ufc-winner-prediction.ipynb

{"cells":[{"metadata":{},"cell_type":"markdown","source":"# UFC WINNER PREDICTION\n\nThis is a UFC dataset which uses bets and odds to favour who will win a particular fight.\nAlthough originally intended for predicting 'bets', I will be using this to predict who will win the fight.\n\nThe following notebook is divided into 3 parts:\n1. Baseline model - Building a simple baseline model with basic modifications\n2. Data Visualization - Using the power of visualization to gain a deeper insight into our data.\n3. Feature Engineering and building the final model - After both the steps above are done, using feature engineering to strengthen our input and build a model more powerful than the baseline\n\nNote: Originally the dataset is built for a **Regression** model, but I have used it to for the purpose of **Classification**."},{"metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","trusted":true},"cell_type":"code","source":"#Import Cell\n#used to import all the libraries and functions used\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nfrom sklearn.model_selection import train_test_split, GridSearchCV\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.preprocessing import LabelEncoder\nfrom sklearn.metrics import accuracy_score\nimport sys, warnings, os\nfrom sklearn.dummy import DummyClassifier","execution_count":1,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"#To ignore max-iteration warnings while cross validating scores\nif not sys.warnoptions:\n    warnings.simplefilter(\"ignore\")\n    os.environ[\"PYTHONWARNINGS\"] = \"ignore\"","execution_count":2,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"#Setting columns and rows to display all the results\npd.set_option(\"display.max_columns\", None, \"display.max_rows\", None)","execution_count":3,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"#Reading the dataset\nufc_master_ds = pd.read_csv(\"../input/ultimate-ufc-dataset/ufc-master.csv\")\n#Seperating label from input\nlabel = ufc_master_ds.Winner\n#I have removed \"B_Women's Featherweight_rank\" because imputing with this feature in the dataset gives me a ton of errors in the baseline model.\nX = ufc_master_ds.drop(['Winner',\"B_Women's Featherweight_rank\"], axis =1)","execution_count":4,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"X.head()","execution_count":5,"outputs":[{"output_type":"execute_result","execution_count":5,"data":{"text/plain":"              R_fighter         B_fighter  R_odds  B_odds        R_ev  \\\n0           Paul Felder  Rafael Dos Anjos     165    -200  165.000000   \n1  Abdul Razak Alhassan    Khaos Williams    -240     185   41.666667   \n2            Kay Hansen      Cory McKenna    -230     180   43.478261   \n3         Brendan Allen   Sean Strickland    -118    -106   84.745763   \n4          Ashley Yoder   Miranda Granger     135    -167  135.000000   \n\n         B_ev        date                location country  title_bout  \\\n0   50.000000  11/14/2020  Las Vegas, Nevada, USA     USA       False   \n1  185.000000  11/14/2020  Las Vegas, Nevada, USA     USA       False   \n2  180.000000  11/14/2020  Las Vegas, Nevada, USA     USA       False   \n3   94.339623  11/14/2020  Las Vegas, Nevada, USA     USA       False   \n4   59.880240  11/14/2020  Las Vegas, Nevada, USA     USA       False   \n\n          weight_class  gender  no_of_rounds  B_current_lose_streak  \\\n0          Lightweight    MALE             5                      2   \n1         Welterweight    MALE             3                      0   \n2  Women's Strawweight  FEMALE             3                      0   \n3         Catch Weight    MALE             3                      0   \n4  Women's Strawweight  FEMALE             3                      1   \n\n   B_current_win_streak  B_draw  B_avg_SIG_STR_landed  B_avg_SIG_STR_pct  \\\n0                     0       0                  3.47               0.45   \n1                     1       0                 22.22               0.58   \n2                     1       0                  3.13               0.49   \n3                     2       0                  4.64               0.36   \n4                     0       0                  3.47               0.43   \n\n   B_avg_SUB_ATT  B_avg_TD_landed  B_avg_TD_pct  B_longest_win_streak  \\\n0            0.6             1.82          0.39                     5   \n1            0.0             0.00          0.00                     1   \n2            0.0             1.00          1.00                     1   \n3            0.4             1.22          0.57                     3   \n4            0.0             0.00          0.00                     1   \n\n   B_losses  B_total_rounds_fought  B_total_title_bouts  \\\n0        11                     86                    4   \n1         0                      1                    0   \n2         0                      3                    0   \n3         3                     28                    0   \n4         1                      4                    0   \n\n   B_win_by_Decision_Majority  B_win_by_Decision_Split  \\\n0                           0                        0   \n1                           0                        0   \n2                           0                        0   \n3                           0                        2   \n4                           0                        0   \n\n   B_win_by_Decision_Unanimous  B_win_by_KO/TKO  B_win_by_Submission  \\\n0                           10                4                    4   \n1                            0                1                    0   \n2                            1                0                    0   \n3                            3                2                    1   \n4                            1                0                    0   \n\n   B_win_by_TKO_Doctor_Stoppage  B_wins  B_Stance  B_Height_cms  B_Reach_cms  \\\n0                             0      18  Southpaw        172.72       177.80   \n1                             0       1  Orthodox        182.88       195.58   \n2                             0       1  Orthodox        160.02       147.32   \n3                             0       8  Orthodox        185.42       193.04   \n4                             0       1  Orthodox        170.18       172.72   \n\n   B_Weight_lbs  R_current_lose_streak  R_current_win_streak  R_draw  \\\n0           155                      1                     0       0   \n1           170                      1                     0       0   \n2           115                      0                     1       0   \n3           185                      0                     4       0   \n4           125                      2                     0       0   \n\n   R_avg_SIG_STR_landed  R_avg_SIG_STR_pct  R_avg_SUB_ATT  R_avg_TD_landed  \\\n0                  3.67               0.43            0.2             0.25   \n1                  4.54               0.48            0.0             0.73   \n2                  2.49               0.56            1.2             2.41   \n3                  2.67               0.60            2.4             1.42   \n4                  2.96               0.40            0.4             1.14   \n\n   R_avg_TD_pct  R_longest_win_streak  R_losses  R_total_rounds_fought  \\\n0          0.25                     3         5                     38   \n1          0.28                     3         2                     10   \n2          0.18                     1         0                      3   \n3          0.37                     4         0                      7   \n4          0.36                     2         5                     21   \n\n   R_total_title_bouts  R_win_by_Decision_Majority  R_win_by_Decision_Split  \\\n0                    0                           0                        2   \n1                    0                           0                        0   \n2                    0                           0                        0   \n3                    0                           0                        0   \n4                    0                           0                        1   \n\n   R_win_by_Decision_Unanimous  R_win_by_KO/TKO  R_win_by_Submission  \\\n0                            2                4                    1   \n1                            0                4                    0   \n2                            0                0                    1   \n3                            1                1                    2   \n4                            1                0                    0   \n\n   R_win_by_TKO_Doctor_Stoppage  R_wins  R_Stance  R_Height_cms  R_Reach_cms  \\\n0                             0       9  Orthodox        180.34       177.80   \n1                             0       4  Orthodox        177.80       185.42   \n2                             0       1  Orthodox        157.48       160.02   \n3                             0       4  Orthodox        187.96       190.50   \n4                             0       2  Southpaw        170.18       175.26   \n\n   R_Weight_lbs  R_age  B_age  lose_streak_dif  win_streak_dif  \\\n0           155     36     36                1               0   \n1           170     35     26               -1               1   \n2           115     21     21                0               0   \n3           185     24     29                0              -2   \n4           115     33     28               -1               0   \n\n   longest_win_streak_dif  win_dif  loss_dif  total_round_dif  \\\n0                       2        9         6               48   \n1                      -2       -3        -2               -9   \n2                       0        0         0                0   \n3                      -1        4         3               21   \n4                      -1       -1        -4              -17   \n\n   total_title_bout_dif  ko_dif  sub_dif  height_dif  reach_dif  age_dif  \\\n0                     4       0        3       -7.62       0.00        0   \n1                     0      -3        0        5.08      10.16       -9   \n2                     0       0       -1        2.54     -12.70        0   \n3                     0       1       -1       -2.54       2.54        5   \n4                     0       0        0        0.00      -2.54       -5   \n\n   sig_str_dif  avg_sub_att_dif  avg_td_dif  empty_arena  constant_1  \\\n0        -0.20              0.4        1.57            1           1   \n1        17.68              0.0       -0.73            1           1   \n2         0.64             -1.2       -1.41            1           1   \n3         1.97             -2.0       -0.20            1           1   \n4         0.51             -0.4       -1.14            1           1   \n\n   B_match_weightclass_rank  R_match_weightclass_rank  \\\n0                      12.0                       7.0   \n1                       NaN                       NaN   \n2                       NaN                       NaN   \n3                       NaN                       NaN   \n4                       NaN                       NaN   \n\n   R_Women's Flyweight_rank  R_Women's Featherweight_rank  \\\n0                       NaN                           NaN   \n1                       NaN                           NaN   \n2                       NaN                           NaN   \n3                       NaN                           NaN   \n4                       NaN                           NaN   \n\n   R_Women's Strawweight_rank  R_Women's Bantamweight_rank  \\\n0                         NaN                          NaN   \n1                         NaN                          NaN   \n2                         NaN                          NaN   \n3                         NaN                          NaN   \n4                         NaN                          NaN   \n\n   R_Heavyweight_rank  R_Light Heavyweight_rank  R_Middleweight_rank  \\\n0                 NaN                       NaN                  NaN   \n1                 NaN                       NaN                  NaN   \n2                 NaN                       NaN                  NaN   \n3                 NaN                       NaN                  NaN   \n4                 NaN                       NaN                  NaN   \n\n   R_Welterweight_rank  R_Lightweight_rank  R_Featherweight_rank  \\\n0                  NaN                 7.0                   NaN   \n1                  NaN                 NaN                   NaN   \n2                  NaN                 NaN                   NaN   \n3                  NaN                 NaN                   NaN   \n4                  NaN                 NaN                   NaN   \n\n   R_Bantamweight_rank  R_Flyweight_rank  R_Pound-for-Pound_rank  \\\n0                  NaN               NaN                     NaN   \n1                  NaN               NaN                     NaN   \n2                  NaN               NaN                     NaN   \n3                  NaN               NaN                     NaN   \n4                  NaN               NaN                     NaN   \n\n   B_Women's Flyweight_rank  B_Women's Strawweight_rank  \\\n0                       NaN                         NaN   \n1                       NaN                         NaN   \n2                       NaN                         NaN   \n3                       NaN                         NaN   \n4                       NaN                         NaN   \n\n   B_Women's Bantamweight_rank  B_Heavyweight_rank  B_Light Heavyweight_rank  \\\n0                          NaN                 NaN                       NaN   \n1                          NaN                 NaN                       NaN   \n2                          NaN                 NaN                       NaN   \n3                          NaN                 NaN                       NaN   \n4                          NaN                 NaN                       NaN   \n\n   B_Middleweight_rank  B_Welterweight_rank  B_Lightweight_rank  \\\n0                  NaN                  NaN                12.0   \n1                  NaN                  NaN                 NaN   \n2                  NaN                  NaN                 NaN   \n3                  NaN                  NaN                 NaN   \n4                  NaN                  NaN                 NaN   \n\n   B_Featherweight_rank  B_Bantamweight_rank  B_Flyweight_rank  \\\n0                   NaN                  NaN               NaN   \n1                   NaN                  NaN               NaN   \n2                   NaN                  NaN               NaN   \n3                   NaN                  NaN               NaN   \n4                   NaN                  NaN               NaN   \n\n   B_Pound-for-Pound_rank better_rank finish finish_details  finish_round  \\\n0                     NaN         Red    NaN            NaN           NaN   \n1                     NaN     neither    NaN            NaN           NaN   \n2                     NaN     neither    NaN            NaN           NaN   \n3                     NaN     neither    NaN            NaN           NaN   \n4                     NaN     neither    NaN            NaN           NaN   \n\n  finish_round_time  total_fight_time_secs  R_kd_bout  B_kd_bout  \\\n0               NaN                    NaN        NaN        NaN   \n1               NaN                    NaN        NaN        NaN   \n2               NaN                    NaN        NaN        NaN   \n3               NaN                    NaN        NaN        NaN   \n4               NaN                    NaN        NaN        NaN   \n\n   R_sig_str_landed_bout  B_sig_str_landed_bout  R_sig_str_attempted_bout  \\\n0                    NaN                    NaN                       NaN   \n1                    NaN                    NaN                       NaN   \n2                    NaN                    NaN                       NaN   \n3                    NaN                    NaN                       NaN   \n4                    NaN                    NaN                       NaN   \n\n   B_sig_str_attempted_bout  R_sig_str_pct_bout  B_sig_str_pct_bout  \\\n0                       NaN                 NaN                 NaN   \n1                       NaN                 NaN                 NaN   \n2                       NaN                 NaN                 NaN   \n3                       NaN                 NaN                 NaN   \n4                       NaN                 NaN                 NaN   \n\n   R_tot_str_landed_bout  B_tot_str_landed_bout  R_tot_str_attempted_bout  \\\n0                    NaN                    NaN                       NaN   \n1                    NaN                    NaN                       NaN   \n2                    NaN                    NaN                       NaN   \n3                    NaN                    NaN                       NaN   \n4                    NaN                    NaN                       NaN   \n\n   B_tot_str_attempted_bout  R_td_landed_bout  B_td_landed_bout  \\\n0                       NaN               NaN               NaN   \n1                       NaN               NaN               NaN   \n2                       NaN               NaN               NaN   \n3                       NaN               NaN               NaN   \n4                       NaN               NaN               NaN   \n\n   R_td_attempted_bout  B_td_attempted_bout  R_td_pct_bout  B_td_pct_bout  \\\n0                  NaN                  NaN            NaN            NaN   \n1                  NaN                  NaN            NaN            NaN   \n2                  NaN                  NaN            NaN            NaN   \n3                  NaN                  NaN            NaN            NaN   \n4                  NaN                  NaN            NaN            NaN   \n\n   R_sub_attempts_bout  B_sub_attempts_bout  R_pass_bout  B_pass_bout  \\\n0                  NaN                  NaN          NaN          NaN   \n1                  NaN                  NaN          NaN          NaN   \n2                  NaN                  NaN          NaN          NaN   \n3                  NaN                  NaN          NaN          NaN   \n4                  NaN                  NaN          NaN          NaN   \n\n   R_rev_bout  B_rev_bout  \n0         NaN         NaN  \n1         NaN         NaN  \n2         NaN         NaN  \n3         NaN         NaN  \n4         NaN         NaN  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>R_fighter</th>\n      <th>B_fighter</th>\n      <th>R_odds</th>\n      <th>B_odds</th>\n      <th>R_ev</th>\n      <th>B_ev</th>\n      <th>date</th>\n      <th>location</th>\n      <th>country</th>\n      <th>title_bout</th>\n      <th>weight_class</th>\n      <th>gender</th>\n      <th>no_of_rounds</th>\n      <th>B_current_lose_streak</th>\n      <th>B_current_win_streak</th>\n      <th>B_draw</th>\n      <th>B_avg_SIG_STR_landed</th>\n      <th>B_avg_SIG_STR_pct</th>\n      <th>B_avg_SUB_ATT</th>\n      <th>B_avg_TD_landed</th>\n      <th>B_avg_TD_pct</th>\n      <th>B_longest_win_streak</th>\n      <th>B_losses</th>\n      <th>B_total_rounds_fought</th>\n      <th>B_total_title_bouts</th>\n      <th>B_win_by_Decision_Majority</th>\n      <th>B_win_by_Decision_Split</th>\n      <th>B_win_by_Decision_Unanimous</th>\n      <th>B_win_by_KO/TKO</th>\n      <th>B_win_by_Submission</th>\n      <th>B_win_by_TKO_Doctor_Stoppage</th>\n      <th>B_wins</th>\n      <th>B_Stance</th>\n      <th>B_Height_cms</th>\n      <th>B_Reach_cms</th>\n      <th>B_Weight_lbs</th>\n      <th>R_current_lose_streak</th>\n      <th>R_current_win_streak</th>\n      <th>R_draw</th>\n      <th>R_avg_SIG_STR_landed</th>\n      <th>R_avg_SIG_STR_pct</th>\n      <th>R_avg_SUB_ATT</th>\n      <th>R_avg_TD_landed</th>\n      <th>R_avg_TD_pct</th>\n      <th>R_longest_win_streak</th>\n      <th>R_losses</th>\n      <th>R_total_rounds_fought</th>\n      <th>R_total_title_bouts</th>\n      <th>R_win_by_Decision_Majority</th>\n      <th>R_win_by_Decision_Split</th>\n      <th>R_win_by_Decision_Unanimous</th>\n      <th>R_win_by_KO/TKO</th>\n      <th>R_win_by_Submission</th>\n      <th>R_win_by_TKO_Doctor_Stoppage</th>\n      <th>R_wins</th>\n      <th>R_Stance</th>\n      <th>R_Height_cms</th>\n      <th>R_Reach_cms</th>\n      <th>R_Weight_lbs</th>\n      <th>R_age</th>\n      <th>B_age</th>\n      <th>lose_streak_dif</th>\n      <th>win_streak_dif</th>\n      <th>longest_win_streak_dif</th>\n      <th>win_dif</th>\n      <th>loss_dif</th>\n      <th>total_round_dif</th>\n      <th>total_title_bout_dif</th>\n      <th>ko_dif</th>\n      <th>sub_dif</th>\n      <th>height_dif</th>\n      <th>reach_dif</th>\n      <th>age_dif</th>\n      <th>sig_str_dif</th>\n      <th>avg_sub_att_dif</th>\n      <th>avg_td_dif</th>\n      <th>empty_arena</th>\n      <th>constant_1</th>\n      <th>B_match_weightclass_rank</th>\n      <th>R_match_weightclass_rank</th>\n      <th>R_Women's Flyweight_rank</th>\n      <th>R_Women's Featherweight_rank</th>\n      <th>R_Women's Strawweight_rank</th>\n      <th>R_Women's Bantamweight_rank</th>\n      <th>R_Heavyweight_rank</th>\n      <th>R_Light Heavyweight_rank</th>\n      <th>R_Middleweight_rank</th>\n      <th>R_Welterweight_rank</th>\n      <th>R_Lightweight_rank</th>\n      <th>R_Featherweight_rank</th>\n      <th>R_Bantamweight_rank</th>\n      <th>R_Flyweight_rank</th>\n      <th>R_Pound-for-Pound_rank</th>\n      <th>B_Women's Flyweight_rank</th>\n      <th>B_Women's Strawweight_rank</th>\n      <th>B_Women's Bantamweight_rank</th>\n      <th>B_Heavyweight_rank</th>\n      <th>B_Light Heavyweight_rank</th>\n      <th>B_Middleweight_rank</th>\n      <th>B_Welterweight_rank</th>\n      <th>B_Lightweight_rank</th>\n      <th>B_Featherweight_rank</th>\n      <th>B_Bantamweight_rank</th>\n      <th>B_Flyweight_rank</th>\n      <th>B_Pound-for-Pound_rank</th>\n      <th>better_rank</th>\n      <th>finish</th>\n      <th>finish_details</th>\n      <th>finish_round</th>\n      <th>finish_round_time</th>\n      <th>total_fight_time_secs</th>\n      <th>R_kd_bout</th>\n      <th>B_kd_bout</th>\n      <th>R_sig_str_landed_bout</th>\n      <th>B_sig_str_landed_bout</th>\n      <th>R_sig_str_attempted_bout</th>\n      <th>B_sig_str_attempted_bout</th>\n      <th>R_sig_str_pct_bout</th>\n      <th>B_sig_str_pct_bout</th>\n      <th>R_tot_str_landed_bout</th>\n      <th>B_tot_str_landed_bout</th>\n      <th>R_tot_str_attempted_bout</th>\n      <th>B_tot_str_attempted_bout</th>\n      <th>R_td_landed_bout</th>\n      <th>B_td_landed_bout</th>\n      <th>R_td_attempted_bout</th>\n      <th>B_td_attempted_bout</th>\n      <th>R_td_pct_bout</th>\n      <th>B_td_pct_bout</th>\n      <th>R_sub_attempts_bout</th>\n      <th>B_sub_attempts_bout</th>\n      <th>R_pass_bout</th>\n      <th>B_pass_bout</th>\n      <th>R_rev_bout</th>\n      <th>B_rev_bout</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Paul Felder</td>\n      <td>Rafael Dos Anjos</td>\n      <td>165</td>\n      <td>-200</td>\n      <td>165.000000</td>\n      <td>50.000000</td>\n      <td>11/14/2020</td>\n      <td>Las Vegas, Nevada, USA</td>\n      <td>USA</td>\n      <td>False</td>\n      <td>Lightweight</td>\n      <td>MALE</td>\n      <td>5</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>3.47</td>\n      <td>0.45</td>\n      <td>0.6</td>\n      <td>1.82</td>\n      <td>0.39</td>\n      <td>5</td>\n      <td>11</td>\n      <td>86</td>\n      <td>4</td>\n      <td>0</td>\n      <td>0</td>\n      <td>10</td>\n      <td>4</td>\n      <td>4</td>\n      <td>0</td>\n      <td>18</td>\n      <td>Southpaw</td>\n      <td>172.72</td>\n      <td>177.80</td>\n      <td>155</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>3.67</td>\n      <td>0.43</td>\n      <td>0.2</td>\n      <td>0.25</td>\n      <td>0.25</td>\n      <td>3</td>\n      <td>5</td>\n      <td>38</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2</td>\n      <td>2</td>\n      <td>4</td>\n      <td>1</td>\n      <td>0</td>\n      <td>9</td>\n      <td>Orthodox</td>\n      <td>180.34</td>\n      <td>177.80</td>\n      <td>155</td>\n      <td>36</td>\n      <td>36</td>\n      <td>1</td>\n      <td>0</td>\n      <td>2</td>\n      <td>9</td>\n      <td>6</td>\n      <td>48</td>\n      <td>4</td>\n      <td>0</td>\n      <td>3</td>\n      <td>-7.62</td>\n      <td>0.00</td>\n      <td>0</td>\n      <td>-0.20</td>\n      <td>0.4</td>\n      <td>1.57</td>\n      <td>1</td>\n      <td>1</td>\n      <td>12.0</td>\n      <td>7.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>7.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>12.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Red</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Abdul Razak Alhassan</td>\n      <td>Khaos Williams</td>\n      <td>-240</td>\n      <td>185</td>\n      <td>41.666667</td>\n      <td>185.000000</td>\n      <td>11/14/2020</td>\n      <td>Las Vegas, Nevada, USA</td>\n      <td>USA</td>\n      <td>False</td>\n      <td>Welterweight</td>\n      <td>MALE</td>\n      <td>3</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>22.22</td>\n      <td>0.58</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.00</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>Orthodox</td>\n      <td>182.88</td>\n      <td>195.58</td>\n      <td>170</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>4.54</td>\n      <td>0.48</td>\n      <td>0.0</td>\n      <td>0.73</td>\n      <td>0.28</td>\n      <td>3</td>\n      <td>2</td>\n      <td>10</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>4</td>\n      <td>0</td>\n      <td>0</td>\n      <td>4</td>\n      <td>Orthodox</td>\n      <td>177.80</td>\n      <td>185.42</td>\n      <td>170</td>\n      <td>35</td>\n      <td>26</td>\n      <td>-1</td>\n      <td>1</td>\n      <td>-2</td>\n      <td>-3</td>\n      <td>-2</td>\n      <td>-9</td>\n      <td>0</td>\n      <td>-3</td>\n      <td>0</td>\n      <td>5.08</td>\n      <td>10.16</td>\n      <td>-9</td>\n      <td>17.68</td>\n      <td>0.0</td>\n      <td>-0.73</td>\n      <td>1</td>\n      <td>1</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>neither</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Kay Hansen</td>\n      <td>Cory McKenna</td>\n      <td>-230</td>\n      <td>180</td>\n      <td>43.478261</td>\n      <td>180.000000</td>\n      <td>11/14/2020</td>\n      <td>Las Vegas, Nevada, USA</td>\n      <td>USA</td>\n      <td>False</td>\n      <td>Women's Strawweight</td>\n      <td>FEMALE</td>\n      <td>3</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>3.13</td>\n      <td>0.49</td>\n      <td>0.0</td>\n      <td>1.00</td>\n      <td>1.00</td>\n      <td>1</td>\n      <td>0</td>\n      <td>3</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>Orthodox</td>\n      <td>160.02</td>\n      <td>147.32</td>\n      <td>115</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>2.49</td>\n      <td>0.56</td>\n      <td>1.2</td>\n      <td>2.41</td>\n      <td>0.18</td>\n      <td>1</td>\n      <td>0</td>\n      <td>3</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>Orthodox</td>\n      <td>157.48</td>\n      <td>160.02</td>\n      <td>115</td>\n      <td>21</td>\n      <td>21</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>-1</td>\n      <td>2.54</td>\n      <td>-12.70</td>\n      <td>0</td>\n      <td>0.64</td>\n      <td>-1.2</td>\n      <td>-1.41</td>\n      <td>1</td>\n      <td>1</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>neither</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Brendan Allen</td>\n      <td>Sean Strickland</td>\n      <td>-118</td>\n      <td>-106</td>\n      <td>84.745763</td>\n      <td>94.339623</td>\n      <td>11/14/2020</td>\n      <td>Las Vegas, Nevada, USA</td>\n      <td>USA</td>\n      <td>False</td>\n      <td>Catch Weight</td>\n      <td>MALE</td>\n      <td>3</td>\n      <td>0</td>\n      <td>2</td>\n      <td>0</td>\n      <td>4.64</td>\n      <td>0.36</td>\n      <td>0.4</td>\n      <td>1.22</td>\n      <td>0.57</td>\n      <td>3</td>\n      <td>3</td>\n      <td>28</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2</td>\n      <td>3</td>\n      <td>2</td>\n      <td>1</td>\n      <td>0</td>\n      <td>8</td>\n      <td>Orthodox</td>\n      <td>185.42</td>\n      <td>193.04</td>\n      <td>185</td>\n      <td>0</td>\n      <td>4</td>\n      <td>0</td>\n      <td>2.67</td>\n      <td>0.60</td>\n      <td>2.4</td>\n      <td>1.42</td>\n      <td>0.37</td>\n      <td>4</td>\n      <td>0</td>\n      <td>7</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>2</td>\n      <td>0</td>\n      <td>4</td>\n      <td>Orthodox</td>\n      <td>187.96</td>\n      <td>190.50</td>\n      <td>185</td>\n      <td>24</td>\n      <td>29</td>\n      <td>0</td>\n      <td>-2</td>\n      <td>-1</td>\n      <td>4</td>\n      <td>3</td>\n      <td>21</td>\n      <td>0</td>\n      <td>1</td>\n      <td>-1</td>\n      <td>-2.54</td>\n      <td>2.54</td>\n      <td>5</td>\n      <td>1.97</td>\n      <td>-2.0</td>\n      <td>-0.20</td>\n      <td>1</td>\n      <td>1</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>neither</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Ashley Yoder</td>\n      <td>Miranda Granger</td>\n      <td>135</td>\n      <td>-167</td>\n      <td>135.000000</td>\n      <td>59.880240</td>\n      <td>11/14/2020</td>\n      <td>Las Vegas, Nevada, USA</td>\n      <td>USA</td>\n      <td>False</td>\n      <td>Women's Strawweight</td>\n      <td>FEMALE</td>\n      <td>3</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>3.47</td>\n      <td>0.43</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.00</td>\n      <td>1</td>\n      <td>1</td>\n      <td>4</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>Orthodox</td>\n      <td>170.18</td>\n      <td>172.72</td>\n      <td>125</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2.96</td>\n      <td>0.40</td>\n      <td>0.4</td>\n      <td>1.14</td>\n      <td>0.36</td>\n      <td>2</td>\n      <td>5</td>\n      <td>21</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2</td>\n      <td>Southpaw</td>\n      <td>170.18</td>\n      <td>175.26</td>\n      <td>115</td>\n      <td>33</td>\n      <td>28</td>\n      <td>-1</td>\n      <td>0</td>\n      <td>-1</td>\n      <td>-1</td>\n      <td>-4</td>\n      <td>-17</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0.00</td>\n      <td>-2.54</td>\n      <td>-5</td>\n      <td>0.51</td>\n      <td>-0.4</td>\n      <td>-1.14</td>\n      <td>1</td>\n      <td>1</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>neither</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"# 1. Creating a baseline model\n\n Since this is a classification task, the target value will be 'Winner'."},{"metadata":{},"cell_type":"markdown","source":"***Encoding categorical variables***\n\nI'll be encoding the categorical columns prior to doing anything so it'll be easier for me to split the data while avoiding data leakage."},{"metadata":{"trusted":true},"cell_type":"code","source":"#Separating the features based on their data types\ncat_col = [col for col in X.columns if X[col].dtypes == 'object']\nnum_col = [col for col in X.columns if col not in cat_col]","execution_count":6,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"enc = LabelEncoder()\nfor i in X[cat_col]:\n    #using astype(str) to avoid columns with 'float and str' to throw errors\n    X[i] = enc.fit_transform(X[i].astype(str))","execution_count":7,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"#Also encoding Label for Red to be 1 and Blue to be 0 \nlabel = [1 if win == 'Red' else 0 for win in label]","execution_count":8,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Now that this is done, I can split the dataset into training and test set."},{"metadata":{"trusted":true},"cell_type":"code","source":"X_train, X_valid, y_train, y_valid = train_test_split(X, label, random_state = 2, test_size = 0.3)","execution_count":9,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"***Finding and filling null values***\n\nBuilding a baseline model would mean imputing all the null values with their 'means' (or mode/ median but I chose mean here) since this is a baseline model"},{"metadata":{"trusted":true},"cell_type":"code","source":"X_train.isnull().sum().sort_values(ascending=False)","execution_count":10,"outputs":[{"output_type":"execute_result","execution_count":10,"data":{"text/plain":"R_Women's Featherweight_rank    3127\nB_Pound-for-Pound_rank          3118\nB_Women's Flyweight_rank        3112\nR_Women's Flyweight_rank        3100\nB_Women's Strawweight_rank      3093\nB_Welterweight_rank             3085\nB_Lightweight_rank              3079\nB_Women's Bantamweight_rank     3078\nB_Bantamweight_rank             3075\nB_Featherweight_rank            3073\nB_Flyweight_rank                3071\nB_Light Heavyweight_rank        3068\nB_Middleweight_rank             3065\nR_Women's Strawweight_rank      3060\nB_Heavyweight_rank              3059\nR_Women's Bantamweight_rank     3052\nR_Welterweight_rank             3048\nR_Lightweight_rank              3048\nR_Middleweight_rank             3043\nR_Flyweight_rank                3041\nR_Featherweight_rank            3039\nR_Light Heavyweight_rank        3039\nR_Bantamweight_rank             3039\nR_Heavyweight_rank              3037\nR_Pound-for-Pound_rank          3030\nB_match_weightclass_rank        2584\nR_match_weightclass_rank        2272\nR_rev_bout                      1188\nB_rev_bout                      1188\nB_td_pct_bout                   1188\nR_pass_bout                     1188\nR_sub_attempts_bout             1188\nB_pass_bout                     1188\nR_td_pct_bout                   1188\nB_td_attempted_bout             1188\nR_td_attempted_bout             1188\nB_td_landed_bout                1188\nR_td_landed_bout                1188\nB_tot_str_attempted_bout        1188\nR_tot_str_attempted_bout        1188\nB_tot_str_landed_bout           1188\nR_tot_str_landed_bout           1188\nB_sig_str_pct_bout              1188\nR_sig_str_pct_bout              1188\nB_sig_str_attempted_bout        1188\nR_sig_str_attempted_bout        1188\nB_sig_str_landed_bout           1188\nR_sig_str_landed_bout           1188\nB_kd_bout                       1188\nR_kd_bout                       1188\nB_sub_attempts_bout             1188\nB_avg_SIG_STR_landed             657\nB_avg_TD_pct                     596\nB_avg_SUB_ATT                    591\nB_avg_TD_landed                  591\nB_avg_SIG_STR_pct                542\nR_avg_SIG_STR_landed             311\nR_avg_TD_pct                     250\nfinish_round                     250\ntotal_fight_time_secs            250\nR_avg_SIG_STR_pct                245\nR_avg_SUB_ATT                    245\nR_avg_TD_landed                  245\nR_current_win_streak               0\nB_win_by_Decision_Split            0\nB_win_by_Decision_Unanimous        0\nB_win_by_KO/TKO                    0\nB_total_title_bouts                0\nB_win_by_Submission                0\nB_win_by_TKO_Doctor_Stoppage       0\nB_wins                             0\nB_Stance                           0\nB_Height_cms                       0\nB_Reach_cms                        0\nB_Weight_lbs                       0\nR_current_lose_streak              0\nB_win_by_Decision_Majority         0\nR_ev                               0\nB_total_rounds_fought              0\nB_losses                           0\nR_odds                             0\nB_fighter                          0\nR_draw                             0\nB_ev                               0\ndate                               0\nlocation                           0\ncountry                            0\ntitle_bout                         0\nB_odds                             0\ngender                             0\nno_of_rounds                       0\nB_current_lose_streak              0\nB_current_win_streak               0\nB_draw                             0\nB_longest_win_streak               0\nweight_class                       0\ntotal_title_bout_dif               0\nR_longest_win_streak               0\nsig_str_dif                        0\nloss_dif                           0\ntotal_round_dif                    0\nko_dif                             0\nsub_dif                            0\nheight_dif                         0\nreach_dif                          0\nage_dif                            0\navg_sub_att_dif                    0\nR_losses                           0\navg_td_dif                         0\nempty_arena                        0\nconstant_1                         0\nbetter_rank                        0\nfinish                             0\nfinish_details                     0\nfinish_round_time                  0\nwin_dif                            0\nlongest_win_streak_dif             0\nwin_streak_dif                     0\nlose_streak_dif                    0\nR_total_rounds_fought              0\nR_total_title_bouts                0\nR_win_by_Decision_Majority         0\nR_win_by_Decision_Split            0\nR_win_by_Decision_Unanimous        0\nR_win_by_KO/TKO                    0\nR_win_by_Submission                0\nR_win_by_TKO_Doctor_Stoppage       0\nR_wins                             0\nR_Stance                           0\nR_Height_cms                       0\nR_Reach_cms                        0\nR_Weight_lbs                       0\nR_age                              0\nB_age                              0\nR_fighter                          0\ndtype: int64"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"Since, it is baseline model, imputation would be pretty basic and simple:\n1. Impute the numerical columns with mean\n2. Fill the categorical columns with 'most_frequent'"},{"metadata":{"trusted":true},"cell_type":"code","source":"imp = SimpleImputer(strategy='most_frequent')\nimp.fit(X_train[num_col])\nX_train[num_col] = imp.transform(X_train[num_col])\nX_valid[num_col] = imp.transform(X_valid[num_col])","execution_count":11,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"cat_imp = SimpleImputer(strategy = 'most_frequent')\ncat_imp.fit(X_train[cat_col])\nX_train[cat_col] = cat_imp.transform(X_train[cat_col])\nX_valid[cat_col] = cat_imp.transform(X_valid[cat_col])","execution_count":12,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"X_train.sample(10)","execution_count":13,"outputs":[{"output_type":"execute_result","execution_count":13,"data":{"text/plain":"      R_fighter  B_fighter  R_odds  B_odds        R_ev        B_ev  date  \\\n4310        263       1245    -250     210   40.000000  210.000000    35   \n4109        999       1383    -375     335   26.666667  335.000000   352   \n3561        923        241    -145     135   68.965517  135.000000   166   \n2582        661       1454    -105    -115   95.238095   86.956522   239   \n1477       1227        938    -370     310   27.027027  310.000000    82   \n555         458         93     180    -220  180.000000   45.454545    16   \n4232         70       1434    -210     190   47.619048  190.000000   157   \n4456        566        935    -320     290   31.250000  290.000000   180   \n2849        365       1254    -150     140   66.666667  140.000000    86   \n3259        227         78     165    -175  165.000000   57.142857   113   \n\n      location  country  title_bout  weight_class  gender  no_of_rounds  \\\n4310         5       21       False             7       1             3   \n4109        99       21       False             5       1             3   \n3561        83        3       False             7       1             3   \n2582        51        2       False             3       1             3   \n1477        88       21       False             4       1             3   \n555        106       21       False            12       0             3   \n4232        66       21        True             7       1             5   \n4456        89       21       False             6       1             3   \n2849       133        1       False             7       1             3   \n3259        66       21        True             7       1             5   \n\n      B_current_lose_streak  B_current_win_streak  B_draw  \\\n4310                      0                     1       0   \n4109                      0                     1       0   \n3561                      0                     3       0   \n2582                      0                     2       0   \n1477                      0                     1       0   \n555                       0                     1       0   \n4232                      0                     1       0   \n4456                      0                     3       0   \n2849                      1                     0       0   \n3259                      1                     0       0   \n\n      B_avg_SIG_STR_landed  B_avg_SIG_STR_pct  B_avg_SUB_ATT  B_avg_TD_landed  \\\n4310             10.666667           0.511667       0.666667         0.500000   \n4109             26.833333           0.531667       0.291667         2.250000   \n3561             48.142857           0.475714       0.428571         0.000000   \n2582             22.750000           0.350000       0.500000         4.250000   \n1477             48.500000           0.500000       0.000000         0.000000   \n555               0.000000           0.500000       0.100000         0.390000   \n4232              9.583333           0.531667       0.500000         0.500000   \n4456             16.833333           0.361667       1.333333         2.333333   \n2849             82.000000           0.480000       0.000000         0.000000   \n3259             28.764706           0.695294       0.352941         0.176471   \n\n      B_avg_TD_pct  B_longest_win_streak  B_losses  B_total_rounds_fought  \\\n4310      0.333333                     1         4                      7   \n4109      0.524583                     6         9                     59   \n3561      0.000000                     3         2                     19   \n2582      0.555000                     2         1                     10   \n1477      0.000000                     1         1                      4   \n555       0.360000                     1         7                     32   \n4232      0.229167                     3         4                     18   \n4456      0.540000                     3         2                     12   \n2849      0.000000                     0         1                      3   \n3259      0.176471                    16         1                     37   \n\n      B_total_title_bouts  B_win_by_Decision_Majority  \\\n4310                    0                           0   \n4109                   10                           0   \n3561                    0                           0   \n2582                    0                           0   \n1477                    0                           0   \n555                     0                           0   \n4232                    3                           0   \n4456                    0                           0   \n2849                    0                           0   \n3259                   12                           0   \n\n      B_win_by_Decision_Split  B_win_by_Decision_Unanimous  B_win_by_KO/TKO  \\\n4310                        0                            0                0   \n4109                        2                            3                7   \n3561                        1                            2                0   \n2582                        0                            2                0   \n1477                        0                            1                0   \n555                         0                            4                1   \n4232                        0                            0                6   \n4456                        0                            1                0   \n2849                        0                            0                0   \n3259                        0                            2               11   \n\n      B_win_by_Submission  B_win_by_TKO_Doctor_Stoppage  B_wins  B_Stance  \\\n4310                    2                             0       2         1   \n4109                    2                             1      15         1   \n3561                    1                             1       5         1   \n2582                    1                             0       3         2   \n1477                    0                             0       1         1   \n555                     0                             0       5         1   \n4232                    1                             1       8         2   \n4456                    3                             0       4         1   \n2849                    0                             0       0         2   \n3259                    3                             0      16         2   \n\n      B_Height_cms  B_Reach_cms  B_Weight_lbs  R_current_lose_streak  \\\n4310        185.42       193.04           185                      0   \n4109        190.50       187.96           205                      0   \n3561        187.96       193.04           185                      0   \n2582        162.56       165.10           125                      0   \n1477        193.04       195.58           240                      1   \n555         160.02       162.56           115                      0   \n4232        182.88       187.96           185                      0   \n4456        172.72       190.50           155                      0   \n2849        187.96       190.50           205                      1   \n3259        187.96       195.58           185                      0   \n\n      R_current_win_streak  R_draw  R_avg_SIG_STR_landed  R_avg_SIG_STR_pct  \\\n4310                     1       0             22.000000           0.370000   \n4109                     2       0             22.916667           0.372500   \n3561                     1       0             49.000000           0.437500   \n2582                     2       0              9.000000           0.346000   \n1477                     0       0             19.750000           0.318750   \n555                      2       0             23.000000           0.450000   \n4232                    12       0             32.666667           0.728333   \n4456                     3       0             29.000000           0.370000   \n2849                     0       0             17.666667           0.560000   \n3259                     6       0             26.166667           0.465000   \n\n      R_avg_SUB_ATT  R_avg_TD_landed  R_avg_TD_pct  R_longest_win_streak  \\\n4310       1.000000         5.000000      0.710000                     1   \n4109       0.000000         3.666667      0.394167                     5   \n3561       0.250000         1.250000      0.250000                     2   \n2582       0.200000         1.400000      0.250000                     2   \n1477       0.125000         0.000000      0.000000                     2   \n555        0.400000         0.410000      1.000000                     2   \n4232       0.500000         0.250000      0.250000                    12   \n4456       1.833333         2.500000      0.390000                     3   \n2849       0.000000         1.333333      0.300000                     2   \n3259       1.000000         2.333333      0.768333                     6   \n\n      R_losses  R_total_rounds_fought  R_total_title_bouts  \\\n4310         0                      2                    1   \n4109         2                     32                    3   \n3561         1                     12                    0   \n2582         2                     10                    0   \n1477         5                     14                    0   \n555          1                      8                    0   \n4232         0                     29                    8   \n4456         1                     15                    0   \n2849         1                      6                    0   \n3259         0                     12                    1   \n\n      R_win_by_Decision_Majority  R_win_by_Decision_Split  \\\n4310                           0                        0   \n4109                           1                        3   \n3561                           0                        0   \n2582                           0                        0   \n1477                           0                        0   \n555                            0                        1   \n4232                           0                        0   \n4456                           0                        0   \n2849                           0                        0   \n3259                           0                        0   \n\n      R_win_by_Decision_Unanimous  R_win_by_KO/TKO  R_win_by_Submission  \\\n4310                            0                0                    1   \n4109                            2                4                    0   \n3561                            2                0                    1   \n2582                            2                0                    1   \n1477                            0                3                    0   \n555                             1                0                    0   \n4232                            2                7                    3   \n4456                            2                1                    2   \n2849                            0                2                    0   \n3259                            2                2                    2   \n\n      R_win_by_TKO_Doctor_Stoppage  R_wins  R_Stance  R_Height_cms  \\\n4310                             0       1         1        180.34   \n4109                             0      10         1        182.88   \n3561                             0       3         2        182.88   \n2582                             0       3         1        165.10   \n1477                             0       3         2        195.58   \n555                              0       2         1        154.94   \n4232                             0      12         2        187.96   \n4456                             0       5         2        172.72   \n2849                             0       2         3        185.42   \n3259                             0       6         1        187.96   \n\n      R_Reach_cms  R_Weight_lbs  R_age  B_age  lose_streak_dif  \\\n4310       190.50           170     25     33                0   \n4109       190.50           205     31     36                0   \n3561       187.96           185     34     26                0   \n2582       170.18           125     30     30                0   \n1477       195.58           250     34     33                1   \n555        157.48           115     27     35                0   \n4232       195.58           185     35     33                0   \n4456       180.34           155     26     28                0   \n2849       185.42           185     34     28                0   \n3259       198.12           185     29     38               -1   \n\n      win_streak_dif  longest_win_streak_dif  win_dif  loss_dif  \\\n4310               0                       0        1        -4   \n4109              -1                       1        5        -7   \n3561               2                       1        2        -1   \n2582               0                       0        0         1   \n1477               1                      -1       -2         4   \n555               -1                      -1        3        -6   \n4232             -11                      -9       -4        -4   \n4456               0                       0       -1        -1   \n2849               0                      -2       -2         0   \n3259              -6                      10       10        -1   \n\n      total_round_dif  total_title_bout_dif  ko_dif  sub_dif  height_dif  \\\n4310                5                    -1       0        1        5.08   \n4109               27                     7       4        2        7.62   \n3561                7                     0       1        0        5.08   \n2582                0                     0       0        0       -2.54   \n1477              -10                     0      -3        0       -2.54   \n555                24                     0       1        0        5.08   \n4232              -11                    -5       0       -2       -5.08   \n4456               -3                     0      -1        1        0.00   \n2849               -3                     0      -2        0        2.54   \n3259               25                    11       9        1        0.00   \n\n      reach_dif  age_dif  sig_str_dif  avg_sub_att_dif  avg_td_dif  \\\n4310       2.54       -8   -11.333333        -0.333333   -4.500000   \n4109      -2.54       -5     3.916667         0.291667   -1.416667   \n3561       5.08        8    -0.857143         0.178571   -1.250000   \n2582      -5.08        0    13.750000         0.300000    2.850000   \n1477       0.00        1    28.750000        -0.125000    0.000000   \n555        5.08       -8     0.000000        -0.300000   -0.020000   \n4232      -7.62        2   -23.083333         0.000000    0.250000   \n4456      10.16       -2   -12.166667        -0.500000   -0.166667   \n2849       5.08        6    64.333333         0.000000   -1.333333   \n3259      -2.54       -9     2.598039        -0.647059   -2.156863   \n\n      empty_arena  constant_1  B_match_weightclass_rank  \\\n4310            0           1                       9.0   \n4109            0           1                       9.0   \n3561            0           1                       9.0   \n2582            0           1                      12.0   \n1477            0           1                       9.0   \n555             0           1                       9.0   \n4232            0           1                       9.0   \n4456            0           1                       9.0   \n2849            0           1                       9.0   \n3259            0           1                       1.0   \n\n      R_match_weightclass_rank  R_Women's Flyweight_rank  \\\n4310                       0.0                       5.0   \n4109                       0.0                       5.0   \n3561                       0.0                       5.0   \n2582                       4.0                       5.0   \n1477                       0.0                       5.0   \n555                        0.0                       5.0   \n4232                       0.0                       5.0   \n4456                       0.0                       5.0   \n2849                       0.0                       5.0   \n3259                       0.0                       5.0   \n\n      R_Women's Featherweight_rank  R_Women's Strawweight_rank  \\\n4310                           0.0                         0.0   \n4109                           0.0                         0.0   \n3561                           0.0                         0.0   \n2582                           0.0                         0.0   \n1477                           0.0                         0.0   \n555                            0.0                         0.0   \n4232                           0.0                         0.0   \n4456                           0.0                         0.0   \n2849                           0.0                         0.0   \n3259                           0.0                         0.0   \n\n      R_Women's Bantamweight_rank  R_Heavyweight_rank  \\\n4310                          0.0                 2.0   \n4109                          0.0                 2.0   \n3561                          0.0                 2.0   \n2582                          0.0                 2.0   \n1477                          0.0                 2.0   \n555                           0.0                 2.0   \n4232                          0.0                 2.0   \n4456                          0.0                 2.0   \n2849                          0.0                 2.0   \n3259                          0.0                 2.0   \n\n      R_Light Heavyweight_rank  R_Middleweight_rank  R_Welterweight_rank  \\\n4310                       4.0                 13.0                  6.0   \n4109                       4.0                 13.0                  6.0   \n3561                       4.0                 13.0                  6.0   \n2582                       4.0                 13.0                  6.0   \n1477                       4.0                 13.0                  6.0   \n555                        4.0                 13.0                  6.0   \n4232                       4.0                 13.0                  6.0   \n4456                       4.0                 13.0                  6.0   \n2849                       4.0                 13.0                  6.0   \n3259                       4.0                  0.0                  6.0   \n\n      R_Lightweight_rank  R_Featherweight_rank  R_Bantamweight_rank  \\\n4310                 5.0                  10.0                  8.0   \n4109                 5.0                  10.0                  8.0   \n3561                 5.0                  10.0                  8.0   \n2582                 5.0                  10.0                  8.0   \n1477                 5.0                  10.0                  8.0   \n555                  5.0                  10.0                  8.0   \n4232                 5.0                  10.0                  8.0   \n4456                 5.0                  10.0                  8.0   \n2849                 5.0                  10.0                  8.0   \n3259                 5.0                  10.0                  8.0   \n\n      R_Flyweight_rank  R_Pound-for-Pound_rank  B_Women's Flyweight_rank  \\\n4310               7.0                     7.0                       6.0   \n4109               7.0                     7.0                       6.0   \n3561               7.0                     7.0                       6.0   \n2582               4.0                     7.0                       6.0   \n1477               7.0                     7.0                       6.0   \n555                7.0                     7.0                       6.0   \n4232               7.0                     7.0                       6.0   \n4456               7.0                     7.0                       6.0   \n2849               7.0                     7.0                       6.0   \n3259               7.0                     8.0                       6.0   \n\n      B_Women's Strawweight_rank  B_Women's Bantamweight_rank  \\\n4310                         9.0                          7.0   \n4109                         9.0                          7.0   \n3561                         9.0                          7.0   \n2582                         9.0                          7.0   \n1477                         9.0                          7.0   \n555                          9.0                          7.0   \n4232                         9.0                          7.0   \n4456                         9.0                          7.0   \n2849                         9.0                          7.0   \n3259                         9.0                          7.0   \n\n      B_Heavyweight_rank  B_Light Heavyweight_rank  B_Middleweight_rank  \\\n4310                 9.0                      12.0                  8.0   \n4109                 9.0                      12.0                  8.0   \n3561                 9.0                      12.0                  8.0   \n2582                 9.0                      12.0                  8.0   \n1477                 9.0                      12.0                  8.0   \n555                  9.0                      12.0                  8.0   \n4232                 9.0                      12.0                  8.0   \n4456                 9.0                      12.0                  8.0   \n2849                 9.0                      12.0                  8.0   \n3259                 9.0                      12.0                  1.0   \n\n      B_Welterweight_rank  B_Lightweight_rank  B_Featherweight_rank  \\\n4310                 12.0                 5.0                   9.0   \n4109                 12.0                 5.0                   9.0   \n3561                 12.0                 5.0                   9.0   \n2582                 12.0                 5.0                   9.0   \n1477                 12.0                 5.0                   9.0   \n555                  12.0                 5.0                   9.0   \n4232                 12.0                 5.0                   9.0   \n4456                 12.0                 5.0                   9.0   \n2849                 12.0                 5.0                   9.0   \n3259                 12.0                 5.0                   9.0   \n\n      B_Bantamweight_rank  B_Flyweight_rank  B_Pound-for-Pound_rank  \\\n4310                  9.0              12.0                    14.0   \n4109                  9.0              12.0                    14.0   \n3561                  9.0              12.0                    14.0   \n2582                  9.0              12.0                    14.0   \n1477                  9.0              12.0                    14.0   \n555                   9.0              12.0                    14.0   \n4232                  9.0              12.0                    14.0   \n4456                  9.0              12.0                    14.0   \n2849                  9.0              12.0                    14.0   \n3259                  9.0              12.0                     3.0   \n\n      better_rank  finish  finish_details  finish_round  finish_round_time  \\\n4310            2       5               2           3.0                 74   \n4109            2       1              15           2.0                280   \n3561            2       4              31           3.0                292   \n2582            1       6              31           3.0                292   \n1477            2       0              31           1.0                261   \n555             2       1               6           2.0                258   \n4232            2       1              12           1.0                198   \n4456            2       6              31           3.0                292   \n2849            2       1              25           1.0                129   \n3259            1       1              31           2.0                 69   \n\n      total_fight_time_secs  R_kd_bout  B_kd_bout  R_sig_str_landed_bout  \\\n4310                  681.0        0.0        1.0                   60.0   \n4109                  588.0        0.0        0.0                    6.0   \n3561                  900.0        0.0        0.0                   86.0   \n2582                  900.0        1.0        0.0                   21.0   \n1477                  269.0        0.0        0.0                    6.0   \n555                   566.0        0.0        0.0                   46.0   \n4232                  205.0        0.0        0.0                    6.0   \n4456                  900.0        0.0        0.0                   23.0   \n2849                  136.0        0.0        0.0                    6.0   \n3259                  376.0        1.0        0.0                   20.0   \n\n      B_sig_str_landed_bout  R_sig_str_attempted_bout  \\\n4310                   36.0                     165.0   \n4109                    1.0                      18.0   \n3561                   64.0                     225.0   \n2582                   28.0                      54.0   \n1477                    1.0                      18.0   \n555                    70.0                     108.0   \n4232                    1.0                      18.0   \n4456                   17.0                      67.0   \n2849                    1.0                      18.0   \n3259                    8.0                      44.0   \n\n      B_sig_str_attempted_bout  R_sig_str_pct_bout  B_sig_str_pct_bout  \\\n4310                     103.0                0.36                0.34   \n4109                      18.0                0.50                0.50   \n3561                     247.0                0.38                0.25   \n2582                      66.0                0.38                0.42   \n1477                      18.0                0.50                0.50   \n555                      155.0                0.42                0.45   \n4232                      18.0                0.50                0.50   \n4456                      45.0                0.34                0.37   \n2849                      18.0                0.50                0.50   \n3259                      15.0                0.45                0.53   \n\n      R_tot_str_landed_bout  B_tot_str_landed_bout  R_tot_str_attempted_bout  \\\n4310                   69.0                   44.0                     174.0   \n4109                    6.0                   10.0                      28.0   \n3561                   86.0                   67.0                     225.0   \n2582                   36.0                   45.0                      71.0   \n1477                    6.0                   10.0                      28.0   \n555                    50.0                   80.0                     114.0   \n4232                    6.0                   10.0                      28.0   \n4456                   42.0                   52.0                      89.0   \n2849                    6.0                   10.0                      28.0   \n3259                   34.0                   32.0                      60.0   \n\n      B_tot_str_attempted_bout  R_td_landed_bout  B_td_landed_bout  \\\n4310                     111.0               2.0               1.0   \n4109                      10.0               0.0               0.0   \n3561                     250.0               0.0               0.0   \n2582                      87.0               4.0               0.0   \n1477                      10.0               0.0               0.0   \n555                      170.0               0.0               1.0   \n4232                      10.0               0.0               0.0   \n4456                      81.0               1.0               4.0   \n2849                      10.0               0.0               0.0   \n3259                      45.0               1.0               0.0   \n\n      R_td_attempted_bout  B_td_attempted_bout  R_td_pct_bout  B_td_pct_bout  \\\n4310                  4.0                  3.0            0.5           0.33   \n4109                  0.0                  0.0            0.0           0.00   \n3561                  1.0                  0.0            0.0           0.00   \n2582                  5.0                  9.0            0.8           0.00   \n1477                  0.0                  0.0            0.0           0.00   \n555                   0.0                  1.0            0.0           1.00   \n4232                  0.0                  0.0            0.0           0.00   \n4456                  2.0                  8.0            0.5           0.50   \n2849                  0.0                  0.0            0.0           0.00   \n3259                  1.0                  0.0            1.0           0.00   \n\n      R_sub_attempts_bout  B_sub_attempts_bout  R_pass_bout  B_pass_bout  \\\n4310                  1.0                  0.0          2.0          1.0   \n4109                  0.0                  0.0          0.0          0.0   \n3561                  0.0                  0.0          0.0          0.0   \n2582                  0.0                  0.0          4.0          0.0   \n1477                  0.0                  0.0          0.0          0.0   \n555                   0.0                  0.0          0.0          2.0   \n4232                  0.0                  0.0          0.0          0.0   \n4456                  3.0                  1.0          2.0          7.0   \n2849                  0.0                  0.0          0.0          0.0   \n3259                  1.0                  0.0          0.0          0.0   \n\n      R_rev_bout  B_rev_bout  \n4310         0.0         0.0  \n4109         0.0         0.0  \n3561         0.0         0.0  \n2582         0.0         1.0  \n1477         0.0         0.0  \n555          0.0         0.0  \n4232         0.0         0.0  \n4456         1.0         0.0  \n2849         0.0         0.0  \n3259         0.0         0.0  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>R_fighter</th>\n      <th>B_fighter</th>\n      <th>R_odds</th>\n      <th>B_odds</th>\n      <th>R_ev</th>\n      <th>B_ev</th>\n      <th>date</th>\n      <th>location</th>\n      <th>country</th>\n      <th>title_bout</th>\n      <th>weight_class</th>\n      <th>gender</th>\n      <th>no_of_rounds</th>\n      <th>B_current_lose_streak</th>\n      <th>B_current_win_streak</th>\n      <th>B_draw</th>\n      <th>B_avg_SIG_STR_landed</th>\n      <th>B_avg_SIG_STR_pct</th>\n      <th>B_avg_SUB_ATT</th>\n      <th>B_avg_TD_landed</th>\n      <th>B_avg_TD_pct</th>\n      <th>B_longest_win_streak</th>\n      <th>B_losses</th>\n      <th>B_total_rounds_fought</th>\n      <th>B_total_title_bouts</th>\n      <th>B_win_by_Decision_Majority</th>\n      <th>B_win_by_Decision_Split</th>\n      <th>B_win_by_Decision_Unanimous</th>\n      <th>B_win_by_KO/TKO</th>\n      <th>B_win_by_Submission</th>\n      <th>B_win_by_TKO_Doctor_Stoppage</th>\n      <th>B_wins</th>\n      <th>B_Stance</th>\n      <th>B_Height_cms</th>\n      <th>B_Reach_cms</th>\n      <th>B_Weight_lbs</th>\n      <th>R_current_lose_streak</th>\n      <th>R_current_win_streak</th>\n      <th>R_draw</th>\n      <th>R_avg_SIG_STR_landed</th>\n      <th>R_avg_SIG_STR_pct</th>\n      <th>R_avg_SUB_ATT</th>\n      <th>R_avg_TD_landed</th>\n      <th>R_avg_TD_pct</th>\n      <th>R_longest_win_streak</th>\n      <th>R_losses</th>\n      <th>R_total_rounds_fought</th>\n      <th>R_total_title_bouts</th>\n      <th>R_win_by_Decision_Majority</th>\n      <th>R_win_by_Decision_Split</th>\n      <th>R_win_by_Decision_Unanimous</th>\n      <th>R_win_by_KO/TKO</th>\n      <th>R_win_by_Submission</th>\n      <th>R_win_by_TKO_Doctor_Stoppage</th>\n      <th>R_wins</th>\n      <th>R_Stance</th>\n      <th>R_Height_cms</th>\n      <th>R_Reach_cms</th>\n      <th>R_Weight_lbs</th>\n      <th>R_age</th>\n      <th>B_age</th>\n      <th>lose_streak_dif</th>\n      <th>win_streak_dif</th>\n      <th>longest_win_streak_dif</th>\n      <th>win_dif</th>\n      <th>loss_dif</th>\n      <th>total_round_dif</th>\n      <th>total_title_bout_dif</th>\n      <th>ko_dif</th>\n      <th>sub_dif</th>\n      <th>height_dif</th>\n      <th>reach_dif</th>\n      <th>age_dif</th>\n      <th>sig_str_dif</th>\n      <th>avg_sub_att_dif</th>\n      <th>avg_td_dif</th>\n      <th>empty_arena</th>\n      <th>constant_1</th>\n      <th>B_match_weightclass_rank</th>\n      <th>R_match_weightclass_rank</th>\n      <th>R_Women's Flyweight_rank</th>\n      <th>R_Women's Featherweight_rank</th>\n      <th>R_Women's Strawweight_rank</th>\n      <th>R_Women's Bantamweight_rank</th>\n      <th>R_Heavyweight_rank</th>\n      <th>R_Light Heavyweight_rank</th>\n      <th>R_Middleweight_rank</th>\n      <th>R_Welterweight_rank</th>\n      <th>R_Lightweight_rank</th>\n      <th>R_Featherweight_rank</th>\n      <th>R_Bantamweight_rank</th>\n      <th>R_Flyweight_rank</th>\n      <th>R_Pound-for-Pound_rank</th>\n      <th>B_Women's Flyweight_rank</th>\n      <th>B_Women's Strawweight_rank</th>\n      <th>B_Women's Bantamweight_rank</th>\n      <th>B_Heavyweight_rank</th>\n      <th>B_Light Heavyweight_rank</th>\n      <th>B_Middleweight_rank</th>\n      <th>B_Welterweight_rank</th>\n      <th>B_Lightweight_rank</th>\n      <th>B_Featherweight_rank</th>\n      <th>B_Bantamweight_rank</th>\n      <th>B_Flyweight_rank</th>\n      <th>B_Pound-for-Pound_rank</th>\n      <th>better_rank</th>\n      <th>finish</th>\n      <th>finish_details</th>\n      <th>finish_round</th>\n      <th>finish_round_time</th>\n      <th>total_fight_time_secs</th>\n      <th>R_kd_bout</th>\n      <th>B_kd_bout</th>\n      <th>R_sig_str_landed_bout</th>\n      <th>B_sig_str_landed_bout</th>\n      <th>R_sig_str_attempted_bout</th>\n      <th>B_sig_str_attempted_bout</th>\n      <th>R_sig_str_pct_bout</th>\n      <th>B_sig_str_pct_bout</th>\n      <th>R_tot_str_landed_bout</th>\n      <th>B_tot_str_landed_bout</th>\n      <th>R_tot_str_attempted_bout</th>\n      <th>B_tot_str_attempted_bout</th>\n      <th>R_td_landed_bout</th>\n      <th>B_td_landed_bout</th>\n      <th>R_td_attempted_bout</th>\n      <th>B_td_attempted_bout</th>\n      <th>R_td_pct_bout</th>\n      <th>B_td_pct_bout</th>\n      <th>R_sub_attempts_bout</th>\n      <th>B_sub_attempts_bout</th>\n      <th>R_pass_bout</th>\n      <th>B_pass_bout</th>\n      <th>R_rev_bout</th>\n      <th>B_rev_bout</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>4310</th>\n      <td>263</td>\n      <td>1245</td>\n      <td>-250</td>\n      <td>210</td>\n      <td>40.000000</td>\n      <td>210.000000</td>\n      <td>35</td>\n      <td>5</td>\n      <td>21</td>\n      <td>False</td>\n      <td>7</td>\n      <td>1</td>\n      <td>3</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>10.666667</td>\n      <td>0.511667</td>\n      <td>0.666667</td>\n      <td>0.500000</td>\n      <td>0.333333</td>\n      <td>1</td>\n      <td>4</td>\n      <td>7</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2</td>\n      <td>0</td>\n      <td>2</td>\n      <td>1</td>\n      <td>185.42</td>\n      <td>193.04</td>\n      <td>185</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>22.000000</td>\n      <td>0.370000</td>\n      <td>1.000000</td>\n      <td>5.000000</td>\n      <td>0.710000</td>\n      <td>1</td>\n      <td>0</td>\n      <td>2</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>180.34</td>\n      <td>190.50</td>\n      <td>170</td>\n      <td>25</td>\n      <td>33</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>-4</td>\n      <td>5</td>\n      <td>-1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>5.08</td>\n      <td>2.54</td>\n      <td>-8</td>\n      <td>-11.333333</td>\n      <td>-0.333333</td>\n      <td>-4.500000</td>\n      <td>0</td>\n      <td>1</td>\n      <td>9.0</td>\n      <td>0.0</td>\n      <td>5.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>4.0</td>\n      <td>13.0</td>\n      <td>6.0</td>\n      <td>5.0</td>\n      <td>10.0</td>\n      <td>8.0</td>\n      <td>7.0</td>\n      <td>7.0</td>\n      <td>6.0</td>\n      <td>9.0</td>\n      <td>7.0</td>\n      <td>9.0</td>\n      <td>12.0</td>\n      <td>8.0</td>\n      <td>12.0</td>\n      <td>5.0</td>\n      <td>9.0</td>\n      <td>9.0</td>\n      <td>12.0</td>\n      <td>14.0</td>\n      <td>2</td>\n      <td>5</td>\n      <td>2</td>\n      <td>3.0</td>\n      <td>74</td>\n      <td>681.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>60.0</td>\n      <td>36.0</td>\n      <td>165.0</td>\n      <td>103.0</td>\n      <td>0.36</td>\n      <td>0.34</td>\n      <td>69.0</td>\n      <td>44.0</td>\n      <td>174.0</td>\n      <td>111.0</td>\n      <td>2.0</td>\n      <td>1.0</td>\n      <td>4.0</td>\n      <td>3.0</td>\n      <td>0.5</td>\n      <td>0.33</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>4109</th>\n      <td>999</td>\n      <td>1383</td>\n      <td>-375</td>\n      <td>335</td>\n      <td>26.666667</td>\n      <td>335.000000</td>\n      <td>352</td>\n      <td>99</td>\n      <td>21</td>\n      <td>False</td>\n      <td>5</td>\n      <td>1</td>\n      <td>3</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>26.833333</td>\n      <td>0.531667</td>\n      <td>0.291667</td>\n      <td>2.250000</td>\n      <td>0.524583</td>\n      <td>6</td>\n      <td>9</td>\n      <td>59</td>\n      <td>10</td>\n      <td>0</td>\n      <td>2</td>\n      <td>3</td>\n      <td>7</td>\n      <td>2</td>\n      <td>1</td>\n      <td>15</td>\n      <td>1</td>\n      <td>190.50</td>\n      <td>187.96</td>\n      <td>205</td>\n      <td>0</td>\n      <td>2</td>\n      <td>0</td>\n      <td>22.916667</td>\n      <td>0.372500</td>\n      <td>0.000000</td>\n      <td>3.666667</td>\n      <td>0.394167</td>\n      <td>5</td>\n      <td>2</td>\n      <td>32</td>\n      <td>3</td>\n      <td>1</td>\n      <td>3</td>\n      <td>2</td>\n      <td>4</td>\n      <td>0</td>\n      <td>0</td>\n      <td>10</td>\n      <td>1</td>\n      <td>182.88</td>\n      <td>190.50</td>\n      <td>205</td>\n      <td>31</td>\n      <td>36</td>\n      <td>0</td>\n      <td>-1</td>\n      <td>1</td>\n      <td>5</td>\n      <td>-7</td>\n      <td>27</td>\n      <td>7</td>\n      <td>4</td>\n      <td>2</td>\n      <td>7.62</td>\n      <td>-2.54</td>\n      <td>-5</td>\n      <td>3.916667</td>\n      <td>0.291667</td>\n      <td>-1.416667</td>\n      <td>0</td>\n      <td>1</td>\n      <td>9.0</td>\n      <td>0.0</td>\n      <td>5.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>4.0</td>\n      <td>13.0</td>\n      <td>6.0</td>\n      <td>5.0</td>\n      <td>10.0</td>\n      <td>8.0</td>\n      <td>7.0</td>\n      <td>7.0</td>\n      <td>6.0</td>\n      <td>9.0</td>\n      <td>7.0</td>\n      <td>9.0</td>\n      <td>12.0</td>\n      <td>8.0</td>\n      <td>12.0</td>\n      <td>5.0</td>\n      <td>9.0</td>\n      <td>9.0</td>\n      <td>12.0</td>\n      <td>14.0</td>\n      <td>2</td>\n      <td>1</td>\n      <td>15</td>\n      <td>2.0</td>\n      <td>280</td>\n      <td>588.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>6.0</td>\n      <td>1.0</td>\n      <td>18.0</td>\n      <td>18.0</td>\n      <td>0.50</td>\n      <td>0.50</td>\n      <td>6.0</td>\n      <td>10.0</td>\n      <td>28.0</td>\n      <td>10.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>3561</th>\n      <td>923</td>\n      <td>241</td>\n      <td>-145</td>\n      <td>135</td>\n      <td>68.965517</td>\n      <td>135.000000</td>\n      <td>166</td>\n      <td>83</td>\n      <td>3</td>\n      <td>False</td>\n      <td>7</td>\n      <td>1</td>\n      <td>3</td>\n      <td>0</td>\n      <td>3</td>\n      <td>0</td>\n      <td>48.142857</td>\n      <td>0.475714</td>\n      <td>0.428571</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>3</td>\n      <td>2</td>\n      <td>19</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>2</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>5</td>\n      <td>1</td>\n      <td>187.96</td>\n      <td>193.04</td>\n      <td>185</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>49.000000</td>\n      <td>0.437500</td>\n      <td>0.250000</td>\n      <td>1.250000</td>\n      <td>0.250000</td>\n      <td>2</td>\n      <td>1</td>\n      <td>12</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>3</td>\n      <td>2</td>\n      <td>182.88</td>\n      <td>187.96</td>\n      <td>185</td>\n      <td>34</td>\n      <td>26</td>\n      <td>0</td>\n      <td>2</td>\n      <td>1</td>\n      <td>2</td>\n      <td>-1</td>\n      <td>7</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>5.08</td>\n      <td>5.08</td>\n      <td>8</td>\n      <td>-0.857143</td>\n      <td>0.178571</td>\n      <td>-1.250000</td>\n      <td>0</td>\n      <td>1</td>\n      <td>9.0</td>\n      <td>0.0</td>\n      <td>5.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>4.0</td>\n      <td>13.0</td>\n      <td>6.0</td>\n      <td>5.0</td>\n      <td>10.0</td>\n      <td>8.0</td>\n      <td>7.0</td>\n      <td>7.0</td>\n      <td>6.0</td>\n      <td>9.0</td>\n      <td>7.0</td>\n      <td>9.0</td>\n      <td>12.0</td>\n      <td>8.0</td>\n      <td>12.0</td>\n      <td>5.0</td>\n      <td>9.0</td>\n      <td>9.0</td>\n      <td>12.0</td>\n      <td>14.0</td>\n      <td>2</td>\n      <td>4</td>\n      <td>31</td>\n      <td>3.0</td>\n      <td>292</td>\n      <td>900.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>86.0</td>\n      <td>64.0</td>\n      <td>225.0</td>\n      <td>247.0</td>\n      <td>0.38</td>\n      <td>0.25</td>\n      <td>86.0</td>\n      <td>67.0</td>\n      <td>225.0</td>\n      <td>250.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>2582</th>\n      <td>661</td>\n      <td>1454</td>\n      <td>-105</td>\n      <td>-115</td>\n      <td>95.238095</td>\n      <td>86.956522</td>\n      <td>239</td>\n      <td>51</td>\n      <td>2</td>\n      <td>False</td>\n      <td>3</td>\n      <td>1</td>\n      <td>3</td>\n      <td>0</td>\n      <td>2</td>\n      <td>0</td>\n      <td>22.750000</td>\n      <td>0.350000</td>\n      <td>0.500000</td>\n      <td>4.250000</td>\n      <td>0.555000</td>\n      <td>2</td>\n      <td>1</td>\n      <td>10</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>3</td>\n      <td>2</td>\n      <td>162.56</td>\n      <td>165.10</td>\n      <td>125</td>\n      <td>0</td>\n      <td>2</td>\n      <td>0</td>\n      <td>9.000000</td>\n      <td>0.346000</td>\n      <td>0.200000</td>\n      <td>1.400000</td>\n      <td>0.250000</td>\n      <td>2</td>\n      <td>2</td>\n      <td>10</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>3</td>\n      <td>1</td>\n      <td>165.10</td>\n      <td>170.18</td>\n      <td>125</td>\n      <td>30</td>\n      <td>30</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>-2.54</td>\n      <td>-5.08</td>\n      <td>0</td>\n      <td>13.750000</td>\n      <td>0.300000</td>\n      <td>2.850000</td>\n      <td>0</td>\n      <td>1</td>\n      <td>12.0</td>\n      <td>4.0</td>\n      <td>5.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>4.0</td>\n      <td>13.0</td>\n      <td>6.0</td>\n      <td>5.0</td>\n      <td>10.0</td>\n      <td>8.0</td>\n      <td>4.0</td>\n      <td>7.0</td>\n      <td>6.0</td>\n      <td>9.0</td>\n      <td>7.0</td>\n      <td>9.0</td>\n      <td>12.0</td>\n      <td>8.0</td>\n      <td>12.0</td>\n      <td>5.0</td>\n      <td>9.0</td>\n      <td>9.0</td>\n      <td>12.0</td>\n      <td>14.0</td>\n      <td>1</td>\n      <td>6</td>\n      <td>31</td>\n      <td>3.0</td>\n      <td>292</td>\n      <td>900.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>21.0</td>\n      <td>28.0</td>\n      <td>54.0</td>\n      <td>66.0</td>\n      <td>0.38</td>\n      <td>0.42</td>\n      <td>36.0</td>\n      <td>45.0</td>\n      <td>71.0</td>\n      <td>87.0</td>\n      <td>4.0</td>\n      <td>0.0</td>\n      <td>5.0</td>\n      <td>9.0</td>\n      <td>0.8</td>\n      <td>0.00</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>4.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>1477</th>\n      <td>1227</td>\n      <td>938</td>\n      <td>-370</td>\n      <td>310</td>\n      <td>27.027027</td>\n      <td>310.000000</td>\n      <td>82</td>\n      <td>88</td>\n      <td>21</td>\n      <td>False</td>\n      <td>4</td>\n      <td>1</td>\n      <td>3</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>48.500000</td>\n      <td>0.500000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>1</td>\n      <td>1</td>\n      <td>4</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>193.04</td>\n      <td>195.58</td>\n      <td>240</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>19.750000</td>\n      <td>0.318750</td>\n      <td>0.125000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>2</td>\n      <td>5</td>\n      <td>14</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>3</td>\n      <td>0</td>\n      <td>0</td>\n      <td>3</td>\n      <td>2</td>\n      <td>195.58</td>\n      <td>195.58</td>\n      <td>250</td>\n      <td>34</td>\n      <td>33</td>\n      <td>1</td>\n      <td>1</td>\n      <td>-1</td>\n      <td>-2</td>\n      <td>4</td>\n      <td>-10</td>\n      <td>0</td>\n      <td>-3</td>\n      <td>0</td>\n      <td>-2.54</td>\n      <td>0.00</td>\n      <td>1</td>\n      <td>28.750000</td>\n      <td>-0.125000</td>\n      <td>0.000000</td>\n      <td>0</td>\n      <td>1</td>\n      <td>9.0</td>\n      <td>0.0</td>\n      <td>5.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>4.0</td>\n      <td>13.0</td>\n      <td>6.0</td>\n      <td>5.0</td>\n      <td>10.0</td>\n      <td>8.0</td>\n      <td>7.0</td>\n      <td>7.0</td>\n      <td>6.0</td>\n      <td>9.0</td>\n      <td>7.0</td>\n      <td>9.0</td>\n      <td>12.0</td>\n      <td>8.0</td>\n      <td>12.0</td>\n      <td>5.0</td>\n      <td>9.0</td>\n      <td>9.0</td>\n      <td>12.0</td>\n      <td>14.0</td>\n      <td>2</td>\n      <td>0</td>\n      <td>31</td>\n      <td>1.0</td>\n      <td>261</td>\n      <td>269.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>6.0</td>\n      <td>1.0</td>\n      <td>18.0</td>\n      <td>18.0</td>\n      <td>0.50</td>\n      <td>0.50</td>\n      <td>6.0</td>\n      <td>10.0</td>\n      <td>28.0</td>\n      <td>10.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>555</th>\n      <td>458</td>\n      <td>93</td>\n      <td>180</td>\n      <td>-220</td>\n      <td>180.000000</td>\n      <td>45.454545</td>\n      <td>16</td>\n      <td>106</td>\n      <td>21</td>\n      <td>False</td>\n      <td>12</td>\n      <td>0</td>\n      <td>3</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0.000000</td>\n      <td>0.500000</td>\n      <td>0.100000</td>\n      <td>0.390000</td>\n      <td>0.360000</td>\n      <td>1</td>\n      <td>7</td>\n      <td>32</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>4</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>5</td>\n      <td>1</td>\n      <td>160.02</td>\n      <td>162.56</td>\n      <td>115</td>\n      <td>0</td>\n      <td>2</td>\n      <td>0</td>\n      <td>23.000000</td>\n      <td>0.450000</td>\n      <td>0.400000</td>\n      <td>0.410000</td>\n      <td>1.000000</td>\n      <td>2</td>\n      <td>1</td>\n      <td>8</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2</td>\n      <td>1</td>\n      <td>154.94</td>\n      <td>157.48</td>\n      <td>115</td>\n      <td>27</td>\n      <td>35</td>\n      <td>0</td>\n      <td>-1</td>\n      <td>-1</td>\n      <td>3</td>\n      <td>-6</td>\n      <td>24</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>5.08</td>\n      <td>5.08</td>\n      <td>-8</td>\n      <td>0.000000</td>\n      <td>-0.300000</td>\n      <td>-0.020000</td>\n      <td>0</td>\n      <td>1</td>\n      <td>9.0</td>\n      <td>0.0</td>\n      <td>5.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>4.0</td>\n      <td>13.0</td>\n      <td>6.0</td>\n      <td>5.0</td>\n      <td>10.0</td>\n      <td>8.0</td>\n      <td>7.0</td>\n      <td>7.0</td>\n      <td>6.0</td>\n      <td>9.0</td>\n      <td>7.0</td>\n      <td>9.0</td>\n      <td>12.0</td>\n      <td>8.0</td>\n      <td>12.0</td>\n      <td>5.0</td>\n      <td>9.0</td>\n      <td>9.0</td>\n      <td>12.0</td>\n      <td>14.0</td>\n      <td>2</td>\n      <td>1</td>\n      <td>6</td>\n      <td>2.0</td>\n      <td>258</td>\n      <td>566.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>46.0</td>\n      <td>70.0</td>\n      <td>108.0</td>\n      <td>155.0</td>\n      <td>0.42</td>\n      <td>0.45</td>\n      <td>50.0</td>\n      <td>80.0</td>\n      <td>114.0</td>\n      <td>170.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.00</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>4232</th>\n      <td>70</td>\n      <td>1434</td>\n      <td>-210</td>\n      <td>190</td>\n      <td>47.619048</td>\n      <td>190.000000</td>\n      <td>157</td>\n      <td>66</td>\n      <td>21</td>\n      <td>True</td>\n      <td>7</td>\n      <td>1</td>\n      <td>5</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>9.583333</td>\n      <td>0.531667</td>\n      <td>0.500000</td>\n      <td>0.500000</td>\n      <td>0.229167</td>\n      <td>3</td>\n      <td>4</td>\n      <td>18</td>\n      <td>3</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>6</td>\n      <td>1</td>\n      <td>1</td>\n      <td>8</td>\n      <td>2</td>\n      <td>182.88</td>\n      <td>187.96</td>\n      <td>185</td>\n      <td>0</td>\n      <td>12</td>\n      <td>0</td>\n      <td>32.666667</td>\n      <td>0.728333</td>\n      <td>0.500000</td>\n      <td>0.250000</td>\n      <td>0.250000</td>\n      <td>12</td>\n      <td>0</td>\n      <td>29</td>\n      <td>8</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2</td>\n      <td>7</td>\n      <td>3</td>\n      <td>0</td>\n      <td>12</td>\n      <td>2</td>\n      <td>187.96</td>\n      <td>195.58</td>\n      <td>185</td>\n      <td>35</td>\n      <td>33</td>\n      <td>0</td>\n      <td>-11</td>\n      <td>-9</td>\n      <td>-4</td>\n      <td>-4</td>\n      <td>-11</td>\n      <td>-5</td>\n      <td>0</td>\n      <td>-2</td>\n      <td>-5.08</td>\n      <td>-7.62</td>\n      <td>2</td>\n      <td>-23.083333</td>\n      <td>0.000000</td>\n      <td>0.250000</td>\n      <td>0</td>\n      <td>1</td>\n      <td>9.0</td>\n      <td>0.0</td>\n      <td>5.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>4.0</td>\n      <td>13.0</td>\n      <td>6.0</td>\n      <td>5.0</td>\n      <td>10.0</td>\n      <td>8.0</td>\n      <td>7.0</td>\n      <td>7.0</td>\n      <td>6.0</td>\n      <td>9.0</td>\n      <td>7.0</td>\n      <td>9.0</td>\n      <td>12.0</td>\n      <td>8.0</td>\n      <td>12.0</td>\n      <td>5.0</td>\n      <td>9.0</td>\n      <td>9.0</td>\n      <td>12.0</td>\n      <td>14.0</td>\n      <td>2</td>\n      <td>1</td>\n      <td>12</td>\n      <td>1.0</td>\n      <td>198</td>\n      <td>205.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>6.0</td>\n      <td>1.0</td>\n      <td>18.0</td>\n      <td>18.0</td>\n      <td>0.50</td>\n      <td>0.50</td>\n      <td>6.0</td>\n      <td>10.0</td>\n      <td>28.0</td>\n      <td>10.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>4456</th>\n      <td>566</td>\n      <td>935</td>\n      <td>-320</td>\n      <td>290</td>\n      <td>31.250000</td>\n      <td>290.000000</td>\n      <td>180</td>\n      <td>89</td>\n      <td>21</td>\n      <td>False</td>\n      <td>6</td>\n      <td>1</td>\n      <td>3</td>\n      <td>0</td>\n      <td>3</td>\n      <td>0</td>\n      <td>16.833333</td>\n      <td>0.361667</td>\n      <td>1.333333</td>\n      <td>2.333333</td>\n      <td>0.540000</td>\n      <td>3</td>\n      <td>2</td>\n      <td>12</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>3</td>\n      <td>0</td>\n      <td>4</td>\n      <td>1</td>\n      <td>172.72</td>\n      <td>190.50</td>\n      <td>155</td>\n      <td>0</td>\n      <td>3</td>\n      <td>0</td>\n      <td>29.000000</td>\n      <td>0.370000</td>\n      <td>1.833333</td>\n      <td>2.500000</td>\n      <td>0.390000</td>\n      <td>3</td>\n      <td>1</td>\n      <td>15</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2</td>\n      <td>1</td>\n      <td>2</td>\n      <td>0</td>\n      <td>5</td>\n      <td>2</td>\n      <td>172.72</td>\n      <td>180.34</td>\n      <td>155</td>\n      <td>26</td>\n      <td>28</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>-1</td>\n      <td>-1</td>\n      <td>-3</td>\n      <td>0</td>\n      <td>-1</td>\n      <td>1</td>\n      <td>0.00</td>\n      <td>10.16</td>\n      <td>-2</td>\n      <td>-12.166667</td>\n      <td>-0.500000</td>\n      <td>-0.166667</td>\n      <td>0</td>\n      <td>1</td>\n      <td>9.0</td>\n      <td>0.0</td>\n      <td>5.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>4.0</td>\n      <td>13.0</td>\n      <td>6.0</td>\n      <td>5.0</td>\n      <td>10.0</td>\n      <td>8.0</td>\n      <td>7.0</td>\n      <td>7.0</td>\n      <td>6.0</td>\n      <td>9.0</td>\n      <td>7.0</td>\n      <td>9.0</td>\n      <td>12.0</td>\n      <td>8.0</td>\n      <td>12.0</td>\n      <td>5.0</td>\n      <td>9.0</td>\n      <td>9.0</td>\n      <td>12.0</td>\n      <td>14.0</td>\n      <td>2</td>\n      <td>6</td>\n      <td>31</td>\n      <td>3.0</td>\n      <td>292</td>\n      <td>900.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>23.0</td>\n      <td>17.0</td>\n      <td>67.0</td>\n      <td>45.0</td>\n      <td>0.34</td>\n      <td>0.37</td>\n      <td>42.0</td>\n      <td>52.0</td>\n      <td>89.0</td>\n      <td>81.0</td>\n      <td>1.0</td>\n      <td>4.0</td>\n      <td>2.0</td>\n      <td>8.0</td>\n      <td>0.5</td>\n      <td>0.50</td>\n      <td>3.0</td>\n      <td>1.0</td>\n      <td>2.0</td>\n      <td>7.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>2849</th>\n      <td>365</td>\n      <td>1254</td>\n      <td>-150</td>\n      <td>140</td>\n      <td>66.666667</td>\n      <td>140.000000</td>\n      <td>86</td>\n      <td>133</td>\n      <td>1</td>\n      <td>False</td>\n      <td>7</td>\n      <td>1</td>\n      <td>3</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>82.000000</td>\n      <td>0.480000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0</td>\n      <td>1</td>\n      <td>3</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2</td>\n      <td>187.96</td>\n      <td>190.50</td>\n      <td>205</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>17.666667</td>\n      <td>0.560000</td>\n      <td>0.000000</td>\n      <td>1.333333</td>\n      <td>0.300000</td>\n      <td>2</td>\n      <td>1</td>\n      <td>6</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2</td>\n      <td>3</td>\n      <td>185.42</td>\n      <td>185.42</td>\n      <td>185</td>\n      <td>34</td>\n      <td>28</td>\n      <td>0</td>\n      <td>0</td>\n      <td>-2</td>\n      <td>-2</td>\n      <td>0</td>\n      <td>-3</td>\n      <td>0</td>\n      <td>-2</td>\n      <td>0</td>\n      <td>2.54</td>\n      <td>5.08</td>\n      <td>6</td>\n      <td>64.333333</td>\n      <td>0.000000</td>\n      <td>-1.333333</td>\n      <td>0</td>\n      <td>1</td>\n      <td>9.0</td>\n      <td>0.0</td>\n      <td>5.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>4.0</td>\n      <td>13.0</td>\n      <td>6.0</td>\n      <td>5.0</td>\n      <td>10.0</td>\n      <td>8.0</td>\n      <td>7.0</td>\n      <td>7.0</td>\n      <td>6.0</td>\n      <td>9.0</td>\n      <td>7.0</td>\n      <td>9.0</td>\n      <td>12.0</td>\n      <td>8.0</td>\n      <td>12.0</td>\n      <td>5.0</td>\n      <td>9.0</td>\n      <td>9.0</td>\n      <td>12.0</td>\n      <td>14.0</td>\n      <td>2</td>\n      <td>1</td>\n      <td>25</td>\n      <td>1.0</td>\n      <td>129</td>\n      <td>136.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>6.0</td>\n      <td>1.0</td>\n      <td>18.0</td>\n      <td>18.0</td>\n      <td>0.50</td>\n      <td>0.50</td>\n      <td>6.0</td>\n      <td>10.0</td>\n      <td>28.0</td>\n      <td>10.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>3259</th>\n      <td>227</td>\n      <td>78</td>\n      <td>165</td>\n      <td>-175</td>\n      <td>165.000000</td>\n      <td>57.142857</td>\n      <td>113</td>\n      <td>66</td>\n      <td>21</td>\n      <td>True</td>\n      <td>7</td>\n      <td>1</td>\n      <td>5</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>28.764706</td>\n      <td>0.695294</td>\n      <td>0.352941</td>\n      <td>0.176471</td>\n      <td>0.176471</td>\n      <td>16</td>\n      <td>1</td>\n      <td>37</td>\n      <td>12</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2</td>\n      <td>11</td>\n      <td>3</td>\n      <td>0</td>\n      <td>16</td>\n      <td>2</td>\n      <td>187.96</td>\n      <td>195.58</td>\n      <td>185</td>\n      <td>0</td>\n      <td>6</td>\n      <td>0</td>\n      <td>26.166667</td>\n      <td>0.465000</td>\n      <td>1.000000</td>\n      <td>2.333333</td>\n      <td>0.768333</td>\n      <td>6</td>\n      <td>0</td>\n      <td>12</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2</td>\n      <td>2</td>\n      <td>2</td>\n      <td>0</td>\n      <td>6</td>\n      <td>1</td>\n      <td>187.96</td>\n      <td>198.12</td>\n      <td>185</td>\n      <td>29</td>\n      <td>38</td>\n      <td>-1</td>\n      <td>-6</td>\n      <td>10</td>\n      <td>10</td>\n      <td>-1</td>\n      <td>25</td>\n      <td>11</td>\n      <td>9</td>\n      <td>1</td>\n      <td>0.00</td>\n      <td>-2.54</td>\n      <td>-9</td>\n      <td>2.598039</td>\n      <td>-0.647059</td>\n      <td>-2.156863</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>5.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>4.0</td>\n      <td>0.0</td>\n      <td>6.0</td>\n      <td>5.0</td>\n      <td>10.0</td>\n      <td>8.0</td>\n      <td>7.0</td>\n      <td>8.0</td>\n      <td>6.0</td>\n      <td>9.0</td>\n      <td>7.0</td>\n      <td>9.0</td>\n      <td>12.0</td>\n      <td>1.0</td>\n      <td>12.0</td>\n      <td>5.0</td>\n      <td>9.0</td>\n      <td>9.0</td>\n      <td>12.0</td>\n      <td>3.0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>31</td>\n      <td>2.0</td>\n      <td>69</td>\n      <td>376.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>20.0</td>\n      <td>8.0</td>\n      <td>44.0</td>\n      <td>15.0</td>\n      <td>0.45</td>\n      <td>0.53</td>\n      <td>34.0</td>\n      <td>32.0</td>\n      <td>60.0</td>\n      <td>45.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.00</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"***Building a baseline model***"},{"metadata":{"trusted":true},"cell_type":"code","source":"#A DummyClassifier is used to be a baseline to compare a better model's performance later on\nbase_model = DummyClassifier(random_state=2)","execution_count":14,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"base_model.fit(X_train,y_train)","execution_count":15,"outputs":[{"output_type":"execute_result","execution_count":15,"data":{"text/plain":"DummyClassifier(random_state=2)"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"preds = base_model.predict(X_valid)\naccuracy_score(y_valid, preds)","execution_count":16,"outputs":[{"output_type":"execute_result","execution_count":16,"data":{"text/plain":"0.49850968703427717"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"49% is the accuracy for this model/ baseline accuracy"},{"metadata":{},"cell_type":"markdown","source":"Now that the baseline model is built let's focus on building an actual model for our prediction"},{"metadata":{},"cell_type":"markdown","source":"# 2. Data Visualization\nFeature visualization on ufc_master_ds to gain some insight into our data "},{"metadata":{"trusted":true},"cell_type":"code","source":"ufc_master_ds.head()","execution_count":17,"outputs":[{"output_type":"execute_result","execution_count":17,"data":{"text/plain":"              R_fighter         B_fighter  R_odds  B_odds        R_ev  \\\n0           Paul Felder  Rafael Dos Anjos     165    -200  165.000000   \n1  Abdul Razak Alhassan    Khaos Williams    -240     185   41.666667   \n2            Kay Hansen      Cory McKenna    -230     180   43.478261   \n3         Brendan Allen   Sean Strickland    -118    -106   84.745763   \n4          Ashley Yoder   Miranda Granger     135    -167  135.000000   \n\n         B_ev        date                location country Winner  title_bout  \\\n0   50.000000  11/14/2020  Las Vegas, Nevada, USA     USA   Blue       False   \n1  185.000000  11/14/2020  Las Vegas, Nevada, USA     USA   Blue       False   \n2  180.000000  11/14/2020  Las Vegas, Nevada, USA     USA   Blue       False   \n3   94.339623  11/14/2020  Las Vegas, Nevada, USA     USA   Blue       False   \n4   59.880240  11/14/2020  Las Vegas, Nevada, USA     USA    Red       False   \n\n          weight_class  gender  no_of_rounds  B_current_lose_streak  \\\n0          Lightweight    MALE             5                      2   \n1         Welterweight    MALE             3                      0   \n2  Women's Strawweight  FEMALE             3                      0   \n3         Catch Weight    MALE             3                      0   \n4  Women's Strawweight  FEMALE             3                      1   \n\n   B_current_win_streak  B_draw  B_avg_SIG_STR_landed  B_avg_SIG_STR_pct  \\\n0                     0       0                  3.47               0.45   \n1                     1       0                 22.22               0.58   \n2                     1       0                  3.13               0.49   \n3                     2       0                  4.64               0.36   \n4                     0       0                  3.47               0.43   \n\n   B_avg_SUB_ATT  B_avg_TD_landed  B_avg_TD_pct  B_longest_win_streak  \\\n0            0.6             1.82          0.39                     5   \n1            0.0             0.00          0.00                     1   \n2            0.0             1.00          1.00                     1   \n3            0.4             1.22          0.57                     3   \n4            0.0             0.00          0.00                     1   \n\n   B_losses  B_total_rounds_fought  B_total_title_bouts  \\\n0        11                     86                    4   \n1         0                      1                    0   \n2         0                      3                    0   \n3         3                     28                    0   \n4         1                      4                    0   \n\n   B_win_by_Decision_Majority  B_win_by_Decision_Split  \\\n0                           0                        0   \n1                           0                        0   \n2                           0                        0   \n3                           0                        2   \n4                           0                        0   \n\n   B_win_by_Decision_Unanimous  B_win_by_KO/TKO  B_win_by_Submission  \\\n0                           10                4                    4   \n1                            0                1                    0   \n2                            1                0                    0   \n3                            3                2                    1   \n4                            1                0                    0   \n\n   B_win_by_TKO_Doctor_Stoppage  B_wins  B_Stance  B_Height_cms  B_Reach_cms  \\\n0                             0      18  Southpaw        172.72       177.80   \n1                             0       1  Orthodox        182.88       195.58   \n2                             0       1  Orthodox        160.02       147.32   \n3                             0       8  Orthodox        185.42       193.04   \n4                             0       1  Orthodox        170.18       172.72   \n\n   B_Weight_lbs  R_current_lose_streak  R_current_win_streak  R_draw  \\\n0           155                      1                     0       0   \n1           170                      1                     0       0   \n2           115                      0                     1       0   \n3           185                      0                     4       0   \n4           125                      2                     0       0   \n\n   R_avg_SIG_STR_landed  R_avg_SIG_STR_pct  R_avg_SUB_ATT  R_avg_TD_landed  \\\n0                  3.67               0.43            0.2             0.25   \n1                  4.54               0.48            0.0             0.73   \n2                  2.49               0.56            1.2             2.41   \n3                  2.67               0.60            2.4             1.42   \n4                  2.96               0.40            0.4             1.14   \n\n   R_avg_TD_pct  R_longest_win_streak  R_losses  R_total_rounds_fought  \\\n0          0.25                     3         5                     38   \n1          0.28                     3         2                     10   \n2          0.18                     1         0                      3   \n3          0.37                     4         0                      7   \n4          0.36                     2         5                     21   \n\n   R_total_title_bouts  R_win_by_Decision_Majority  R_win_by_Decision_Split  \\\n0                    0                           0                        2   \n1                    0                           0                        0   \n2                    0                           0                        0   \n3                    0                           0                        0   \n4                    0                           0                        1   \n\n   R_win_by_Decision_Unanimous  R_win_by_KO/TKO  R_win_by_Submission  \\\n0                            2                4                    1   \n1                            0                4                    0   \n2                            0                0                    1   \n3                            1                1                    2   \n4                            1                0                    0   \n\n   R_win_by_TKO_Doctor_Stoppage  R_wins  R_Stance  R_Height_cms  R_Reach_cms  \\\n0                             0       9  Orthodox        180.34       177.80   \n1                             0       4  Orthodox        177.80       185.42   \n2                             0       1  Orthodox        157.48       160.02   \n3                             0       4  Orthodox        187.96       190.50   \n4                             0       2  Southpaw        170.18       175.26   \n\n   R_Weight_lbs  R_age  B_age  lose_streak_dif  win_streak_dif  \\\n0           155     36     36                1               0   \n1           170     35     26               -1               1   \n2           115     21     21                0               0   \n3           185     24     29                0              -2   \n4           115     33     28               -1               0   \n\n   longest_win_streak_dif  win_dif  loss_dif  total_round_dif  \\\n0                       2        9         6               48   \n1                      -2       -3        -2               -9   \n2                       0        0         0                0   \n3                      -1        4         3               21   \n4                      -1       -1        -4              -17   \n\n   total_title_bout_dif  ko_dif  sub_dif  height_dif  reach_dif  age_dif  \\\n0                     4       0        3       -7.62       0.00        0   \n1                     0      -3        0        5.08      10.16       -9   \n2                     0       0       -1        2.54     -12.70        0   \n3                     0       1       -1       -2.54       2.54        5   \n4                     0       0        0        0.00      -2.54       -5   \n\n   sig_str_dif  avg_sub_att_dif  avg_td_dif  empty_arena  constant_1  \\\n0        -0.20              0.4        1.57            1           1   \n1        17.68              0.0       -0.73            1           1   \n2         0.64             -1.2       -1.41            1           1   \n3         1.97             -2.0       -0.20            1           1   \n4         0.51             -0.4       -1.14            1           1   \n\n   B_match_weightclass_rank  R_match_weightclass_rank  \\\n0                      12.0                       7.0   \n1                       NaN                       NaN   \n2                       NaN                       NaN   \n3                       NaN                       NaN   \n4                       NaN                       NaN   \n\n   R_Women's Flyweight_rank  R_Women's Featherweight_rank  \\\n0                       NaN                           NaN   \n1                       NaN                           NaN   \n2                       NaN                           NaN   \n3                       NaN                           NaN   \n4                       NaN                           NaN   \n\n   R_Women's Strawweight_rank  R_Women's Bantamweight_rank  \\\n0                         NaN                          NaN   \n1                         NaN                          NaN   \n2                         NaN                          NaN   \n3                         NaN                          NaN   \n4                         NaN                          NaN   \n\n   R_Heavyweight_rank  R_Light Heavyweight_rank  R_Middleweight_rank  \\\n0                 NaN                       NaN                  NaN   \n1                 NaN                       NaN                  NaN   \n2                 NaN                       NaN                  NaN   \n3                 NaN                       NaN                  NaN   \n4                 NaN                       NaN                  NaN   \n\n   R_Welterweight_rank  R_Lightweight_rank  R_Featherweight_rank  \\\n0                  NaN                 7.0                   NaN   \n1                  NaN                 NaN                   NaN   \n2                  NaN                 NaN                   NaN   \n3                  NaN                 NaN                   NaN   \n4                  NaN                 NaN                   NaN   \n\n   R_Bantamweight_rank  R_Flyweight_rank  R_Pound-for-Pound_rank  \\\n0                  NaN               NaN                     NaN   \n1                  NaN               NaN                     NaN   \n2                  NaN               NaN                     NaN   \n3                  NaN               NaN                     NaN   \n4                  NaN               NaN                     NaN   \n\n   B_Women's Flyweight_rank  B_Women's Featherweight_rank  \\\n0                       NaN                           NaN   \n1                       NaN                           NaN   \n2                       NaN                           NaN   \n3                       NaN                           NaN   \n4                       NaN                           NaN   \n\n   B_Women's Strawweight_rank  B_Women's Bantamweight_rank  \\\n0                         NaN                          NaN   \n1                         NaN                          NaN   \n2                         NaN                          NaN   \n3                         NaN                          NaN   \n4                         NaN                          NaN   \n\n   B_Heavyweight_rank  B_Light Heavyweight_rank  B_Middleweight_rank  \\\n0                 NaN                       NaN                  NaN   \n1                 NaN                       NaN                  NaN   \n2                 NaN                       NaN                  NaN   \n3                 NaN                       NaN                  NaN   \n4                 NaN                       NaN                  NaN   \n\n   B_Welterweight_rank  B_Lightweight_rank  B_Featherweight_rank  \\\n0                  NaN                12.0                   NaN   \n1                  NaN                 NaN                   NaN   \n2                  NaN                 NaN                   NaN   \n3                  NaN                 NaN                   NaN   \n4                  NaN                 NaN                   NaN   \n\n   B_Bantamweight_rank  B_Flyweight_rank  B_Pound-for-Pound_rank better_rank  \\\n0                  NaN               NaN                     NaN         Red   \n1                  NaN               NaN                     NaN     neither   \n2                  NaN               NaN                     NaN     neither   \n3                  NaN               NaN                     NaN     neither   \n4                  NaN               NaN                     NaN     neither   \n\n  finish finish_details  finish_round finish_round_time  \\\n0    NaN            NaN           NaN               NaN   \n1    NaN            NaN           NaN               NaN   \n2    NaN            NaN           NaN               NaN   \n3    NaN            NaN           NaN               NaN   \n4    NaN            NaN           NaN               NaN   \n\n   total_fight_time_secs  R_kd_bout  B_kd_bout  R_sig_str_landed_bout  \\\n0                    NaN        NaN        NaN                    NaN   \n1                    NaN        NaN        NaN                    NaN   \n2                    NaN        NaN        NaN                    NaN   \n3                    NaN        NaN        NaN                    NaN   \n4                    NaN        NaN        NaN                    NaN   \n\n   B_sig_str_landed_bout  R_sig_str_attempted_bout  B_sig_str_attempted_bout  \\\n0                    NaN                       NaN                       NaN   \n1                    NaN                       NaN                       NaN   \n2                    NaN                       NaN                       NaN   \n3                    NaN                       NaN                       NaN   \n4                    NaN                       NaN                       NaN   \n\n   R_sig_str_pct_bout  B_sig_str_pct_bout  R_tot_str_landed_bout  \\\n0                 NaN                 NaN                    NaN   \n1                 NaN                 NaN                    NaN   \n2                 NaN                 NaN                    NaN   \n3                 NaN                 NaN                    NaN   \n4                 NaN                 NaN                    NaN   \n\n   B_tot_str_landed_bout  R_tot_str_attempted_bout  B_tot_str_attempted_bout  \\\n0                    NaN                       NaN                       NaN   \n1                    NaN                       NaN                       NaN   \n2                    NaN                       NaN                       NaN   \n3                    NaN                       NaN                       NaN   \n4                    NaN                       NaN                       NaN   \n\n   R_td_landed_bout  B_td_landed_bout  R_td_attempted_bout  \\\n0               NaN               NaN                  NaN   \n1               NaN               NaN                  NaN   \n2               NaN               NaN                  NaN   \n3               NaN               NaN                  NaN   \n4               NaN               NaN                  NaN   \n\n   B_td_attempted_bout  R_td_pct_bout  B_td_pct_bout  R_sub_attempts_bout  \\\n0                  NaN            NaN            NaN                  NaN   \n1                  NaN            NaN            NaN                  NaN   \n2                  NaN            NaN            NaN                  NaN   \n3                  NaN            NaN            NaN                  NaN   \n4                  NaN            NaN            NaN                  NaN   \n\n   B_sub_attempts_bout  R_pass_bout  B_pass_bout  R_rev_bout  B_rev_bout  \n0                  NaN          NaN          NaN         NaN         NaN  \n1                  NaN          NaN          NaN         NaN         NaN  \n2                  NaN          NaN          NaN         NaN         NaN  \n3                  NaN          NaN          NaN         NaN         NaN  \n4                  NaN          NaN          NaN         NaN         NaN  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>R_fighter</th>\n      <th>B_fighter</th>\n      <th>R_odds</th>\n      <th>B_odds</th>\n      <th>R_ev</th>\n      <th>B_ev</th>\n      <th>date</th>\n      <th>location</th>\n      <th>country</th>\n      <th>Winner</th>\n      <th>title_bout</th>\n      <th>weight_class</th>\n      <th>gender</th>\n      <th>no_of_rounds</th>\n      <th>B_current_lose_streak</th>\n      <th>B_current_win_streak</th>\n      <th>B_draw</th>\n      <th>B_avg_SIG_STR_landed</th>\n      <th>B_avg_SIG_STR_pct</th>\n      <th>B_avg_SUB_ATT</th>\n      <th>B_avg_TD_landed</th>\n      <th>B_avg_TD_pct</th>\n      <th>B_longest_win_streak</th>\n      <th>B_losses</th>\n      <th>B_total_rounds_fought</th>\n      <th>B_total_title_bouts</th>\n      <th>B_win_by_Decision_Majority</th>\n      <th>B_win_by_Decision_Split</th>\n      <th>B_win_by_Decision_Unanimous</th>\n      <th>B_win_by_KO/TKO</th>\n      <th>B_win_by_Submission</th>\n      <th>B_win_by_TKO_Doctor_Stoppage</th>\n      <th>B_wins</th>\n      <th>B_Stance</th>\n      <th>B_Height_cms</th>\n      <th>B_Reach_cms</th>\n      <th>B_Weight_lbs</th>\n      <th>R_current_lose_streak</th>\n      <th>R_current_win_streak</th>\n      <th>R_draw</th>\n      <th>R_avg_SIG_STR_landed</th>\n      <th>R_avg_SIG_STR_pct</th>\n      <th>R_avg_SUB_ATT</th>\n      <th>R_avg_TD_landed</th>\n      <th>R_avg_TD_pct</th>\n      <th>R_longest_win_streak</th>\n      <th>R_losses</th>\n      <th>R_total_rounds_fought</th>\n      <th>R_total_title_bouts</th>\n      <th>R_win_by_Decision_Majority</th>\n      <th>R_win_by_Decision_Split</th>\n      <th>R_win_by_Decision_Unanimous</th>\n      <th>R_win_by_KO/TKO</th>\n      <th>R_win_by_Submission</th>\n      <th>R_win_by_TKO_Doctor_Stoppage</th>\n      <th>R_wins</th>\n      <th>R_Stance</th>\n      <th>R_Height_cms</th>\n      <th>R_Reach_cms</th>\n      <th>R_Weight_lbs</th>\n      <th>R_age</th>\n      <th>B_age</th>\n      <th>lose_streak_dif</th>\n      <th>win_streak_dif</th>\n      <th>longest_win_streak_dif</th>\n      <th>win_dif</th>\n      <th>loss_dif</th>\n      <th>total_round_dif</th>\n      <th>total_title_bout_dif</th>\n      <th>ko_dif</th>\n      <th>sub_dif</th>\n      <th>height_dif</th>\n      <th>reach_dif</th>\n      <th>age_dif</th>\n      <th>sig_str_dif</th>\n      <th>avg_sub_att_dif</th>\n      <th>avg_td_dif</th>\n      <th>empty_arena</th>\n      <th>constant_1</th>\n      <th>B_match_weightclass_rank</th>\n      <th>R_match_weightclass_rank</th>\n      <th>R_Women's Flyweight_rank</th>\n      <th>R_Women's Featherweight_rank</th>\n      <th>R_Women's Strawweight_rank</th>\n      <th>R_Women's Bantamweight_rank</th>\n      <th>R_Heavyweight_rank</th>\n      <th>R_Light Heavyweight_rank</th>\n      <th>R_Middleweight_rank</th>\n      <th>R_Welterweight_rank</th>\n      <th>R_Lightweight_rank</th>\n      <th>R_Featherweight_rank</th>\n      <th>R_Bantamweight_rank</th>\n      <th>R_Flyweight_rank</th>\n      <th>R_Pound-for-Pound_rank</th>\n      <th>B_Women's Flyweight_rank</th>\n      <th>B_Women's Featherweight_rank</th>\n      <th>B_Women's Strawweight_rank</th>\n      <th>B_Women's Bantamweight_rank</th>\n      <th>B_Heavyweight_rank</th>\n      <th>B_Light Heavyweight_rank</th>\n      <th>B_Middleweight_rank</th>\n      <th>B_Welterweight_rank</th>\n      <th>B_Lightweight_rank</th>\n      <th>B_Featherweight_rank</th>\n      <th>B_Bantamweight_rank</th>\n      <th>B_Flyweight_rank</th>\n      <th>B_Pound-for-Pound_rank</th>\n      <th>better_rank</th>\n      <th>finish</th>\n      <th>finish_details</th>\n      <th>finish_round</th>\n      <th>finish_round_time</th>\n      <th>total_fight_time_secs</th>\n      <th>R_kd_bout</th>\n      <th>B_kd_bout</th>\n      <th>R_sig_str_landed_bout</th>\n      <th>B_sig_str_landed_bout</th>\n      <th>R_sig_str_attempted_bout</th>\n      <th>B_sig_str_attempted_bout</th>\n      <th>R_sig_str_pct_bout</th>\n      <th>B_sig_str_pct_bout</th>\n      <th>R_tot_str_landed_bout</th>\n      <th>B_tot_str_landed_bout</th>\n      <th>R_tot_str_attempted_bout</th>\n      <th>B_tot_str_attempted_bout</th>\n      <th>R_td_landed_bout</th>\n      <th>B_td_landed_bout</th>\n      <th>R_td_attempted_bout</th>\n      <th>B_td_attempted_bout</th>\n      <th>R_td_pct_bout</th>\n      <th>B_td_pct_bout</th>\n      <th>R_sub_attempts_bout</th>\n      <th>B_sub_attempts_bout</th>\n      <th>R_pass_bout</th>\n      <th>B_pass_bout</th>\n      <th>R_rev_bout</th>\n      <th>B_rev_bout</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Paul Felder</td>\n      <td>Rafael Dos Anjos</td>\n      <td>165</td>\n      <td>-200</td>\n      <td>165.000000</td>\n      <td>50.000000</td>\n      <td>11/14/2020</td>\n      <td>Las Vegas, Nevada, USA</td>\n      <td>USA</td>\n      <td>Blue</td>\n      <td>False</td>\n      <td>Lightweight</td>\n      <td>MALE</td>\n      <td>5</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>3.47</td>\n      <td>0.45</td>\n      <td>0.6</td>\n      <td>1.82</td>\n      <td>0.39</td>\n      <td>5</td>\n      <td>11</td>\n      <td>86</td>\n      <td>4</td>\n      <td>0</td>\n      <td>0</td>\n      <td>10</td>\n      <td>4</td>\n      <td>4</td>\n      <td>0</td>\n      <td>18</td>\n      <td>Southpaw</td>\n      <td>172.72</td>\n      <td>177.80</td>\n      <td>155</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>3.67</td>\n      <td>0.43</td>\n      <td>0.2</td>\n      <td>0.25</td>\n      <td>0.25</td>\n      <td>3</td>\n      <td>5</td>\n      <td>38</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2</td>\n      <td>2</td>\n      <td>4</td>\n      <td>1</td>\n      <td>0</td>\n      <td>9</td>\n      <td>Orthodox</td>\n      <td>180.34</td>\n      <td>177.80</td>\n      <td>155</td>\n      <td>36</td>\n      <td>36</td>\n      <td>1</td>\n      <td>0</td>\n      <td>2</td>\n      <td>9</td>\n      <td>6</td>\n      <td>48</td>\n      <td>4</td>\n      <td>0</td>\n      <td>3</td>\n      <td>-7.62</td>\n      <td>0.00</td>\n      <td>0</td>\n      <td>-0.20</td>\n      <td>0.4</td>\n      <td>1.57</td>\n      <td>1</td>\n      <td>1</td>\n      <td>12.0</td>\n      <td>7.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>7.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>12.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Red</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Abdul Razak Alhassan</td>\n      <td>Khaos Williams</td>\n      <td>-240</td>\n      <td>185</td>\n      <td>41.666667</td>\n      <td>185.000000</td>\n      <td>11/14/2020</td>\n      <td>Las Vegas, Nevada, USA</td>\n      <td>USA</td>\n      <td>Blue</td>\n      <td>False</td>\n      <td>Welterweight</td>\n      <td>MALE</td>\n      <td>3</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>22.22</td>\n      <td>0.58</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.00</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>Orthodox</td>\n      <td>182.88</td>\n      <td>195.58</td>\n      <td>170</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>4.54</td>\n      <td>0.48</td>\n      <td>0.0</td>\n      <td>0.73</td>\n      <td>0.28</td>\n      <td>3</td>\n      <td>2</td>\n      <td>10</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>4</td>\n      <td>0</td>\n      <td>0</td>\n      <td>4</td>\n      <td>Orthodox</td>\n      <td>177.80</td>\n      <td>185.42</td>\n      <td>170</td>\n      <td>35</td>\n      <td>26</td>\n      <td>-1</td>\n      <td>1</td>\n      <td>-2</td>\n      <td>-3</td>\n      <td>-2</td>\n      <td>-9</td>\n      <td>0</td>\n      <td>-3</td>\n      <td>0</td>\n      <td>5.08</td>\n      <td>10.16</td>\n      <td>-9</td>\n      <td>17.68</td>\n      <td>0.0</td>\n      <td>-0.73</td>\n      <td>1</td>\n      <td>1</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>neither</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Kay Hansen</td>\n      <td>Cory McKenna</td>\n      <td>-230</td>\n      <td>180</td>\n      <td>43.478261</td>\n      <td>180.000000</td>\n      <td>11/14/2020</td>\n      <td>Las Vegas, Nevada, USA</td>\n      <td>USA</td>\n      <td>Blue</td>\n      <td>False</td>\n      <td>Women's Strawweight</td>\n      <td>FEMALE</td>\n      <td>3</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>3.13</td>\n      <td>0.49</td>\n      <td>0.0</td>\n      <td>1.00</td>\n      <td>1.00</td>\n      <td>1</td>\n      <td>0</td>\n      <td>3</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>Orthodox</td>\n      <td>160.02</td>\n      <td>147.32</td>\n      <td>115</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>2.49</td>\n      <td>0.56</td>\n      <td>1.2</td>\n      <td>2.41</td>\n      <td>0.18</td>\n      <td>1</td>\n      <td>0</td>\n      <td>3</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>Orthodox</td>\n      <td>157.48</td>\n      <td>160.02</td>\n      <td>115</td>\n      <td>21</td>\n      <td>21</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>-1</td>\n      <td>2.54</td>\n      <td>-12.70</td>\n      <td>0</td>\n      <td>0.64</td>\n      <td>-1.2</td>\n      <td>-1.41</td>\n      <td>1</td>\n      <td>1</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>neither</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Brendan Allen</td>\n      <td>Sean Strickland</td>\n      <td>-118</td>\n      <td>-106</td>\n      <td>84.745763</td>\n      <td>94.339623</td>\n      <td>11/14/2020</td>\n      <td>Las Vegas, Nevada, USA</td>\n      <td>USA</td>\n      <td>Blue</td>\n      <td>False</td>\n      <td>Catch Weight</td>\n      <td>MALE</td>\n      <td>3</td>\n      <td>0</td>\n      <td>2</td>\n      <td>0</td>\n      <td>4.64</td>\n      <td>0.36</td>\n      <td>0.4</td>\n      <td>1.22</td>\n      <td>0.57</td>\n      <td>3</td>\n      <td>3</td>\n      <td>28</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2</td>\n      <td>3</td>\n      <td>2</td>\n      <td>1</td>\n      <td>0</td>\n      <td>8</td>\n      <td>Orthodox</td>\n      <td>185.42</td>\n      <td>193.04</td>\n      <td>185</td>\n      <td>0</td>\n      <td>4</td>\n      <td>0</td>\n      <td>2.67</td>\n      <td>0.60</td>\n      <td>2.4</td>\n      <td>1.42</td>\n      <td>0.37</td>\n      <td>4</td>\n      <td>0</td>\n      <td>7</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>2</td>\n      <td>0</td>\n      <td>4</td>\n      <td>Orthodox</td>\n      <td>187.96</td>\n      <td>190.50</td>\n      <td>185</td>\n      <td>24</td>\n      <td>29</td>\n      <td>0</td>\n      <td>-2</td>\n      <td>-1</td>\n      <td>4</td>\n      <td>3</td>\n      <td>21</td>\n      <td>0</td>\n      <td>1</td>\n      <td>-1</td>\n      <td>-2.54</td>\n      <td>2.54</td>\n      <td>5</td>\n      <td>1.97</td>\n      <td>-2.0</td>\n      <td>-0.20</td>\n      <td>1</td>\n      <td>1</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>neither</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Ashley Yoder</td>\n      <td>Miranda Granger</td>\n      <td>135</td>\n      <td>-167</td>\n      <td>135.000000</td>\n      <td>59.880240</td>\n      <td>11/14/2020</td>\n      <td>Las Vegas, Nevada, USA</td>\n      <td>USA</td>\n      <td>Red</td>\n      <td>False</td>\n      <td>Women's Strawweight</td>\n      <td>FEMALE</td>\n      <td>3</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>3.47</td>\n      <td>0.43</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.00</td>\n      <td>1</td>\n      <td>1</td>\n      <td>4</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>Orthodox</td>\n      <td>170.18</td>\n      <td>172.72</td>\n      <td>125</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2.96</td>\n      <td>0.40</td>\n      <td>0.4</td>\n      <td>1.14</td>\n      <td>0.36</td>\n      <td>2</td>\n      <td>5</td>\n      <td>21</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2</td>\n      <td>Southpaw</td>\n      <td>170.18</td>\n      <td>175.26</td>\n      <td>115</td>\n      <td>33</td>\n      <td>28</td>\n      <td>-1</td>\n      <td>0</td>\n      <td>-1</td>\n      <td>-1</td>\n      <td>-4</td>\n      <td>-17</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0.00</td>\n      <td>-2.54</td>\n      <td>-5</td>\n      <td>0.51</td>\n      <td>-0.4</td>\n      <td>-1.14</td>\n      <td>1</td>\n      <td>1</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>neither</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"#Encoding label so it is easier to find correlation\nufc_master_ds['Winner'] = [1 if winner == 'Red' else 0 for winner in ufc_master_ds.Winner]","execution_count":18,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Now, let's see the correlation between variables and target and then select the appropriate variable to visualize"},{"metadata":{"trusted":true},"cell_type":"code","source":"num_corr_col = [col for col in ufc_master_ds.columns if ufc_master_ds[col].dtype == 'int64' or ufc_master_ds[col].dtype == 'float64']\ncorr_dict = {}\n#Getting absolute values of correlation since we would need to inspect negative correlation too\nfor col in num_corr_col:\n    corr_dict[col] = abs(ufc_master_ds[col].corr(ufc_master_ds['Winner']))","execution_count":19,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"for w in sorted(corr_dict, key=corr_dict.get):\n    print(w, corr_dict[w])","execution_count":20,"outputs":[{"output_type":"stream","text":"B_Pound-for-Pound_rank 4.0438642413036e-18\nB_avg_TD_pct 0.00012087014772744039\nR_Welterweight_rank 0.00025674863303685514\nB_win_by_Submission 0.001921970700803313\nR_Pound-for-Pound_rank 0.0022287169288472074\nR_draw 0.0022689430916459806\nR_Weight_lbs 0.003057520088610938\nR_Height_cms 0.0037943455887552947\nB_avg_SUB_ATT 0.004167108124258807\nR_win_by_Decision_Unanimous 0.0042820543616267845\nB_longest_win_streak 0.005064693501448341\nR_win_by_KO/TKO 0.005143504980737794\ntotal_title_bout_dif 0.00654198940495853\nR_win_by_Submission 0.007146079004340056\nsub_dif 0.007394412534057697\nR_wins 0.00863537589373594\nB_win_by_TKO_Doctor_Stoppage 0.01008498554047256\nR_rev_bout 0.01052663529382445\nB_wins 0.011624583559467262\nB_win_by_Decision_Majority 0.012640281159421701\nempty_arena 0.013073493242466401\nR_win_by_Decision_Majority 0.014170122345257273\nB_win_by_KO/TKO 0.01506906024897687\nR_Reach_cms 0.015153621330814706\nB_win_by_Decision_Unanimous 0.016033821891185986\nB_draw 0.016214935675339985\nB_avg_SIG_STR_pct 0.016501058410236105\nB_Weight_lbs 0.019674385960646534\nwin_dif 0.01968165883396887\nB_total_rounds_fought 0.01981987855162967\nB_current_lose_streak 0.021648806166682043\nko_dif 0.02194154367045564\nR_total_title_bouts 0.02258254258875796\nR_Lightweight_rank 0.023517506233656987\navg_sub_att_dif 0.024452991541061214\nB_Middleweight_rank 0.024532673800591993\ntotal_fight_time_secs 0.025061752737682484\nfinish_round 0.02577799963008899\nR_avg_SIG_STR_landed 0.026253793203216042\nR_total_rounds_fought 0.0265944711493032\nR_avg_SUB_ATT 0.027624789286347883\nB_Height_cms 0.029943337150663153\nR_win_by_TKO_Doctor_Stoppage 0.030047864807815745\nR_longest_win_streak 0.03092723678624766\nno_of_rounds 0.03150499539465877\nB_losses 0.032815960421498404\nB_Bantamweight_rank 0.03590787932901787\nB_win_by_Decision_Split 0.03736122933946951\nheight_dif 0.03750138951460594\nlongest_win_streak_dif 0.03789144194312468\nB_rev_bout 0.03824474972195452\nB_avg_TD_landed 0.04018505627773451\ntotal_round_dif 0.04136305617539945\nB_Reach_cms 0.04272907982003152\nB_total_title_bouts 0.04330731048415467\nB_current_win_streak 0.044705466576715376\nR_Middleweight_rank 0.045677093238108944\nB_avg_SIG_STR_landed 0.045707114263715355\nR_Featherweight_rank 0.051876358302552834\nR_current_lose_streak 0.052483168858832786\nlose_streak_dif 0.053039802625660736\nR_Women's Flyweight_rank 0.05359330785703403\nsig_str_dif 0.05534040237630357\nB_Lightweight_rank 0.05563697541718596\nB_Light Heavyweight_rank 0.05963318664048127\nR_Light Heavyweight_rank 0.06141912343659836\nR_current_win_streak 0.06455332948841463\nR_avg_TD_pct 0.06607804057327828\nR_avg_SIG_STR_pct 0.07060894752999475\nR_win_by_Decision_Split 0.07166778271198596\nreach_dif 0.07248119761177446\nR_avg_TD_landed 0.07748048512365746\nR_td_attempted_bout 0.08037661201789775\nR_losses 0.0810194979634776\navg_td_dif 0.08220098993657818\nloss_dif 0.08544901787420617\nR_Women's Strawweight_rank 0.09450875876038257\nR_match_weightclass_rank 0.09498578466369675\nB_Flyweight_rank 0.09692487608800007\nwin_streak_dif 0.09750035737615846\nR_age 0.10320215007626332\nB_match_weightclass_rank 0.10460627754938132\nR_sig_str_attempted_bout 0.10611405172435617\nB_age 0.10734635694106708\nB_Women's Flyweight_rank 0.11276017713050743\nB_td_attempted_bout 0.11465201428077773\nR_Flyweight_rank 0.13261735799075047\nR_Bantamweight_rank 0.13376078137366262\nage_dif 0.14557064385916996\nconstant_1 nan\nR_Women's Featherweight_rank nan\nB_Featherweight_rank 0.09963402895232808\nB_Heavyweight_rank 0.11515344724291655\nB_sig_str_attempted_bout 0.1508821937041635\nB_sub_attempts_bout 0.15672306300932698\nR_Women's Bantamweight_rank 0.16787597045805258\nB_Welterweight_rank 0.17478311293991372\nR_tot_str_attempted_bout 0.17528101859430364\nB_tot_str_attempted_bout 0.1872300696766775\nB_Women's Bantamweight_rank 0.19016464890807985\nR_Heavyweight_rank 0.21012602461803115\nB_Women's Featherweight_rank nan\nR_sub_attempts_bout 0.17582326902583706\nB_Women's Strawweight_rank 0.22591549768969907\nR_sig_str_landed_bout 0.22948755482620395\nB_td_pct_bout 0.23060349058749638\nR_td_landed_bout 0.23519081114557155\nB_td_landed_bout 0.25608124267311666\nR_td_pct_bout 0.25863818059084637\nB_sig_str_landed_bout 0.260187165198815\nB_tot_str_landed_bout 0.2682526512407526\nR_tot_str_landed_bout 0.2855103090298856\nR_pass_bout 0.2865745363744149\nR_kd_bout 0.2886602774893558\nB_sig_str_pct_bout 0.29213159185130844\nB_pass_bout 0.3017654676883408\nB_ev 0.31036383173528564\nR_ev 0.3142815820902688\nR_odds 0.3324760128092555\nB_odds 0.33824434930371344\nR_sig_str_pct_bout 0.34310171081158264\nB_kd_bout 0.363214782622317\nWinner 1.0\n","name":"stdout"}]},{"metadata":{},"cell_type":"markdown","source":"The biggest contributors i.e. columns with a greater than 0.25 correlation (in increasing order of importance) are:\n* B_td_landed_bout,\n* R_td_pct_bout,\n* B_sig_str_landed_bout,\n* B_tot_str_landed_bout,\n* R_tot_str_landed_bout,\n* R_pass_bout,\n* R_kd_bout,\n* B_sig_str_pct_bout,\n* B_pass_bout,\n* B_ev,\n* R_ev,\n* R_odds,\n* B_odds,\n* R_sig_str_pct_bout,\n* B_kd_bout"},{"metadata":{},"cell_type":"markdown","source":"Since none of the bout variables have any information on them, I'll have to do some inspection and visualization and see if I can find out the meaning of these."},{"metadata":{"trusted":true},"cell_type":"code","source":"ufc_master_ds['B_kd_bout'].unique()","execution_count":21,"outputs":[{"output_type":"execute_result","execution_count":21,"data":{"text/plain":"array([nan,  0.,  1.,  2.,  3.,  4.])"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"#Getting null values percentage\n(ufc_master_ds['B_kd_bout'].isnull().sum()/ufc_master_ds.shape[0])*100","execution_count":22,"outputs":[{"output_type":"execute_result","execution_count":22,"data":{"text/plain":"38.028169014084504"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"More than 38% of these values are null"},{"metadata":{"trusted":true},"cell_type":"code","source":"#For visualization purposes\nufc_master_ds['Winner'] = ['Red' if winner == 1 else 'Blue' for winner in ufc_master_ds.Winner]","execution_count":23,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Let's see the countplot"},{"metadata":{"trusted":true},"cell_type":"code","source":"sns.countplot(x=ufc_master_ds['B_kd_bout'], hue = ufc_master_ds['Winner']);","execution_count":24,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{},"cell_type":"markdown","source":"A majority of these values belong to 0, then 1,2,3 with a rare appearance in 4. All belong to float64 type. Also, these are bout related."},{"metadata":{},"cell_type":"markdown","source":"According to the official UFC site, 'kd' refers to knockdown, so these could be knockdowns dealt to/by the blue player"},{"metadata":{"trusted":true},"cell_type":"code","source":"#Analysing \"_odds\" variables\nsns.scatterplot(x=\"B_odds\", y=\"R_odds\", hue=\"Winner\", data = ufc_master_ds);","execution_count":25,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 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\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{},"cell_type":"markdown","source":"As B_odds increase, there are more \"Red\" winners. Same for blue, so it seems that, with the exception of a few outliers, all of the winner are the ones that are bet against. These seem like odds to lose rather than odds to win"},{"metadata":{"trusted":true},"cell_type":"code","source":"#Just to be sure\nufc_master_ds[\"Winner\"].loc[ufc_master_ds[\"B_odds\"]>1].value_counts()","execution_count":26,"outputs":[{"output_type":"execute_result","execution_count":26,"data":{"text/plain":"Red     1906\nBlue     846\nName: Winner, dtype: int64"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"These are the winner distributions where the odds of Blue player winning are greater than 0, but the winners are clearly Red players.\nSame would be applicable for B_odds"},{"metadata":{"trusted":true},"cell_type":"code","source":"#Null values in _sig_str_pct_bout variables\n[(ufc_master_ds[col].isnull().sum()/ufc_master_ds.shape[0])*100 for col in ['R_sig_str_pct_bout','B_sig_str_pct_bout']]\n#Same number of missing values as _kd_bout variables","execution_count":27,"outputs":[{"output_type":"execute_result","execution_count":27,"data":{"text/plain":"[38.028169014084504, 38.028169014084504]"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"sns.scatterplot(x='R_sig_str_pct_bout',y='B_sig_str_pct_bout',hue = 'Winner', data=ufc_master_ds);","execution_count":28,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{},"cell_type":"markdown","source":"There does seem to be somewhat of a linear correlation in significant striking accuracy and winner of the bout"},{"metadata":{"trusted":true},"cell_type":"code","source":"#Lets inspect _ev variables\nsns.scatterplot(x='B_ev', y='R_ev',hue = 'Winner', data=ufc_master_ds);","execution_count":29,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 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\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{},"cell_type":"markdown","source":"When B_ev is increasing in value, the winners are Red and when R_ev is increasing, the winners are Blue."},{"metadata":{"trusted":true},"cell_type":"code","source":"fig, ax = plt.subplots(1,2, figsize=(15,7))\nsns.scatterplot(x='B_ev', y='R_odds',hue = 'Winner', data=ufc_master_ds, ax=ax[0]);\nsns.scatterplot(x='B_ev', y='B_odds',hue = 'Winner', data=ufc_master_ds, ax=ax[1]);\nfig.show()","execution_count":30,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1080x504 with 2 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{},"cell_type":"markdown","source":"As the chances of Red winning increases (i.e. R_odds decreases), the profit i.e. B_ev increases.\nThis indicates that B_ev is the profit on Red rather than blue and the other way round would be the same for R_ev"},{"metadata":{},"cell_type":"markdown","source":"The _bout features would have more or less similar relationship with one another"},{"metadata":{},"cell_type":"markdown","source":"Let's move to _Stance variables"},{"metadata":{"trusted":true},"cell_type":"code","source":"fig, ax = plt.subplots(1,2, figsize=(15,7))\nsns.countplot(ufc_master_ds['Winner'], hue = ufc_master_ds['R_Stance'], ax=ax[0])\nsns.countplot(ufc_master_ds['Winner'], hue = ufc_master_ds['B_Stance'], ax=ax[1])\nax[0].title.set_text('Stances of Red Players')\nax[1].title.set_text('Stances of Blue Players')\nfig.show()","execution_count":31,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1080x504 with 2 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{},"cell_type":"markdown","source":"Orthodox is the superior stance followed by Southpaw and then Switch"},{"metadata":{},"cell_type":"markdown","source":"# 3. Feature Engineering, Filling null values and Final Model"},{"metadata":{"trusted":true},"cell_type":"code","source":"ufc_master_ds.head()","execution_count":32,"outputs":[{"output_type":"execute_result","execution_count":32,"data":{"text/plain":"              R_fighter         B_fighter  R_odds  B_odds        R_ev  \\\n0           Paul Felder  Rafael Dos Anjos     165    -200  165.000000   \n1  Abdul Razak Alhassan    Khaos Williams    -240     185   41.666667   \n2            Kay Hansen      Cory McKenna    -230     180   43.478261   \n3         Brendan Allen   Sean Strickland    -118    -106   84.745763   \n4          Ashley Yoder   Miranda Granger     135    -167  135.000000   \n\n         B_ev        date                location country Winner  title_bout  \\\n0   50.000000  11/14/2020  Las Vegas, Nevada, USA     USA   Blue       False   \n1  185.000000  11/14/2020  Las Vegas, Nevada, USA     USA   Blue       False   \n2  180.000000  11/14/2020  Las Vegas, Nevada, USA     USA   Blue       False   \n3   94.339623  11/14/2020  Las Vegas, Nevada, USA     USA   Blue       False   \n4   59.880240  11/14/2020  Las Vegas, Nevada, USA     USA    Red       False   \n\n          weight_class  gender  no_of_rounds  B_current_lose_streak  \\\n0          Lightweight    MALE             5                      2   \n1         Welterweight    MALE             3                      0   \n2  Women's Strawweight  FEMALE             3                      0   \n3         Catch Weight    MALE             3                      0   \n4  Women's Strawweight  FEMALE             3                      1   \n\n   B_current_win_streak  B_draw  B_avg_SIG_STR_landed  B_avg_SIG_STR_pct  \\\n0                     0       0                  3.47               0.45   \n1                     1       0                 22.22               0.58   \n2                     1       0                  3.13               0.49   \n3                     2       0                  4.64               0.36   \n4                     0       0                  3.47               0.43   \n\n   B_avg_SUB_ATT  B_avg_TD_landed  B_avg_TD_pct  B_longest_win_streak  \\\n0            0.6             1.82          0.39                     5   \n1            0.0             0.00          0.00                     1   \n2            0.0             1.00          1.00                     1   \n3            0.4             1.22          0.57                     3   \n4            0.0             0.00          0.00                     1   \n\n   B_losses  B_total_rounds_fought  B_total_title_bouts  \\\n0        11                     86                    4   \n1         0                      1                    0   \n2         0                      3                    0   \n3         3                     28                    0   \n4         1                      4                    0   \n\n   B_win_by_Decision_Majority  B_win_by_Decision_Split  \\\n0                           0                        0   \n1                           0                        0   \n2                           0                        0   \n3                           0                        2   \n4                           0                        0   \n\n   B_win_by_Decision_Unanimous  B_win_by_KO/TKO  B_win_by_Submission  \\\n0                           10                4                    4   \n1                            0                1                    0   \n2                            1                0                    0   \n3                            3                2                    1   \n4                            1                0                    0   \n\n   B_win_by_TKO_Doctor_Stoppage  B_wins  B_Stance  B_Height_cms  B_Reach_cms  \\\n0                             0      18  Southpaw        172.72       177.80   \n1                             0       1  Orthodox        182.88       195.58   \n2                             0       1  Orthodox        160.02       147.32   \n3                             0       8  Orthodox        185.42       193.04   \n4                             0       1  Orthodox        170.18       172.72   \n\n   B_Weight_lbs  R_current_lose_streak  R_current_win_streak  R_draw  \\\n0           155                      1                     0       0   \n1           170                      1                     0       0   \n2           115                      0                     1       0   \n3           185                      0                     4       0   \n4           125                      2                     0       0   \n\n   R_avg_SIG_STR_landed  R_avg_SIG_STR_pct  R_avg_SUB_ATT  R_avg_TD_landed  \\\n0                  3.67               0.43            0.2             0.25   \n1                  4.54               0.48            0.0             0.73   \n2                  2.49               0.56            1.2             2.41   \n3                  2.67               0.60            2.4             1.42   \n4                  2.96               0.40            0.4             1.14   \n\n   R_avg_TD_pct  R_longest_win_streak  R_losses  R_total_rounds_fought  \\\n0          0.25                     3         5                     38   \n1          0.28                     3         2                     10   \n2          0.18                     1         0                      3   \n3          0.37                     4         0                      7   \n4          0.36                     2         5                     21   \n\n   R_total_title_bouts  R_win_by_Decision_Majority  R_win_by_Decision_Split  \\\n0                    0                           0                        2   \n1                    0                           0                        0   \n2                    0                           0                        0   \n3                    0                           0                        0   \n4                    0                           0                        1   \n\n   R_win_by_Decision_Unanimous  R_win_by_KO/TKO  R_win_by_Submission  \\\n0                            2                4                    1   \n1                            0                4                    0   \n2                            0                0                    1   \n3                            1                1                    2   \n4                            1                0                    0   \n\n   R_win_by_TKO_Doctor_Stoppage  R_wins  R_Stance  R_Height_cms  R_Reach_cms  \\\n0                             0       9  Orthodox        180.34       177.80   \n1                             0       4  Orthodox        177.80       185.42   \n2                             0       1  Orthodox        157.48       160.02   \n3                             0       4  Orthodox        187.96       190.50   \n4                             0       2  Southpaw        170.18       175.26   \n\n   R_Weight_lbs  R_age  B_age  lose_streak_dif  win_streak_dif  \\\n0           155     36     36                1               0   \n1           170     35     26               -1               1   \n2           115     21     21                0               0   \n3           185     24     29                0              -2   \n4           115     33     28               -1               0   \n\n   longest_win_streak_dif  win_dif  loss_dif  total_round_dif  \\\n0                       2        9         6               48   \n1                      -2       -3        -2               -9   \n2                       0        0         0                0   \n3                      -1        4         3               21   \n4                      -1       -1        -4              -17   \n\n   total_title_bout_dif  ko_dif  sub_dif  height_dif  reach_dif  age_dif  \\\n0                     4       0        3       -7.62       0.00        0   \n1                     0      -3        0        5.08      10.16       -9   \n2                     0       0       -1        2.54     -12.70        0   \n3                     0       1       -1       -2.54       2.54        5   \n4                     0       0        0        0.00      -2.54       -5   \n\n   sig_str_dif  avg_sub_att_dif  avg_td_dif  empty_arena  constant_1  \\\n0        -0.20              0.4        1.57            1           1   \n1        17.68              0.0       -0.73            1           1   \n2         0.64             -1.2       -1.41            1           1   \n3         1.97             -2.0       -0.20            1           1   \n4         0.51             -0.4       -1.14            1           1   \n\n   B_match_weightclass_rank  R_match_weightclass_rank  \\\n0                      12.0                       7.0   \n1                       NaN                       NaN   \n2                       NaN                       NaN   \n3                       NaN                       NaN   \n4                       NaN                       NaN   \n\n   R_Women's Flyweight_rank  R_Women's Featherweight_rank  \\\n0                       NaN                           NaN   \n1                       NaN                           NaN   \n2                       NaN                           NaN   \n3                       NaN                           NaN   \n4                       NaN                           NaN   \n\n   R_Women's Strawweight_rank  R_Women's Bantamweight_rank  \\\n0                         NaN                          NaN   \n1                         NaN                          NaN   \n2                         NaN                          NaN   \n3                         NaN                          NaN   \n4                         NaN                          NaN   \n\n   R_Heavyweight_rank  R_Light Heavyweight_rank  R_Middleweight_rank  \\\n0                 NaN                       NaN                  NaN   \n1                 NaN                       NaN                  NaN   \n2                 NaN                       NaN                  NaN   \n3                 NaN                       NaN                  NaN   \n4                 NaN                       NaN                  NaN   \n\n   R_Welterweight_rank  R_Lightweight_rank  R_Featherweight_rank  \\\n0                  NaN                 7.0                   NaN   \n1                  NaN                 NaN                   NaN   \n2                  NaN                 NaN                   NaN   \n3                  NaN                 NaN                   NaN   \n4                  NaN                 NaN                   NaN   \n\n   R_Bantamweight_rank  R_Flyweight_rank  R_Pound-for-Pound_rank  \\\n0                  NaN               NaN                     NaN   \n1                  NaN               NaN                     NaN   \n2                  NaN               NaN                     NaN   \n3                  NaN               NaN                     NaN   \n4                  NaN               NaN                     NaN   \n\n   B_Women's Flyweight_rank  B_Women's Featherweight_rank  \\\n0                       NaN                           NaN   \n1                       NaN                           NaN   \n2                       NaN                           NaN   \n3                       NaN                           NaN   \n4                       NaN                           NaN   \n\n   B_Women's Strawweight_rank  B_Women's Bantamweight_rank  \\\n0                         NaN                          NaN   \n1                         NaN                          NaN   \n2                         NaN                          NaN   \n3                         NaN                          NaN   \n4                         NaN                          NaN   \n\n   B_Heavyweight_rank  B_Light Heavyweight_rank  B_Middleweight_rank  \\\n0                 NaN                       NaN                  NaN   \n1                 NaN                       NaN                  NaN   \n2                 NaN                       NaN                  NaN   \n3                 NaN                       NaN                  NaN   \n4                 NaN                       NaN                  NaN   \n\n   B_Welterweight_rank  B_Lightweight_rank  B_Featherweight_rank  \\\n0                  NaN                12.0                   NaN   \n1                  NaN                 NaN                   NaN   \n2                  NaN                 NaN                   NaN   \n3                  NaN                 NaN                   NaN   \n4                  NaN                 NaN                   NaN   \n\n   B_Bantamweight_rank  B_Flyweight_rank  B_Pound-for-Pound_rank better_rank  \\\n0                  NaN               NaN                     NaN         Red   \n1                  NaN               NaN                     NaN     neither   \n2                  NaN               NaN                     NaN     neither   \n3                  NaN               NaN                     NaN     neither   \n4                  NaN               NaN                     NaN     neither   \n\n  finish finish_details  finish_round finish_round_time  \\\n0    NaN            NaN           NaN               NaN   \n1    NaN            NaN           NaN               NaN   \n2    NaN            NaN           NaN               NaN   \n3    NaN            NaN           NaN               NaN   \n4    NaN            NaN           NaN               NaN   \n\n   total_fight_time_secs  R_kd_bout  B_kd_bout  R_sig_str_landed_bout  \\\n0                    NaN        NaN        NaN                    NaN   \n1                    NaN        NaN        NaN                    NaN   \n2                    NaN        NaN        NaN                    NaN   \n3                    NaN        NaN        NaN                    NaN   \n4                    NaN        NaN        NaN                    NaN   \n\n   B_sig_str_landed_bout  R_sig_str_attempted_bout  B_sig_str_attempted_bout  \\\n0                    NaN                       NaN                       NaN   \n1                    NaN                       NaN                       NaN   \n2                    NaN                       NaN                       NaN   \n3                    NaN                       NaN                       NaN   \n4                    NaN                       NaN                       NaN   \n\n   R_sig_str_pct_bout  B_sig_str_pct_bout  R_tot_str_landed_bout  \\\n0                 NaN                 NaN                    NaN   \n1                 NaN                 NaN                    NaN   \n2                 NaN                 NaN                    NaN   \n3                 NaN                 NaN                    NaN   \n4                 NaN                 NaN                    NaN   \n\n   B_tot_str_landed_bout  R_tot_str_attempted_bout  B_tot_str_attempted_bout  \\\n0                    NaN                       NaN                       NaN   \n1                    NaN                       NaN                       NaN   \n2                    NaN                       NaN                       NaN   \n3                    NaN                       NaN                       NaN   \n4                    NaN                       NaN                       NaN   \n\n   R_td_landed_bout  B_td_landed_bout  R_td_attempted_bout  \\\n0               NaN               NaN                  NaN   \n1               NaN               NaN                  NaN   \n2               NaN               NaN                  NaN   \n3               NaN               NaN                  NaN   \n4               NaN               NaN                  NaN   \n\n   B_td_attempted_bout  R_td_pct_bout  B_td_pct_bout  R_sub_attempts_bout  \\\n0                  NaN            NaN            NaN                  NaN   \n1                  NaN            NaN            NaN                  NaN   \n2                  NaN            NaN            NaN                  NaN   \n3                  NaN            NaN            NaN                  NaN   \n4                  NaN            NaN            NaN                  NaN   \n\n   B_sub_attempts_bout  R_pass_bout  B_pass_bout  R_rev_bout  B_rev_bout  \n0                  NaN          NaN          NaN         NaN         NaN  \n1                  NaN          NaN          NaN         NaN         NaN  \n2                  NaN          NaN          NaN         NaN         NaN  \n3                  NaN          NaN          NaN         NaN         NaN  \n4                  NaN          NaN          NaN         NaN         NaN  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>R_fighter</th>\n      <th>B_fighter</th>\n      <th>R_odds</th>\n      <th>B_odds</th>\n      <th>R_ev</th>\n      <th>B_ev</th>\n      <th>date</th>\n      <th>location</th>\n      <th>country</th>\n      <th>Winner</th>\n      <th>title_bout</th>\n      <th>weight_class</th>\n      <th>gender</th>\n      <th>no_of_rounds</th>\n      <th>B_current_lose_streak</th>\n      <th>B_current_win_streak</th>\n      <th>B_draw</th>\n      <th>B_avg_SIG_STR_landed</th>\n      <th>B_avg_SIG_STR_pct</th>\n      <th>B_avg_SUB_ATT</th>\n      <th>B_avg_TD_landed</th>\n      <th>B_avg_TD_pct</th>\n      <th>B_longest_win_streak</th>\n      <th>B_losses</th>\n      <th>B_total_rounds_fought</th>\n      <th>B_total_title_bouts</th>\n      <th>B_win_by_Decision_Majority</th>\n      <th>B_win_by_Decision_Split</th>\n      <th>B_win_by_Decision_Unanimous</th>\n      <th>B_win_by_KO/TKO</th>\n      <th>B_win_by_Submission</th>\n      <th>B_win_by_TKO_Doctor_Stoppage</th>\n      <th>B_wins</th>\n      <th>B_Stance</th>\n      <th>B_Height_cms</th>\n      <th>B_Reach_cms</th>\n      <th>B_Weight_lbs</th>\n      <th>R_current_lose_streak</th>\n      <th>R_current_win_streak</th>\n      <th>R_draw</th>\n      <th>R_avg_SIG_STR_landed</th>\n      <th>R_avg_SIG_STR_pct</th>\n      <th>R_avg_SUB_ATT</th>\n      <th>R_avg_TD_landed</th>\n      <th>R_avg_TD_pct</th>\n      <th>R_longest_win_streak</th>\n      <th>R_losses</th>\n      <th>R_total_rounds_fought</th>\n      <th>R_total_title_bouts</th>\n      <th>R_win_by_Decision_Majority</th>\n      <th>R_win_by_Decision_Split</th>\n      <th>R_win_by_Decision_Unanimous</th>\n      <th>R_win_by_KO/TKO</th>\n      <th>R_win_by_Submission</th>\n      <th>R_win_by_TKO_Doctor_Stoppage</th>\n      <th>R_wins</th>\n      <th>R_Stance</th>\n      <th>R_Height_cms</th>\n      <th>R_Reach_cms</th>\n      <th>R_Weight_lbs</th>\n      <th>R_age</th>\n      <th>B_age</th>\n      <th>lose_streak_dif</th>\n      <th>win_streak_dif</th>\n      <th>longest_win_streak_dif</th>\n      <th>win_dif</th>\n      <th>loss_dif</th>\n      <th>total_round_dif</th>\n      <th>total_title_bout_dif</th>\n      <th>ko_dif</th>\n      <th>sub_dif</th>\n      <th>height_dif</th>\n      <th>reach_dif</th>\n      <th>age_dif</th>\n      <th>sig_str_dif</th>\n      <th>avg_sub_att_dif</th>\n      <th>avg_td_dif</th>\n      <th>empty_arena</th>\n      <th>constant_1</th>\n      <th>B_match_weightclass_rank</th>\n      <th>R_match_weightclass_rank</th>\n      <th>R_Women's Flyweight_rank</th>\n      <th>R_Women's Featherweight_rank</th>\n      <th>R_Women's Strawweight_rank</th>\n      <th>R_Women's Bantamweight_rank</th>\n      <th>R_Heavyweight_rank</th>\n      <th>R_Light Heavyweight_rank</th>\n      <th>R_Middleweight_rank</th>\n      <th>R_Welterweight_rank</th>\n      <th>R_Lightweight_rank</th>\n      <th>R_Featherweight_rank</th>\n      <th>R_Bantamweight_rank</th>\n      <th>R_Flyweight_rank</th>\n      <th>R_Pound-for-Pound_rank</th>\n      <th>B_Women's Flyweight_rank</th>\n      <th>B_Women's Featherweight_rank</th>\n      <th>B_Women's Strawweight_rank</th>\n      <th>B_Women's Bantamweight_rank</th>\n      <th>B_Heavyweight_rank</th>\n      <th>B_Light Heavyweight_rank</th>\n      <th>B_Middleweight_rank</th>\n      <th>B_Welterweight_rank</th>\n      <th>B_Lightweight_rank</th>\n      <th>B_Featherweight_rank</th>\n      <th>B_Bantamweight_rank</th>\n      <th>B_Flyweight_rank</th>\n      <th>B_Pound-for-Pound_rank</th>\n      <th>better_rank</th>\n      <th>finish</th>\n      <th>finish_details</th>\n      <th>finish_round</th>\n      <th>finish_round_time</th>\n      <th>total_fight_time_secs</th>\n      <th>R_kd_bout</th>\n      <th>B_kd_bout</th>\n      <th>R_sig_str_landed_bout</th>\n      <th>B_sig_str_landed_bout</th>\n      <th>R_sig_str_attempted_bout</th>\n      <th>B_sig_str_attempted_bout</th>\n      <th>R_sig_str_pct_bout</th>\n      <th>B_sig_str_pct_bout</th>\n      <th>R_tot_str_landed_bout</th>\n      <th>B_tot_str_landed_bout</th>\n      <th>R_tot_str_attempted_bout</th>\n      <th>B_tot_str_attempted_bout</th>\n      <th>R_td_landed_bout</th>\n      <th>B_td_landed_bout</th>\n      <th>R_td_attempted_bout</th>\n      <th>B_td_attempted_bout</th>\n      <th>R_td_pct_bout</th>\n      <th>B_td_pct_bout</th>\n      <th>R_sub_attempts_bout</th>\n      <th>B_sub_attempts_bout</th>\n      <th>R_pass_bout</th>\n      <th>B_pass_bout</th>\n      <th>R_rev_bout</th>\n      <th>B_rev_bout</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Paul Felder</td>\n      <td>Rafael Dos Anjos</td>\n      <td>165</td>\n      <td>-200</td>\n      <td>165.000000</td>\n      <td>50.000000</td>\n      <td>11/14/2020</td>\n      <td>Las Vegas, Nevada, USA</td>\n      <td>USA</td>\n      <td>Blue</td>\n      <td>False</td>\n      <td>Lightweight</td>\n      <td>MALE</td>\n      <td>5</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>3.47</td>\n      <td>0.45</td>\n      <td>0.6</td>\n      <td>1.82</td>\n      <td>0.39</td>\n      <td>5</td>\n      <td>11</td>\n      <td>86</td>\n      <td>4</td>\n      <td>0</td>\n      <td>0</td>\n      <td>10</td>\n      <td>4</td>\n      <td>4</td>\n      <td>0</td>\n      <td>18</td>\n      <td>Southpaw</td>\n      <td>172.72</td>\n      <td>177.80</td>\n      <td>155</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>3.67</td>\n      <td>0.43</td>\n      <td>0.2</td>\n      <td>0.25</td>\n      <td>0.25</td>\n      <td>3</td>\n      <td>5</td>\n      <td>38</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2</td>\n      <td>2</td>\n      <td>4</td>\n      <td>1</td>\n      <td>0</td>\n      <td>9</td>\n      <td>Orthodox</td>\n      <td>180.34</td>\n      <td>177.80</td>\n      <td>155</td>\n      <td>36</td>\n      <td>36</td>\n      <td>1</td>\n      <td>0</td>\n      <td>2</td>\n      <td>9</td>\n      <td>6</td>\n      <td>48</td>\n      <td>4</td>\n      <td>0</td>\n      <td>3</td>\n      <td>-7.62</td>\n      <td>0.00</td>\n      <td>0</td>\n      <td>-0.20</td>\n      <td>0.4</td>\n      <td>1.57</td>\n      <td>1</td>\n      <td>1</td>\n      <td>12.0</td>\n      <td>7.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>7.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>12.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Red</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Abdul Razak Alhassan</td>\n      <td>Khaos Williams</td>\n      <td>-240</td>\n      <td>185</td>\n      <td>41.666667</td>\n      <td>185.000000</td>\n      <td>11/14/2020</td>\n      <td>Las Vegas, Nevada, USA</td>\n      <td>USA</td>\n      <td>Blue</td>\n      <td>False</td>\n      <td>Welterweight</td>\n      <td>MALE</td>\n      <td>3</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>22.22</td>\n      <td>0.58</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.00</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>Orthodox</td>\n      <td>182.88</td>\n      <td>195.58</td>\n      <td>170</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>4.54</td>\n      <td>0.48</td>\n      <td>0.0</td>\n      <td>0.73</td>\n      <td>0.28</td>\n      <td>3</td>\n      <td>2</td>\n      <td>10</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>4</td>\n      <td>0</td>\n      <td>0</td>\n      <td>4</td>\n      <td>Orthodox</td>\n      <td>177.80</td>\n      <td>185.42</td>\n      <td>170</td>\n      <td>35</td>\n      <td>26</td>\n      <td>-1</td>\n      <td>1</td>\n      <td>-2</td>\n      <td>-3</td>\n      <td>-2</td>\n      <td>-9</td>\n      <td>0</td>\n      <td>-3</td>\n      <td>0</td>\n      <td>5.08</td>\n      <td>10.16</td>\n      <td>-9</td>\n      <td>17.68</td>\n      <td>0.0</td>\n      <td>-0.73</td>\n      <td>1</td>\n      <td>1</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>neither</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Kay Hansen</td>\n      <td>Cory McKenna</td>\n      <td>-230</td>\n      <td>180</td>\n      <td>43.478261</td>\n      <td>180.000000</td>\n      <td>11/14/2020</td>\n      <td>Las Vegas, Nevada, USA</td>\n      <td>USA</td>\n      <td>Blue</td>\n      <td>False</td>\n      <td>Women's Strawweight</td>\n      <td>FEMALE</td>\n      <td>3</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>3.13</td>\n      <td>0.49</td>\n      <td>0.0</td>\n      <td>1.00</td>\n      <td>1.00</td>\n      <td>1</td>\n      <td>0</td>\n      <td>3</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>Orthodox</td>\n      <td>160.02</td>\n      <td>147.32</td>\n      <td>115</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>2.49</td>\n      <td>0.56</td>\n      <td>1.2</td>\n      <td>2.41</td>\n      <td>0.18</td>\n      <td>1</td>\n      <td>0</td>\n      <td>3</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>Orthodox</td>\n      <td>157.48</td>\n      <td>160.02</td>\n      <td>115</td>\n      <td>21</td>\n      <td>21</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>-1</td>\n      <td>2.54</td>\n      <td>-12.70</td>\n      <td>0</td>\n      <td>0.64</td>\n      <td>-1.2</td>\n      <td>-1.41</td>\n      <td>1</td>\n      <td>1</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>neither</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Brendan Allen</td>\n      <td>Sean Strickland</td>\n      <td>-118</td>\n      <td>-106</td>\n      <td>84.745763</td>\n      <td>94.339623</td>\n      <td>11/14/2020</td>\n      <td>Las Vegas, Nevada, USA</td>\n      <td>USA</td>\n      <td>Blue</td>\n      <td>False</td>\n      <td>Catch Weight</td>\n      <td>MALE</td>\n      <td>3</td>\n      <td>0</td>\n      <td>2</td>\n      <td>0</td>\n      <td>4.64</td>\n      <td>0.36</td>\n      <td>0.4</td>\n      <td>1.22</td>\n      <td>0.57</td>\n      <td>3</td>\n      <td>3</td>\n      <td>28</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2</td>\n      <td>3</td>\n      <td>2</td>\n      <td>1</td>\n      <td>0</td>\n      <td>8</td>\n      <td>Orthodox</td>\n      <td>185.42</td>\n      <td>193.04</td>\n      <td>185</td>\n      <td>0</td>\n      <td>4</td>\n      <td>0</td>\n      <td>2.67</td>\n      <td>0.60</td>\n      <td>2.4</td>\n      <td>1.42</td>\n      <td>0.37</td>\n      <td>4</td>\n      <td>0</td>\n      <td>7</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>2</td>\n      <td>0</td>\n      <td>4</td>\n      <td>Orthodox</td>\n      <td>187.96</td>\n      <td>190.50</td>\n      <td>185</td>\n      <td>24</td>\n      <td>29</td>\n      <td>0</td>\n      <td>-2</td>\n      <td>-1</td>\n      <td>4</td>\n      <td>3</td>\n      <td>21</td>\n      <td>0</td>\n      <td>1</td>\n      <td>-1</td>\n      <td>-2.54</td>\n      <td>2.54</td>\n      <td>5</td>\n      <td>1.97</td>\n      <td>-2.0</td>\n      <td>-0.20</td>\n      <td>1</td>\n      <td>1</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>neither</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Ashley Yoder</td>\n      <td>Miranda Granger</td>\n      <td>135</td>\n      <td>-167</td>\n      <td>135.000000</td>\n      <td>59.880240</td>\n      <td>11/14/2020</td>\n      <td>Las Vegas, Nevada, USA</td>\n      <td>USA</td>\n      <td>Red</td>\n      <td>False</td>\n      <td>Women's Strawweight</td>\n      <td>FEMALE</td>\n      <td>3</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>3.47</td>\n      <td>0.43</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.00</td>\n      <td>1</td>\n      <td>1</td>\n      <td>4</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>Orthodox</td>\n      <td>170.18</td>\n      <td>172.72</td>\n      <td>125</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2.96</td>\n      <td>0.40</td>\n      <td>0.4</td>\n      <td>1.14</td>\n      <td>0.36</td>\n      <td>2</td>\n      <td>5</td>\n      <td>21</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2</td>\n      <td>Southpaw</td>\n      <td>170.18</td>\n      <td>175.26</td>\n      <td>115</td>\n      <td>33</td>\n      <td>28</td>\n      <td>-1</td>\n      <td>0</td>\n      <td>-1</td>\n      <td>-1</td>\n      <td>-4</td>\n      <td>-17</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0.00</td>\n      <td>-2.54</td>\n      <td>-5</td>\n      <td>0.51</td>\n      <td>-0.4</td>\n      <td>-1.14</td>\n      <td>1</td>\n      <td>1</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>neither</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"Considering the number of variables, it would make sense to combine similar variables to increase their correlation and reduce complexity by limiting the input features"},{"metadata":{},"cell_type":"markdown","source":"Some variables are already engineered. They have combined features, so I'll just be doing those that haven't yet been touched.\nThese are the already engineered features:\n1. lose_streak_dif: (Blue lose streak) - (Red lose streak) \n2. winstreakdif: (Blue win streak) - (Red win streak)\n3. longest_win_streak_dif: (Blue longest win streak) - (Red longest win streak)\n4. win_dif: (Blue wins) - (Red wins)\n5. loss_dif: (Blue losses) - (Red losses)\n6. total_round_dif: (Blue total rounds fought) - (Red total rounds fought)\n7. total_title_bout_dif: (Blue number of title fights) - (Red number of title fights)\n8. ko_dif: (Blue wins by KO/TKO) - (Red wins by KO/TKO)\n9. sub_dif: (Blue wins by submission) - (Red wins by submission)\n10. height_dif: (Blue height) - (Red height) in cms\n11. reach_dif: (Blue reach) - (Red reach) in cms\n12. age_dif: (Blue age) - (Red age)\n13. sig_str_dif: (Blue sig strikes per minute) - (Red sig strikes per minute)\n14. avg_sub_att_dif: (Blue submission attempts) - (Red submission attempts)\n15. avg_td_dif: (Blue TD attempts) - (Red TD attempts)"},{"metadata":{"trusted":true},"cell_type":"code","source":"ufc_master_ds['draw_diff'] = (ufc_master_ds['B_draw']-ufc_master_ds['R_draw'])\nufc_master_ds['avg_sig_str_pct_diff'] = (ufc_master_ds['B_avg_SIG_STR_pct']-ufc_master_ds['R_avg_SIG_STR_pct'])\nufc_master_ds['avg_TD_pct_diff'] = (ufc_master_ds['B_avg_TD_pct']-ufc_master_ds['B_avg_TD_pct'])\nufc_master_ds['win_by_Decision_Majority_diff'] = (ufc_master_ds['B_win_by_Decision_Majority']-ufc_master_ds['R_win_by_Decision_Majority'])\nufc_master_ds['win_by_Decision_Split_diff'] = (ufc_master_ds['B_win_by_Decision_Split']-ufc_master_ds['R_win_by_Decision_Split'])\nufc_master_ds['win_by_Decision_Unanimous_diff'] = (ufc_master_ds['B_win_by_Decision_Unanimous']-ufc_master_ds['R_win_by_Decision_Unanimous'])\nufc_master_ds['win_by_TKO_Doctor_Stoppage_diff'] = (ufc_master_ds['B_win_by_TKO_Doctor_Stoppage']-ufc_master_ds['R_win_by_TKO_Doctor_Stoppage'])","execution_count":33,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"ufc_master_ds['odds_diff'] = (ufc_master_ds['B_odds']-ufc_master_ds['R_odds'])\nufc_master_ds['ev_diff'] = (ufc_master_ds['B_ev']-ufc_master_ds['R_ev'])\n\nufc_master_ds['kd_bout_diff']=(ufc_master_ds['B_kd_bout']-ufc_master_ds['R_kd_bout'])\nufc_master_ds['sig_str_landed_bout_diff']=(ufc_master_ds['B_sig_str_landed_bout']-ufc_master_ds['R_sig_str_landed_bout'])\nufc_master_ds['sig_str_attempted_bout_diff']=(ufc_master_ds['B_sig_str_attempted_bout']-ufc_master_ds['R_sig_str_attempted_bout'])\nufc_master_ds['sig_str_attempted_bout_diff']=(ufc_master_ds['B_sig_str_attempted_bout']-ufc_master_ds['R_sig_str_attempted_bout'])\nufc_master_ds['sig_str_pct_bout_diff']=(ufc_master_ds['B_sig_str_pct_bout']-ufc_master_ds['R_sig_str_pct_bout'])\nufc_master_ds['tot_str_landed_bout_diff']=(ufc_master_ds['B_tot_str_landed_bout']-ufc_master_ds['R_tot_str_landed_bout'])\nufc_master_ds['tot_str_attempted_bout_diff']=(ufc_master_ds['B_tot_str_attempted_bout']-ufc_master_ds['R_tot_str_attempted_bout'])\nufc_master_ds['td_landed_bout_diff']=(ufc_master_ds['B_td_landed_bout']-ufc_master_ds['R_td_landed_bout'])\nufc_master_ds['td_attempted_bout_diff']=(ufc_master_ds['B_td_attempted_bout']-ufc_master_ds['R_td_attempted_bout'])\nufc_master_ds['td_pct_bout_diff']=(ufc_master_ds['B_td_pct_bout']-ufc_master_ds['R_td_pct_bout'])\nufc_master_ds['td_pct_bout_diff']=(ufc_master_ds['B_td_pct_bout']-ufc_master_ds['R_td_pct_bout'])\nufc_master_ds['sub_attempts_bout_diff']=(ufc_master_ds['B_sub_attempts_bout']-ufc_master_ds['R_sub_attempts_bout'])\nufc_master_ds['pass_bout_diff']=(ufc_master_ds['B_pass_bout']-ufc_master_ds['R_pass_bout'])\nufc_master_ds['rev_bout_diff']=(ufc_master_ds['B_rev_bout']-ufc_master_ds['R_rev_bout'])","execution_count":34,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"After extracting the necessary information from these variables, there's no more need for them. So, I'll just drop them"},{"metadata":{"trusted":true},"cell_type":"code","source":"#Dropping variables\nvar_drop = [\n'B_odds',\n'R_odds',\n'B_ev',\n'R_ev',\n'R_kd_bout',\n'B_kd_bout',\n'R_sig_str_landed_bout',\n'B_sig_str_landed_bout',\n'R_sig_str_attempted_bout',\n'B_sig_str_attempted_bout',\n'R_sig_str_pct_bout',\n'B_sig_str_pct_bout',\n'R_tot_str_landed_bout',\n'B_tot_str_landed_bout',\n'R_tot_str_attempted_bout',\n'B_tot_str_attempted_bout',\n'R_td_landed_bout',\n'B_td_landed_bout',\n'R_td_attempted_bout',\n'B_td_attempted_bout',\n'R_td_pct_bout',\n'B_td_pct_bout',\n'R_sub_attempts_bout',\n'B_sub_attempts_bout',\n'R_pass_bout',\n'B_pass_bout',\n'R_rev_bout',\n'B_rev_bout',\n'B_current_lose_streak', 'R_current_lose_streak',\n'B_current_win_streak', 'R_current_win_streak',\n'B_longest_win_streak', 'R_longest_win_streak',\n'B_wins', 'R_wins',\n'B_losses', 'R_losses',\n'B_total_rounds_fought', 'R_total_rounds_fought',\n'B_total_title_bouts', 'R_total_title_bouts',\n'B_win_by_KO/TKO', 'R_win_by_KO/TKO',\n'B_win_by_Submission', 'R_win_by_Submission',\n'B_Height_cms', 'R_Height_cms',\n'B_Reach_cms', 'R_Reach_cms',\n'B_age', 'R_age',\n'B_avg_SIG_STR_landed', 'R_avg_SIG_STR_landed',\n'B_avg_SUB_ATT', 'R_avg_SUB_ATT',\n'B_avg_TD_landed', 'R_avg_TD_landed',\n'B_draw','B_avg_SIG_STR_pct','B_avg_TD_pct','B_win_by_Decision_Majority','B_win_by_Decision_Split','B_win_by_Decision_Unanimous','B_win_by_TKO_Doctor_Stoppage',\n'R_draw','R_avg_SIG_STR_pct','R_avg_TD_pct','R_win_by_Decision_Majority','R_win_by_Decision_Split','R_win_by_Decision_Unanimous','R_win_by_TKO_Doctor_Stoppage']\nufc_master_ds.drop(var_drop, axis=1, inplace = True)","execution_count":35,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Next step will be dropping the variables that are common to both the fighters(like date, country, etc.) and thus provide no advantage to anyone"},{"metadata":{"trusted":true},"cell_type":"code","source":"comm_drop = [\n'date','location','country','weight_class','gender','no_of_rounds','empty_arena','constant_1','finish','finish_details','finish_round','finish_round_time','total_fight_time_secs','B_Weight_lbs','R_Weight_lbs'\n]\nufc_master_ds.drop(comm_drop, axis=1, inplace = True)","execution_count":36,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Before moving on to the rank variables, the _Stance variables need some encoding. I'll encode them as I visualized them above.i.e. Orthodox is superior so it will be 4, Southpaw after that will be 3, Switch, 2, and Open Stance 1"},{"metadata":{"trusted":true},"cell_type":"code","source":"ufc_master_ds.B_Stance.unique()","execution_count":37,"outputs":[{"output_type":"execute_result","execution_count":37,"data":{"text/plain":"array(['Southpaw', 'Orthodox', 'Switch', 'Switch ', 'Open Stance'],\n      dtype=object)"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"#It has one spelling mistake\nufc_master_ds['B_Stance'].loc[ufc_master_ds['B_Stance']=='Switch '] = 'Switch'\n#R_Stance doesn't have this error, so we're cool","execution_count":38,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"stance = ['B_Stance', 'R_Stance']","execution_count":39,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"for x in stance:\n    ufc_master_ds[x] = [4 if st == 'Orthodox'\n                           else 3 if st == 'Southpaw'\n                           else 2 if st == 'Switch'\n                           else 1 for st in ufc_master_ds[x]]\n#using -1 and 1 for both red and blue so there is no misunderstanding that one variable is better than the other    \nufc_master_ds['better_rank'] = [-1 if rank == 'Red'\n                               else 1 if rank == 'Blue'\n                               else 0 for rank in ufc_master_ds['better_rank']]\n\nufc_master_ds['title_bout'] = [1 if tb==True else 0 for tb in ufc_master_ds['title_bout']]","execution_count":40,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"ufc_master_ds['Stance_diff'] = (ufc_master_ds['B_Stance'] - ufc_master_ds['R_Stance'])\nufc_master_ds.drop(stance, axis = 1, inplace = True)","execution_count":41,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"ufc_master_ds.head()","execution_count":42,"outputs":[{"output_type":"execute_result","execution_count":42,"data":{"text/plain":"              R_fighter         B_fighter Winner  title_bout  lose_streak_dif  \\\n0           Paul Felder  Rafael Dos Anjos   Blue           0                1   \n1  Abdul Razak Alhassan    Khaos Williams   Blue           0               -1   \n2            Kay Hansen      Cory McKenna   Blue           0                0   \n3         Brendan Allen   Sean Strickland   Blue           0                0   \n4          Ashley Yoder   Miranda Granger    Red           0               -1   \n\n   win_streak_dif  longest_win_streak_dif  win_dif  loss_dif  total_round_dif  \\\n0               0                       2        9         6               48   \n1               1                      -2       -3        -2               -9   \n2               0                       0        0         0                0   \n3              -2                      -1        4         3               21   \n4               0                      -1       -1        -4              -17   \n\n   total_title_bout_dif  ko_dif  sub_dif  height_dif  reach_dif  age_dif  \\\n0                     4       0        3       -7.62       0.00        0   \n1                     0      -3        0        5.08      10.16       -9   \n2                     0       0       -1        2.54     -12.70        0   \n3                     0       1       -1       -2.54       2.54        5   \n4                     0       0        0        0.00      -2.54       -5   \n\n   sig_str_dif  avg_sub_att_dif  avg_td_dif  B_match_weightclass_rank  \\\n0        -0.20              0.4        1.57                      12.0   \n1        17.68              0.0       -0.73                       NaN   \n2         0.64             -1.2       -1.41                       NaN   \n3         1.97             -2.0       -0.20                       NaN   \n4         0.51             -0.4       -1.14                       NaN   \n\n   R_match_weightclass_rank  R_Women's Flyweight_rank  \\\n0                       7.0                       NaN   \n1                       NaN                       NaN   \n2                       NaN                       NaN   \n3                       NaN                       NaN   \n4                       NaN                       NaN   \n\n   R_Women's Featherweight_rank  R_Women's Strawweight_rank  \\\n0                           NaN                         NaN   \n1                           NaN                         NaN   \n2                           NaN                         NaN   \n3                           NaN                         NaN   \n4                           NaN                         NaN   \n\n   R_Women's Bantamweight_rank  R_Heavyweight_rank  R_Light Heavyweight_rank  \\\n0                          NaN                 NaN                       NaN   \n1                          NaN                 NaN                       NaN   \n2                          NaN                 NaN                       NaN   \n3                          NaN                 NaN                       NaN   \n4                          NaN                 NaN                       NaN   \n\n   R_Middleweight_rank  R_Welterweight_rank  R_Lightweight_rank  \\\n0                  NaN                  NaN                 7.0   \n1                  NaN                  NaN                 NaN   \n2                  NaN                  NaN                 NaN   \n3                  NaN                  NaN                 NaN   \n4                  NaN                  NaN                 NaN   \n\n   R_Featherweight_rank  R_Bantamweight_rank  R_Flyweight_rank  \\\n0                   NaN                  NaN               NaN   \n1                   NaN                  NaN               NaN   \n2                   NaN                  NaN               NaN   \n3                   NaN                  NaN               NaN   \n4                   NaN                  NaN               NaN   \n\n   R_Pound-for-Pound_rank  B_Women's Flyweight_rank  \\\n0                     NaN                       NaN   \n1                     NaN                       NaN   \n2                     NaN                       NaN   \n3                     NaN                       NaN   \n4                     NaN                       NaN   \n\n   B_Women's Featherweight_rank  B_Women's Strawweight_rank  \\\n0                           NaN                         NaN   \n1                           NaN                         NaN   \n2                           NaN                         NaN   \n3                           NaN                         NaN   \n4                           NaN                         NaN   \n\n   B_Women's Bantamweight_rank  B_Heavyweight_rank  B_Light Heavyweight_rank  \\\n0                          NaN                 NaN                       NaN   \n1                          NaN                 NaN                       NaN   \n2                          NaN                 NaN                       NaN   \n3                          NaN                 NaN                       NaN   \n4                          NaN                 NaN                       NaN   \n\n   B_Middleweight_rank  B_Welterweight_rank  B_Lightweight_rank  \\\n0                  NaN                  NaN                12.0   \n1                  NaN                  NaN                 NaN   \n2                  NaN                  NaN                 NaN   \n3                  NaN                  NaN                 NaN   \n4                  NaN                  NaN                 NaN   \n\n   B_Featherweight_rank  B_Bantamweight_rank  B_Flyweight_rank  \\\n0                   NaN                  NaN               NaN   \n1                   NaN                  NaN               NaN   \n2                   NaN                  NaN               NaN   \n3                   NaN                  NaN               NaN   \n4                   NaN                  NaN               NaN   \n\n   B_Pound-for-Pound_rank  better_rank  draw_diff  avg_sig_str_pct_diff  \\\n0                     NaN           -1          0                  0.02   \n1                     NaN            0          0                  0.10   \n2                     NaN            0          0                 -0.07   \n3                     NaN            0          0                 -0.24   \n4                     NaN            0          0                  0.03   \n\n   avg_TD_pct_diff  win_by_Decision_Majority_diff  win_by_Decision_Split_diff  \\\n0              0.0                              0                          -2   \n1              0.0                              0                           0   \n2              0.0                              0                           0   \n3              0.0                              0                           2   \n4              0.0                              0                          -1   \n\n   win_by_Decision_Unanimous_diff  win_by_TKO_Doctor_Stoppage_diff  odds_diff  \\\n0                               8                                0       -365   \n1                               0                                0        425   \n2                               1                                0        410   \n3                               2                                0         12   \n4                               0                                0       -302   \n\n      ev_diff  kd_bout_diff  sig_str_landed_bout_diff  \\\n0 -115.000000           NaN                       NaN   \n1  143.333333           NaN                       NaN   \n2  136.521739           NaN                       NaN   \n3    9.593860           NaN                       NaN   \n4  -75.119760           NaN                       NaN   \n\n   sig_str_attempted_bout_diff  sig_str_pct_bout_diff  \\\n0                          NaN                    NaN   \n1                          NaN                    NaN   \n2                          NaN                    NaN   \n3                          NaN                    NaN   \n4                          NaN                    NaN   \n\n   tot_str_landed_bout_diff  tot_str_attempted_bout_diff  td_landed_bout_diff  \\\n0                       NaN                          NaN                  NaN   \n1                       NaN                          NaN                  NaN   \n2                       NaN                          NaN                  NaN   \n3                       NaN                          NaN                  NaN   \n4                       NaN                          NaN                  NaN   \n\n   td_attempted_bout_diff  td_pct_bout_diff  sub_attempts_bout_diff  \\\n0                     NaN               NaN                     NaN   \n1                     NaN               NaN                     NaN   \n2                     NaN               NaN                     NaN   \n3                     NaN               NaN                     NaN   \n4                     NaN               NaN                     NaN   \n\n   pass_bout_diff  rev_bout_diff  Stance_diff  \n0             NaN            NaN           -1  \n1             NaN            NaN            0  \n2             NaN            NaN            0  \n3             NaN            NaN            0  \n4             NaN            NaN            1  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>R_fighter</th>\n      <th>B_fighter</th>\n      <th>Winner</th>\n      <th>title_bout</th>\n      <th>lose_streak_dif</th>\n      <th>win_streak_dif</th>\n      <th>longest_win_streak_dif</th>\n      <th>win_dif</th>\n      <th>loss_dif</th>\n      <th>total_round_dif</th>\n      <th>total_title_bout_dif</th>\n      <th>ko_dif</th>\n      <th>sub_dif</th>\n      <th>height_dif</th>\n      <th>reach_dif</th>\n      <th>age_dif</th>\n      <th>sig_str_dif</th>\n      <th>avg_sub_att_dif</th>\n      <th>avg_td_dif</th>\n      <th>B_match_weightclass_rank</th>\n      <th>R_match_weightclass_rank</th>\n      <th>R_Women's Flyweight_rank</th>\n      <th>R_Women's Featherweight_rank</th>\n      <th>R_Women's Strawweight_rank</th>\n      <th>R_Women's Bantamweight_rank</th>\n      <th>R_Heavyweight_rank</th>\n      <th>R_Light Heavyweight_rank</th>\n      <th>R_Middleweight_rank</th>\n      <th>R_Welterweight_rank</th>\n      <th>R_Lightweight_rank</th>\n      <th>R_Featherweight_rank</th>\n      <th>R_Bantamweight_rank</th>\n      <th>R_Flyweight_rank</th>\n      <th>R_Pound-for-Pound_rank</th>\n      <th>B_Women's Flyweight_rank</th>\n      <th>B_Women's Featherweight_rank</th>\n      <th>B_Women's Strawweight_rank</th>\n      <th>B_Women's Bantamweight_rank</th>\n      <th>B_Heavyweight_rank</th>\n      <th>B_Light Heavyweight_rank</th>\n      <th>B_Middleweight_rank</th>\n      <th>B_Welterweight_rank</th>\n      <th>B_Lightweight_rank</th>\n      <th>B_Featherweight_rank</th>\n      <th>B_Bantamweight_rank</th>\n      <th>B_Flyweight_rank</th>\n      <th>B_Pound-for-Pound_rank</th>\n      <th>better_rank</th>\n      <th>draw_diff</th>\n      <th>avg_sig_str_pct_diff</th>\n      <th>avg_TD_pct_diff</th>\n      <th>win_by_Decision_Majority_diff</th>\n      <th>win_by_Decision_Split_diff</th>\n      <th>win_by_Decision_Unanimous_diff</th>\n      <th>win_by_TKO_Doctor_Stoppage_diff</th>\n      <th>odds_diff</th>\n      <th>ev_diff</th>\n      <th>kd_bout_diff</th>\n      <th>sig_str_landed_bout_diff</th>\n      <th>sig_str_attempted_bout_diff</th>\n      <th>sig_str_pct_bout_diff</th>\n      <th>tot_str_landed_bout_diff</th>\n      <th>tot_str_attempted_bout_diff</th>\n      <th>td_landed_bout_diff</th>\n      <th>td_attempted_bout_diff</th>\n      <th>td_pct_bout_diff</th>\n      <th>sub_attempts_bout_diff</th>\n      <th>pass_bout_diff</th>\n      <th>rev_bout_diff</th>\n      <th>Stance_diff</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Paul Felder</td>\n      <td>Rafael Dos Anjos</td>\n      <td>Blue</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>2</td>\n      <td>9</td>\n      <td>6</td>\n      <td>48</td>\n      <td>4</td>\n      <td>0</td>\n      <td>3</td>\n      <td>-7.62</td>\n      <td>0.00</td>\n      <td>0</td>\n      <td>-0.20</td>\n      <td>0.4</td>\n      <td>1.57</td>\n      <td>12.0</td>\n      <td>7.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>7.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>12.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>-1</td>\n      <td>0</td>\n      <td>0.02</td>\n      <td>0.0</td>\n      <td>0</td>\n      <td>-2</td>\n      <td>8</td>\n      <td>0</td>\n      <td>-365</td>\n      <td>-115.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>-1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Abdul Razak Alhassan</td>\n      <td>Khaos Williams</td>\n      <td>Blue</td>\n      <td>0</td>\n      <td>-1</td>\n      <td>1</td>\n      <td>-2</td>\n      <td>-3</td>\n      <td>-2</td>\n      <td>-9</td>\n      <td>0</td>\n      <td>-3</td>\n      <td>0</td>\n      <td>5.08</td>\n      <td>10.16</td>\n      <td>-9</td>\n      <td>17.68</td>\n      <td>0.0</td>\n      <td>-0.73</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0.10</td>\n      <td>0.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>425</td>\n      <td>143.333333</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Kay Hansen</td>\n      <td>Cory McKenna</td>\n      <td>Blue</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>-1</td>\n      <td>2.54</td>\n      <td>-12.70</td>\n      <td>0</td>\n      <td>0.64</td>\n      <td>-1.2</td>\n      <td>-1.41</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0</td>\n      <td>0</td>\n      <td>-0.07</td>\n      <td>0.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>410</td>\n      <td>136.521739</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Brendan Allen</td>\n      <td>Sean Strickland</td>\n      <td>Blue</td>\n      <td>0</td>\n      <td>0</td>\n      <td>-2</td>\n      <td>-1</td>\n      <td>4</td>\n      <td>3</td>\n      <td>21</td>\n      <td>0</td>\n      <td>1</td>\n      <td>-1</td>\n      <td>-2.54</td>\n      <td>2.54</td>\n      <td>5</td>\n      <td>1.97</td>\n      <td>-2.0</td>\n      <td>-0.20</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0</td>\n      <td>0</td>\n      <td>-0.24</td>\n      <td>0.0</td>\n      <td>0</td>\n      <td>2</td>\n      <td>2</td>\n      <td>0</td>\n      <td>12</td>\n      <td>9.593860</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Ashley Yoder</td>\n      <td>Miranda Granger</td>\n      <td>Red</td>\n      <td>0</td>\n      <td>-1</td>\n      <td>0</td>\n      <td>-1</td>\n      <td>-1</td>\n      <td>-4</td>\n      <td>-17</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0.00</td>\n      <td>-2.54</td>\n      <td>-5</td>\n      <td>0.51</td>\n      <td>-0.4</td>\n      <td>-1.14</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0.03</td>\n      <td>0.0</td>\n      <td>0</td>\n      <td>-1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>-302</td>\n      <td>-75.119760</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"#Encoding label so it is easier to find correlation\nufc_master_ds['Winner'] = [1 if winner == 'Red' else 0 for winner in ufc_master_ds.Winner]","execution_count":43,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"ufc_master_ds.loc[:,'B_match_weightclass_rank':'better_rank'].isnull().sum()","execution_count":44,"outputs":[{"output_type":"execute_result","execution_count":44,"data":{"text/plain":"B_match_weightclass_rank        3684\nR_match_weightclass_rank        3268\nR_Women's Flyweight_rank        4431\nR_Women's Featherweight_rank    4467\nR_Women's Strawweight_rank      4378\nR_Women's Bantamweight_rank     4364\nR_Heavyweight_rank              4342\nR_Light Heavyweight_rank        4345\nR_Middleweight_rank             4354\nR_Welterweight_rank             4343\nR_Lightweight_rank              4347\nR_Featherweight_rank            4349\nR_Bantamweight_rank             4348\nR_Flyweight_rank                4343\nR_Pound-for-Pound_rank          4328\nB_Women's Flyweight_rank        4443\nB_Women's Featherweight_rank    4473\nB_Women's Strawweight_rank      4419\nB_Women's Bantamweight_rank     4399\nB_Heavyweight_rank              4373\nB_Light Heavyweight_rank        4388\nB_Middleweight_rank             4384\nB_Welterweight_rank             4392\nB_Lightweight_rank              4392\nB_Featherweight_rank            4390\nB_Bantamweight_rank             4395\nB_Flyweight_rank                4386\nB_Pound-for-Pound_rank          4445\nbetter_rank                        0\ndtype: int64"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"Almost all of _rank variables except better_rank are empty. I'll keep it and remove the rest"},{"metadata":{"trusted":true},"cell_type":"code","source":"ufc_master_ds.drop(ufc_master_ds.loc[:,'B_match_weightclass_rank':'B_Pound-for-Pound_rank'], axis=1, inplace = True)","execution_count":45,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"ufc_master_ds.sample(10)","execution_count":46,"outputs":[{"output_type":"execute_result","execution_count":46,"data":{"text/plain":"               R_fighter             B_fighter  Winner  title_bout  \\\n1720    Cynthia Calvillo        Pearl Gonzalez       1           0   \n3668        Mauricio Rua  Alexander Gustafsson       0           0   \n4450       Gerald Harris         Mario Miranda       1           0   \n2415       Chris Cariaso         Sergio Pettis       0           0   \n1742        Mauricio Rua         Gian Villante       1           0   \n2940     Francis Carmont         Thales Leites       0           0   \n2498  Daniel Omielanczuk     Chris de la Rocha       1           0   \n3624     Simeon Thoresen        David Mitchell       0           0   \n1356        Stipe Miocic       Francis Ngannou       1           1   \n745         Mitch Gagnon            Cole Smith       0           0   \n\n      lose_streak_dif  win_streak_dif  longest_win_streak_dif  win_dif  \\\n1720                0              -1                      -1       -1   \n3668                0               4                       3        1   \n4450                0              -1                      -1       -1   \n2415                1               0                      -1       -4   \n1742                0              -1                       0       -3   \n2940                2               3                      -1        2   \n2498                2               0                      -1       -1   \n3624               -1               0                      -1       -1   \n1356                0               1                       1       -5   \n745                 2               0                      -4       -4   \n\n      loss_dif  total_round_dif  total_title_bout_dif  ko_dif  sub_dif  \\\n1720         0               -1                     0       0       -1   \n3668         3              -15                    -3      -2        2   \n4450         0               -3                     0      -1        0   \n2415         3              -20                    -1      -1        0   \n1742         4              -19                    -3      -2        0   \n2940        -1                5                     1       1        1   \n2498         2               -9                     0      -1        0   \n3624        -1                3                     0       0       -1   \n1356         2              -21                    -3      -3        1   \n745          3              -15                     0       0       -3   \n\n      height_dif  reach_dif  age_dif  sig_str_dif  avg_sub_att_dif  \\\n1720        0.00      10.16       -1   -13.000000        -3.000000   \n3668       10.16       7.62        6   -21.333333         0.380952   \n4450       10.16      -7.62        0   -21.000000         0.000000   \n2415        7.62      15.24       12    14.033333         0.500000   \n1742        5.08       0.00        4     6.944444        -0.250000   \n2940       -5.08       0.00        0    -4.022727         0.556818   \n2498        7.62      15.24       -4   -31.666667        -0.333333   \n3624       -2.54      -7.62       -5    -9.000000         1.500000   \n1356        0.00       7.62        4   -34.153846         0.166667   \n745        15.24       5.08        4   -28.571429        -1.142857   \n\n      avg_td_dif  better_rank  draw_diff  avg_sig_str_pct_diff  \\\n1720   -1.000000            0          0                   NaN   \n3668   -1.095238            0          0             -0.201429   \n4450   -1.000000            0          0                   NaN   \n2415   -0.033333           -1          0             -0.010333   \n1742   -0.631944           -1          0             -0.028611   \n2940    0.465909           -1          0             -0.055568   \n2498   -1.000000            0          0                   NaN   \n3624    0.500000            0          0              0.025000   \n1356   -1.307692           -1          0              0.043590   \n745    -0.857143            0          0             -0.131429   \n\n      avg_TD_pct_diff  win_by_Decision_Majority_diff  \\\n1720              NaN                              0   \n3668              0.0                              0   \n4450              NaN                              0   \n2415              0.0                              0   \n1742              0.0                              0   \n2940              0.0                              0   \n2498              NaN                              0   \n3624              0.0                              0   \n1356              0.0                              0   \n745               NaN                              0   \n\n      win_by_Decision_Split_diff  win_by_Decision_Unanimous_diff  \\\n1720                           0                               0   \n3668                           0                               1   \n4450                           0                               0   \n2415                          -2                              -1   \n1742                           0                              -1   \n2940                           0                               0   \n2498                           0                               0   \n3624                           0                               0   \n1356                           0                              -3   \n745                            0                              -1   \n\n      win_by_TKO_Doctor_Stoppage_diff  odds_diff     ev_diff  kd_bout_diff  \\\n1720                                0        520  204.285714           NaN   \n3668                                0       -520 -204.285714           NaN   \n4450                                0        290   70.483871          -1.0   \n2415                                0       -370 -120.000000           NaN   \n1742                                0        290   70.483871          -2.0   \n2940                                0        400  142.380952           1.0   \n2498                                0        250   43.076923          -1.0   \n3624                                0        440  166.521739           0.0   \n1356                                1       -320  -91.176471           0.0   \n745                                 0        220   16.666667           NaN   \n\n      sig_str_landed_bout_diff  sig_str_attempted_bout_diff  \\\n1720                       NaN                          NaN   \n3668                       NaN                          NaN   \n4450                      -1.0                          3.0   \n2415                       NaN                          NaN   \n1742                     -39.0                        -22.0   \n2940                       0.0                         -8.0   \n2498                     -14.0                         -8.0   \n3624                      11.0                         44.0   \n1356                     -49.0                         18.0   \n745                        NaN                          NaN   \n\n      sig_str_pct_bout_diff  tot_str_landed_bout_diff  \\\n1720                    NaN                       NaN   \n3668                    NaN                       NaN   \n4450                  -0.05                      16.0   \n2415                    NaN                       NaN   \n1742                  -0.18                     -39.0   \n2940                   0.06                       1.0   \n2498                   0.05                     -14.0   \n3624                  -0.05                      69.0   \n1356                  -0.55                    -167.0   \n745                     NaN                       NaN   \n\n      tot_str_attempted_bout_diff  td_landed_bout_diff  \\\n1720                          NaN                  NaN   \n3668                          NaN                  NaN   \n4450                         26.0                  0.0   \n2415                          NaN                  NaN   \n1742                        -23.0                 -2.0   \n2940                         -7.0                  0.0   \n2498                         -8.0                  0.0   \n3624                        115.0                  0.0   \n1356                       -118.0                 -6.0   \n745                           NaN                  NaN   \n\n      td_attempted_bout_diff  td_pct_bout_diff  sub_attempts_bout_diff  \\\n1720                     NaN               NaN                     NaN   \n3668                     NaN               NaN                     NaN   \n4450                     0.0              0.00                     0.0   \n2415                     NaN               NaN                     NaN   \n1742                    -2.0             -0.66                     0.0   \n2940                     0.0              0.00                     0.0   \n2498                     0.0              0.00                     0.0   \n3624                     1.0             -0.50                     2.0   \n1356                   -13.0             -0.42                     1.0   \n745                      NaN               NaN                     NaN   \n\n      pass_bout_diff  rev_bout_diff  Stance_diff  \n1720             NaN            NaN            0  \n3668             NaN            NaN            0  \n4450             0.0            0.0            1  \n2415             NaN            NaN            1  \n1742             0.0            0.0            0  \n2940             0.0            0.0            0  \n2498             0.0            0.0            1  \n3624             0.0            1.0            0  \n1356            -4.0            0.0            0  \n745              NaN            NaN            1  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>R_fighter</th>\n      <th>B_fighter</th>\n      <th>Winner</th>\n      <th>title_bout</th>\n      <th>lose_streak_dif</th>\n      <th>win_streak_dif</th>\n      <th>longest_win_streak_dif</th>\n      <th>win_dif</th>\n      <th>loss_dif</th>\n      <th>total_round_dif</th>\n      <th>total_title_bout_dif</th>\n      <th>ko_dif</th>\n      <th>sub_dif</th>\n      <th>height_dif</th>\n      <th>reach_dif</th>\n      <th>age_dif</th>\n      <th>sig_str_dif</th>\n      <th>avg_sub_att_dif</th>\n      <th>avg_td_dif</th>\n      <th>better_rank</th>\n      <th>draw_diff</th>\n      <th>avg_sig_str_pct_diff</th>\n      <th>avg_TD_pct_diff</th>\n      <th>win_by_Decision_Majority_diff</th>\n      <th>win_by_Decision_Split_diff</th>\n      <th>win_by_Decision_Unanimous_diff</th>\n      <th>win_by_TKO_Doctor_Stoppage_diff</th>\n      <th>odds_diff</th>\n      <th>ev_diff</th>\n      <th>kd_bout_diff</th>\n      <th>sig_str_landed_bout_diff</th>\n      <th>sig_str_attempted_bout_diff</th>\n      <th>sig_str_pct_bout_diff</th>\n      <th>tot_str_landed_bout_diff</th>\n      <th>tot_str_attempted_bout_diff</th>\n      <th>td_landed_bout_diff</th>\n      <th>td_attempted_bout_diff</th>\n      <th>td_pct_bout_diff</th>\n      <th>sub_attempts_bout_diff</th>\n      <th>pass_bout_diff</th>\n      <th>rev_bout_diff</th>\n      <th>Stance_diff</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1720</th>\n      <td>Cynthia Calvillo</td>\n      <td>Pearl Gonzalez</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>-1</td>\n      <td>-1</td>\n      <td>-1</td>\n      <td>0</td>\n      <td>-1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>-1</td>\n      <td>0.00</td>\n      <td>10.16</td>\n      <td>-1</td>\n      <td>-13.000000</td>\n      <td>-3.000000</td>\n      <td>-1.000000</td>\n      <td>0</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>520</td>\n      <td>204.285714</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3668</th>\n      <td>Mauricio Rua</td>\n      <td>Alexander Gustafsson</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>4</td>\n      <td>3</td>\n      <td>1</td>\n      <td>3</td>\n      <td>-15</td>\n      <td>-3</td>\n      <td>-2</td>\n      <td>2</td>\n      <td>10.16</td>\n      <td>7.62</td>\n      <td>6</td>\n      <td>-21.333333</td>\n      <td>0.380952</td>\n      <td>-1.095238</td>\n      <td>0</td>\n      <td>0</td>\n      <td>-0.201429</td>\n      <td>0.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>-520</td>\n      <td>-204.285714</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4450</th>\n      <td>Gerald Harris</td>\n      <td>Mario Miranda</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>-1</td>\n      <td>-1</td>\n      <td>-1</td>\n      <td>0</td>\n      <td>-3</td>\n      <td>0</td>\n      <td>-1</td>\n      <td>0</td>\n      <td>10.16</td>\n      <td>-7.62</td>\n      <td>0</td>\n      <td>-21.000000</td>\n      <td>0.000000</td>\n      <td>-1.000000</td>\n      <td>0</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>290</td>\n      <td>70.483871</td>\n      <td>-1.0</td>\n      <td>-1.0</td>\n      <td>3.0</td>\n      <td>-0.05</td>\n      <td>16.0</td>\n      <td>26.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>2415</th>\n      <td>Chris Cariaso</td>\n      <td>Sergio Pettis</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>-1</td>\n      <td>-4</td>\n      <td>3</td>\n      <td>-20</td>\n      <td>-1</td>\n      <td>-1</td>\n      <td>0</td>\n      <td>7.62</td>\n      <td>15.24</td>\n      <td>12</td>\n      <td>14.033333</td>\n      <td>0.500000</td>\n      <td>-0.033333</td>\n      <td>-1</td>\n      <td>0</td>\n      <td>-0.010333</td>\n      <td>0.0</td>\n      <td>0</td>\n      <td>-2</td>\n      <td>-1</td>\n      <td>0</td>\n      <td>-370</td>\n      <td>-120.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1742</th>\n      <td>Mauricio Rua</td>\n      <td>Gian Villante</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>-1</td>\n      <td>0</td>\n      <td>-3</td>\n      <td>4</td>\n      <td>-19</td>\n      <td>-3</td>\n      <td>-2</td>\n      <td>0</td>\n      <td>5.08</td>\n      <td>0.00</td>\n      <td>4</td>\n      <td>6.944444</td>\n      <td>-0.250000</td>\n      <td>-0.631944</td>\n      <td>-1</td>\n      <td>0</td>\n      <td>-0.028611</td>\n      <td>0.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>-1</td>\n      <td>0</td>\n      <td>290</td>\n      <td>70.483871</td>\n      <td>-2.0</td>\n      <td>-39.0</td>\n      <td>-22.0</td>\n      <td>-0.18</td>\n      <td>-39.0</td>\n      <td>-23.0</td>\n      <td>-2.0</td>\n      <td>-2.0</td>\n      <td>-0.66</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2940</th>\n      <td>Francis Carmont</td>\n      <td>Thales Leites</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2</td>\n      <td>3</td>\n      <td>-1</td>\n      <td>2</td>\n      <td>-1</td>\n      <td>5</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>-5.08</td>\n      <td>0.00</td>\n      <td>0</td>\n      <td>-4.022727</td>\n      <td>0.556818</td>\n      <td>0.465909</td>\n      <td>-1</td>\n      <td>0</td>\n      <td>-0.055568</td>\n      <td>0.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>400</td>\n      <td>142.380952</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>-8.0</td>\n      <td>0.06</td>\n      <td>1.0</td>\n      <td>-7.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2498</th>\n      <td>Daniel Omielanczuk</td>\n      <td>Chris de la Rocha</td>\n      <td>1</td>\n      <td>0</td>\n      <td>2</td>\n      <td>0</td>\n      <td>-1</td>\n      <td>-1</td>\n      <td>2</td>\n      <td>-9</td>\n      <td>0</td>\n      <td>-1</td>\n      <td>0</td>\n      <td>7.62</td>\n      <td>15.24</td>\n      <td>-4</td>\n      <td>-31.666667</td>\n      <td>-0.333333</td>\n      <td>-1.000000</td>\n      <td>0</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>250</td>\n      <td>43.076923</td>\n      <td>-1.0</td>\n      <td>-14.0</td>\n      <td>-8.0</td>\n      <td>0.05</td>\n      <td>-14.0</td>\n      <td>-8.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>3624</th>\n      <td>Simeon Thoresen</td>\n      <td>David Mitchell</td>\n      <td>0</td>\n      <td>0</td>\n      <td>-1</td>\n      <td>0</td>\n      <td>-1</td>\n      <td>-1</td>\n      <td>-1</td>\n      <td>3</td>\n      <td>0</td>\n      <td>0</td>\n      <td>-1</td>\n      <td>-2.54</td>\n      <td>-7.62</td>\n      <td>-5</td>\n      <td>-9.000000</td>\n      <td>1.500000</td>\n      <td>0.500000</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0.025000</td>\n      <td>0.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>440</td>\n      <td>166.521739</td>\n      <td>0.0</td>\n      <td>11.0</td>\n      <td>44.0</td>\n      <td>-0.05</td>\n      <td>69.0</td>\n      <td>115.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>-0.50</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1356</th>\n      <td>Stipe Miocic</td>\n      <td>Francis Ngannou</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>-5</td>\n      <td>2</td>\n      <td>-21</td>\n      <td>-3</td>\n      <td>-3</td>\n      <td>1</td>\n      <td>0.00</td>\n      <td>7.62</td>\n      <td>4</td>\n      <td>-34.153846</td>\n      <td>0.166667</td>\n      <td>-1.307692</td>\n      <td>-1</td>\n      <td>0</td>\n      <td>0.043590</td>\n      <td>0.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>-3</td>\n      <td>1</td>\n      <td>-320</td>\n      <td>-91.176471</td>\n      <td>0.0</td>\n      <td>-49.0</td>\n      <td>18.0</td>\n      <td>-0.55</td>\n      <td>-167.0</td>\n      <td>-118.0</td>\n      <td>-6.0</td>\n      <td>-13.0</td>\n      <td>-0.42</td>\n      <td>1.0</td>\n      <td>-4.0</td>\n      <td>0.0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>745</th>\n      <td>Mitch Gagnon</td>\n      <td>Cole Smith</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2</td>\n      <td>0</td>\n      <td>-4</td>\n      <td>-4</td>\n      <td>3</td>\n      <td>-15</td>\n      <td>0</td>\n      <td>0</td>\n      <td>-3</td>\n      <td>15.24</td>\n      <td>5.08</td>\n      <td>4</td>\n      <td>-28.571429</td>\n      <td>-1.142857</td>\n      <td>-0.857143</td>\n      <td>0</td>\n      <td>0</td>\n      <td>-0.131429</td>\n      <td>NaN</td>\n      <td>0</td>\n      <td>0</td>\n      <td>-1</td>\n      <td>0</td>\n      <td>220</td>\n      <td>16.666667</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"label = ufc_master_ds.Winner\nufc_master_ds.drop(['Winner'], axis=1, inplace = True)","execution_count":47,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"#Encoding the remaining categorical variables\ncat_col = ['R_fighter', 'B_fighter']\nenc = LabelEncoder()\nfor i in ufc_master_ds[cat_col]:\n    ufc_master_ds[i] = enc.fit_transform(ufc_master_ds[i])","execution_count":48,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"***Splitting the data in training and testing data set***"},{"metadata":{"trusted":true},"cell_type":"code","source":"X_train, X_valid, y_train, y_valid = train_test_split(ufc_master_ds, label, test_size = 0.3, random_state=2)","execution_count":49,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"#At this point all the null values are the ones that have been left empty by error/mistake and are not left empty deliberately.\n#So, it would make sense to fill in these with mean rather than 0 or anything else\nimpute = SimpleImputer(strategy = 'mean')\nimpute.fit(X_train)\nX_train = impute.transform(X_train)\nX_valid = impute.transform(X_valid)","execution_count":50,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"RF_model = RandomForestClassifier(random_state=2)","execution_count":51,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"RF_model.fit(X_train, y_train)","execution_count":52,"outputs":[{"output_type":"execute_result","execution_count":52,"data":{"text/plain":"RandomForestClassifier(random_state=2)"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"preds = RF_model.predict(X_valid)\naccuracy_score(y_valid, preds)","execution_count":53,"outputs":[{"output_type":"execute_result","execution_count":53,"data":{"text/plain":"0.7734724292101341"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"**77% is the accuracy after feature engineering**"},{"metadata":{"trusted":true},"cell_type":"code","source":"#Built a model after doing GridSearch but not putting the code here because the cell takes up a lot of time\nRF_model = RandomForestClassifier(n_estimators = 350, max_depth = 12, random_state = 2)","execution_count":54,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"RF_model.fit(X_train, y_train)\npreds = RF_model.predict(X_valid)\naccuracy_score(y_valid, preds)","execution_count":55,"outputs":[{"output_type":"execute_result","execution_count":55,"data":{"text/plain":"0.7794336810730254"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"**77.9% which is almost 78% accuracy**"},{"metadata":{},"cell_type":"markdown","source":"So, after careful feature engineering and data visualization, the accuracy of our model increased by almost 30%.\n\nNote : It should be considered that the model used as a baseline was a DummyClassifier and not a proper model.\n"}],"metadata":{"kernelspec":{"name":"python3","display_name":"Python 3","language":"python"},"language_info":{"name":"python","version":"3.7.6","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat":4,"nbformat_minor":4}