predictive.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "e7f3ea8c-c502-4c52-ac10-0495ad0102bc",
   "metadata": {},
   "source": [
    "Predicting NBA games to 64% using a ridge regresion model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ed8cb84e-26f6-4dee-92b6-a542ff88337b",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "197645bc-7677-408c-b8c2-9124d7923a37",
   "metadata": {},
   "outputs": [],
   "source": [
    "#First load the data from a csv dataset\n",
    "nbaData = pd.read_csv(\"nba_games.csv\", index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "25ec312b-105a-48f2-bd87-f124b8c4fdda",
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   "outputs": [
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       "      <td>27.1</td>\n",
       "      <td>150.0</td>\n",
       "      <td>126.0</td>\n",
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       "          mp   mp.1    fg    fga    fg%    3p   3pa    3p%    ft   fta  ...  \\\n",
       "0      240.0  240.0  39.0   81.0  0.481   6.0  20.0  0.300  14.0  18.0  ...   \n",
       "1      240.0  240.0  36.0  100.0  0.360   7.0  31.0  0.226  16.0  19.0  ...   \n",
       "2      240.0  240.0  37.0   85.0  0.435   8.0  19.0  0.421  17.0  23.0  ...   \n",
       "3      240.0  240.0  41.0   89.0  0.461   8.0  21.0  0.381  17.0  19.0  ...   \n",
       "4      240.0  240.0  27.0   86.0  0.314   6.0  26.0  0.231  15.0  20.0  ...   \n",
       "...      ...    ...   ...    ...    ...   ...   ...    ...   ...   ...  ...   \n",
       "17767  240.0  240.0  35.0   81.0  0.432  11.0  26.0  0.423  27.0  36.0  ...   \n",
       "17768  240.0  240.0  37.0   74.0  0.500  13.0  25.0  0.520  26.0  37.0  ...   \n",
       "17769  240.0  240.0  42.0   89.0  0.472  14.0  33.0  0.424  10.0  20.0  ...   \n",
       "17770  240.0  240.0  41.0   85.0  0.482   9.0  26.0  0.346  26.0  30.0  ...   \n",
       "17771  240.0  240.0  33.0   85.0  0.388  12.0  44.0  0.273  28.0  34.0  ...   \n",
       "\n",
       "       tov%_max_opp  usg%_max_opp  ortg_max_opp  drtg_max_opp  team_opp  \\\n",
       "0              22.8          29.0         178.0         111.0       DAL   \n",
       "1              50.0          32.6         152.0         111.0       ATL   \n",
       "2              20.0          30.9         148.0         116.0       SAS   \n",
       "3              28.6          30.9         138.0         118.0       MIN   \n",
       "4              16.8          30.9         157.0          90.0       MEM   \n",
       "...             ...           ...           ...           ...       ...   \n",
       "17767          34.2          33.7         160.0         118.0       OKC   \n",
       "17768          25.0          30.0         139.0         129.0       ORL   \n",
       "17769          25.6          29.9         175.0         126.0       LAC   \n",
       "17770          27.7          27.1         150.0         126.0       MIA   \n",
       "17771          51.5          36.2         141.0         114.0       BOS   \n",
       "\n",
       "       total_opp  home_opp  season        date    won  \n",
       "0             95         1    2016  2015-12-09   True  \n",
       "1             98         0    2016  2015-12-09  False  \n",
       "2            107         1    2018  2017-10-18  False  \n",
       "3             99         0    2018  2017-10-18   True  \n",
       "4             92         1    2021  2021-04-30  False  \n",
       "...          ...       ...     ...         ...    ...  \n",
       "17767         92         0    2019  2018-10-19   True  \n",
       "17768        108         1    2017  2016-12-14   True  \n",
       "17769        113         0    2017  2016-12-14  False  \n",
       "17770        106         1    2020  2020-09-19   True  \n",
       "17771        117         0    2020  2020-09-19  False  \n",
       "\n",
       "[17772 rows x 150 columns]"
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     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nbaData"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "10afef48-4765-403f-b16d-9b6dcea7483d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Sort by data\n",
    "nbaData = nbaData.sort_values('date')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6819b756-477b-4fc1-a1c3-cbaf5f6c9590",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Reset index\n",
    "nbaData = nbaData.reset_index(drop=True)"
   ]
  },
  {
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   "execution_count": 6,
   "id": "9aa58686-a439-41b8-9c95-d1a92dcc1346",
   "metadata": {},
   "outputs": [
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       "      <td>138.0</td>\n",
       "      <td>105.0</td>\n",
       "      <td>CLE</td>\n",
       "      <td>95</td>\n",
       "      <td>0</td>\n",
       "      <td>2016</td>\n",
       "      <td>2015-10-27</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>240.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>0.427</td>\n",
       "      <td>9.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.300</td>\n",
       "      <td>20.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>...</td>\n",
       "      <td>37.5</td>\n",
       "      <td>38.9</td>\n",
       "      <td>201.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>NOP</td>\n",
       "      <td>95</td>\n",
       "      <td>0</td>\n",
       "      <td>2016</td>\n",
       "      <td>2015-10-27</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>240.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>0.451</td>\n",
       "      <td>8.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.296</td>\n",
       "      <td>12.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>...</td>\n",
       "      <td>33.3</td>\n",
       "      <td>23.6</td>\n",
       "      <td>132.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>DET</td>\n",
       "      <td>106</td>\n",
       "      <td>0</td>\n",
       "      <td>2016</td>\n",
       "      <td>2015-10-27</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17767</th>\n",
       "      <td>240.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>0.400</td>\n",
       "      <td>15.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>0.395</td>\n",
       "      <td>14.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>...</td>\n",
       "      <td>42.9</td>\n",
       "      <td>36.3</td>\n",
       "      <td>133.0</td>\n",
       "      <td>112.0</td>\n",
       "      <td>GSW</td>\n",
       "      <td>107</td>\n",
       "      <td>0</td>\n",
       "      <td>2022</td>\n",
       "      <td>2022-06-10</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17768</th>\n",
       "      <td>240.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>0.466</td>\n",
       "      <td>9.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0.225</td>\n",
       "      <td>13.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>...</td>\n",
       "      <td>45.0</td>\n",
       "      <td>94.4</td>\n",
       "      <td>300.0</td>\n",
       "      <td>112.0</td>\n",
       "      <td>BOS</td>\n",
       "      <td>94</td>\n",
       "      <td>0</td>\n",
       "      <td>2022</td>\n",
       "      <td>2022-06-13</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17769</th>\n",
       "      <td>240.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>0.413</td>\n",
       "      <td>11.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0.344</td>\n",
       "      <td>21.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>...</td>\n",
       "      <td>33.3</td>\n",
       "      <td>36.2</td>\n",
       "      <td>222.0</td>\n",
       "      <td>107.0</td>\n",
       "      <td>GSW</td>\n",
       "      <td>104</td>\n",
       "      <td>1</td>\n",
       "      <td>2022</td>\n",
       "      <td>2022-06-13</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17770</th>\n",
       "      <td>240.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>0.425</td>\n",
       "      <td>11.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.393</td>\n",
       "      <td>11.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>...</td>\n",
       "      <td>33.3</td>\n",
       "      <td>31.5</td>\n",
       "      <td>186.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>GSW</td>\n",
       "      <td>103</td>\n",
       "      <td>0</td>\n",
       "      <td>2022</td>\n",
       "      <td>2022-06-16</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17771</th>\n",
       "      <td>240.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>0.413</td>\n",
       "      <td>19.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>0.413</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>...</td>\n",
       "      <td>100.0</td>\n",
       "      <td>42.6</td>\n",
       "      <td>141.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>BOS</td>\n",
       "      <td>90</td>\n",
       "      <td>1</td>\n",
       "      <td>2022</td>\n",
       "      <td>2022-06-16</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>17772 rows × 150 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          mp   mp.1    fg   fga    fg%    3p   3pa    3p%    ft   fta  ...  \\\n",
       "0      240.0  240.0  35.0  83.0  0.422   6.0  18.0  0.333  19.0  27.0  ...   \n",
       "1      240.0  240.0  38.0  94.0  0.404   9.0  29.0  0.310  10.0  17.0  ...   \n",
       "2      240.0  240.0  37.0  87.0  0.425   7.0  19.0  0.368  16.0  23.0  ...   \n",
       "3      240.0  240.0  41.0  96.0  0.427   9.0  30.0  0.300  20.0  22.0  ...   \n",
       "4      240.0  240.0  37.0  82.0  0.451   8.0  27.0  0.296  12.0  15.0  ...   \n",
       "...      ...    ...   ...   ...    ...   ...   ...    ...   ...   ...  ...   \n",
       "17767  240.0  240.0  34.0  85.0  0.400  15.0  38.0  0.395  14.0  19.0  ...   \n",
       "17768  240.0  240.0  41.0  88.0  0.466   9.0  40.0  0.225  13.0  15.0  ...   \n",
       "17769  240.0  240.0  31.0  75.0  0.413  11.0  32.0  0.344  21.0  31.0  ...   \n",
       "17770  240.0  240.0  34.0  80.0  0.425  11.0  28.0  0.393  11.0  12.0  ...   \n",
       "17771  240.0  240.0  38.0  92.0  0.413  19.0  46.0  0.413   8.0   8.0  ...   \n",
       "\n",
       "       tov%_max_opp  usg%_max_opp  ortg_max_opp  drtg_max_opp  team_opp  \\\n",
       "0              69.4          43.7         206.0         104.0       GSW   \n",
       "1              53.2          34.6         162.0         104.0       CHI   \n",
       "2              30.4          29.0         138.0         105.0       CLE   \n",
       "3              37.5          38.9         201.0         120.0       NOP   \n",
       "4              33.3          23.6         132.0         104.0       DET   \n",
       "...             ...           ...           ...           ...       ...   \n",
       "17767          42.9          36.3         133.0         112.0       GSW   \n",
       "17768          45.0          94.4         300.0         112.0       BOS   \n",
       "17769          33.3          36.2         222.0         107.0       GSW   \n",
       "17770          33.3          31.5         186.0         111.0       GSW   \n",
       "17771         100.0          42.6         141.0         126.0       BOS   \n",
       "\n",
       "       total_opp  home_opp  season        date    won  \n",
       "0            111         1    2016  2015-10-27  False  \n",
       "1             97         1    2016  2015-10-27  False  \n",
       "2             95         0    2016  2015-10-27   True  \n",
       "3             95         0    2016  2015-10-27   True  \n",
       "4            106         0    2016  2015-10-27  False  \n",
       "...          ...       ...     ...         ...    ...  \n",
       "17767        107         0    2022  2022-06-10  False  \n",
       "17768         94         0    2022  2022-06-13   True  \n",
       "17769        104         1    2022  2022-06-13  False  \n",
       "17770        103         0    2022  2022-06-16  False  \n",
       "17771         90         1    2022  2022-06-16   True  \n",
       "\n",
       "[17772 rows x 150 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nbaData"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "bdf2331c-a013-475e-b323-f57045f81303",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mp</th>\n",
       "      <th>fg</th>\n",
       "      <th>fga</th>\n",
       "      <th>fg%</th>\n",
       "      <th>3p</th>\n",
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       "      <th>tov%_max_opp</th>\n",
       "      <th>usg%_max_opp</th>\n",
       "      <th>ortg_max_opp</th>\n",
       "      <th>drtg_max_opp</th>\n",
       "      <th>team_opp</th>\n",
       "      <th>total_opp</th>\n",
       "      <th>home_opp</th>\n",
       "      <th>season</th>\n",
       "      <th>date</th>\n",
       "      <th>won</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>240.0</td>\n",
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       "      <td>83.0</td>\n",
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       "      <td>19.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.704</td>\n",
       "      <td>...</td>\n",
       "      <td>69.4</td>\n",
       "      <td>43.7</td>\n",
       "      <td>206.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>GSW</td>\n",
       "      <td>111</td>\n",
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       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>240.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>0.404</td>\n",
       "      <td>9.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>0.310</td>\n",
       "      <td>10.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.588</td>\n",
       "      <td>...</td>\n",
       "      <td>53.2</td>\n",
       "      <td>34.6</td>\n",
       "      <td>162.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>CHI</td>\n",
       "      <td>97</td>\n",
       "      <td>1</td>\n",
       "      <td>2016</td>\n",
       "      <td>2015-10-27</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>240.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>0.425</td>\n",
       "      <td>7.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0.368</td>\n",
       "      <td>16.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0.696</td>\n",
       "      <td>...</td>\n",
       "      <td>30.4</td>\n",
       "      <td>29.0</td>\n",
       "      <td>138.0</td>\n",
       "      <td>105.0</td>\n",
       "      <td>CLE</td>\n",
       "      <td>95</td>\n",
       "      <td>0</td>\n",
       "      <td>2016</td>\n",
       "      <td>2015-10-27</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>240.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>0.427</td>\n",
       "      <td>9.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.300</td>\n",
       "      <td>20.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.909</td>\n",
       "      <td>...</td>\n",
       "      <td>37.5</td>\n",
       "      <td>38.9</td>\n",
       "      <td>201.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>NOP</td>\n",
       "      <td>95</td>\n",
       "      <td>0</td>\n",
       "      <td>2016</td>\n",
       "      <td>2015-10-27</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>240.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>0.451</td>\n",
       "      <td>8.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.296</td>\n",
       "      <td>12.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.800</td>\n",
       "      <td>...</td>\n",
       "      <td>33.3</td>\n",
       "      <td>23.6</td>\n",
       "      <td>132.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>DET</td>\n",
       "      <td>106</td>\n",
       "      <td>0</td>\n",
       "      <td>2016</td>\n",
       "      <td>2015-10-27</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17767</th>\n",
       "      <td>240.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>0.400</td>\n",
       "      <td>15.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>0.395</td>\n",
       "      <td>14.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0.737</td>\n",
       "      <td>...</td>\n",
       "      <td>42.9</td>\n",
       "      <td>36.3</td>\n",
       "      <td>133.0</td>\n",
       "      <td>112.0</td>\n",
       "      <td>GSW</td>\n",
       "      <td>107</td>\n",
       "      <td>0</td>\n",
       "      <td>2022</td>\n",
       "      <td>2022-06-10</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>17768</th>\n",
       "      <td>240.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>0.466</td>\n",
       "      <td>9.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0.225</td>\n",
       "      <td>13.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.867</td>\n",
       "      <td>...</td>\n",
       "      <td>45.0</td>\n",
       "      <td>94.4</td>\n",
       "      <td>300.0</td>\n",
       "      <td>112.0</td>\n",
       "      <td>BOS</td>\n",
       "      <td>94</td>\n",
       "      <td>0</td>\n",
       "      <td>2022</td>\n",
       "      <td>2022-06-13</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17769</th>\n",
       "      <td>240.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>0.413</td>\n",
       "      <td>11.0</td>\n",
       "      <td>32.0</td>\n",
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       "      <td>21.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0.677</td>\n",
       "      <td>...</td>\n",
       "      <td>33.3</td>\n",
       "      <td>36.2</td>\n",
       "      <td>222.0</td>\n",
       "      <td>107.0</td>\n",
       "      <td>GSW</td>\n",
       "      <td>104</td>\n",
       "      <td>1</td>\n",
       "      <td>2022</td>\n",
       "      <td>2022-06-13</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17770</th>\n",
       "      <td>240.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>0.425</td>\n",
       "      <td>11.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.393</td>\n",
       "      <td>11.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.917</td>\n",
       "      <td>...</td>\n",
       "      <td>33.3</td>\n",
       "      <td>31.5</td>\n",
       "      <td>186.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>GSW</td>\n",
       "      <td>103</td>\n",
       "      <td>0</td>\n",
       "      <td>2022</td>\n",
       "      <td>2022-06-16</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17771</th>\n",
       "      <td>240.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>0.413</td>\n",
       "      <td>19.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>0.413</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.000</td>\n",
       "      <td>...</td>\n",
       "      <td>100.0</td>\n",
       "      <td>42.6</td>\n",
       "      <td>141.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>BOS</td>\n",
       "      <td>90</td>\n",
       "      <td>1</td>\n",
       "      <td>2022</td>\n",
       "      <td>2022-06-16</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>17772 rows × 147 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          mp    fg   fga    fg%    3p   3pa    3p%    ft   fta    ft%  ...  \\\n",
       "0      240.0  35.0  83.0  0.422   6.0  18.0  0.333  19.0  27.0  0.704  ...   \n",
       "1      240.0  38.0  94.0  0.404   9.0  29.0  0.310  10.0  17.0  0.588  ...   \n",
       "2      240.0  37.0  87.0  0.425   7.0  19.0  0.368  16.0  23.0  0.696  ...   \n",
       "3      240.0  41.0  96.0  0.427   9.0  30.0  0.300  20.0  22.0  0.909  ...   \n",
       "4      240.0  37.0  82.0  0.451   8.0  27.0  0.296  12.0  15.0  0.800  ...   \n",
       "...      ...   ...   ...    ...   ...   ...    ...   ...   ...    ...  ...   \n",
       "17767  240.0  34.0  85.0  0.400  15.0  38.0  0.395  14.0  19.0  0.737  ...   \n",
       "17768  240.0  41.0  88.0  0.466   9.0  40.0  0.225  13.0  15.0  0.867  ...   \n",
       "17769  240.0  31.0  75.0  0.413  11.0  32.0  0.344  21.0  31.0  0.677  ...   \n",
       "17770  240.0  34.0  80.0  0.425  11.0  28.0  0.393  11.0  12.0  0.917  ...   \n",
       "17771  240.0  38.0  92.0  0.413  19.0  46.0  0.413   8.0   8.0  1.000  ...   \n",
       "\n",
       "       tov%_max_opp  usg%_max_opp  ortg_max_opp  drtg_max_opp  team_opp  \\\n",
       "0              69.4          43.7         206.0         104.0       GSW   \n",
       "1              53.2          34.6         162.0         104.0       CHI   \n",
       "2              30.4          29.0         138.0         105.0       CLE   \n",
       "3              37.5          38.9         201.0         120.0       NOP   \n",
       "4              33.3          23.6         132.0         104.0       DET   \n",
       "...             ...           ...           ...           ...       ...   \n",
       "17767          42.9          36.3         133.0         112.0       GSW   \n",
       "17768          45.0          94.4         300.0         112.0       BOS   \n",
       "17769          33.3          36.2         222.0         107.0       GSW   \n",
       "17770          33.3          31.5         186.0         111.0       GSW   \n",
       "17771         100.0          42.6         141.0         126.0       BOS   \n",
       "\n",
       "       total_opp  home_opp  season        date    won  \n",
       "0            111         1    2016  2015-10-27  False  \n",
       "1             97         1    2016  2015-10-27  False  \n",
       "2             95         0    2016  2015-10-27   True  \n",
       "3             95         0    2016  2015-10-27   True  \n",
       "4            106         0    2016  2015-10-27  False  \n",
       "...          ...       ...     ...         ...    ...  \n",
       "17767        107         0    2022  2022-06-10  False  \n",
       "17768         94         0    2022  2022-06-13   True  \n",
       "17769        104         1    2022  2022-06-13  False  \n",
       "17770        103         0    2022  2022-06-16  False  \n",
       "17771         90         1    2022  2022-06-16   True  \n",
       "\n",
       "[17772 rows x 147 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#deleting not important rows\n",
    "del nbaData[\"mp.1\"]\n",
    "del nbaData[\"mp_opp.1\"]\n",
    "del nbaData[\"index_opp\"]\n",
    "nbaData"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "41d54f5b-6fee-4d4a-827a-f465caf1501e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Test\\AppData\\Local\\Temp\\ipykernel_20236\\2928047337.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  team[\"nextGame\"] = team[\"won\"].shift(-1)\n",
      "C:\\Users\\Test\\AppData\\Local\\Temp\\ipykernel_20236\\2928047337.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  team[\"nextGame\"] = team[\"won\"].shift(-1)\n",
      "C:\\Users\\Test\\AppData\\Local\\Temp\\ipykernel_20236\\2928047337.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  team[\"nextGame\"] = team[\"won\"].shift(-1)\n",
      "C:\\Users\\Test\\AppData\\Local\\Temp\\ipykernel_20236\\2928047337.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  team[\"nextGame\"] = team[\"won\"].shift(-1)\n",
      "C:\\Users\\Test\\AppData\\Local\\Temp\\ipykernel_20236\\2928047337.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  team[\"nextGame\"] = team[\"won\"].shift(-1)\n",
      "C:\\Users\\Test\\AppData\\Local\\Temp\\ipykernel_20236\\2928047337.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  team[\"nextGame\"] = team[\"won\"].shift(-1)\n",
      "C:\\Users\\Test\\AppData\\Local\\Temp\\ipykernel_20236\\2928047337.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  team[\"nextGame\"] = team[\"won\"].shift(-1)\n",
      "C:\\Users\\Test\\AppData\\Local\\Temp\\ipykernel_20236\\2928047337.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  team[\"nextGame\"] = team[\"won\"].shift(-1)\n",
      "C:\\Users\\Test\\AppData\\Local\\Temp\\ipykernel_20236\\2928047337.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  team[\"nextGame\"] = team[\"won\"].shift(-1)\n",
      "C:\\Users\\Test\\AppData\\Local\\Temp\\ipykernel_20236\\2928047337.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  team[\"nextGame\"] = team[\"won\"].shift(-1)\n",
      "C:\\Users\\Test\\AppData\\Local\\Temp\\ipykernel_20236\\2928047337.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  team[\"nextGame\"] = team[\"won\"].shift(-1)\n",
      "C:\\Users\\Test\\AppData\\Local\\Temp\\ipykernel_20236\\2928047337.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  team[\"nextGame\"] = team[\"won\"].shift(-1)\n",
      "C:\\Users\\Test\\AppData\\Local\\Temp\\ipykernel_20236\\2928047337.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  team[\"nextGame\"] = team[\"won\"].shift(-1)\n",
      "C:\\Users\\Test\\AppData\\Local\\Temp\\ipykernel_20236\\2928047337.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  team[\"nextGame\"] = team[\"won\"].shift(-1)\n",
      "C:\\Users\\Test\\AppData\\Local\\Temp\\ipykernel_20236\\2928047337.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  team[\"nextGame\"] = team[\"won\"].shift(-1)\n",
      "C:\\Users\\Test\\AppData\\Local\\Temp\\ipykernel_20236\\2928047337.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  team[\"nextGame\"] = team[\"won\"].shift(-1)\n",
      "C:\\Users\\Test\\AppData\\Local\\Temp\\ipykernel_20236\\2928047337.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  team[\"nextGame\"] = team[\"won\"].shift(-1)\n",
      "C:\\Users\\Test\\AppData\\Local\\Temp\\ipykernel_20236\\2928047337.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  team[\"nextGame\"] = team[\"won\"].shift(-1)\n",
      "C:\\Users\\Test\\AppData\\Local\\Temp\\ipykernel_20236\\2928047337.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  team[\"nextGame\"] = team[\"won\"].shift(-1)\n",
      "C:\\Users\\Test\\AppData\\Local\\Temp\\ipykernel_20236\\2928047337.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  team[\"nextGame\"] = team[\"won\"].shift(-1)\n",
      "C:\\Users\\Test\\AppData\\Local\\Temp\\ipykernel_20236\\2928047337.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  team[\"nextGame\"] = team[\"won\"].shift(-1)\n",
      "C:\\Users\\Test\\AppData\\Local\\Temp\\ipykernel_20236\\2928047337.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  team[\"nextGame\"] = team[\"won\"].shift(-1)\n",
      "C:\\Users\\Test\\AppData\\Local\\Temp\\ipykernel_20236\\2928047337.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  team[\"nextGame\"] = team[\"won\"].shift(-1)\n",
      "C:\\Users\\Test\\AppData\\Local\\Temp\\ipykernel_20236\\2928047337.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  team[\"nextGame\"] = team[\"won\"].shift(-1)\n",
      "C:\\Users\\Test\\AppData\\Local\\Temp\\ipykernel_20236\\2928047337.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  team[\"nextGame\"] = team[\"won\"].shift(-1)\n",
      "C:\\Users\\Test\\AppData\\Local\\Temp\\ipykernel_20236\\2928047337.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  team[\"nextGame\"] = team[\"won\"].shift(-1)\n",
      "C:\\Users\\Test\\AppData\\Local\\Temp\\ipykernel_20236\\2928047337.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  team[\"nextGame\"] = team[\"won\"].shift(-1)\n",
      "C:\\Users\\Test\\AppData\\Local\\Temp\\ipykernel_20236\\2928047337.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  team[\"nextGame\"] = team[\"won\"].shift(-1)\n",
      "C:\\Users\\Test\\AppData\\Local\\Temp\\ipykernel_20236\\2928047337.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  team[\"nextGame\"] = team[\"won\"].shift(-1)\n",
      "C:\\Users\\Test\\AppData\\Local\\Temp\\ipykernel_20236\\2928047337.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  team[\"nextGame\"] = team[\"won\"].shift(-1)\n",
      "C:\\Users\\Test\\AppData\\Local\\Temp\\ipykernel_20236\\2928047337.py:7: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
      "  nbaData = nbaData.groupby(\"team\", group_keys=False).apply(add_target)\n"
     ]
    }
   ],
   "source": [
    "#over here, we create a new column called target, which gets the next games outcome\n",
    "def add_target(team):\n",
    "    team[\"nextGame\"] = team[\"won\"].shift(-1)\n",
    "    return team\n",
    "\n",
    "#Target is the next games match outcome, so we shift the next games match result into its own column\n",
    "nbaData = nbaData.groupby(\"team\", group_keys=False).apply(add_target)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a01932dc-d8de-4598-ac7e-520f1b653d75",
   "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>mp</th>\n",
       "      <th>fg</th>\n",
       "      <th>fga</th>\n",
       "      <th>fg%</th>\n",
       "      <th>3p</th>\n",
       "      <th>3pa</th>\n",
       "      <th>3p%</th>\n",
       "      <th>ft</th>\n",
       "      <th>fta</th>\n",
       "      <th>ft%</th>\n",
       "      <th>...</th>\n",
       "      <th>usg%_max_opp</th>\n",
       "      <th>ortg_max_opp</th>\n",
       "      <th>drtg_max_opp</th>\n",
       "      <th>team_opp</th>\n",
       "      <th>total_opp</th>\n",
       "      <th>home_opp</th>\n",
       "      <th>season</th>\n",
       "      <th>date</th>\n",
       "      <th>won</th>\n",
       "      <th>nextGame</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>240.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>0.393</td>\n",
       "      <td>7.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.250</td>\n",
       "      <td>15.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.600</td>\n",
       "      <td>...</td>\n",
       "      <td>25.2</td>\n",
       "      <td>134.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>ORL</td>\n",
       "      <td>87</td>\n",
       "      <td>1</td>\n",
       "      <td>2016</td>\n",
       "      <td>2015-10-28</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>240.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>0.515</td>\n",
       "      <td>12.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.571</td>\n",
       "      <td>36.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>0.818</td>\n",
       "      <td>...</td>\n",
       "      <td>32.4</td>\n",
       "      <td>138.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>MIL</td>\n",
       "      <td>113</td>\n",
       "      <td>1</td>\n",
       "      <td>2016</td>\n",
       "      <td>2015-10-30</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>240.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>0.422</td>\n",
       "      <td>6.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.273</td>\n",
       "      <td>28.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0.848</td>\n",
       "      <td>...</td>\n",
       "      <td>28.7</td>\n",
       "      <td>153.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>NYK</td>\n",
       "      <td>117</td>\n",
       "      <td>0</td>\n",
       "      <td>2016</td>\n",
       "      <td>2015-10-31</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>240.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>0.483</td>\n",
       "      <td>8.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0.348</td>\n",
       "      <td>10.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.714</td>\n",
       "      <td>...</td>\n",
       "      <td>30.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>109.0</td>\n",
       "      <td>SAS</td>\n",
       "      <td>99</td>\n",
       "      <td>0</td>\n",
       "      <td>2016</td>\n",
       "      <td>2015-11-04</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>161</th>\n",
       "      <td>240.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>0.409</td>\n",
       "      <td>8.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.320</td>\n",
       "      <td>18.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0.783</td>\n",
       "      <td>...</td>\n",
       "      <td>41.6</td>\n",
       "      <td>146.0</td>\n",
       "      <td>103.0</td>\n",
       "      <td>BOS</td>\n",
       "      <td>118</td>\n",
       "      <td>1</td>\n",
       "      <td>2016</td>\n",
       "      <td>2015-11-06</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17471</th>\n",
       "      <td>240.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>0.463</td>\n",
       "      <td>8.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.381</td>\n",
       "      <td>18.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.900</td>\n",
       "      <td>...</td>\n",
       "      <td>34.4</td>\n",
       "      <td>245.0</td>\n",
       "      <td>116.0</td>\n",
       "      <td>BOS</td>\n",
       "      <td>144</td>\n",
       "      <td>1</td>\n",
       "      <td>2022</td>\n",
       "      <td>2022-04-03</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17483</th>\n",
       "      <td>240.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>0.554</td>\n",
       "      <td>13.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.433</td>\n",
       "      <td>7.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.500</td>\n",
       "      <td>...</td>\n",
       "      <td>32.8</td>\n",
       "      <td>168.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>MIN</td>\n",
       "      <td>114</td>\n",
       "      <td>1</td>\n",
       "      <td>2022</td>\n",
       "      <td>2022-04-05</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17510</th>\n",
       "      <td>240.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>0.488</td>\n",
       "      <td>10.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0.286</td>\n",
       "      <td>9.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.818</td>\n",
       "      <td>...</td>\n",
       "      <td>60.2</td>\n",
       "      <td>159.0</td>\n",
       "      <td>114.0</td>\n",
       "      <td>ATL</td>\n",
       "      <td>118</td>\n",
       "      <td>1</td>\n",
       "      <td>2022</td>\n",
       "      <td>2022-04-06</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17538</th>\n",
       "      <td>240.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>0.507</td>\n",
       "      <td>9.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.321</td>\n",
       "      <td>13.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.765</td>\n",
       "      <td>...</td>\n",
       "      <td>35.5</td>\n",
       "      <td>135.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>NYK</td>\n",
       "      <td>114</td>\n",
       "      <td>0</td>\n",
       "      <td>2022</td>\n",
       "      <td>2022-04-08</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17558</th>\n",
       "      <td>240.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>0.447</td>\n",
       "      <td>8.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0.348</td>\n",
       "      <td>16.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0.615</td>\n",
       "      <td>...</td>\n",
       "      <td>37.3</td>\n",
       "      <td>161.0</td>\n",
       "      <td>118.0</td>\n",
       "      <td>CHO</td>\n",
       "      <td>124</td>\n",
       "      <td>1</td>\n",
       "      <td>2022</td>\n",
       "      <td>2022-04-10</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>580 rows × 148 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          mp    fg    fga    fg%    3p   3pa    3p%    ft   fta    ft%  ...  \\\n",
       "11     240.0  33.0   84.0  0.393   7.0  28.0  0.250  15.0  25.0  0.600  ...   \n",
       "63     240.0  35.0   68.0  0.515  12.0  21.0  0.571  36.0  44.0  0.818  ...   \n",
       "69     240.0  38.0   90.0  0.422   6.0  22.0  0.273  28.0  33.0  0.848  ...   \n",
       "124    240.0  42.0   87.0  0.483   8.0  23.0  0.348  10.0  14.0  0.714  ...   \n",
       "161    240.0  36.0   88.0  0.409   8.0  25.0  0.320  18.0  23.0  0.783  ...   \n",
       "...      ...   ...    ...    ...   ...   ...    ...   ...   ...    ...  ...   \n",
       "17471  240.0  38.0   82.0  0.463   8.0  21.0  0.381  18.0  20.0  0.900  ...   \n",
       "17483  240.0  56.0  101.0  0.554  13.0  30.0  0.433   7.0  14.0  0.500  ...   \n",
       "17510  240.0  42.0   86.0  0.488  10.0  35.0  0.286   9.0  11.0  0.818  ...   \n",
       "17538  240.0  35.0   69.0  0.507   9.0  28.0  0.321  13.0  17.0  0.765  ...   \n",
       "17558  240.0  42.0   94.0  0.447   8.0  23.0  0.348  16.0  26.0  0.615  ...   \n",
       "\n",
       "       usg%_max_opp  ortg_max_opp  drtg_max_opp  team_opp  total_opp  \\\n",
       "11             25.2         134.0          98.0       ORL         87   \n",
       "63             32.4         138.0         122.0       MIL        113   \n",
       "69             28.7         153.0         108.0       NYK        117   \n",
       "124            30.0         160.0         109.0       SAS         99   \n",
       "161            41.6         146.0         103.0       BOS        118   \n",
       "...             ...           ...           ...       ...        ...   \n",
       "17471          34.4         245.0         116.0       BOS        144   \n",
       "17483          32.8         168.0         132.0       MIN        114   \n",
       "17510          60.2         159.0         114.0       ATL        118   \n",
       "17538          35.5         135.0         104.0       NYK        114   \n",
       "17558          37.3         161.0         118.0       CHO        124   \n",
       "\n",
       "       home_opp  season        date    won  nextGame  \n",
       "11            1    2016  2015-10-28   True      True  \n",
       "63            1    2016  2015-10-30   True     False  \n",
       "69            0    2016  2015-10-31  False      True  \n",
       "124           0    2016  2015-11-04   True     False  \n",
       "161           1    2016  2015-11-06  False     False  \n",
       "...         ...     ...         ...    ...       ...  \n",
       "17471         1    2022  2022-04-03  False      True  \n",
       "17483         1    2022  2022-04-05   True     False  \n",
       "17510         1    2022  2022-04-06  False     False  \n",
       "17538         0    2022  2022-04-08  False     False  \n",
       "17558         1    2022  2022-04-10  False       NaN  \n",
       "\n",
       "[580 rows x 148 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nbaData[nbaData[\"team\"]==\"WAS\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "fc5fb154-b84c-4c30-88e5-033d4afaeda3",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Test\\AppData\\Local\\Temp\\ipykernel_20236\\3062041193.py:1: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n",
      "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n",
      "A typical example is when you are setting values in a column of a DataFrame, like:\n",
      "\n",
      "df[\"col\"][row_indexer] = value\n",
      "\n",
      "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\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",
      "\n",
      "  nbaData[\"nextGame\"][pd.isnull(nbaData[\"nextGame\"])]=2\n",
      "C:\\Users\\Test\\AppData\\Local\\Temp\\ipykernel_20236\\3062041193.py:1: 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",
      "  nbaData[\"nextGame\"][pd.isnull(nbaData[\"nextGame\"])]=2\n"
     ]
    }
   ],
   "source": [
    "nbaData[\"nextGame\"][pd.isnull(nbaData[\"nextGame\"])]=2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "48065cbe-660f-4ec6-a8e6-3836ffc9cbea",
   "metadata": {},
   "outputs": [
    {
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       "      <td>29.0</td>\n",
       "      <td>138.0</td>\n",
       "      <td>105.0</td>\n",
       "      <td>CLE</td>\n",
       "      <td>95</td>\n",
       "      <td>0</td>\n",
       "      <td>2016</td>\n",
       "      <td>2015-10-27</td>\n",
       "      <td>True</td>\n",
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       "      <td>88.0</td>\n",
       "      <td>0.466</td>\n",
       "      <td>9.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0.225</td>\n",
       "      <td>13.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.867</td>\n",
       "      <td>...</td>\n",
       "      <td>94.4</td>\n",
       "      <td>300.0</td>\n",
       "      <td>112.0</td>\n",
       "      <td>BOS</td>\n",
       "      <td>94</td>\n",
       "      <td>0</td>\n",
       "      <td>2022</td>\n",
       "      <td>2022-06-13</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17769</th>\n",
       "      <td>240.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>0.413</td>\n",
       "      <td>11.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0.344</td>\n",
       "      <td>21.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0.677</td>\n",
       "      <td>...</td>\n",
       "      <td>36.2</td>\n",
       "      <td>222.0</td>\n",
       "      <td>107.0</td>\n",
       "      <td>GSW</td>\n",
       "      <td>104</td>\n",
       "      <td>1</td>\n",
       "      <td>2022</td>\n",
       "      <td>2022-06-13</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17770</th>\n",
       "      <td>240.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>0.425</td>\n",
       "      <td>11.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.393</td>\n",
       "      <td>11.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.917</td>\n",
       "      <td>...</td>\n",
       "      <td>31.5</td>\n",
       "      <td>186.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>GSW</td>\n",
       "      <td>103</td>\n",
       "      <td>0</td>\n",
       "      <td>2022</td>\n",
       "      <td>2022-06-16</td>\n",
       "      <td>False</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17771</th>\n",
       "      <td>240.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>0.413</td>\n",
       "      <td>19.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>0.413</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.000</td>\n",
       "      <td>...</td>\n",
       "      <td>42.6</td>\n",
       "      <td>141.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>BOS</td>\n",
       "      <td>90</td>\n",
       "      <td>1</td>\n",
       "      <td>2022</td>\n",
       "      <td>2022-06-16</td>\n",
       "      <td>True</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>17772 rows × 148 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          mp    fg   fga    fg%    3p   3pa    3p%    ft   fta    ft%  ...  \\\n",
       "0      240.0  35.0  83.0  0.422   6.0  18.0  0.333  19.0  27.0  0.704  ...   \n",
       "1      240.0  38.0  94.0  0.404   9.0  29.0  0.310  10.0  17.0  0.588  ...   \n",
       "2      240.0  37.0  87.0  0.425   7.0  19.0  0.368  16.0  23.0  0.696  ...   \n",
       "3      240.0  41.0  96.0  0.427   9.0  30.0  0.300  20.0  22.0  0.909  ...   \n",
       "4      240.0  37.0  82.0  0.451   8.0  27.0  0.296  12.0  15.0  0.800  ...   \n",
       "...      ...   ...   ...    ...   ...   ...    ...   ...   ...    ...  ...   \n",
       "17767  240.0  34.0  85.0  0.400  15.0  38.0  0.395  14.0  19.0  0.737  ...   \n",
       "17768  240.0  41.0  88.0  0.466   9.0  40.0  0.225  13.0  15.0  0.867  ...   \n",
       "17769  240.0  31.0  75.0  0.413  11.0  32.0  0.344  21.0  31.0  0.677  ...   \n",
       "17770  240.0  34.0  80.0  0.425  11.0  28.0  0.393  11.0  12.0  0.917  ...   \n",
       "17771  240.0  38.0  92.0  0.413  19.0  46.0  0.413   8.0   8.0  1.000  ...   \n",
       "\n",
       "       usg%_max_opp  ortg_max_opp  drtg_max_opp  team_opp  total_opp  \\\n",
       "0              43.7         206.0         104.0       GSW        111   \n",
       "1              34.6         162.0         104.0       CHI         97   \n",
       "2              29.0         138.0         105.0       CLE         95   \n",
       "3              38.9         201.0         120.0       NOP         95   \n",
       "4              23.6         132.0         104.0       DET        106   \n",
       "...             ...           ...           ...       ...        ...   \n",
       "17767          36.3         133.0         112.0       GSW        107   \n",
       "17768          94.4         300.0         112.0       BOS         94   \n",
       "17769          36.2         222.0         107.0       GSW        104   \n",
       "17770          31.5         186.0         111.0       GSW        103   \n",
       "17771          42.6         141.0         126.0       BOS         90   \n",
       "\n",
       "       home_opp  season        date    won  nextGame  \n",
       "0             1    2016  2015-10-27  False     False  \n",
       "1             1    2016  2015-10-27  False      True  \n",
       "2             0    2016  2015-10-27   True      True  \n",
       "3             0    2016  2015-10-27   True      True  \n",
       "4             0    2016  2015-10-27  False      True  \n",
       "...         ...     ...         ...    ...       ...  \n",
       "17767         0    2022  2022-06-10  False     False  \n",
       "17768         0    2022  2022-06-13   True      True  \n",
       "17769         1    2022  2022-06-13  False     False  \n",
       "17770         0    2022  2022-06-16  False         2  \n",
       "17771         1    2022  2022-06-16   True         2  \n",
       "\n",
       "[17772 rows x 148 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nbaData"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "0adab9d7-8da8-4397-a2f7-917eb3ee8a58",
   "metadata": {},
   "outputs": [],
   "source": [
    "#This value gives a 1 or 0 value to the nextGame column to sognigy a win or a loss\n",
    "nbaData[\"nextGame\"] = nbaData[\"nextGame\"].astype(int, errors=\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c57ec4c4-293e-4207-9021-29c2c76a8db8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "won\n",
       "False    8886\n",
       "True     8886\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#this gives how many wins and losses there are bc always 1 team has to win or loose\n",
    "nbaData[\"won\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "508933e7-b5ef-45c3-8e80-331c8ca8a170",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "nextGame\n",
       "1    8872\n",
       "0    8870\n",
       "2      30\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nbaData[\"nextGame\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "432e387c-f1cc-4bbf-80bf-86af2d998115",
   "metadata": {},
   "outputs": [],
   "source": [
    "# What this does is that it changes all the not null values to false\n",
    "nulls = pd.isnull(nbaData)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "40469a93-a1fd-4e25-8c34-f033998c104d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#This gives the count of all the null values, or all the true values in each row\n",
    "nulls = nulls.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "5ad2008d-8f66-46a1-9aa0-ee7946fede93",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "mp          0\n",
       "fg          0\n",
       "fga         0\n",
       "fg%         0\n",
       "3p          0\n",
       "           ..\n",
       "home_opp    0\n",
       "season      0\n",
       "date        0\n",
       "won         0\n",
       "nextGame    0\n",
       "Length: 148, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#This gives us the how many null values are in each column\n",
    "nulls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "483b96c1-e1d0-4982-9c8a-9136142f1025",
   "metadata": {},
   "outputs": [],
   "source": [
    "#This gives us all the columns which have null values: nulls[nulls>0]\n",
    "# We are now going to delete these values\n",
    "nulls = nulls[nulls>0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "a175f2b6-1211-42cc-9ba4-93c5979d28b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "+/-             17772\n",
       "mp_max          17772\n",
       "mp_max.1        17772\n",
       "+/-_opp         17772\n",
       "mp_max_opp      17772\n",
       "mp_max_opp.1    17772\n",
       "dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nulls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "6debf5a3-c976-4af7-b377-37bbd2aa821a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#This creates a list of columns that are not null ~ is a negation so it only puts the values that have values\n",
    "valid_columns = nbaData.columns[~nbaData.columns.isin(nulls.index)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "96c7aaf5-7c64-4e08-879f-dfb28c1f2b3c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#now we want to assign the columns that are only valid  do the dataframe using .copy\n",
    "nbaData = nbaData[valid_columns].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "3494a17a-b6c7-4ce0-b33c-4f7d3a1364c8",
   "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>mp</th>\n",
       "      <th>fg</th>\n",
       "      <th>fga</th>\n",
       "      <th>fg%</th>\n",
       "      <th>3p</th>\n",
       "      <th>3pa</th>\n",
       "      <th>3p%</th>\n",
       "      <th>ft</th>\n",
       "      <th>fta</th>\n",
       "      <th>ft%</th>\n",
       "      <th>...</th>\n",
       "      <th>usg%_max_opp</th>\n",
       "      <th>ortg_max_opp</th>\n",
       "      <th>drtg_max_opp</th>\n",
       "      <th>team_opp</th>\n",
       "      <th>total_opp</th>\n",
       "      <th>home_opp</th>\n",
       "      <th>season</th>\n",
       "      <th>date</th>\n",
       "      <th>won</th>\n",
       "      <th>nextGame</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>240.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>0.422</td>\n",
       "      <td>6.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.333</td>\n",
       "      <td>19.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.704</td>\n",
       "      <td>...</td>\n",
       "      <td>43.7</td>\n",
       "      <td>206.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>GSW</td>\n",
       "      <td>111</td>\n",
       "      <td>1</td>\n",
       "      <td>2016</td>\n",
       "      <td>2015-10-27</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>240.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>0.404</td>\n",
       "      <td>9.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>0.310</td>\n",
       "      <td>10.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.588</td>\n",
       "      <td>...</td>\n",
       "      <td>34.6</td>\n",
       "      <td>162.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>CHI</td>\n",
       "      <td>97</td>\n",
       "      <td>1</td>\n",
       "      <td>2016</td>\n",
       "      <td>2015-10-27</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>240.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>0.425</td>\n",
       "      <td>7.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0.368</td>\n",
       "      <td>16.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0.696</td>\n",
       "      <td>...</td>\n",
       "      <td>29.0</td>\n",
       "      <td>138.0</td>\n",
       "      <td>105.0</td>\n",
       "      <td>CLE</td>\n",
       "      <td>95</td>\n",
       "      <td>0</td>\n",
       "      <td>2016</td>\n",
       "      <td>2015-10-27</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>240.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>0.427</td>\n",
       "      <td>9.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.300</td>\n",
       "      <td>20.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.909</td>\n",
       "      <td>...</td>\n",
       "      <td>38.9</td>\n",
       "      <td>201.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>NOP</td>\n",
       "      <td>95</td>\n",
       "      <td>0</td>\n",
       "      <td>2016</td>\n",
       "      <td>2015-10-27</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>240.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>0.451</td>\n",
       "      <td>8.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.296</td>\n",
       "      <td>12.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.800</td>\n",
       "      <td>...</td>\n",
       "      <td>23.6</td>\n",
       "      <td>132.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>DET</td>\n",
       "      <td>106</td>\n",
       "      <td>0</td>\n",
       "      <td>2016</td>\n",
       "      <td>2015-10-27</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17767</th>\n",
       "      <td>240.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>0.400</td>\n",
       "      <td>15.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>0.395</td>\n",
       "      <td>14.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0.737</td>\n",
       "      <td>...</td>\n",
       "      <td>36.3</td>\n",
       "      <td>133.0</td>\n",
       "      <td>112.0</td>\n",
       "      <td>GSW</td>\n",
       "      <td>107</td>\n",
       "      <td>0</td>\n",
       "      <td>2022</td>\n",
       "      <td>2022-06-10</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17768</th>\n",
       "      <td>240.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>0.466</td>\n",
       "      <td>9.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0.225</td>\n",
       "      <td>13.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.867</td>\n",
       "      <td>...</td>\n",
       "      <td>94.4</td>\n",
       "      <td>300.0</td>\n",
       "      <td>112.0</td>\n",
       "      <td>BOS</td>\n",
       "      <td>94</td>\n",
       "      <td>0</td>\n",
       "      <td>2022</td>\n",
       "      <td>2022-06-13</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17769</th>\n",
       "      <td>240.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>0.413</td>\n",
       "      <td>11.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0.344</td>\n",
       "      <td>21.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0.677</td>\n",
       "      <td>...</td>\n",
       "      <td>36.2</td>\n",
       "      <td>222.0</td>\n",
       "      <td>107.0</td>\n",
       "      <td>GSW</td>\n",
       "      <td>104</td>\n",
       "      <td>1</td>\n",
       "      <td>2022</td>\n",
       "      <td>2022-06-13</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17770</th>\n",
       "      <td>240.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>0.425</td>\n",
       "      <td>11.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.393</td>\n",
       "      <td>11.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.917</td>\n",
       "      <td>...</td>\n",
       "      <td>31.5</td>\n",
       "      <td>186.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>GSW</td>\n",
       "      <td>103</td>\n",
       "      <td>0</td>\n",
       "      <td>2022</td>\n",
       "      <td>2022-06-16</td>\n",
       "      <td>False</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17771</th>\n",
       "      <td>240.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>0.413</td>\n",
       "      <td>19.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>0.413</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.000</td>\n",
       "      <td>...</td>\n",
       "      <td>42.6</td>\n",
       "      <td>141.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>BOS</td>\n",
       "      <td>90</td>\n",
       "      <td>1</td>\n",
       "      <td>2022</td>\n",
       "      <td>2022-06-16</td>\n",
       "      <td>True</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>17772 rows × 142 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          mp    fg   fga    fg%    3p   3pa    3p%    ft   fta    ft%  ...  \\\n",
       "0      240.0  35.0  83.0  0.422   6.0  18.0  0.333  19.0  27.0  0.704  ...   \n",
       "1      240.0  38.0  94.0  0.404   9.0  29.0  0.310  10.0  17.0  0.588  ...   \n",
       "2      240.0  37.0  87.0  0.425   7.0  19.0  0.368  16.0  23.0  0.696  ...   \n",
       "3      240.0  41.0  96.0  0.427   9.0  30.0  0.300  20.0  22.0  0.909  ...   \n",
       "4      240.0  37.0  82.0  0.451   8.0  27.0  0.296  12.0  15.0  0.800  ...   \n",
       "...      ...   ...   ...    ...   ...   ...    ...   ...   ...    ...  ...   \n",
       "17767  240.0  34.0  85.0  0.400  15.0  38.0  0.395  14.0  19.0  0.737  ...   \n",
       "17768  240.0  41.0  88.0  0.466   9.0  40.0  0.225  13.0  15.0  0.867  ...   \n",
       "17769  240.0  31.0  75.0  0.413  11.0  32.0  0.344  21.0  31.0  0.677  ...   \n",
       "17770  240.0  34.0  80.0  0.425  11.0  28.0  0.393  11.0  12.0  0.917  ...   \n",
       "17771  240.0  38.0  92.0  0.413  19.0  46.0  0.413   8.0   8.0  1.000  ...   \n",
       "\n",
       "       usg%_max_opp  ortg_max_opp  drtg_max_opp  team_opp  total_opp  \\\n",
       "0              43.7         206.0         104.0       GSW        111   \n",
       "1              34.6         162.0         104.0       CHI         97   \n",
       "2              29.0         138.0         105.0       CLE         95   \n",
       "3              38.9         201.0         120.0       NOP         95   \n",
       "4              23.6         132.0         104.0       DET        106   \n",
       "...             ...           ...           ...       ...        ...   \n",
       "17767          36.3         133.0         112.0       GSW        107   \n",
       "17768          94.4         300.0         112.0       BOS         94   \n",
       "17769          36.2         222.0         107.0       GSW        104   \n",
       "17770          31.5         186.0         111.0       GSW        103   \n",
       "17771          42.6         141.0         126.0       BOS         90   \n",
       "\n",
       "       home_opp  season        date    won  nextGame  \n",
       "0             1    2016  2015-10-27  False         0  \n",
       "1             1    2016  2015-10-27  False         1  \n",
       "2             0    2016  2015-10-27   True         1  \n",
       "3             0    2016  2015-10-27   True         1  \n",
       "4             0    2016  2015-10-27  False         1  \n",
       "...         ...     ...         ...    ...       ...  \n",
       "17767         0    2022  2022-06-10  False         0  \n",
       "17768         0    2022  2022-06-13   True         1  \n",
       "17769         1    2022  2022-06-13  False         0  \n",
       "17770         0    2022  2022-06-16  False         2  \n",
       "17771         1    2022  2022-06-16   True         2  \n",
       "\n",
       "[17772 rows x 142 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nbaData"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "ea5c652e-523f-48d6-8a77-b3367c91fa15",
   "metadata": {},
   "outputs": [],
   "source": [
    "##############################################################################################\n",
    "#All the above steps were data cleaning\n",
    "#############################################################################################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "0c94d815-edaa-4db1-899d-0a0490fae286",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import TimeSeriesSplit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "d56f0354-0960-4db0-87cc-d30d490bf848",
   "metadata": {},
   "outputs": [],
   "source": [
    "#We dont want to learn all the data, we want to train some data, then predicts the rest\n",
    "#We split it up, we train on one set, then we predict another set\n",
    "#   CROSS \n",
    "#Because we time ordered, we train past data to predict future data\n",
    "# Use data from 2015 to predict 2022"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "ad97974e-0c46-42a0-8bb3-ef2e847e74cf",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "from sklearn.feature_selection import SequentialFeatureSelector\n",
    "#Our machine learning model, uses ridge regression to do classification\n",
    "from sklearn.linear_model import RidgeClassifier\n",
    "\n",
    "rr = RidgeClassifier(alpha = 1)\n",
    "split = TimeSeriesSplit(n_splits=3)\n",
    "\n",
    "#This picks the best feature for us, pick about 20% so 30 features, direction is forward because\n",
    "#This is time sequenced\n",
    "sfs = SequentialFeatureSelector(rr, n_features_to_select = 30, direction = \"forward\", cv=split)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "8e38305b-c688-4583-9539-9d528e50228c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Now we need to scale the data, scale all the columns so they fall between 0 and 1\n",
    "# remove the columns that cannot be scaled\n",
    "removed_columns = [\"season\", \"date\", \"won\", \"nextGame\", \"team\", \"team_opp\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "4a2eabbb-407e-4e7e-970d-b10e38d674a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "selected_col = nbaData.columns[~nbaData.columns.isin(removed_columns)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "0181bb12-4bdd-486c-ae61-5147b4bbd032",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Now we are ready to scale, this will scale the data between 0 and  1\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "scaler = MinMaxScaler()\n",
    "#Why we do it like this is because we only want to scale the numerical values columns, so this way is good\n",
    "nbaData[selected_col] = scaler.fit_transform(nbaData[selected_col])\n",
    "\n",
    "#Makes rige regression perform better"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "6f9b8bd8-1a35-4b3b-8368-65a3f2dab045",
   "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>mp</th>\n",
       "      <th>fg</th>\n",
       "      <th>fga</th>\n",
       "      <th>fg%</th>\n",
       "      <th>3p</th>\n",
       "      <th>3pa</th>\n",
       "      <th>3p%</th>\n",
       "      <th>ft</th>\n",
       "      <th>fta</th>\n",
       "      <th>ft%</th>\n",
       "      <th>...</th>\n",
       "      <th>usg%_max_opp</th>\n",
       "      <th>ortg_max_opp</th>\n",
       "      <th>drtg_max_opp</th>\n",
       "      <th>team_opp</th>\n",
       "      <th>total_opp</th>\n",
       "      <th>home_opp</th>\n",
       "      <th>season</th>\n",
       "      <th>date</th>\n",
       "      <th>won</th>\n",
       "      <th>nextGame</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.366029</td>\n",
       "      <td>0.206897</td>\n",
       "      <td>0.212121</td>\n",
       "      <td>0.395487</td>\n",
       "      <td>0.418605</td>\n",
       "      <td>0.412698</td>\n",
       "      <td>0.654609</td>\n",
       "      <td>...</td>\n",
       "      <td>0.277279</td>\n",
       "      <td>0.554502</td>\n",
       "      <td>0.317647</td>\n",
       "      <td>GSW</td>\n",
       "      <td>0.451923</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2016</td>\n",
       "      <td>2015-10-27</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.431818</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.322967</td>\n",
       "      <td>0.310345</td>\n",
       "      <td>0.378788</td>\n",
       "      <td>0.368171</td>\n",
       "      <td>0.209302</td>\n",
       "      <td>0.253968</td>\n",
       "      <td>0.519253</td>\n",
       "      <td>...</td>\n",
       "      <td>0.160462</td>\n",
       "      <td>0.345972</td>\n",
       "      <td>0.317647</td>\n",
       "      <td>CHI</td>\n",
       "      <td>0.317308</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2016</td>\n",
       "      <td>2015-10-27</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.409091</td>\n",
       "      <td>0.397059</td>\n",
       "      <td>0.373206</td>\n",
       "      <td>0.241379</td>\n",
       "      <td>0.227273</td>\n",
       "      <td>0.437055</td>\n",
       "      <td>0.348837</td>\n",
       "      <td>0.349206</td>\n",
       "      <td>0.645274</td>\n",
       "      <td>...</td>\n",
       "      <td>0.088575</td>\n",
       "      <td>0.232227</td>\n",
       "      <td>0.329412</td>\n",
       "      <td>CLE</td>\n",
       "      <td>0.298077</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2016</td>\n",
       "      <td>2015-10-27</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.529412</td>\n",
       "      <td>0.377990</td>\n",
       "      <td>0.310345</td>\n",
       "      <td>0.393939</td>\n",
       "      <td>0.356295</td>\n",
       "      <td>0.441860</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.893816</td>\n",
       "      <td>...</td>\n",
       "      <td>0.215661</td>\n",
       "      <td>0.530806</td>\n",
       "      <td>0.505882</td>\n",
       "      <td>NOP</td>\n",
       "      <td>0.298077</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2016</td>\n",
       "      <td>2015-10-27</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.409091</td>\n",
       "      <td>0.323529</td>\n",
       "      <td>0.435407</td>\n",
       "      <td>0.275862</td>\n",
       "      <td>0.348485</td>\n",
       "      <td>0.351544</td>\n",
       "      <td>0.255814</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.766628</td>\n",
       "      <td>...</td>\n",
       "      <td>0.019255</td>\n",
       "      <td>0.203791</td>\n",
       "      <td>0.317647</td>\n",
       "      <td>DET</td>\n",
       "      <td>0.403846</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2016</td>\n",
       "      <td>2015-10-27</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
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       "      <td>...</td>\n",
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       "      <td>0.313397</td>\n",
       "      <td>0.517241</td>\n",
       "      <td>0.515152</td>\n",
       "      <td>0.469121</td>\n",
       "      <td>0.302326</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>0.693116</td>\n",
       "      <td>...</td>\n",
       "      <td>0.182285</td>\n",
       "      <td>0.208531</td>\n",
       "      <td>0.411765</td>\n",
       "      <td>GSW</td>\n",
       "      <td>0.413462</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2022</td>\n",
       "      <td>2022-06-10</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17768</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.411765</td>\n",
       "      <td>0.471292</td>\n",
       "      <td>0.310345</td>\n",
       "      <td>0.545455</td>\n",
       "      <td>0.267221</td>\n",
       "      <td>0.279070</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.844807</td>\n",
       "      <td>...</td>\n",
       "      <td>0.928113</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.411765</td>\n",
       "      <td>BOS</td>\n",
       "      <td>0.288462</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2022</td>\n",
       "      <td>2022-06-13</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17769</th>\n",
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       "      <td>0.344498</td>\n",
       "      <td>0.379310</td>\n",
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       "      <td>0.408551</td>\n",
       "      <td>0.465116</td>\n",
       "      <td>0.476190</td>\n",
       "      <td>0.623104</td>\n",
       "      <td>...</td>\n",
       "      <td>0.181001</td>\n",
       "      <td>0.630332</td>\n",
       "      <td>0.352941</td>\n",
       "      <td>GSW</td>\n",
       "      <td>0.384615</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2022</td>\n",
       "      <td>2022-06-13</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17770</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.340909</td>\n",
       "      <td>0.294118</td>\n",
       "      <td>0.373206</td>\n",
       "      <td>0.379310</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>0.466746</td>\n",
       "      <td>0.232558</td>\n",
       "      <td>0.174603</td>\n",
       "      <td>0.903151</td>\n",
       "      <td>...</td>\n",
       "      <td>0.120668</td>\n",
       "      <td>0.459716</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>GSW</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2022</td>\n",
       "      <td>2022-06-16</td>\n",
       "      <td>False</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17771</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.431818</td>\n",
       "      <td>0.470588</td>\n",
       "      <td>0.344498</td>\n",
       "      <td>0.655172</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>0.490499</td>\n",
       "      <td>0.162791</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>0.246445</td>\n",
       "      <td>0.576471</td>\n",
       "      <td>BOS</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2022</td>\n",
       "      <td>2022-06-16</td>\n",
       "      <td>True</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>17772 rows × 142 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        mp        fg       fga       fg%        3p       3pa       3p%  \\\n",
       "0      0.0  0.363636  0.338235  0.366029  0.206897  0.212121  0.395487   \n",
       "1      0.0  0.431818  0.500000  0.322967  0.310345  0.378788  0.368171   \n",
       "2      0.0  0.409091  0.397059  0.373206  0.241379  0.227273  0.437055   \n",
       "3      0.0  0.500000  0.529412  0.377990  0.310345  0.393939  0.356295   \n",
       "4      0.0  0.409091  0.323529  0.435407  0.275862  0.348485  0.351544   \n",
       "...    ...       ...       ...       ...       ...       ...       ...   \n",
       "17767  0.0  0.340909  0.367647  0.313397  0.517241  0.515152  0.469121   \n",
       "17768  0.0  0.500000  0.411765  0.471292  0.310345  0.545455  0.267221   \n",
       "17769  0.0  0.272727  0.220588  0.344498  0.379310  0.424242  0.408551   \n",
       "17770  0.0  0.340909  0.294118  0.373206  0.379310  0.363636  0.466746   \n",
       "17771  0.0  0.431818  0.470588  0.344498  0.655172  0.636364  0.490499   \n",
       "\n",
       "             ft       fta       ft%  ...  usg%_max_opp  ortg_max_opp  \\\n",
       "0      0.418605  0.412698  0.654609  ...      0.277279      0.554502   \n",
       "1      0.209302  0.253968  0.519253  ...      0.160462      0.345972   \n",
       "2      0.348837  0.349206  0.645274  ...      0.088575      0.232227   \n",
       "3      0.441860  0.333333  0.893816  ...      0.215661      0.530806   \n",
       "4      0.255814  0.222222  0.766628  ...      0.019255      0.203791   \n",
       "...         ...       ...       ...  ...           ...           ...   \n",
       "17767  0.302326  0.285714  0.693116  ...      0.182285      0.208531   \n",
       "17768  0.279070  0.222222  0.844807  ...      0.928113      1.000000   \n",
       "17769  0.465116  0.476190  0.623104  ...      0.181001      0.630332   \n",
       "17770  0.232558  0.174603  0.903151  ...      0.120668      0.459716   \n",
       "17771  0.162791  0.111111  1.000000  ...      0.263158      0.246445   \n",
       "\n",
       "       drtg_max_opp  team_opp  total_opp  home_opp  season        date    won  \\\n",
       "0          0.317647       GSW   0.451923       1.0    2016  2015-10-27  False   \n",
       "1          0.317647       CHI   0.317308       1.0    2016  2015-10-27  False   \n",
       "2          0.329412       CLE   0.298077       0.0    2016  2015-10-27   True   \n",
       "3          0.505882       NOP   0.298077       0.0    2016  2015-10-27   True   \n",
       "4          0.317647       DET   0.403846       0.0    2016  2015-10-27  False   \n",
       "...             ...       ...        ...       ...     ...         ...    ...   \n",
       "17767      0.411765       GSW   0.413462       0.0    2022  2022-06-10  False   \n",
       "17768      0.411765       BOS   0.288462       0.0    2022  2022-06-13   True   \n",
       "17769      0.352941       GSW   0.384615       1.0    2022  2022-06-13  False   \n",
       "17770      0.400000       GSW   0.375000       0.0    2022  2022-06-16  False   \n",
       "17771      0.576471       BOS   0.250000       1.0    2022  2022-06-16   True   \n",
       "\n",
       "       nextGame  \n",
       "0             0  \n",
       "1             1  \n",
       "2             1  \n",
       "3             1  \n",
       "4             1  \n",
       "...         ...  \n",
       "17767         0  \n",
       "17768         1  \n",
       "17769         0  \n",
       "17770         2  \n",
       "17771         2  \n",
       "\n",
       "[17772 rows x 142 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nbaData"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "a989b75e-e09f-482d-9141-45cec23834c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SequentialFeatureSelector(cv=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None),\n",
       "                          estimator=RidgeClassifier(alpha=1),\n",
       "                          n_features_to_select=30)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;SequentialFeatureSelector<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.feature_selection.SequentialFeatureSelector.html\">?<span>Documentation for SequentialFeatureSelector</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>SequentialFeatureSelector(cv=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None),\n",
       "                          estimator=RidgeClassifier(alpha=1),\n",
       "                          n_features_to_select=30)</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">estimator: RidgeClassifier</label><div class=\"sk-toggleable__content fitted\"><pre>RidgeClassifier(alpha=1)</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;RidgeClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.RidgeClassifier.html\">?<span>Documentation for RidgeClassifier</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>RidgeClassifier(alpha=1)</pre></div> </div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "SequentialFeatureSelector(cv=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None),\n",
       "                          estimator=RidgeClassifier(alpha=1),\n",
       "                          n_features_to_select=30)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sfs.fit(nbaData[selected_col], nbaData[\"nextGame\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "c565537c-dbc0-457a-b3d3-7d4fdf83dd11",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Get the list of our predictor columns out of our sequential selector\n",
    "#If the feature selected has picked that column to use in our model, this will be true, else it will be false\n",
    "predictors = list(selected_col[sfs.get_support()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "d3e2aa76-8b02-4aa1-8528-698ef63bf4b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['mp',\n",
       " 'fg%',\n",
       " '3p%',\n",
       " 'orb',\n",
       " 'ts%',\n",
       " 'usg%',\n",
       " '3p%_max',\n",
       " 'ft_max',\n",
       " 'fta_max',\n",
       " '+/-_max',\n",
       " 'drb%_max',\n",
       " 'trb%_max',\n",
       " 'tov%_max',\n",
       " 'usg%_max',\n",
       " 'mp_opp',\n",
       " 'fg_opp',\n",
       " '3p_opp',\n",
       " 'ft%_opp',\n",
       " 'blk_opp',\n",
       " 'usg%_opp',\n",
       " 'fga_max_opp',\n",
       " '3p_max_opp',\n",
       " 'ft_max_opp',\n",
       " 'ft%_max_opp',\n",
       " 'blk_max_opp',\n",
       " 'pf_max_opp',\n",
       " 'pts_max_opp',\n",
       " 'drb%_max_opp',\n",
       " 'blk%_max_opp',\n",
       " 'usg%_max_opp']"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#These are the 30 columns that we will use to train our data to make predictons\n",
    "# This makes sense because the team will be a team will usually have similar stats to the other team\n",
    "predictors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "da4a1ec3-1af6-4494-83c8-4deb8b9f7f3d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Will help us make the predictons\n",
    "# we have time series data, we have to use historic data to predict future data, \n",
    "# will split by season to predict the past season to predict fututre season\n",
    "# start parameter is 2 bc we need atleast 2 seasons of data to start making predictions\n",
    "# will use 2016, 2017 data to make predictions for 2018\n",
    "#Then we will use 16, 17, 18 to predict 2019\n",
    "def backtest(data, model, predictors, start=3, step=1):\n",
    "    all_predictions=[]\n",
    "\n",
    "    seasons = sorted(data[\"season\"].unique())\n",
    "\n",
    "    for i in range (start, len(seasons), step):\n",
    "        season = seasons[i]\n",
    "\n",
    "        train = data[data[\"season\"]< season]\n",
    "        test = data[data[\"season\"] == season]\n",
    "\n",
    "        model.fit(train[predictors], train[\"nextGame\"])\n",
    "\n",
    "        preds = model.predict(test[predictors])\n",
    "        preds = pd.Series(preds, index=test.index)\n",
    "\n",
    "        #Axis 1 will create the result as 2 seperate columns\n",
    "        combined = pd.concat([test[\"nextGame\"], preds], axis=1)\n",
    "        combined.columns=[\"actual\", \"prediction\"]\n",
    "\n",
    "        all_predictions.append(combined)\n",
    "    return pd.concat(all_predictions)\n",
    "\n",
    "          "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "a4b8e73e-e509-474c-9a66-6eb104a1d59c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# sorted(nbaData[\"season\"].unique())\n",
    "predictions= backtest(nbaData, rr, predictors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "590e0c9e-270c-48e1-a612-29c135e63e7d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       actual  prediction\n",
      "7874        1           0\n",
      "7875        0           1\n",
      "7876        0           0\n",
      "7877        1           1\n",
      "7878        0           1\n",
      "...       ...         ...\n",
      "17767       0           0\n",
      "17768       1           1\n",
      "17769       0           1\n",
      "17770       2           1\n",
      "17771       2           1\n",
      "\n",
      "[9898 rows x 2 columns]\n"
     ]
    }
   ],
   "source": [
    "print(predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "caa7579f-2123-49e9-9706-ee51013a84a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5487434130522902"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#This acciracy score function will pass in the actual and predicted values and gives percent of prediction\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "#We will take the 2 value with is NaN value remove \n",
    "predictions = predictions[predictions[\"actual\"] !=2]\n",
    "\n",
    "#This is the accuray score function which returns accuracy in percent\n",
    "accuracy_score(predictions[\"actual\"], predictions[\"prediction\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "de00f295-6223-4c95-b5fb-99f7d831a6ab",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Test\\AppData\\Local\\Temp\\ipykernel_20236\\3910938873.py:2: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
      "  nbaData.groupby(\"home\").apply(lambda x: x[x[\"won\"]==1].shape[0] / x.shape[0])\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "home\n",
       "0.0    0.428314\n",
       "1.0    0.571686\n",
       "dtype: float64"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#We will check to see when the team is home, comaprerd to when they win, so percent of the time they win\n",
    "nbaData.groupby(\"home\").apply(lambda x: x[x[\"won\"]==1].shape[0] / x.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "95fe25e4-0709-409a-8da9-7c013befefa3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 10 games avg time\n",
    "#We are using the last game to predict the next game, so to better get a prediction, we will use the last 10 game data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "cc2ad8dd-bf1e-4327-86f4-9e3a923fe614",
   "metadata": {},
   "outputs": [],
   "source": [
    "#We will use rolling, which uses the selected columns from before, adnd we add a few new columns\n",
    "# Bring back some of the columns we removed \n",
    "nbaData_rolling = nbaData[list(selected_col) + [\"won\", \"team\", \"season\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "842f47d1-d844-44aa-b7c4-753da5abd9dc",
   "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>mp</th>\n",
       "      <th>fg</th>\n",
       "      <th>fga</th>\n",
       "      <th>fg%</th>\n",
       "      <th>3p</th>\n",
       "      <th>3pa</th>\n",
       "      <th>3p%</th>\n",
       "      <th>ft</th>\n",
       "      <th>fta</th>\n",
       "      <th>ft%</th>\n",
       "      <th>...</th>\n",
       "      <th>blk%_max_opp</th>\n",
       "      <th>tov%_max_opp</th>\n",
       "      <th>usg%_max_opp</th>\n",
       "      <th>ortg_max_opp</th>\n",
       "      <th>drtg_max_opp</th>\n",
       "      <th>total_opp</th>\n",
       "      <th>home_opp</th>\n",
       "      <th>won</th>\n",
       "      <th>team</th>\n",
       "      <th>season</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>0.338235</td>\n",
       "      <td>0.366029</td>\n",
       "      <td>0.206897</td>\n",
       "      <td>0.212121</td>\n",
       "      <td>0.395487</td>\n",
       "      <td>0.418605</td>\n",
       "      <td>0.412698</td>\n",
       "      <td>0.654609</td>\n",
       "      <td>...</td>\n",
       "      <td>0.079</td>\n",
       "      <td>0.679245</td>\n",
       "      <td>0.277279</td>\n",
       "      <td>0.554502</td>\n",
       "      <td>0.317647</td>\n",
       "      <td>0.451923</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>NOP</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.431818</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.322967</td>\n",
       "      <td>0.310345</td>\n",
       "      <td>0.378788</td>\n",
       "      <td>0.368171</td>\n",
       "      <td>0.209302</td>\n",
       "      <td>0.253968</td>\n",
       "      <td>0.519253</td>\n",
       "      <td>...</td>\n",
       "      <td>0.140</td>\n",
       "      <td>0.509434</td>\n",
       "      <td>0.160462</td>\n",
       "      <td>0.345972</td>\n",
       "      <td>0.317647</td>\n",
       "      <td>0.317308</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>CLE</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.409091</td>\n",
       "      <td>0.397059</td>\n",
       "      <td>0.373206</td>\n",
       "      <td>0.241379</td>\n",
       "      <td>0.227273</td>\n",
       "      <td>0.437055</td>\n",
       "      <td>0.348837</td>\n",
       "      <td>0.349206</td>\n",
       "      <td>0.645274</td>\n",
       "      <td>...</td>\n",
       "      <td>0.185</td>\n",
       "      <td>0.270440</td>\n",
       "      <td>0.088575</td>\n",
       "      <td>0.232227</td>\n",
       "      <td>0.329412</td>\n",
       "      <td>0.298077</td>\n",
       "      <td>0.0</td>\n",
       "      <td>True</td>\n",
       "      <td>CHI</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.529412</td>\n",
       "      <td>0.377990</td>\n",
       "      <td>0.310345</td>\n",
       "      <td>0.393939</td>\n",
       "      <td>0.356295</td>\n",
       "      <td>0.441860</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.893816</td>\n",
       "      <td>...</td>\n",
       "      <td>0.063</td>\n",
       "      <td>0.344864</td>\n",
       "      <td>0.215661</td>\n",
       "      <td>0.530806</td>\n",
       "      <td>0.505882</td>\n",
       "      <td>0.298077</td>\n",
       "      <td>0.0</td>\n",
       "      <td>True</td>\n",
       "      <td>GSW</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.409091</td>\n",
       "      <td>0.323529</td>\n",
       "      <td>0.435407</td>\n",
       "      <td>0.275862</td>\n",
       "      <td>0.348485</td>\n",
       "      <td>0.351544</td>\n",
       "      <td>0.255814</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.766628</td>\n",
       "      <td>...</td>\n",
       "      <td>0.047</td>\n",
       "      <td>0.300839</td>\n",
       "      <td>0.019255</td>\n",
       "      <td>0.203791</td>\n",
       "      <td>0.317647</td>\n",
       "      <td>0.403846</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>ATL</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.367647</td>\n",
       "      <td>0.313397</td>\n",
       "      <td>0.517241</td>\n",
       "      <td>0.515152</td>\n",
       "      <td>0.469121</td>\n",
       "      <td>0.302326</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>0.693116</td>\n",
       "      <td>...</td>\n",
       "      <td>0.103</td>\n",
       "      <td>0.401468</td>\n",
       "      <td>0.182285</td>\n",
       "      <td>0.208531</td>\n",
       "      <td>0.411765</td>\n",
       "      <td>0.413462</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>BOS</td>\n",
       "      <td>2022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.844807</td>\n",
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       "      <td>0.124</td>\n",
       "      <td>0.423480</td>\n",
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       "      <td>0.288462</td>\n",
       "      <td>0.0</td>\n",
       "      <td>True</td>\n",
       "      <td>GSW</td>\n",
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       "      <td>0.465116</td>\n",
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       "      <td>0.623104</td>\n",
       "      <td>...</td>\n",
       "      <td>0.076</td>\n",
       "      <td>0.300839</td>\n",
       "      <td>0.181001</td>\n",
       "      <td>0.630332</td>\n",
       "      <td>0.352941</td>\n",
       "      <td>0.384615</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>BOS</td>\n",
       "      <td>2022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17770</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.340909</td>\n",
       "      <td>0.294118</td>\n",
       "      <td>0.373206</td>\n",
       "      <td>0.379310</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>0.466746</td>\n",
       "      <td>0.232558</td>\n",
       "      <td>0.174603</td>\n",
       "      <td>0.903151</td>\n",
       "      <td>...</td>\n",
       "      <td>0.063</td>\n",
       "      <td>0.300839</td>\n",
       "      <td>0.120668</td>\n",
       "      <td>0.459716</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>BOS</td>\n",
       "      <td>2022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17771</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.431818</td>\n",
       "      <td>0.470588</td>\n",
       "      <td>0.344498</td>\n",
       "      <td>0.655172</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>0.490499</td>\n",
       "      <td>0.162791</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.160</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>0.246445</td>\n",
       "      <td>0.576471</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>True</td>\n",
       "      <td>GSW</td>\n",
       "      <td>2022</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>17772 rows × 139 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        mp        fg       fga       fg%        3p       3pa       3p%  \\\n",
       "0      0.0  0.363636  0.338235  0.366029  0.206897  0.212121  0.395487   \n",
       "1      0.0  0.431818  0.500000  0.322967  0.310345  0.378788  0.368171   \n",
       "2      0.0  0.409091  0.397059  0.373206  0.241379  0.227273  0.437055   \n",
       "3      0.0  0.500000  0.529412  0.377990  0.310345  0.393939  0.356295   \n",
       "4      0.0  0.409091  0.323529  0.435407  0.275862  0.348485  0.351544   \n",
       "...    ...       ...       ...       ...       ...       ...       ...   \n",
       "17767  0.0  0.340909  0.367647  0.313397  0.517241  0.515152  0.469121   \n",
       "17768  0.0  0.500000  0.411765  0.471292  0.310345  0.545455  0.267221   \n",
       "17769  0.0  0.272727  0.220588  0.344498  0.379310  0.424242  0.408551   \n",
       "17770  0.0  0.340909  0.294118  0.373206  0.379310  0.363636  0.466746   \n",
       "17771  0.0  0.431818  0.470588  0.344498  0.655172  0.636364  0.490499   \n",
       "\n",
       "             ft       fta       ft%  ...  blk%_max_opp  tov%_max_opp  \\\n",
       "0      0.418605  0.412698  0.654609  ...         0.079      0.679245   \n",
       "1      0.209302  0.253968  0.519253  ...         0.140      0.509434   \n",
       "2      0.348837  0.349206  0.645274  ...         0.185      0.270440   \n",
       "3      0.441860  0.333333  0.893816  ...         0.063      0.344864   \n",
       "4      0.255814  0.222222  0.766628  ...         0.047      0.300839   \n",
       "...         ...       ...       ...  ...           ...           ...   \n",
       "17767  0.302326  0.285714  0.693116  ...         0.103      0.401468   \n",
       "17768  0.279070  0.222222  0.844807  ...         0.124      0.423480   \n",
       "17769  0.465116  0.476190  0.623104  ...         0.076      0.300839   \n",
       "17770  0.232558  0.174603  0.903151  ...         0.063      0.300839   \n",
       "17771  0.162791  0.111111  1.000000  ...         0.160      1.000000   \n",
       "\n",
       "       usg%_max_opp  ortg_max_opp  drtg_max_opp  total_opp  home_opp    won  \\\n",
       "0          0.277279      0.554502      0.317647   0.451923       1.0  False   \n",
       "1          0.160462      0.345972      0.317647   0.317308       1.0  False   \n",
       "2          0.088575      0.232227      0.329412   0.298077       0.0   True   \n",
       "3          0.215661      0.530806      0.505882   0.298077       0.0   True   \n",
       "4          0.019255      0.203791      0.317647   0.403846       0.0  False   \n",
       "...             ...           ...           ...        ...       ...    ...   \n",
       "17767      0.182285      0.208531      0.411765   0.413462       0.0  False   \n",
       "17768      0.928113      1.000000      0.411765   0.288462       0.0   True   \n",
       "17769      0.181001      0.630332      0.352941   0.384615       1.0  False   \n",
       "17770      0.120668      0.459716      0.400000   0.375000       0.0  False   \n",
       "17771      0.263158      0.246445      0.576471   0.250000       1.0   True   \n",
       "\n",
       "       team  season  \n",
       "0       NOP    2016  \n",
       "1       CLE    2016  \n",
       "2       CHI    2016  \n",
       "3       GSW    2016  \n",
       "4       ATL    2016  \n",
       "...     ...     ...  \n",
       "17767   BOS    2022  \n",
       "17768   GSW    2022  \n",
       "17769   BOS    2022  \n",
       "17770   BOS    2022  \n",
       "17771   GSW    2022  \n",
       "\n",
       "[17772 rows x 139 columns]"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nbaData_rolling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "92fe1153-cdf3-44bc-9167-8a63ce553518",
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_team_averages(team):\n",
    "    # Select only numeric columns for the rolling mean\n",
    "    rolling = team.select_dtypes(include=[float, int]).rolling(10).mean()\n",
    "    \n",
    "    # Return the rolling means first, then append the non-numeric columns like 'team' and 'season'\n",
    "    return pd.concat([rolling.reset_index(drop=True), team[['team', 'season']].reset_index(drop=True)], axis=1)\n",
    "\n",
    "# Group by team and season, and apply the rolling function\n",
    "nbaData_rolling = nbaData.groupby([\"team\", \"season\"]).apply(find_team_averages)\n",
    "\n",
    "# Reset index if necessary after groupby\n",
    "nbaData_rolling = nbaData_rolling.reset_index(drop=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "15cc14e8-c7a6-418c-adc0-9520ff7ca5c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "nbaData_rolling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c252caf7-0386-426f-9dc4-4b6eed44ba07",
   "metadata": {},
   "outputs": [],
   "source": [
    "rolling_cols = [f\"{col}_10\" for col in nbaData_rolling.columns]\n",
    "nbaData_rolling.columns=rolling_cols\n",
    "#Axis 1 makes a long data frame, combines them to make a wider dataframe\n",
    "nbaData = pd.concat([nbaData, nbaData_rolling], axis=1)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "b33c229a-b37f-4866-b817-b286dbe46a4e",
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'DataFrame' object has no attribute 'Dropna'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_20236\\1653711398.py\u001b[0m in \u001b[0;36m?\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mnbaData\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnbaData\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDropna\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36m?\u001b[1;34m(self, name)\u001b[0m\n\u001b[0;32m   6295\u001b[0m             \u001b[1;32mand\u001b[0m \u001b[0mname\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_accessors\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   6296\u001b[0m             \u001b[1;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_info_axis\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_can_hold_identifiers_and_holds_name\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   6297\u001b[0m         \u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   6298\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 6299\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m: 'DataFrame' object has no attribute 'Dropna'"
     ]
    }
   ],
   "source": [
    "nbaData = nbaData.Dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c90eaada-b4a6-445e-a3f9-6b9f3ebe38ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "nbaData"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "160cbabf-2d44-4838-b25c-1cbc8c17eb71",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'shift' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[81], line 8\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21madd_col\u001b[39m(nbaData, col_name):\n\u001b[0;32m      6\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m nbaData\u001b[38;5;241m.\u001b[39mgroupby(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mteam\u001b[39m\u001b[38;5;124m\"\u001b[39m, group_keys\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\u001b[38;5;241m.\u001b[39mapply(\u001b[38;5;28;01mlambda\u001b[39;00m x: shift(x, col_name))\n\u001b[1;32m----> 8\u001b[0m df[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhome_next\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[43madd_col\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnbaData\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mhome\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m      9\u001b[0m df[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mteam_opp_next\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m add_col(nbaData, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mteam_opp\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m     10\u001b[0m df[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdate_next\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m add_col(nbaData, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mteam_opp\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "Cell \u001b[1;32mIn[81], line 6\u001b[0m, in \u001b[0;36madd_col\u001b[1;34m(nbaData, col_name)\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21madd_col\u001b[39m(nbaData, col_name):\n\u001b[1;32m----> 6\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mnbaData\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgroupby\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mteam\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mgroup_keys\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43;01mlambda\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mshift\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcol_name\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\pandas\\core\\groupby\\groupby.py:1824\u001b[0m, in \u001b[0;36mGroupBy.apply\u001b[1;34m(self, func, include_groups, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1822\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m option_context(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmode.chained_assignment\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[0;32m   1823\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m-> 1824\u001b[0m         result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_python_apply_general\u001b[49m\u001b[43m(\u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_selected_obj\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1825\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m (\n\u001b[0;32m   1826\u001b[0m             \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobj, Series)\n\u001b[0;32m   1827\u001b[0m             \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_selection \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m   1828\u001b[0m             \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_selected_obj\u001b[38;5;241m.\u001b[39mshape \u001b[38;5;241m!=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_obj_with_exclusions\u001b[38;5;241m.\u001b[39mshape\n\u001b[0;32m   1829\u001b[0m         ):\n\u001b[0;32m   1830\u001b[0m             warnings\u001b[38;5;241m.\u001b[39mwarn(\n\u001b[0;32m   1831\u001b[0m                 message\u001b[38;5;241m=\u001b[39m_apply_groupings_depr\u001b[38;5;241m.\u001b[39mformat(\n\u001b[0;32m   1832\u001b[0m                     \u001b[38;5;28mtype\u001b[39m(\u001b[38;5;28mself\u001b[39m)\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mapply\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   1835\u001b[0m                 stacklevel\u001b[38;5;241m=\u001b[39mfind_stack_level(),\n\u001b[0;32m   1836\u001b[0m             )\n",
      "File \u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\pandas\\core\\groupby\\groupby.py:1885\u001b[0m, in \u001b[0;36mGroupBy._python_apply_general\u001b[1;34m(self, f, data, not_indexed_same, is_transform, is_agg)\u001b[0m\n\u001b[0;32m   1850\u001b[0m \u001b[38;5;129m@final\u001b[39m\n\u001b[0;32m   1851\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_python_apply_general\u001b[39m(\n\u001b[0;32m   1852\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   1857\u001b[0m     is_agg: \u001b[38;5;28mbool\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[0;32m   1858\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m NDFrameT:\n\u001b[0;32m   1859\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m   1860\u001b[0m \u001b[38;5;124;03m    Apply function f in python space\u001b[39;00m\n\u001b[0;32m   1861\u001b[0m \n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   1883\u001b[0m \u001b[38;5;124;03m        data after applying f\u001b[39;00m\n\u001b[0;32m   1884\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m-> 1885\u001b[0m     values, mutated \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_grouper\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply_groupwise\u001b[49m\u001b[43m(\u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1886\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m not_indexed_same \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m   1887\u001b[0m         not_indexed_same \u001b[38;5;241m=\u001b[39m mutated\n",
      "File \u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\pandas\\core\\groupby\\ops.py:919\u001b[0m, in \u001b[0;36mBaseGrouper.apply_groupwise\u001b[1;34m(self, f, data, axis)\u001b[0m\n\u001b[0;32m    917\u001b[0m \u001b[38;5;66;03m# group might be modified\u001b[39;00m\n\u001b[0;32m    918\u001b[0m group_axes \u001b[38;5;241m=\u001b[39m group\u001b[38;5;241m.\u001b[39maxes\n\u001b[1;32m--> 919\u001b[0m res \u001b[38;5;241m=\u001b[39m \u001b[43mf\u001b[49m\u001b[43m(\u001b[49m\u001b[43mgroup\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    920\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m mutated \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m _is_indexed_like(res, group_axes, axis):\n\u001b[0;32m    921\u001b[0m     mutated \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n",
      "Cell \u001b[1;32mIn[81], line 6\u001b[0m, in \u001b[0;36madd_col.<locals>.<lambda>\u001b[1;34m(x)\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21madd_col\u001b[39m(nbaData, col_name):\n\u001b[1;32m----> 6\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m nbaData\u001b[38;5;241m.\u001b[39mgroupby(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mteam\u001b[39m\u001b[38;5;124m\"\u001b[39m, group_keys\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\u001b[38;5;241m.\u001b[39mapply(\u001b[38;5;28;01mlambda\u001b[39;00m x: \u001b[43mshift\u001b[49m(x, col_name))\n",
      "\u001b[1;31mNameError\u001b[0m: name 'shift' is not defined"
     ]
    }
   ],
   "source": [
    "def shift_col(team, col_name):\n",
    "    next_col = team[col_name].shift(-1)\n",
    "    return next_col\n",
    "\n",
    "def add_col(nbaData, col_name):\n",
    "    return nbaData.groupby(\"team\", group_keys=False).apply(lambda x: shift(x, col_name))\n",
    "\n",
    "df[\"home_next\"] = add_col(nbaData, \"home\")\n",
    "df[\"team_opp_next\"] = add_col(nbaData, \"team_opp\")\n",
    "df[\"date_next\"] = add_col(nbaData, \"team_opp\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "befe019b-6105-4204-a75c-274e2fd42870",
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'DataFrame' object has no attribute 'coy'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_20236\\4185526796.py\u001b[0m in \u001b[0;36m?\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mnbaData\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnbaData\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcoy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36m?\u001b[1;34m(self, name)\u001b[0m\n\u001b[0;32m   6295\u001b[0m             \u001b[1;32mand\u001b[0m \u001b[0mname\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_accessors\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   6296\u001b[0m             \u001b[1;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_info_axis\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_can_hold_identifiers_and_holds_name\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   6297\u001b[0m         \u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   6298\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 6299\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m: 'DataFrame' object has no attribute 'coy'"
     ]
    }
   ],
   "source": [
    "nbaData = nbaData.coy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "017d1f69-bd43-481a-84e1-5ab0bb18f38a",
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "\"['team_opp_next', 'date_next'] not in index\"",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[84], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m full \u001b[38;5;241m=\u001b[39m nbaData\u001b[38;5;241m.\u001b[39mmerge(\u001b[43mnbaData\u001b[49m\u001b[43m[\u001b[49m\u001b[43mrolling_cols\u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mteam_opp_next\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mdate_next\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mteam\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m]\u001b[49m, \n\u001b[0;32m      2\u001b[0m                     left_on \u001b[38;5;241m=\u001b[39m [\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mteam\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdate_next\u001b[39m\u001b[38;5;124m\"\u001b[39m],\n\u001b[0;32m      3\u001b[0m                      right_on \u001b[38;5;241m=\u001b[39m [\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mteam_opp_next\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdate_next\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[0;32m      4\u001b[0m                     )\n",
      "File \u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\pandas\\core\\frame.py:4108\u001b[0m, in \u001b[0;36mDataFrame.__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   4106\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m is_iterator(key):\n\u001b[0;32m   4107\u001b[0m         key \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(key)\n\u001b[1;32m-> 4108\u001b[0m     indexer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcolumns\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_indexer_strict\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcolumns\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m[\u001b[38;5;241m1\u001b[39m]\n\u001b[0;32m   4110\u001b[0m \u001b[38;5;66;03m# take() does not accept boolean indexers\u001b[39;00m\n\u001b[0;32m   4111\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(indexer, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdtype\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m) \u001b[38;5;241m==\u001b[39m \u001b[38;5;28mbool\u001b[39m:\n",
      "File \u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\pandas\\core\\indexes\\base.py:6200\u001b[0m, in \u001b[0;36mIndex._get_indexer_strict\u001b[1;34m(self, key, axis_name)\u001b[0m\n\u001b[0;32m   6197\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m   6198\u001b[0m     keyarr, indexer, new_indexer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_reindex_non_unique(keyarr)\n\u001b[1;32m-> 6200\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_raise_if_missing\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkeyarr\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mindexer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis_name\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   6202\u001b[0m keyarr \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtake(indexer)\n\u001b[0;32m   6203\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(key, Index):\n\u001b[0;32m   6204\u001b[0m     \u001b[38;5;66;03m# GH 42790 - Preserve name from an Index\u001b[39;00m\n",
      "File \u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\pandas\\core\\indexes\\base.py:6252\u001b[0m, in \u001b[0;36mIndex._raise_if_missing\u001b[1;34m(self, key, indexer, axis_name)\u001b[0m\n\u001b[0;32m   6249\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNone of [\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mkey\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m] are in the [\u001b[39m\u001b[38;5;132;01m{\u001b[39;00maxis_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m]\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m   6251\u001b[0m not_found \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(ensure_index(key)[missing_mask\u001b[38;5;241m.\u001b[39mnonzero()[\u001b[38;5;241m0\u001b[39m]]\u001b[38;5;241m.\u001b[39munique())\n\u001b[1;32m-> 6252\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mnot_found\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m not in index\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "\u001b[1;31mKeyError\u001b[0m: \"['team_opp_next', 'date_next'] not in index\""
     ]
    }
   ],
   "source": [
    "full = nbaData.merge(nbaData[rolling_cols+[\"team_opp_next\", \"date_next\", \"team\"]], \n",
    "                    left_on = [\"team\", \"date_next\"],\n",
    "                     right_on = [\"team_opp_next\", \"date_next\"]\n",
    "                    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "97d8af3a-2a12-49f5-9c30-e3d23122d81d",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'full' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[86], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m removed_columns \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(\u001b[43mfull\u001b[49m\u001b[38;5;241m.\u001b[39mcolumns[full\u001b[38;5;241m.\u001b[39mdtypes \u001b[38;5;241m==\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mobject\u001b[39m\u001b[38;5;124m\"\u001b[39m]) \u001b[38;5;241m+\u001b[39m removed_columns\n",
      "\u001b[1;31mNameError\u001b[0m: name 'full' is not defined"
     ]
    }
   ],
   "source": [
    "removed_columns = list(full.columns[full.dtypes ==\"object\"]) + removed_columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "7828cbe6-b56c-4315-a20f-f6c31e882e5e",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'full' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[87], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m selected_columns \u001b[38;5;241m=\u001b[39m \u001b[43mfull\u001b[49m\u001b[38;5;241m.\u001b[39mcolumns(\u001b[38;5;241m~\u001b[39mfull\u001b[38;5;241m.\u001b[39mcolumns\u001b[38;5;241m.\u001b[39misin(removed_columns))\n",
      "\u001b[1;31mNameError\u001b[0m: name 'full' is not defined"
     ]
    }
   ],
   "source": [
    "selected_columns = full.columns(~full.columns.isin(removed_columns))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "9f1df513-b04d-4476-a729-c476414affb3",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'full' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[88], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m sfs\u001b[38;5;241m.\u001b[39mfit(\u001b[43mfull\u001b[49m[selected_columns[sfs\u001b[38;5;241m.\u001b[39mget_support()]])\n",
      "\u001b[1;31mNameError\u001b[0m: name 'full' is not defined"
     ]
    }
   ],
   "source": [
    "sfs.fit(full[selected_columns[sfs.get_support()]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "e1ccaef6-0ebb-44f1-8c8f-a58181ad16e9",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'selected_columns' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[89], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m predictors \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(\u001b[43mselected_columns\u001b[49m[sfs\u001b[38;5;241m.\u001b[39mget_support()])\n",
      "\u001b[1;31mNameError\u001b[0m: name 'selected_columns' is not defined"
     ]
    }
   ],
   "source": [
    "predictors = list(selected_columns[sfs.get_support()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "a7089415-d034-4db7-b14f-490a9bdc6a9d",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'full' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[90], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m predictions\u001b[38;5;241m=\u001b[39mbacktest(\u001b[43mfull\u001b[49m, rr, predicotrs)\n",
      "\u001b[1;31mNameError\u001b[0m: name 'full' is not defined"
     ]
    }
   ],
   "source": [
    "predictions=backtest(full, rr, predicotrs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2f7b7a6a-5708-4cc9-b41c-5bdde6816a62",
   "metadata": {},
   "outputs": [],
   "source": [
    "accuracy_score(predictors)"
   ]
  }
 ],
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