NBA_API.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "03c79429",
   "metadata": {},
   "source": [
    "Here I use the NBA API to determine how well the Golden State Warriors performed against the Toronto Raptors.\n",
    "I use the API to determine the number of points the Golden State Warriors won or lost by for each game. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "7a3cabed",
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install nba_api\n",
    "from nba_api.stats.static import teams\n",
    "from nba_api.stats.endpoints import leaguegamefinder\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "02007e7e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>full_name</th>\n",
       "      <th>abbreviation</th>\n",
       "      <th>nickname</th>\n",
       "      <th>city</th>\n",
       "      <th>state</th>\n",
       "      <th>year_founded</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1610612737</td>\n",
       "      <td>Atlanta Hawks</td>\n",
       "      <td>ATL</td>\n",
       "      <td>Hawks</td>\n",
       "      <td>Atlanta</td>\n",
       "      <td>Georgia</td>\n",
       "      <td>1949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1610612738</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>BOS</td>\n",
       "      <td>Celtics</td>\n",
       "      <td>Boston</td>\n",
       "      <td>Massachusetts</td>\n",
       "      <td>1946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1610612739</td>\n",
       "      <td>Cleveland Cavaliers</td>\n",
       "      <td>CLE</td>\n",
       "      <td>Cavaliers</td>\n",
       "      <td>Cleveland</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>1970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1610612740</td>\n",
       "      <td>New Orleans Pelicans</td>\n",
       "      <td>NOP</td>\n",
       "      <td>Pelicans</td>\n",
       "      <td>New Orleans</td>\n",
       "      <td>Louisiana</td>\n",
       "      <td>2002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1610612741</td>\n",
       "      <td>Chicago Bulls</td>\n",
       "      <td>CHI</td>\n",
       "      <td>Bulls</td>\n",
       "      <td>Chicago</td>\n",
       "      <td>Illinois</td>\n",
       "      <td>1966</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           id             full_name abbreviation   nickname         city  \\\n",
       "0  1610612737         Atlanta Hawks          ATL      Hawks      Atlanta   \n",
       "1  1610612738        Boston Celtics          BOS    Celtics       Boston   \n",
       "2  1610612739   Cleveland Cavaliers          CLE  Cavaliers    Cleveland   \n",
       "3  1610612740  New Orleans Pelicans          NOP   Pelicans  New Orleans   \n",
       "4  1610612741         Chicago Bulls          CHI      Bulls      Chicago   \n",
       "\n",
       "           state  year_founded  \n",
       "0        Georgia          1949  \n",
       "1  Massachusetts          1946  \n",
       "2           Ohio          1970  \n",
       "3      Louisiana          2002  \n",
       "4       Illinois          1966  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Create dictionary from data\n",
    "def one_dict(list_dict):\n",
    "    keys=list_dict[0].keys()\n",
    "    out_dict={key:[] for key in keys}\n",
    "    for dict_ in list_dict:\n",
    "        for key, value in dict_.items():\n",
    "            out_dict[key].append(value)\n",
    "    return out_dict\n",
    "\n",
    "nba_teams = teams.get_teams()\n",
    "dict_nba_team=one_dict(nba_teams)\n",
    "df_teams=pd.DataFrame(dict_nba_team)\n",
    "df_teams.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "ad7b5b01",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>full_name</th>\n",
       "      <th>abbreviation</th>\n",
       "      <th>nickname</th>\n",
       "      <th>city</th>\n",
       "      <th>state</th>\n",
       "      <th>year_founded</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1610612744</td>\n",
       "      <td>Golden State Warriors</td>\n",
       "      <td>GSW</td>\n",
       "      <td>Warriors</td>\n",
       "      <td>Golden State</td>\n",
       "      <td>California</td>\n",
       "      <td>1946</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           id              full_name abbreviation  nickname          city  \\\n",
       "7  1610612744  Golden State Warriors          GSW  Warriors  Golden State   \n",
       "\n",
       "        state  year_founded  \n",
       "7  California          1946  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_warriors=df_teams[df_teams['nickname']=='Warriors']\n",
    "df_warriors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "3da152bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1610612744"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "id_warriors=df_warriors[['id']].values[0][0]\n",
    "# we now have an integer that can be used to request the Warriors information \n",
    "id_warriors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "1ca4fecb",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SEASON_ID</th>\n",
       "      <th>TEAM_ID</th>\n",
       "      <th>TEAM_ABBREVIATION</th>\n",
       "      <th>TEAM_NAME</th>\n",
       "      <th>GAME_ID</th>\n",
       "      <th>GAME_DATE</th>\n",
       "      <th>MATCHUP</th>\n",
       "      <th>WL</th>\n",
       "      <th>MIN</th>\n",
       "      <th>PTS</th>\n",
       "      <th>...</th>\n",
       "      <th>FT_PCT</th>\n",
       "      <th>OREB</th>\n",
       "      <th>DREB</th>\n",
       "      <th>REB</th>\n",
       "      <th>AST</th>\n",
       "      <th>STL</th>\n",
       "      <th>BLK</th>\n",
       "      <th>TOV</th>\n",
       "      <th>PF</th>\n",
       "      <th>PLUS_MINUS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>52023</td>\n",
       "      <td>1610612744</td>\n",
       "      <td>GSW</td>\n",
       "      <td>Golden State Warriors</td>\n",
       "      <td>0052300131</td>\n",
       "      <td>2024-04-16</td>\n",
       "      <td>GSW @ SAC</td>\n",
       "      <td>L</td>\n",
       "      <td>239</td>\n",
       "      <td>94</td>\n",
       "      <td>...</td>\n",
       "      <td>0.783</td>\n",
       "      <td>8.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>19</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>17</td>\n",
       "      <td>-24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>22023</td>\n",
       "      <td>1610612744</td>\n",
       "      <td>GSW</td>\n",
       "      <td>Golden State Warriors</td>\n",
       "      <td>0022301198</td>\n",
       "      <td>2024-04-14</td>\n",
       "      <td>GSW vs. UTA</td>\n",
       "      <td>W</td>\n",
       "      <td>239</td>\n",
       "      <td>123</td>\n",
       "      <td>...</td>\n",
       "      <td>0.826</td>\n",
       "      <td>12.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>35</td>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>14</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>22023</td>\n",
       "      <td>1610612744</td>\n",
       "      <td>GSW</td>\n",
       "      <td>Golden State Warriors</td>\n",
       "      <td>0022301182</td>\n",
       "      <td>2024-04-12</td>\n",
       "      <td>GSW vs. NOP</td>\n",
       "      <td>L</td>\n",
       "      <td>240</td>\n",
       "      <td>109</td>\n",
       "      <td>...</td>\n",
       "      <td>0.762</td>\n",
       "      <td>11.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>27</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>16</td>\n",
       "      <td>15</td>\n",
       "      <td>-5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>22023</td>\n",
       "      <td>1610612744</td>\n",
       "      <td>GSW</td>\n",
       "      <td>Golden State Warriors</td>\n",
       "      <td>0022301169</td>\n",
       "      <td>2024-04-11</td>\n",
       "      <td>GSW @ POR</td>\n",
       "      <td>W</td>\n",
       "      <td>240</td>\n",
       "      <td>100</td>\n",
       "      <td>...</td>\n",
       "      <td>0.800</td>\n",
       "      <td>10.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>24</td>\n",
       "      <td>8</td>\n",
       "      <td>13</td>\n",
       "      <td>16</td>\n",
       "      <td>18</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>22023</td>\n",
       "      <td>1610612744</td>\n",
       "      <td>GSW</td>\n",
       "      <td>Golden State Warriors</td>\n",
       "      <td>0022301155</td>\n",
       "      <td>2024-04-09</td>\n",
       "      <td>GSW @ LAL</td>\n",
       "      <td>W</td>\n",
       "      <td>241</td>\n",
       "      <td>134</td>\n",
       "      <td>...</td>\n",
       "      <td>0.667</td>\n",
       "      <td>4.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>37</td>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "      <td>15</td>\n",
       "      <td>17</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  SEASON_ID     TEAM_ID TEAM_ABBREVIATION              TEAM_NAME     GAME_ID  \\\n",
       "0     52023  1610612744               GSW  Golden State Warriors  0052300131   \n",
       "1     22023  1610612744               GSW  Golden State Warriors  0022301198   \n",
       "2     22023  1610612744               GSW  Golden State Warriors  0022301182   \n",
       "3     22023  1610612744               GSW  Golden State Warriors  0022301169   \n",
       "4     22023  1610612744               GSW  Golden State Warriors  0022301155   \n",
       "\n",
       "    GAME_DATE      MATCHUP WL  MIN  PTS  ...  FT_PCT  OREB  DREB   REB  AST  \\\n",
       "0  2024-04-16    GSW @ SAC  L  239   94  ...   0.783   8.0  34.0  42.0   19   \n",
       "1  2024-04-14  GSW vs. UTA  W  239  123  ...   0.826  12.0  30.0  42.0   35   \n",
       "2  2024-04-12  GSW vs. NOP  L  240  109  ...   0.762  11.0  42.0  53.0   27   \n",
       "3  2024-04-11    GSW @ POR  W  240  100  ...   0.800  10.0  36.0  46.0   24   \n",
       "4  2024-04-09    GSW @ LAL  W  241  134  ...   0.667   4.0  38.0  42.0   37   \n",
       "\n",
       "   STL  BLK  TOV  PF  PLUS_MINUS  \n",
       "0    5    3   16  17       -24.0  \n",
       "1   10    6    9  14         7.0  \n",
       "2    6    5   16  15        -5.0  \n",
       "3    8   13   16  18         8.0  \n",
       "4    3   12   15  17        14.0  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gamefinder = leaguegamefinder.LeagueGameFinder(team_id_nullable=id_warriors)\n",
    "gamefinder.get_json()\n",
    "games = gamefinder.get_data_frames()[0]\n",
    "games.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "a7dbdbe5",
   "metadata": {},
   "outputs": [],
   "source": [
    "games_home=games[games['MATCHUP']=='GSW vs. TOR']\n",
    "games_away=games[games['MATCHUP']=='GSW @ TOR']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af7c8ac6",
   "metadata": {},
   "source": [
    "We can plot out the PLUS MINUS column for the dataframes games_home and  games_away. We see the warriors played better at home."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "7c453f38",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.730769230769231"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "games_home['PLUS_MINUS'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "34cb71ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.6071428571428571"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "games_away['PLUS_MINUS'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "cffd4b6c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "games_away.plot(x='GAME_DATE',y='PLUS_MINUS', ax=ax)\n",
    "games_home.plot(x='GAME_DATE',y='PLUS_MINUS', ax=ax)\n",
    "ax.legend([\"away\", \"home\"])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "4bf00829",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "106.8076923076923"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7827fb1e",
   "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": "3.11.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}