1-Data Preprocessing.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d393fdea",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b7807d01",
   "metadata": {},
   "outputs": [],
   "source": [
    "_dictionary = pd.read_csv(\"data_dictionary.csv\")\n",
    "_data = pd.read_csv(\"Wimbledon_featured_matches.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5e883345",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>variables</th>\n",
       "      <th>explanation</th>\n",
       "      <th>example</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>match_id</td>\n",
       "      <td>match identification</td>\n",
       "      <td>2023-wimbledon-1701 (\"7\" is the round, and \"01...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>player1</td>\n",
       "      <td>first and last name of the first player</td>\n",
       "      <td>Carlos Alcaraz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>player2</td>\n",
       "      <td>first and last name of the second player</td>\n",
       "      <td>Novak Djokovic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>elapsed_time</td>\n",
       "      <td>time elapsed since start of first point to sta...</td>\n",
       "      <td>0:10:27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>set_no</td>\n",
       "      <td>set number in match</td>\n",
       "      <td>1, 2, 3, 4, or 5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>game_no</td>\n",
       "      <td>game number in set</td>\n",
       "      <td>1, 2, ...,7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>point_no</td>\n",
       "      <td>point number in game</td>\n",
       "      <td>1, 2, 3... etc.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>p1_sets</td>\n",
       "      <td>sets won by player 1</td>\n",
       "      <td>0, 1, or 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>p2_sets</td>\n",
       "      <td>sets won by player 2</td>\n",
       "      <td>0, 1, or 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>p1_games</td>\n",
       "      <td>games won by player 1 in current set</td>\n",
       "      <td>0, 1,...,6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>p2_games</td>\n",
       "      <td>games won by player 2 in current set</td>\n",
       "      <td>0, 1,...,6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>p1_score</td>\n",
       "      <td>player 1's score within current game</td>\n",
       "      <td>0 (love), 15, 30, 40, AD (advantage)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>p2_score</td>\n",
       "      <td>player 2's score within current game</td>\n",
       "      <td>0 (love), 15, 30, 40, AD (advantage)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>server</td>\n",
       "      <td>server of the point</td>\n",
       "      <td>1: player 1, 2: player 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>serve_no</td>\n",
       "      <td>first or second serve</td>\n",
       "      <td>1: first serve, 2: second serve</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>point_victor</td>\n",
       "      <td>winner of the point</td>\n",
       "      <td>1 if player 1 wins, 2 if player 2 wins</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>p1_points_won</td>\n",
       "      <td>number of points won by player 1 in match</td>\n",
       "      <td>0, 1, 2... etc.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>p2_points_won</td>\n",
       "      <td>number of points won by player 2 in match</td>\n",
       "      <td>0, 1, 2... etc.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>game_victor</td>\n",
       "      <td>a player won a game this point</td>\n",
       "      <td>0: no one, 1: player 1, 2: player 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>set_victor</td>\n",
       "      <td>a player won a set this point</td>\n",
       "      <td>0: no one, 1: player 1, 2: player 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>p1_ace</td>\n",
       "      <td>player 1 hit an untouchable winning serve</td>\n",
       "      <td>0 or 1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>p2_ace</td>\n",
       "      <td>player 2 hit an untouchable winning serve</td>\n",
       "      <td>0 or 1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>p1_winner</td>\n",
       "      <td>player 1 hit an untouchable winning shot</td>\n",
       "      <td>0 or 1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>p2_winner</td>\n",
       "      <td>player 2 hit an untouchable winning shot</td>\n",
       "      <td>0 or 1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>winner_shot_type</td>\n",
       "      <td>category of untouchable shot</td>\n",
       "      <td>F: Forehand, B: Backhand</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>p1_double_fault</td>\n",
       "      <td>player 1 missed both serves and lost the point</td>\n",
       "      <td>0 or 1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>p2_double_fault</td>\n",
       "      <td>player 2 missed both serves and lost the point</td>\n",
       "      <td>0 or 1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>p1_unf_err</td>\n",
       "      <td>player 1 made an unforced error</td>\n",
       "      <td>0 or 1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>p2_unf_err</td>\n",
       "      <td>player 2 made an unforced error</td>\n",
       "      <td>0 or 1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>p1_net_pt</td>\n",
       "      <td>player 1 made it to the net</td>\n",
       "      <td>0 or 1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>p2_net_pt</td>\n",
       "      <td>player 2 made it to the net</td>\n",
       "      <td>0 or 1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>p1_net_pt_won</td>\n",
       "      <td>player 1 won the point while at the net</td>\n",
       "      <td>0 or 1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>p2_net_pt_won</td>\n",
       "      <td>player 2 won the point while at the net</td>\n",
       "      <td>0 or 1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>p1_break_pt</td>\n",
       "      <td>player 1 has an opportunity to win a game play...</td>\n",
       "      <td>0 or 1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>p2_break_pt</td>\n",
       "      <td>player 2 has an opportunity to win a game play...</td>\n",
       "      <td>0 or 1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>p1_break_pt_won</td>\n",
       "      <td>player 1 won the game player 2 is serving</td>\n",
       "      <td>0 or 1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>p2_break_pt_won</td>\n",
       "      <td>player 2 won the game player 1 is serving</td>\n",
       "      <td>0 or 1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>p1_break_pt_missed</td>\n",
       "      <td>player 1 missed an opportunity to win a game p...</td>\n",
       "      <td>0 or 1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>p2_break_pt_missed</td>\n",
       "      <td>player 2 missed an opportunity to win a game p...</td>\n",
       "      <td>0 or 1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>p1_distance_run</td>\n",
       "      <td>player 1's distance ran during point (meters)</td>\n",
       "      <td>5.376, 21.384, etc.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>p2_distance_run</td>\n",
       "      <td>player 2's distance ran during point (meters)</td>\n",
       "      <td>6.485, 12.473, etc.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>rally_count</td>\n",
       "      <td>number of shots during the point</td>\n",
       "      <td>1, 2, 4, etc. (includes serve)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>speed_mph</td>\n",
       "      <td>speed of serve (miles per hour; mph)</td>\n",
       "      <td>81, 124, etc.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>serve_width</td>\n",
       "      <td>direction of serve</td>\n",
       "      <td>B: Body, BC: Body/Center, BW: Body/Wide, C: Ce...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>serve_depth</td>\n",
       "      <td>depth of serve</td>\n",
       "      <td>CTL: Close To Line, NCTL: Not Close To Line</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>return_depth</td>\n",
       "      <td>depth of return</td>\n",
       "      <td>D: Deep, ND: Not Deep</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             variables                                        explanation  \\\n",
       "0             match_id                               match identification   \n",
       "1              player1            first and last name of the first player   \n",
       "2              player2           first and last name of the second player   \n",
       "3         elapsed_time  time elapsed since start of first point to sta...   \n",
       "4               set_no                                set number in match   \n",
       "5              game_no                                 game number in set   \n",
       "6             point_no                               point number in game   \n",
       "7              p1_sets                               sets won by player 1   \n",
       "8              p2_sets                               sets won by player 2   \n",
       "9             p1_games               games won by player 1 in current set   \n",
       "10            p2_games               games won by player 2 in current set   \n",
       "11            p1_score               player 1's score within current game   \n",
       "12            p2_score               player 2's score within current game   \n",
       "13              server                                server of the point   \n",
       "14            serve_no                              first or second serve   \n",
       "15        point_victor                                winner of the point   \n",
       "16       p1_points_won          number of points won by player 1 in match   \n",
       "17       p2_points_won          number of points won by player 2 in match   \n",
       "18         game_victor                     a player won a game this point   \n",
       "19          set_victor                      a player won a set this point   \n",
       "20              p1_ace          player 1 hit an untouchable winning serve   \n",
       "21              p2_ace          player 2 hit an untouchable winning serve   \n",
       "22           p1_winner           player 1 hit an untouchable winning shot   \n",
       "23           p2_winner           player 2 hit an untouchable winning shot   \n",
       "24    winner_shot_type                       category of untouchable shot   \n",
       "25     p1_double_fault     player 1 missed both serves and lost the point   \n",
       "26     p2_double_fault     player 2 missed both serves and lost the point   \n",
       "27          p1_unf_err                    player 1 made an unforced error   \n",
       "28          p2_unf_err                    player 2 made an unforced error   \n",
       "29           p1_net_pt                        player 1 made it to the net   \n",
       "30           p2_net_pt                        player 2 made it to the net   \n",
       "31       p1_net_pt_won            player 1 won the point while at the net   \n",
       "32       p2_net_pt_won            player 2 won the point while at the net   \n",
       "33         p1_break_pt  player 1 has an opportunity to win a game play...   \n",
       "34         p2_break_pt  player 2 has an opportunity to win a game play...   \n",
       "35     p1_break_pt_won          player 1 won the game player 2 is serving   \n",
       "36     p2_break_pt_won          player 2 won the game player 1 is serving   \n",
       "37  p1_break_pt_missed  player 1 missed an opportunity to win a game p...   \n",
       "38  p2_break_pt_missed  player 2 missed an opportunity to win a game p...   \n",
       "39     p1_distance_run      player 1's distance ran during point (meters)   \n",
       "40     p2_distance_run      player 2's distance ran during point (meters)   \n",
       "41         rally_count                   number of shots during the point   \n",
       "42           speed_mph               speed of serve (miles per hour; mph)   \n",
       "43         serve_width                                 direction of serve   \n",
       "44         serve_depth                                     depth of serve   \n",
       "45        return_depth                                    depth of return   \n",
       "\n",
       "                                              example  \n",
       "0   2023-wimbledon-1701 (\"7\" is the round, and \"01...  \n",
       "1                                      Carlos Alcaraz  \n",
       "2                                      Novak Djokovic  \n",
       "3                                             0:10:27  \n",
       "4                                    1, 2, 3, 4, or 5  \n",
       "5                                         1, 2, ...,7  \n",
       "6                                     1, 2, 3... etc.  \n",
       "7                                          0, 1, or 2  \n",
       "8                                          0, 1, or 2  \n",
       "9                                          0, 1,...,6  \n",
       "10                                         0, 1,...,6  \n",
       "11               0 (love), 15, 30, 40, AD (advantage)  \n",
       "12               0 (love), 15, 30, 40, AD (advantage)  \n",
       "13                           1: player 1, 2: player 2  \n",
       "14                    1: first serve, 2: second serve  \n",
       "15             1 if player 1 wins, 2 if player 2 wins  \n",
       "16                                    0, 1, 2... etc.  \n",
       "17                                    0, 1, 2... etc.  \n",
       "18                0: no one, 1: player 1, 2: player 2  \n",
       "19                0: no one, 1: player 1, 2: player 2  \n",
       "20                                             0 or 1  \n",
       "21                                             0 or 1  \n",
       "22                                             0 or 1  \n",
       "23                                             0 or 1  \n",
       "24                           F: Forehand, B: Backhand  \n",
       "25                                             0 or 1  \n",
       "26                                             0 or 1  \n",
       "27                                             0 or 1  \n",
       "28                                             0 or 1  \n",
       "29                                             0 or 1  \n",
       "30                                             0 or 1  \n",
       "31                                             0 or 1  \n",
       "32                                             0 or 1  \n",
       "33                                             0 or 1  \n",
       "34                                             0 or 1  \n",
       "35                                             0 or 1  \n",
       "36                                             0 or 1  \n",
       "37                                             0 or 1  \n",
       "38                                             0 or 1  \n",
       "39                                5.376, 21.384, etc.  \n",
       "40                                6.485, 12.473, etc.  \n",
       "41                     1, 2, 4, etc. (includes serve)  \n",
       "42                                      81, 124, etc.  \n",
       "43  B: Body, BC: Body/Center, BW: Body/Wide, C: Ce...  \n",
       "44        CTL: Close To Line, NCTL: Not Close To Line  \n",
       "45                              D: Deep, ND: Not Deep  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dictionary = _dictionary.copy()\n",
    "dictionary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ce5b341d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>match_id</th>\n",
       "      <th>player1</th>\n",
       "      <th>player2</th>\n",
       "      <th>elapsed_time</th>\n",
       "      <th>set_no</th>\n",
       "      <th>game_no</th>\n",
       "      <th>point_no</th>\n",
       "      <th>p1_sets</th>\n",
       "      <th>p2_sets</th>\n",
       "      <th>p1_games</th>\n",
       "      <th>...</th>\n",
       "      <th>p2_break_pt_won</th>\n",
       "      <th>p1_break_pt_missed</th>\n",
       "      <th>p2_break_pt_missed</th>\n",
       "      <th>p1_distance_run</th>\n",
       "      <th>p2_distance_run</th>\n",
       "      <th>rally_count</th>\n",
       "      <th>speed_mph</th>\n",
       "      <th>serve_width</th>\n",
       "      <th>serve_depth</th>\n",
       "      <th>return_depth</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2023-wimbledon-1301</td>\n",
       "      <td>Carlos Alcaraz</td>\n",
       "      <td>Nicolas Jarry</td>\n",
       "      <td>00:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6.000</td>\n",
       "      <td>7.840</td>\n",
       "      <td>2</td>\n",
       "      <td>95.0</td>\n",
       "      <td>BC</td>\n",
       "      <td>NCTL</td>\n",
       "      <td>ND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2023-wimbledon-1301</td>\n",
       "      <td>Carlos Alcaraz</td>\n",
       "      <td>Nicolas Jarry</td>\n",
       "      <td>00:00:38</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5.253</td>\n",
       "      <td>7.094</td>\n",
       "      <td>1</td>\n",
       "      <td>118.0</td>\n",
       "      <td>B</td>\n",
       "      <td>CTL</td>\n",
       "      <td>ND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2023-wimbledon-1301</td>\n",
       "      <td>Carlos Alcaraz</td>\n",
       "      <td>Nicolas Jarry</td>\n",
       "      <td>00:01:01</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.800</td>\n",
       "      <td>19.808</td>\n",
       "      <td>4</td>\n",
       "      <td>120.0</td>\n",
       "      <td>B</td>\n",
       "      <td>NCTL</td>\n",
       "      <td>D</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2023-wimbledon-1301</td>\n",
       "      <td>Carlos Alcaraz</td>\n",
       "      <td>Nicolas Jarry</td>\n",
       "      <td>00:01:31</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>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>51.108</td>\n",
       "      <td>75.631</td>\n",
       "      <td>13</td>\n",
       "      <td>130.0</td>\n",
       "      <td>BW</td>\n",
       "      <td>CTL</td>\n",
       "      <td>D</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2023-wimbledon-1301</td>\n",
       "      <td>Carlos Alcaraz</td>\n",
       "      <td>Nicolas Jarry</td>\n",
       "      <td>00:02:21</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.649</td>\n",
       "      <td>0.813</td>\n",
       "      <td>1</td>\n",
       "      <td>112.0</td>\n",
       "      <td>W</td>\n",
       "      <td>NCTL</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 46 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              match_id         player1        player2 elapsed_time  set_no  \\\n",
       "0  2023-wimbledon-1301  Carlos Alcaraz  Nicolas Jarry     00:00:00       1   \n",
       "1  2023-wimbledon-1301  Carlos Alcaraz  Nicolas Jarry     00:00:38       1   \n",
       "2  2023-wimbledon-1301  Carlos Alcaraz  Nicolas Jarry     00:01:01       1   \n",
       "3  2023-wimbledon-1301  Carlos Alcaraz  Nicolas Jarry     00:01:31       1   \n",
       "4  2023-wimbledon-1301  Carlos Alcaraz  Nicolas Jarry     00:02:21       1   \n",
       "\n",
       "   game_no  point_no  p1_sets  p2_sets  p1_games  ...  p2_break_pt_won  \\\n",
       "0        1         1        0        0         0  ...                0   \n",
       "1        1         2        0        0         0  ...                0   \n",
       "2        1         3        0        0         0  ...                0   \n",
       "3        1         4        0        0         0  ...                0   \n",
       "4        1         5        0        0         0  ...                0   \n",
       "\n",
       "  p1_break_pt_missed p2_break_pt_missed  p1_distance_run  p2_distance_run  \\\n",
       "0                  0                  0            6.000            7.840   \n",
       "1                  0                  0            5.253            7.094   \n",
       "2                  0                  0           13.800           19.808   \n",
       "3                  0                  0           51.108           75.631   \n",
       "4                  0                  0            0.649            0.813   \n",
       "\n",
       "   rally_count  speed_mph  serve_width  serve_depth  return_depth  \n",
       "0            2       95.0           BC         NCTL            ND  \n",
       "1            1      118.0            B          CTL            ND  \n",
       "2            4      120.0            B         NCTL             D  \n",
       "3           13      130.0           BW          CTL             D  \n",
       "4            1      112.0            W         NCTL           NaN  \n",
       "\n",
       "[5 rows x 46 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = _data.copy()\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d26d0635",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['match_id', 'player1', 'player2', 'elapsed_time', 'set_no', 'game_no',\n",
      "       'point_no', 'p1_sets', 'p2_sets', 'p1_games', 'p2_games', 'p1_score',\n",
      "       'p2_score', 'server', 'serve_no', 'point_victor', 'p1_points_won',\n",
      "       'p2_points_won', 'game_victor', 'set_victor', 'p1_ace', 'p2_ace',\n",
      "       'p1_winner', 'p2_winner', 'winner_shot_type', 'p1_double_fault',\n",
      "       'p2_double_fault', 'p1_unf_err', 'p2_unf_err', 'p1_net_pt', 'p2_net_pt',\n",
      "       'p1_net_pt_won', 'p2_net_pt_won', 'p1_break_pt', 'p2_break_pt',\n",
      "       'p1_break_pt_won', 'p2_break_pt_won', 'p1_break_pt_missed',\n",
      "       'p2_break_pt_missed', 'p1_distance_run', 'p2_distance_run',\n",
      "       'rally_count', 'speed_mph', 'serve_width', 'serve_depth',\n",
      "       'return_depth'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(data.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "016ccf03",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>Inertia</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Carlos Alcaraz</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alexander Zverev</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Frances Tiafoe</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alejandro Davidovich Fokina</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Daniil Medvedev</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jiri Lehecka</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Christopher Eubanks</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Laslo Djere</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jannik Sinner</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Daniel Elahi Galan</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Guido Pella</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Denis Shapovalov</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Andrey Rublev</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alexander Bublik</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lorenzo Musetti</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Stan Wawrinka</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Grigor Dimitrov</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Roman Safiullin</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hubert Hurkacz</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nicolas Jarry</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Matteo Berrettini</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Holger Rune</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Marton Fucsovics</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tommy Paul</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Christopher O'Connell</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Stefanos Tsitsipas</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Quentin Halys</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mikael Ymer</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Liam Broady</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>David Goffin</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maximilian Marterer</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Novak Djokovic</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             Inertia\n",
       "0                                   \n",
       "Carlos Alcaraz                     0\n",
       "Alexander Zverev                   0\n",
       "Frances Tiafoe                     0\n",
       "Alejandro Davidovich Fokina        0\n",
       "Daniil Medvedev                    0\n",
       "Jiri Lehecka                       0\n",
       "Christopher Eubanks                0\n",
       "Laslo Djere                        0\n",
       "Jannik Sinner                      0\n",
       "Daniel Elahi Galan                 0\n",
       "Guido Pella                        0\n",
       "Denis Shapovalov                   0\n",
       "Andrey Rublev                      0\n",
       "Alexander Bublik                   0\n",
       "Lorenzo Musetti                    0\n",
       "Stan Wawrinka                      0\n",
       "Grigor Dimitrov                    0\n",
       "Roman Safiullin                    0\n",
       "Hubert Hurkacz                     0\n",
       "Nicolas Jarry                      0\n",
       "Matteo Berrettini                  0\n",
       "Holger Rune                        0\n",
       "Marton Fucsovics                   0\n",
       "Tommy Paul                         0\n",
       "Christopher O'Connell              0\n",
       "Stefanos Tsitsipas                 0\n",
       "Quentin Halys                      0\n",
       "Mikael Ymer                        0\n",
       "Liam Broady                        0\n",
       "David Goffin                       0\n",
       "Maximilian Marterer                0\n",
       "Novak Djokovic                     0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "players = data[\"player1\"]\n",
    "players = players._append(data[\"player2\"], ignore_index=True).drop_duplicates().reset_index().set_index(0)\n",
    "players[\"Inertia\"] = 0\n",
    "players = players.drop(\"index\", axis = 1)\n",
    "players"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "62994bca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>player1</th>\n",
       "      <th>player2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Carlos Alcaraz</td>\n",
       "      <td>Nicolas Jarry</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Alexander Zverev</td>\n",
       "      <td>Matteo Berrettini</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Frances Tiafoe</td>\n",
       "      <td>Grigor Dimitrov</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Alejandro Davidovich Fokina</td>\n",
       "      <td>Holger Rune</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Daniil Medvedev</td>\n",
       "      <td>Marton Fucsovics</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Jiri Lehecka</td>\n",
       "      <td>Tommy Paul</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Christopher Eubanks</td>\n",
       "      <td>Christopher O'Connell</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Laslo Djere</td>\n",
       "      <td>Stefanos Tsitsipas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Jannik Sinner</td>\n",
       "      <td>Quentin Halys</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Daniel Elahi Galan</td>\n",
       "      <td>Mikael Ymer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Guido Pella</td>\n",
       "      <td>Roman Safiullin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Denis Shapovalov</td>\n",
       "      <td>Liam Broady</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Andrey Rublev</td>\n",
       "      <td>David Goffin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Alexander Bublik</td>\n",
       "      <td>Maximilian Marterer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Lorenzo Musetti</td>\n",
       "      <td>Hubert Hurkacz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Stan Wawrinka</td>\n",
       "      <td>Novak Djokovic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Carlos Alcaraz</td>\n",
       "      <td>Matteo Berrettini</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Grigor Dimitrov</td>\n",
       "      <td>Holger Rune</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Daniil Medvedev</td>\n",
       "      <td>Jiri Lehecka</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Christopher Eubanks</td>\n",
       "      <td>Stefanos Tsitsipas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Jannik Sinner</td>\n",
       "      <td>Daniel Elahi Galan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Roman Safiullin</td>\n",
       "      <td>Denis Shapovalov</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Andrey Rublev</td>\n",
       "      <td>Alexander Bublik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Hubert Hurkacz</td>\n",
       "      <td>Novak Djokovic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Carlos Alcaraz</td>\n",
       "      <td>Holger Rune</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Daniil Medvedev</td>\n",
       "      <td>Christopher Eubanks</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Jannik Sinner</td>\n",
       "      <td>Roman Safiullin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Andrey Rublev</td>\n",
       "      <td>Novak Djokovic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Carlos Alcaraz</td>\n",
       "      <td>Daniil Medvedev</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Jannik Sinner</td>\n",
       "      <td>Novak Djokovic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Carlos Alcaraz</td>\n",
       "      <td>Novak Djokovic</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        player1                player2\n",
       "0                Carlos Alcaraz          Nicolas Jarry\n",
       "1              Alexander Zverev      Matteo Berrettini\n",
       "2                Frances Tiafoe        Grigor Dimitrov\n",
       "3   Alejandro Davidovich Fokina            Holger Rune\n",
       "4               Daniil Medvedev       Marton Fucsovics\n",
       "5                  Jiri Lehecka             Tommy Paul\n",
       "6           Christopher Eubanks  Christopher O'Connell\n",
       "7                   Laslo Djere     Stefanos Tsitsipas\n",
       "8                 Jannik Sinner          Quentin Halys\n",
       "9            Daniel Elahi Galan            Mikael Ymer\n",
       "10                  Guido Pella        Roman Safiullin\n",
       "11             Denis Shapovalov            Liam Broady\n",
       "12                Andrey Rublev           David Goffin\n",
       "13             Alexander Bublik    Maximilian Marterer\n",
       "14              Lorenzo Musetti         Hubert Hurkacz\n",
       "15                Stan Wawrinka         Novak Djokovic\n",
       "16               Carlos Alcaraz      Matteo Berrettini\n",
       "17              Grigor Dimitrov            Holger Rune\n",
       "18              Daniil Medvedev           Jiri Lehecka\n",
       "19          Christopher Eubanks     Stefanos Tsitsipas\n",
       "20                Jannik Sinner     Daniel Elahi Galan\n",
       "21              Roman Safiullin       Denis Shapovalov\n",
       "22                Andrey Rublev       Alexander Bublik\n",
       "23               Hubert Hurkacz         Novak Djokovic\n",
       "24               Carlos Alcaraz            Holger Rune\n",
       "25              Daniil Medvedev    Christopher Eubanks\n",
       "26                Jannik Sinner        Roman Safiullin\n",
       "27                Andrey Rublev         Novak Djokovic\n",
       "28               Carlos Alcaraz        Daniil Medvedev\n",
       "29                Jannik Sinner         Novak Djokovic\n",
       "30               Carlos Alcaraz         Novak Djokovic"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "player_pairs = data[[\"player1\",\"player2\"]].drop_duplicates().reset_index()[[\"player1\",\"player2\"]]\n",
    "player_pairs.to_csv(\"data/0-1-PlayerPairs.csv\")\n",
    "player_pairs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b18e08d1",
   "metadata": {},
   "source": [
    "# Inertia Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e3454ca-d0f3-48ef-a405-5f49366ab220",
   "metadata": {},
   "source": [
    "$$\n",
    "I_i\\propto W_i= \\frac{N(W)}{N}\n",
    "$$\n",
    "\n",
    "- where $N(W)$ is the number of winning points"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "386ed9b9",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_2632529/2929302636.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  win_lose[\"p1_score\"][win_lose[\"p1_score\"]=='AD'] = 60\n",
      "/tmp/ipykernel_2632529/2929302636.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  win_lose[\"p2_score\"][win_lose[\"p2_score\"]=='AD'] = 60\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0     0\n",
       "1     0\n",
       "2    15\n",
       "3    15\n",
       "4    30\n",
       "5    40\n",
       "6    40\n",
       "7    60\n",
       "8    40\n",
       "9    60\n",
       "Name: p1_score, dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "win_lose = data.copy()\n",
    "win_lose[\"p1_score\"][win_lose[\"p1_score\"]=='AD'] = 60\n",
    "win_lose[\"p2_score\"][win_lose[\"p2_score\"]=='AD'] = 60\n",
    "win_lose[\"p1_score\"].iloc[:10]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "db40a4ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>player1</th>\n",
       "      <th>player2</th>\n",
       "      <th>p1_score</th>\n",
       "      <th>p2_score</th>\n",
       "      <th>p1_win</th>\n",
       "      <th>p2_win</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Carlos Alcaraz</td>\n",
       "      <td>Nicolas Jarry</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Carlos Alcaraz</td>\n",
       "      <td>Nicolas Jarry</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Carlos Alcaraz</td>\n",
       "      <td>Nicolas Jarry</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Carlos Alcaraz</td>\n",
       "      <td>Nicolas Jarry</td>\n",
       "      <td>15</td>\n",
       "      <td>30</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Carlos Alcaraz</td>\n",
       "      <td>Nicolas Jarry</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7279</th>\n",
       "      <td>Carlos Alcaraz</td>\n",
       "      <td>Novak Djokovic</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7280</th>\n",
       "      <td>Carlos Alcaraz</td>\n",
       "      <td>Novak Djokovic</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7281</th>\n",
       "      <td>Carlos Alcaraz</td>\n",
       "      <td>Novak Djokovic</td>\n",
       "      <td>30</td>\n",
       "      <td>15</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7282</th>\n",
       "      <td>Carlos Alcaraz</td>\n",
       "      <td>Novak Djokovic</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7283</th>\n",
       "      <td>Carlos Alcaraz</td>\n",
       "      <td>Novak Djokovic</td>\n",
       "      <td>40</td>\n",
       "      <td>30</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7284 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             player1         player2 p1_score p2_score  p1_win  p2_win\n",
       "0     Carlos Alcaraz   Nicolas Jarry        0        0   False   False\n",
       "1     Carlos Alcaraz   Nicolas Jarry        0       15   False    True\n",
       "2     Carlos Alcaraz   Nicolas Jarry       15       15    True   False\n",
       "3     Carlos Alcaraz   Nicolas Jarry       15       30   False    True\n",
       "4     Carlos Alcaraz   Nicolas Jarry       30       30    True   False\n",
       "...              ...             ...      ...      ...     ...     ...\n",
       "7279  Carlos Alcaraz  Novak Djokovic        0       15   False    True\n",
       "7280  Carlos Alcaraz  Novak Djokovic       15       15    True   False\n",
       "7281  Carlos Alcaraz  Novak Djokovic       30       15    True   False\n",
       "7282  Carlos Alcaraz  Novak Djokovic       30       30   False    True\n",
       "7283  Carlos Alcaraz  Novak Djokovic       40       30    True   False\n",
       "\n",
       "[7284 rows x 6 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "win_lose = win_lose[[\"player1\", \"player2\", \"p1_score\", \"p2_score\"]]\n",
    "win_lose[\"p1_win\"] = win_lose[\"p1_score\"].astype(int)!=win_lose[\"p1_score\"].astype(int).shift(1).fillna(0)\n",
    "win_lose[\"p2_win\"] = win_lose[\"p2_score\"].astype(int)!=win_lose[\"p2_score\"].astype(int).shift(1).fillna(0)\n",
    "win_lose"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "405392cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "910"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(win_lose['p1_win']==win_lose['p2_win'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e3df0e38",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>p1_win</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>player1</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Alejandro Davidovich Fokina</th>\n",
       "      <td>0.563798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alexander Bublik</th>\n",
       "      <td>0.632432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alexander Zverev</th>\n",
       "      <td>0.567164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Andrey Rublev</th>\n",
       "      <td>0.565632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Carlos Alcaraz</th>\n",
       "      <td>0.594035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Christopher Eubanks</th>\n",
       "      <td>0.554054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Daniel Elahi Galan</th>\n",
       "      <td>0.547170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Daniil Medvedev</th>\n",
       "      <td>0.591398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Denis Shapovalov</th>\n",
       "      <td>0.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Frances Tiafoe</th>\n",
       "      <td>0.492537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Grigor Dimitrov</th>\n",
       "      <td>0.551095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Guido Pella</th>\n",
       "      <td>0.458824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hubert Hurkacz</th>\n",
       "      <td>0.531365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jannik Sinner</th>\n",
       "      <td>0.601458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jiri Lehecka</th>\n",
       "      <td>0.581325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Laslo Djere</th>\n",
       "      <td>0.542105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lorenzo Musetti</th>\n",
       "      <td>0.540404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Roman Safiullin</th>\n",
       "      <td>0.625641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Stan Wawrinka</th>\n",
       "      <td>0.502994</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               p1_win\n",
       "player1                              \n",
       "Alejandro Davidovich Fokina  0.563798\n",
       "Alexander Bublik             0.632432\n",
       "Alexander Zverev             0.567164\n",
       "Andrey Rublev                0.565632\n",
       "Carlos Alcaraz               0.594035\n",
       "Christopher Eubanks          0.554054\n",
       "Daniel Elahi Galan           0.547170\n",
       "Daniil Medvedev              0.591398\n",
       "Denis Shapovalov             0.600000\n",
       "Frances Tiafoe               0.492537\n",
       "Grigor Dimitrov              0.551095\n",
       "Guido Pella                  0.458824\n",
       "Hubert Hurkacz               0.531365\n",
       "Jannik Sinner                0.601458\n",
       "Jiri Lehecka                 0.581325\n",
       "Laslo Djere                  0.542105\n",
       "Lorenzo Musetti              0.540404\n",
       "Roman Safiullin              0.625641\n",
       "Stan Wawrinka                0.502994"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p1 = win_lose[[\"player1\",'p1_win']].groupby(\"player1\").mean()\n",
    "p2 = win_lose[[\"player2\",'p2_win']].groupby(\"player2\").mean()\n",
    "p1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "724ad007",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_2632529/1829948580.py:4: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '0.5940347970173985' has dtype incompatible with int64, please explicitly cast to a compatible dtype first.\n",
      "  players['Inertia'][player] = p1['p1_win'][player]\n"
     ]
    }
   ],
   "source": [
    "for player in players.index:\n",
    "    \n",
    "    if player in player_pairs['player1'].values:\n",
    "        players['Inertia'][player] = p1['p1_win'][player]\n",
    "    else:\n",
    "        players['Inertia'][player] = p2['p2_win'][player]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "aa8d93e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>Inertia</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Alexander Bublik</th>\n",
       "      <td>0.632432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Roman Safiullin</th>\n",
       "      <td>0.625641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jannik Sinner</th>\n",
       "      <td>0.601458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Denis Shapovalov</th>\n",
       "      <td>0.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Carlos Alcaraz</th>\n",
       "      <td>0.594035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Daniil Medvedev</th>\n",
       "      <td>0.591398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Stefanos Tsitsipas</th>\n",
       "      <td>0.588235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Novak Djokovic</th>\n",
       "      <td>0.581864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jiri Lehecka</th>\n",
       "      <td>0.581325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mikael Ymer</th>\n",
       "      <td>0.575472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alexander Zverev</th>\n",
       "      <td>0.567164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Andrey Rublev</th>\n",
       "      <td>0.565632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alejandro Davidovich Fokina</th>\n",
       "      <td>0.563798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Christopher Eubanks</th>\n",
       "      <td>0.554054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Holger Rune</th>\n",
       "      <td>0.551250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tommy Paul</th>\n",
       "      <td>0.551205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Grigor Dimitrov</th>\n",
       "      <td>0.551095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Marton Fucsovics</th>\n",
       "      <td>0.548780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Daniel Elahi Galan</th>\n",
       "      <td>0.547170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Quentin Halys</th>\n",
       "      <td>0.544601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Christopher O'Connell</th>\n",
       "      <td>0.543103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Laslo Djere</th>\n",
       "      <td>0.542105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Liam Broady</th>\n",
       "      <td>0.541818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lorenzo Musetti</th>\n",
       "      <td>0.540404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nicolas Jarry</th>\n",
       "      <td>0.540000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>David Goffin</th>\n",
       "      <td>0.534483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hubert Hurkacz</th>\n",
       "      <td>0.531365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Matteo Berrettini</th>\n",
       "      <td>0.528169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Stan Wawrinka</th>\n",
       "      <td>0.502994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maximilian Marterer</th>\n",
       "      <td>0.502703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Frances Tiafoe</th>\n",
       "      <td>0.492537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Guido Pella</th>\n",
       "      <td>0.458824</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              Inertia\n",
       "0                                    \n",
       "Alexander Bublik             0.632432\n",
       "Roman Safiullin              0.625641\n",
       "Jannik Sinner                0.601458\n",
       "Denis Shapovalov             0.600000\n",
       "Carlos Alcaraz               0.594035\n",
       "Daniil Medvedev              0.591398\n",
       "Stefanos Tsitsipas           0.588235\n",
       "Novak Djokovic               0.581864\n",
       "Jiri Lehecka                 0.581325\n",
       "Mikael Ymer                  0.575472\n",
       "Alexander Zverev             0.567164\n",
       "Andrey Rublev                0.565632\n",
       "Alejandro Davidovich Fokina  0.563798\n",
       "Christopher Eubanks          0.554054\n",
       "Holger Rune                  0.551250\n",
       "Tommy Paul                   0.551205\n",
       "Grigor Dimitrov              0.551095\n",
       "Marton Fucsovics             0.548780\n",
       "Daniel Elahi Galan           0.547170\n",
       "Quentin Halys                0.544601\n",
       "Christopher O'Connell        0.543103\n",
       "Laslo Djere                  0.542105\n",
       "Liam Broady                  0.541818\n",
       "Lorenzo Musetti              0.540404\n",
       "Nicolas Jarry                0.540000\n",
       "David Goffin                 0.534483\n",
       "Hubert Hurkacz               0.531365\n",
       "Matteo Berrettini            0.528169\n",
       "Stan Wawrinka                0.502994\n",
       "Maximilian Marterer          0.502703\n",
       "Frances Tiafoe               0.492537\n",
       "Guido Pella                  0.458824"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "players = players.sort_values('Inertia', ascending=False)\n",
    "#players[\"Inertia\"] **=3\n",
    "#players[\"Inertia\"] *=10\n",
    "players"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72e07d36",
   "metadata": {},
   "source": [
    "### General Ability Ranking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "737c82c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from utils import visualize as vs\n",
    "sns.set_style(\"dark\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c2bf4c6d-0248-4f91-8e06-c575d8148b24",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/yz812/anaconda3/lib/python3.11/site-packages/seaborn/categorical.py:641: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
      "  grouped_vals = vals.groupby(grouper)\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1500x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 6))\n",
    "sns.barplot(x=players.index, y='Inertia', data=players)\n",
    "vs.set_label('Ranks of Inertia', \"Athlete Names\", \"Inertia\")\n",
    "plt.xticks(rotation=90)\n",
    "vs.save('0-1-IneriaRanks')\n",
    "plt.show()\n",
    "\n",
    "players.to_csv(\"data/0-1-IneriaRanks.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "70ab7def-50b7-45ac-9417-95d011f5e130",
   "metadata": {},
   "outputs": [],
   "source": [
    "inputs = {\n",
    "    'p2': ['p2_win', 'p1_win','p1_game_win','p2_game_win', 'p2_score_diff', 'p2_ser'],\n",
    "    'p1': ['p1_win', 'p2_win','p1_game_win','p2_game_win', 'p1_score_diff', 'p1_ser'],\n",
    "    'gen':[]\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e9826a07-c98f-4361-9a6b-7e5a985883cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>match_id</th>\n",
       "      <th>player1</th>\n",
       "      <th>player2</th>\n",
       "      <th>elapsed_time</th>\n",
       "      <th>set_no</th>\n",
       "      <th>game_no</th>\n",
       "      <th>point_no</th>\n",
       "      <th>p1_sets</th>\n",
       "      <th>p2_sets</th>\n",
       "      <th>p1_games</th>\n",
       "      <th>...</th>\n",
       "      <th>p2_break_pt_won</th>\n",
       "      <th>p1_break_pt_missed</th>\n",
       "      <th>p2_break_pt_missed</th>\n",
       "      <th>p1_distance_run</th>\n",
       "      <th>p2_distance_run</th>\n",
       "      <th>rally_count</th>\n",
       "      <th>speed_mph</th>\n",
       "      <th>serve_width</th>\n",
       "      <th>serve_depth</th>\n",
       "      <th>return_depth</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2023-wimbledon-1301</td>\n",
       "      <td>Carlos Alcaraz</td>\n",
       "      <td>Nicolas Jarry</td>\n",
       "      <td>00:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6.000</td>\n",
       "      <td>7.840</td>\n",
       "      <td>2</td>\n",
       "      <td>95.0</td>\n",
       "      <td>BC</td>\n",
       "      <td>NCTL</td>\n",
       "      <td>ND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2023-wimbledon-1301</td>\n",
       "      <td>Carlos Alcaraz</td>\n",
       "      <td>Nicolas Jarry</td>\n",
       "      <td>00:00:38</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5.253</td>\n",
       "      <td>7.094</td>\n",
       "      <td>1</td>\n",
       "      <td>118.0</td>\n",
       "      <td>B</td>\n",
       "      <td>CTL</td>\n",
       "      <td>ND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2023-wimbledon-1301</td>\n",
       "      <td>Carlos Alcaraz</td>\n",
       "      <td>Nicolas Jarry</td>\n",
       "      <td>00:01:01</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.800</td>\n",
       "      <td>19.808</td>\n",
       "      <td>4</td>\n",
       "      <td>120.0</td>\n",
       "      <td>B</td>\n",
       "      <td>NCTL</td>\n",
       "      <td>D</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2023-wimbledon-1301</td>\n",
       "      <td>Carlos Alcaraz</td>\n",
       "      <td>Nicolas Jarry</td>\n",
       "      <td>00:01:31</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>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>51.108</td>\n",
       "      <td>75.631</td>\n",
       "      <td>13</td>\n",
       "      <td>130.0</td>\n",
       "      <td>BW</td>\n",
       "      <td>CTL</td>\n",
       "      <td>D</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2023-wimbledon-1301</td>\n",
       "      <td>Carlos Alcaraz</td>\n",
       "      <td>Nicolas Jarry</td>\n",
       "      <td>00:02:21</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.649</td>\n",
       "      <td>0.813</td>\n",
       "      <td>1</td>\n",
       "      <td>112.0</td>\n",
       "      <td>W</td>\n",
       "      <td>NCTL</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 46 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              match_id         player1        player2 elapsed_time  set_no  \\\n",
       "0  2023-wimbledon-1301  Carlos Alcaraz  Nicolas Jarry     00:00:00       1   \n",
       "1  2023-wimbledon-1301  Carlos Alcaraz  Nicolas Jarry     00:00:38       1   \n",
       "2  2023-wimbledon-1301  Carlos Alcaraz  Nicolas Jarry     00:01:01       1   \n",
       "3  2023-wimbledon-1301  Carlos Alcaraz  Nicolas Jarry     00:01:31       1   \n",
       "4  2023-wimbledon-1301  Carlos Alcaraz  Nicolas Jarry     00:02:21       1   \n",
       "\n",
       "   game_no  point_no  p1_sets  p2_sets  p1_games  ...  p2_break_pt_won  \\\n",
       "0        1         1        0        0         0  ...                0   \n",
       "1        1         2        0        0         0  ...                0   \n",
       "2        1         3        0        0         0  ...                0   \n",
       "3        1         4        0        0         0  ...                0   \n",
       "4        1         5        0        0         0  ...                0   \n",
       "\n",
       "  p1_break_pt_missed p2_break_pt_missed  p1_distance_run  p2_distance_run  \\\n",
       "0                  0                  0            6.000            7.840   \n",
       "1                  0                  0            5.253            7.094   \n",
       "2                  0                  0           13.800           19.808   \n",
       "3                  0                  0           51.108           75.631   \n",
       "4                  0                  0            0.649            0.813   \n",
       "\n",
       "   rally_count  speed_mph  serve_width  serve_depth  return_depth  \n",
       "0            2       95.0           BC         NCTL            ND  \n",
       "1            1      118.0            B          CTL            ND  \n",
       "2            4      120.0            B         NCTL             D  \n",
       "3           13      130.0           BW          CTL             D  \n",
       "4            1      112.0            W         NCTL           NaN  \n",
       "\n",
       "[5 rows x 46 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "players = pd.read_csv(\"data/0-1-IneriaRanks.csv\")\n",
    "players=players.set_index(players.columns[0])\n",
    "pairs = pd.read_csv(\"data/0-1-PlayerPairs.csv\")\n",
    "pairs.head()\n",
    "\n",
    "_dictionary = pd.read_csv(\"data_dictionary.csv\")\n",
    "_data = pd.read_csv(\"Wimbledon_featured_matches.csv\")\n",
    "\n",
    "dictionary = _dictionary.copy()\n",
    "data = _data.copy()\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1e87167c-4e79-4d1c-9f24-7517c0dca491",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'tr' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[5], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m unit \u001b[38;5;241m=\u001b[39m tr\u001b[38;5;241m.\u001b[39mModelUnit(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCarlos Alcaraz\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNicolas Jarry\u001b[39m\u001b[38;5;124m\"\u001b[39m, data)\n\u001b[1;32m      3\u001b[0m unit\u001b[38;5;241m.\u001b[39mpreprocess()\n\u001b[1;32m      4\u001b[0m unit\u001b[38;5;241m.\u001b[39mprefilter()\n",
      "\u001b[0;31mNameError\u001b[0m: name 'tr' is not defined"
     ]
    }
   ],
   "source": [
    "unit = tr.ModelUnit(\"Carlos Alcaraz\", \"Nicolas Jarry\", data)\n",
    "\n",
    "unit.preprocess()\n",
    "unit.prefilter()\n",
    "unit.show_components()\n",
    "\n",
    "unit.calc_torque(players[\"Inertia\"])\n",
    "unit.show_calculated_torque()\n",
    "\n",
    "unit.train(inputs)\n",
    "unit.construct_momentum()\n",
    "\n",
    "unit.compare_constructed()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2bc756c7-1eba-4d08-b368-2d3b4dfb7521",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}