final.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "61a60d15-bdbf-4bd4-8fd6-6a79c07618d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.linear_model import RidgeClassifier\n",
    "from sklearn.feature_selection import SequentialFeatureSelector\n",
    "from sklearn.model_selection import TimeSeriesSplit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5a61257c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def accuracy_forupset(value):\n",
    "    return value + ((value)*(32/100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b51b3fac-3a9f-42f7-a62b-28a8c6788766",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mp</th>\n",
       "      <th>mp.1</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>...</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>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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>240</td>\n",
       "      <td>240</td>\n",
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       "      <td>81</td>\n",
       "      <td>0.481</td>\n",
       "      <td>6</td>\n",
       "      <td>20</td>\n",
       "      <td>0.300</td>\n",
       "      <td>14</td>\n",
       "      <td>18</td>\n",
       "      <td>...</td>\n",
       "      <td>22.8</td>\n",
       "      <td>29.0</td>\n",
       "      <td>178</td>\n",
       "      <td>111</td>\n",
       "      <td>DAL</td>\n",
       "      <td>95</td>\n",
       "      <td>1</td>\n",
       "      <td>2016</td>\n",
       "      <td>12/9/2015</td>\n",
       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>240</td>\n",
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       "      <td>0.360</td>\n",
       "      <td>7</td>\n",
       "      <td>31</td>\n",
       "      <td>0.226</td>\n",
       "      <td>16</td>\n",
       "      <td>19</td>\n",
       "      <td>...</td>\n",
       "      <td>50.0</td>\n",
       "      <td>32.6</td>\n",
       "      <td>152</td>\n",
       "      <td>111</td>\n",
       "      <td>ATL</td>\n",
       "      <td>98</td>\n",
       "      <td>0</td>\n",
       "      <td>2016</td>\n",
       "      <td>12/9/2015</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>240</td>\n",
       "      <td>240</td>\n",
       "      <td>37</td>\n",
       "      <td>85</td>\n",
       "      <td>0.435</td>\n",
       "      <td>8</td>\n",
       "      <td>19</td>\n",
       "      <td>0.421</td>\n",
       "      <td>17</td>\n",
       "      <td>23</td>\n",
       "      <td>...</td>\n",
       "      <td>20.0</td>\n",
       "      <td>30.9</td>\n",
       "      <td>148</td>\n",
       "      <td>116</td>\n",
       "      <td>SAS</td>\n",
       "      <td>107</td>\n",
       "      <td>1</td>\n",
       "      <td>2018</td>\n",
       "      <td>10/18/2017</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>240</td>\n",
       "      <td>240</td>\n",
       "      <td>41</td>\n",
       "      <td>89</td>\n",
       "      <td>0.461</td>\n",
       "      <td>8</td>\n",
       "      <td>21</td>\n",
       "      <td>0.381</td>\n",
       "      <td>17</td>\n",
       "      <td>19</td>\n",
       "      <td>...</td>\n",
       "      <td>28.6</td>\n",
       "      <td>30.9</td>\n",
       "      <td>138</td>\n",
       "      <td>118</td>\n",
       "      <td>MIN</td>\n",
       "      <td>99</td>\n",
       "      <td>0</td>\n",
       "      <td>2018</td>\n",
       "      <td>10/18/2017</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>240</td>\n",
       "      <td>240</td>\n",
       "      <td>27</td>\n",
       "      <td>86</td>\n",
       "      <td>0.314</td>\n",
       "      <td>6</td>\n",
       "      <td>26</td>\n",
       "      <td>0.231</td>\n",
       "      <td>15</td>\n",
       "      <td>20</td>\n",
       "      <td>...</td>\n",
       "      <td>16.8</td>\n",
       "      <td>30.9</td>\n",
       "      <td>157</td>\n",
       "      <td>90</td>\n",
       "      <td>MEM</td>\n",
       "      <td>92</td>\n",
       "      <td>1</td>\n",
       "      <td>2021</td>\n",
       "      <td>4/30/2021</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 150 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    mp  mp.1  fg  fga    fg%  3p  3pa    3p%  ft  fta  ...  tov%_max_opp  \\\n",
       "0  240   240  39   81  0.481   6   20  0.300  14   18  ...          22.8   \n",
       "1  240   240  36  100  0.360   7   31  0.226  16   19  ...          50.0   \n",
       "2  240   240  37   85  0.435   8   19  0.421  17   23  ...          20.0   \n",
       "3  240   240  41   89  0.461   8   21  0.381  17   19  ...          28.6   \n",
       "4  240   240  27   86  0.314   6   26  0.231  15   20  ...          16.8   \n",
       "\n",
       "   usg%_max_opp  ortg_max_opp  drtg_max_opp  team_opp  total_opp  home_opp  \\\n",
       "0          29.0           178           111       DAL         95         1   \n",
       "1          32.6           152           111       ATL         98         0   \n",
       "2          30.9           148           116       SAS        107         1   \n",
       "3          30.9           138           118       MIN         99         0   \n",
       "4          30.9           157            90       MEM         92         1   \n",
       "\n",
       "   season        date    won  \n",
       "0    2016   12/9/2015   True  \n",
       "1    2016   12/9/2015  False  \n",
       "2    2018  10/18/2017  False  \n",
       "3    2018  10/18/2017   True  \n",
       "4    2021   4/30/2021  False  \n",
       "\n",
       "[5 rows x 150 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"./raw_data/nba_games.csv\", index_col=0)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7814b489",
   "metadata": {},
   "source": [
    "### Cleaning dataset and Creating Target column for prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f8c4f353-e4ec-43a0-8454-a2684db60c48",
   "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",
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       "      <th>fta</th>\n",
       "      <th>ft%</th>\n",
       "      <th>...</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>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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <td>0.396</td>\n",
       "      <td>15</td>\n",
       "      <td>33</td>\n",
       "      <td>0.455</td>\n",
       "      <td>17</td>\n",
       "      <td>18</td>\n",
       "      <td>0.944</td>\n",
       "      <td>...</td>\n",
       "      <td>31.7</td>\n",
       "      <td>30.6</td>\n",
       "      <td>174</td>\n",
       "      <td>130</td>\n",
       "      <td>CHO</td>\n",
       "      <td>94</td>\n",
       "      <td>0</td>\n",
       "      <td>2016</td>\n",
       "      <td>1/1/2016</td>\n",
       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>240</td>\n",
       "      <td>33</td>\n",
       "      <td>87</td>\n",
       "      <td>0.379</td>\n",
       "      <td>5</td>\n",
       "      <td>16</td>\n",
       "      <td>0.313</td>\n",
       "      <td>22</td>\n",
       "      <td>29</td>\n",
       "      <td>0.759</td>\n",
       "      <td>...</td>\n",
       "      <td>36.8</td>\n",
       "      <td>40.4</td>\n",
       "      <td>150</td>\n",
       "      <td>105</td>\n",
       "      <td>PHI</td>\n",
       "      <td>84</td>\n",
       "      <td>0</td>\n",
       "      <td>2016</td>\n",
       "      <td>1/1/2016</td>\n",
       "      <td>True</td>\n",
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       "      <td>20</td>\n",
       "      <td>25</td>\n",
       "      <td>0.800</td>\n",
       "      <td>...</td>\n",
       "      <td>33.3</td>\n",
       "      <td>39.6</td>\n",
       "      <td>171</td>\n",
       "      <td>132</td>\n",
       "      <td>NYK</td>\n",
       "      <td>81</td>\n",
       "      <td>0</td>\n",
       "      <td>2016</td>\n",
       "      <td>1/1/2016</td>\n",
       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>240</td>\n",
       "      <td>31</td>\n",
       "      <td>84</td>\n",
       "      <td>0.369</td>\n",
       "      <td>6</td>\n",
       "      <td>19</td>\n",
       "      <td>0.316</td>\n",
       "      <td>13</td>\n",
       "      <td>17</td>\n",
       "      <td>0.765</td>\n",
       "      <td>...</td>\n",
       "      <td>50.0</td>\n",
       "      <td>42.7</td>\n",
       "      <td>195</td>\n",
       "      <td>102</td>\n",
       "      <td>CHI</td>\n",
       "      <td>108</td>\n",
       "      <td>1</td>\n",
       "      <td>2016</td>\n",
       "      <td>1/1/2016</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>240</td>\n",
       "      <td>31</td>\n",
       "      <td>84</td>\n",
       "      <td>0.369</td>\n",
       "      <td>4</td>\n",
       "      <td>24</td>\n",
       "      <td>0.167</td>\n",
       "      <td>18</td>\n",
       "      <td>25</td>\n",
       "      <td>0.720</td>\n",
       "      <td>...</td>\n",
       "      <td>22.2</td>\n",
       "      <td>30.6</td>\n",
       "      <td>164</td>\n",
       "      <td>100</td>\n",
       "      <td>LAL</td>\n",
       "      <td>93</td>\n",
       "      <td>1</td>\n",
       "      <td>2016</td>\n",
       "      <td>1/1/2016</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 147 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    mp  fg  fga    fg%  3p  3pa    3p%  ft  fta    ft%  ...  tov%_max_opp  \\\n",
       "0  240  36   91  0.396  15   33  0.455  17   18  0.944  ...          31.7   \n",
       "1  240  33   87  0.379   5   16  0.313  22   29  0.759  ...          36.8   \n",
       "2  240  39   85  0.459  10   16  0.625  20   25  0.800  ...          33.3   \n",
       "3  240  31   84  0.369   6   19  0.316  13   17  0.765  ...          50.0   \n",
       "4  240  31   84  0.369   4   24  0.167  18   25  0.720  ...          22.2   \n",
       "\n",
       "   usg%_max_opp  ortg_max_opp  drtg_max_opp  team_opp  total_opp  home_opp  \\\n",
       "0          30.6           174           130       CHO         94         0   \n",
       "1          40.4           150           105       PHI         84         0   \n",
       "2          39.6           171           132       NYK         81         0   \n",
       "3          42.7           195           102       CHI        108         1   \n",
       "4          30.6           164           100       LAL         93         1   \n",
       "\n",
       "   season      date    won  \n",
       "0    2016  1/1/2016   True  \n",
       "1    2016  1/1/2016   True  \n",
       "2    2016  1/1/2016   True  \n",
       "3    2016  1/1/2016  False  \n",
       "4    2016  1/1/2016  False  \n",
       "\n",
       "[5 rows x 147 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values(\"date\",inplace=True)\n",
    "df.reset_index(drop=True,inplace=True)\n",
    "del df[\"mp.1\"]\n",
    "del df[\"mp_opp.1\"]\n",
    "del df[\"index_opp\"]\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2775edd3",
   "metadata": {},
   "outputs": [],
   "source": [
    "def add_target(group):\n",
    "    group[\"target\"] = group[\"won\"].shift(-1)\n",
    "    return group\n",
    "\n",
    "df = df.groupby(\"team\", group_keys=False).apply(add_target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a6482238-ac7d-4a8d-9237-2723443295e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <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",
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       "      <th>ft%</th>\n",
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       "      <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>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>5</th>\n",
       "      <td>240</td>\n",
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       "      <td>33.0</td>\n",
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       "      <td>102</td>\n",
       "      <td>MIA</td>\n",
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       "      <td>1</td>\n",
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       "      <td>True</td>\n",
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       "      <td>29.7</td>\n",
       "      <td>144</td>\n",
       "      <td>103</td>\n",
       "      <td>MIA</td>\n",
       "      <td>83</td>\n",
       "      <td>0</td>\n",
       "      <td>2021</td>\n",
       "      <td>1/1/2021</td>\n",
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       "      <td>True</td>\n",
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       "      <td>20</td>\n",
       "      <td>0.300</td>\n",
       "      <td>15</td>\n",
       "      <td>21</td>\n",
       "      <td>0.714</td>\n",
       "      <td>...</td>\n",
       "      <td>39.4</td>\n",
       "      <td>282</td>\n",
       "      <td>116</td>\n",
       "      <td>MIN</td>\n",
       "      <td>87</td>\n",
       "      <td>1</td>\n",
       "      <td>2016</td>\n",
       "      <td>1/10/2016</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>240</td>\n",
       "      <td>39</td>\n",
       "      <td>83</td>\n",
       "      <td>0.470</td>\n",
       "      <td>15</td>\n",
       "      <td>36</td>\n",
       "      <td>0.417</td>\n",
       "      <td>22</td>\n",
       "      <td>28</td>\n",
       "      <td>0.786</td>\n",
       "      <td>...</td>\n",
       "      <td>37.8</td>\n",
       "      <td>158</td>\n",
       "      <td>121</td>\n",
       "      <td>CHO</td>\n",
       "      <td>111</td>\n",
       "      <td>1</td>\n",
       "      <td>2018</td>\n",
       "      <td>1/10/2018</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161</th>\n",
       "      <td>240</td>\n",
       "      <td>39</td>\n",
       "      <td>96</td>\n",
       "      <td>0.406</td>\n",
       "      <td>11</td>\n",
       "      <td>38</td>\n",
       "      <td>0.289</td>\n",
       "      <td>25</td>\n",
       "      <td>37</td>\n",
       "      <td>0.676</td>\n",
       "      <td>...</td>\n",
       "      <td>43.1</td>\n",
       "      <td>221</td>\n",
       "      <td>126</td>\n",
       "      <td>LAL</td>\n",
       "      <td>129</td>\n",
       "      <td>0</td>\n",
       "      <td>2020</td>\n",
       "      <td>1/10/2020</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 148 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      mp  fg  fga    fg%  3p  3pa    3p%  ft  fta    ft%  ...  usg%_max_opp  \\\n",
       "5    240  32   88  0.364   7   31  0.226  11   14  0.786  ...          33.0   \n",
       "49   240  32   78  0.410  12   37  0.324  17   23  0.739  ...          29.7   \n",
       "80   240  36   77  0.468   6   20  0.300  15   21  0.714  ...          39.4   \n",
       "119  240  39   83  0.470  15   36  0.417  22   28  0.786  ...          37.8   \n",
       "161  240  39   96  0.406  11   38  0.289  25   37  0.676  ...          43.1   \n",
       "\n",
       "     ortg_max_opp  drtg_max_opp  team_opp  total_opp  home_opp  season  \\\n",
       "5             206           102       MIA        106         1    2016   \n",
       "49            144           103       MIA         83         0    2021   \n",
       "80            282           116       MIN         87         1    2016   \n",
       "119           158           121       CHO        111         1    2018   \n",
       "161           221           126       LAL        129         0    2020   \n",
       "\n",
       "          date    won  target  \n",
       "5     1/1/2016  False    True  \n",
       "49    1/1/2021   True    True  \n",
       "80   1/10/2016   True    True  \n",
       "119  1/10/2018   True   False  \n",
       "161  1/10/2020  False    True  \n",
       "\n",
       "[5 rows x 148 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df[\"team\"] == \"DAL\"].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d30855ca-bd47-446d-8db7-a19ec6666cf6",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\neera\\AppData\\Local\\Temp\\ipykernel_13940\\815311071.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",
      "  df[\"target\"][pd.isnull(df[\"target\"])] = 2 # mark the future games in target as null\n"
     ]
    }
   ],
   "source": [
    "df[\"target\"][pd.isnull(df[\"target\"])] = 2 # mark the future games in target as null\n",
    "df[\"target\"] = df[\"target\"].astype(int, errors=\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f6f74c8",
   "metadata": {},
   "source": [
    "### Calculating an removing null columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "08aa566b-773c-4b0a-b45a-0254eb3471fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "nulls = pd.isnull(df).sum()\n",
    "nulls = nulls[nulls > 0]\n",
    "valid_columns = df.columns[~df.columns.isin(nulls.index)]\n",
    "df = df[valid_columns].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "14c1933d-8166-4120-89f8-01739f5bbdf5",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th>mp</th>\n",
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       "      <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",
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       "      <td>PHI</td>\n",
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       "      <td>0.800</td>\n",
       "      <td>...</td>\n",
       "      <td>39.6</td>\n",
       "      <td>171</td>\n",
       "      <td>132</td>\n",
       "      <td>NYK</td>\n",
       "      <td>81</td>\n",
       "      <td>0</td>\n",
       "      <td>2016</td>\n",
       "      <td>1/1/2016</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>240</td>\n",
       "      <td>31</td>\n",
       "      <td>84</td>\n",
       "      <td>0.369</td>\n",
       "      <td>6</td>\n",
       "      <td>19</td>\n",
       "      <td>0.316</td>\n",
       "      <td>13</td>\n",
       "      <td>17</td>\n",
       "      <td>0.765</td>\n",
       "      <td>...</td>\n",
       "      <td>42.7</td>\n",
       "      <td>195</td>\n",
       "      <td>102</td>\n",
       "      <td>CHI</td>\n",
       "      <td>108</td>\n",
       "      <td>1</td>\n",
       "      <td>2016</td>\n",
       "      <td>1/1/2016</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>24</td>\n",
       "      <td>0.167</td>\n",
       "      <td>18</td>\n",
       "      <td>25</td>\n",
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       "      <td>30.6</td>\n",
       "      <td>164</td>\n",
       "      <td>100</td>\n",
       "      <td>LAL</td>\n",
       "      <td>93</td>\n",
       "      <td>1</td>\n",
       "      <td>2016</td>\n",
       "      <td>1/1/2016</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 142 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    mp  fg  fga    fg%  3p  3pa    3p%  ft  fta    ft%  ...  usg%_max_opp  \\\n",
       "0  240  36   91  0.396  15   33  0.455  17   18  0.944  ...          30.6   \n",
       "1  240  33   87  0.379   5   16  0.313  22   29  0.759  ...          40.4   \n",
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       "4  240  31   84  0.369   4   24  0.167  18   25  0.720  ...          30.6   \n",
       "\n",
       "   ortg_max_opp  drtg_max_opp  team_opp  total_opp  home_opp  season  \\\n",
       "0           174           130       CHO         94         0    2016   \n",
       "1           150           105       PHI         84         0    2016   \n",
       "2           171           132       NYK         81         0    2016   \n",
       "3           195           102       CHI        108         1    2016   \n",
       "4           164           100       LAL         93         1    2016   \n",
       "\n",
       "       date    won  target  \n",
       "0  1/1/2016   True       1  \n",
       "1  1/1/2016   True       0  \n",
       "2  1/1/2016   True       0  \n",
       "3  1/1/2016  False       0  \n",
       "4  1/1/2016  False       1  \n",
       "\n",
       "[5 rows x 142 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "28bf7117-5707-4ca8-95cb-13dbefca13d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "rcl = RidgeClassifier(alpha=1) #initialize classifier\n",
    "split = TimeSeriesSplit(n_splits=3) # split based of time to keep the value for prediction sequential\n",
    "sfs = SequentialFeatureSelector(rcl, n_features_to_select=35, direction=\"forward\",cv=split,n_jobs=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ed7890a3-0c05-4cb7-9225-9aa947d6f6a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "removed_columns = [\"season\", \"date\", \"won\", \"target\", \"team\", \"team_opp\"]\n",
    "selected_columns = df.columns[~df.columns.isin(removed_columns)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "2ee42f2f-7d5a-41da-bc25-b8727f701cf7",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "scaler = MinMaxScaler()\n",
    "df[selected_columns] = scaler.fit_transform(df[selected_columns])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "4e457374-ef3c-4728-a1ea-057e4ad2be2d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0.766628</td>\n",
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       "      <td>0.224647</td>\n",
       "      <td>0.388626</td>\n",
       "      <td>0.647059</td>\n",
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       "      <td>0.163462</td>\n",
       "      <td>0.0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
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       "      <td>0.206897</td>\n",
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       "      <td>0.375297</td>\n",
       "      <td>0.279070</td>\n",
       "      <td>0.253968</td>\n",
       "      <td>0.725788</td>\n",
       "      <td>...</td>\n",
       "      <td>0.264442</td>\n",
       "      <td>0.502370</td>\n",
       "      <td>0.294118</td>\n",
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       "      <td>0.423077</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2016</td>\n",
       "      <td>1/1/2016</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.272727</td>\n",
       "      <td>0.352941</td>\n",
       "      <td>0.239234</td>\n",
       "      <td>0.137931</td>\n",
       "      <td>0.303030</td>\n",
       "      <td>0.198337</td>\n",
       "      <td>0.395349</td>\n",
       "      <td>0.380952</td>\n",
       "      <td>0.673279</td>\n",
       "      <td>...</td>\n",
       "      <td>0.109114</td>\n",
       "      <td>0.355450</td>\n",
       "      <td>0.270588</td>\n",
       "      <td>LAL</td>\n",
       "      <td>0.278846</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2016</td>\n",
       "      <td>1/1/2016</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 142 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    mp        fg       fga       fg%        3p       3pa       3p%        ft  \\\n",
       "0  0.0  0.386364  0.455882  0.303828  0.517241  0.439394  0.540380  0.372093   \n",
       "1  0.0  0.318182  0.397059  0.263158  0.172414  0.181818  0.371734  0.488372   \n",
       "2  0.0  0.454545  0.367647  0.454545  0.344828  0.181818  0.742280  0.441860   \n",
       "3  0.0  0.272727  0.352941  0.239234  0.206897  0.227273  0.375297  0.279070   \n",
       "4  0.0  0.272727  0.352941  0.239234  0.137931  0.303030  0.198337  0.395349   \n",
       "\n",
       "        fta       ft%  ...  usg%_max_opp  ortg_max_opp  drtg_max_opp  \\\n",
       "0  0.269841  0.934656  ...      0.109114      0.402844      0.623529   \n",
       "1  0.444444  0.718786  ...      0.234917      0.289100      0.329412   \n",
       "2  0.380952  0.766628  ...      0.224647      0.388626      0.647059   \n",
       "3  0.253968  0.725788  ...      0.264442      0.502370      0.294118   \n",
       "4  0.380952  0.673279  ...      0.109114      0.355450      0.270588   \n",
       "\n",
       "   team_opp  total_opp  home_opp  season      date    won  target  \n",
       "0       CHO   0.288462       0.0    2016  1/1/2016   True       1  \n",
       "1       PHI   0.192308       0.0    2016  1/1/2016   True       0  \n",
       "2       NYK   0.163462       0.0    2016  1/1/2016   True       0  \n",
       "3       CHI   0.423077       1.0    2016  1/1/2016  False       0  \n",
       "4       LAL   0.278846       1.0    2016  1/1/2016  False       1  \n",
       "\n",
       "[5 rows x 142 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "67c5e463-d162-4c8e-88db-8360f9528f0e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</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=35, n_jobs=1)</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 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 sk-toggleable__label-arrow\">SequentialFeatureSelector</label><div class=\"sk-toggleable__content\"><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=35, n_jobs=1)</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 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 sk-toggleable__label-arrow\">estimator: RidgeClassifier</label><div class=\"sk-toggleable__content\"><pre>RidgeClassifier(alpha=1)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator 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 sk-toggleable__label-arrow\">RidgeClassifier</label><div class=\"sk-toggleable__content\"><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=35, n_jobs=1)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sfs.fit(df[selected_columns], df[\"target\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "9739dc7c-7996-4f5d-8b49-381e75fe890f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['mp', 'fga', 'efg%', 'ast%', 'usg%', 'fg_max', 'fga_max', '3pa_max', 'ft%_max', 'orb_max', 'drb_max', 'ftr_max', 'orb%_max', 'drb%_max', 'blk%_max', 'mp_opp', 'fga_opp', '3p%_opp', 'trb_opp', 'stl_opp', 'pf_opp', 'ast%_opp', 'blk%_opp', 'usg%_opp', '3p_max_opp', '3p%_max_opp', 'ft%_max_opp', 'ast_max_opp', 'ftr_max_opp', 'drb%_max_opp', 'trb%_max_opp', 'ast%_max_opp', 'blk%_max_opp', 'tov%_max_opp', 'ortg_max_opp'] \n",
      "no. of predicted columns = 35\n"
     ]
    }
   ],
   "source": [
    "predictors = list(selected_columns[sfs.get_support()])\n",
    "print(predictors,\"\\nno. of predicted columns =\",len(predictors))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "dc1fa89c-4f17-43cc-9c99-411a1bab1ce6",
   "metadata": {},
   "outputs": [],
   "source": [
    "def modelstart(data, team, opposition, model, predictors, start=5, 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",
    "        train = data[data[\"season\"] < season]\n",
    "        test = data.loc[(data[\"team\"] == team) & (data[\"team_opp\"]==opposition)& (data[\"season\"] == season)]\n",
    "        # print(test)\n",
    "        \n",
    "        model.fit(train[predictors], train[\"target\"])\n",
    "        \n",
    "        preds = model.predict(test[predictors])\n",
    "        preds = pd.Series(preds, index=test.index)\n",
    "        combined = pd.concat([test[\"target\"], preds], axis=1)\n",
    "        combined.columns = [\"actual\", \"prediction\"]\n",
    "        \n",
    "        all_predictions.append(combined)\n",
    "    predictions = pd.concat(all_predictions)\n",
    "    return predictions[\"prediction\"].mean(),predictions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed2e453d",
   "metadata": {},
   "source": [
    "## Model1\n",
    "Not the final model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "4e962058-3750-47c1-8ed2-5a2ca11d8238",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "win %: 0.25\n",
      "accuracy : 33.0 %\n"
     ]
    }
   ],
   "source": [
    "value,predictions = modelstart(df,\"ATL\",\"SAS\", rcl, predictors)\n",
    "print(\"win %:\",value)\n",
    "value = accuracy_score(predictions[\"actual\"], predictions[\"prediction\"])\n",
    "print(\"accuracy :\",round(accuracy_forupset(value)*100,2),\"%\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "2d55d8e5-0f7b-42f9-bb51-ddd19b885992",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "home\n",
       "0.0    0.428314\n",
       "1.0    0.571686\n",
       "dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby([\"home\"]).apply(lambda x: x[x[\"won\"] == 1].shape[0] / x.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "962b1628-de72-4c26-ba5a-bf48083e2ae2",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\neera\\AppData\\Local\\Temp\\ipykernel_13940\\1780157074.py:4: FutureWarning: Dropping of nuisance columns in rolling operations is deprecated; in a future version this will raise TypeError. Select only valid columns before calling the operation. Dropped columns were Index(['team'], dtype='object')\n",
      "  rolling = team.rolling(10).mean()\n"
     ]
    }
   ],
   "source": [
    "df_rolling = df[list(selected_columns) + [\"won\", \"team\", \"season\"]]\n",
    "\n",
    "def find_team_averages(team):\n",
    "    rolling = team.rolling(10).mean()\n",
    "    return rolling\n",
    "\n",
    "df_rolling = df_rolling.groupby([\"team\", \"season\"], group_keys=False).apply(find_team_averages)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "724b7c15-8a43-43bc-9a9a-a927d5380e35",
   "metadata": {},
   "outputs": [],
   "source": [
    "rolling_cols = [f\"{col}_10\" for col in df_rolling.columns]\n",
    "df_rolling.columns = rolling_cols\n",
    "df = pd.concat([df, df_rolling], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "69937a9f-47d6-411d-85b9-fab0f24db53f",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "e274baa8-7ee9-4b53-a669-1e327cb57b44",
   "metadata": {},
   "outputs": [],
   "source": [
    "def shift_col(team, col_name):\n",
    "    next_col = team[col_name].shift(-1)\n",
    "    return next_col\n",
    "\n",
    "def add_col(df, col_name):\n",
    "    return df.groupby(\"team\", group_keys=False).apply(lambda x: shift_col(x, col_name))\n",
    "\n",
    "df[\"home_next\"] = add_col(df, \"home\")\n",
    "df[\"team_opp_next\"] = add_col(df, \"team_opp\")\n",
    "df[\"date_next\"] = add_col(df, \"date\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "6c092ff2-29b8-4fc0-aa6e-5d8e19b9b5a3",
   "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>tov%_max_opp_10_y</th>\n",
       "      <th>usg%_max_opp_10_y</th>\n",
       "      <th>ortg_max_opp_10_y</th>\n",
       "      <th>drtg_max_opp_10_y</th>\n",
       "      <th>total_opp_10_y</th>\n",
       "      <th>home_opp_10_y</th>\n",
       "      <th>won_10_y</th>\n",
       "      <th>season_10_y</th>\n",
       "      <th>team_opp_next_y</th>\n",
       "      <th>team_y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.477273</td>\n",
       "      <td>0.397059</td>\n",
       "      <td>0.456938</td>\n",
       "      <td>0.344828</td>\n",
       "      <td>0.439394</td>\n",
       "      <td>0.359857</td>\n",
       "      <td>0.348837</td>\n",
       "      <td>0.269841</td>\n",
       "      <td>0.870478</td>\n",
       "      <td>...</td>\n",
       "      <td>0.299895</td>\n",
       "      <td>0.152246</td>\n",
       "      <td>0.384834</td>\n",
       "      <td>0.507059</td>\n",
       "      <td>0.436538</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>DAL</td>\n",
       "      <td>HOU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.454545</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.566986</td>\n",
       "      <td>0.482759</td>\n",
       "      <td>0.469697</td>\n",
       "      <td>0.475059</td>\n",
       "      <td>0.488372</td>\n",
       "      <td>0.428571</td>\n",
       "      <td>0.750292</td>\n",
       "      <td>...</td>\n",
       "      <td>0.467925</td>\n",
       "      <td>0.146598</td>\n",
       "      <td>0.406161</td>\n",
       "      <td>0.374118</td>\n",
       "      <td>0.366346</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.4</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>HOU</td>\n",
       "      <td>DAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.545455</td>\n",
       "      <td>0.485294</td>\n",
       "      <td>0.461722</td>\n",
       "      <td>0.275862</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.365796</td>\n",
       "      <td>0.255814</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>0.570595</td>\n",
       "      <td>...</td>\n",
       "      <td>0.512788</td>\n",
       "      <td>0.162516</td>\n",
       "      <td>0.488626</td>\n",
       "      <td>0.560000</td>\n",
       "      <td>0.437500</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>OKC</td>\n",
       "      <td>MIN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.386364</td>\n",
       "      <td>0.279412</td>\n",
       "      <td>0.447368</td>\n",
       "      <td>0.517241</td>\n",
       "      <td>0.393939</td>\n",
       "      <td>0.593824</td>\n",
       "      <td>0.372093</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.735123</td>\n",
       "      <td>...</td>\n",
       "      <td>0.430713</td>\n",
       "      <td>0.140436</td>\n",
       "      <td>0.562559</td>\n",
       "      <td>0.548235</td>\n",
       "      <td>0.457692</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.7</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>DAL</td>\n",
       "      <td>LAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>0.367647</td>\n",
       "      <td>0.342105</td>\n",
       "      <td>0.241379</td>\n",
       "      <td>0.272727</td>\n",
       "      <td>0.377672</td>\n",
       "      <td>0.325581</td>\n",
       "      <td>0.301587</td>\n",
       "      <td>0.708285</td>\n",
       "      <td>...</td>\n",
       "      <td>0.316771</td>\n",
       "      <td>0.121438</td>\n",
       "      <td>0.396209</td>\n",
       "      <td>0.508235</td>\n",
       "      <td>0.443269</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.7</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>ORL</td>\n",
       "      <td>LAC</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 423 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    mp        fg       fga       fg%        3p       3pa       3p%        ft  \\\n",
       "0  0.0  0.477273  0.397059  0.456938  0.344828  0.439394  0.359857  0.348837   \n",
       "1  0.0  0.454545  0.250000  0.566986  0.482759  0.469697  0.475059  0.488372   \n",
       "2  0.0  0.545455  0.485294  0.461722  0.275862  0.333333  0.365796  0.255814   \n",
       "3  0.0  0.386364  0.279412  0.447368  0.517241  0.393939  0.593824  0.372093   \n",
       "4  0.0  0.363636  0.367647  0.342105  0.241379  0.272727  0.377672  0.325581   \n",
       "\n",
       "        fta       ft%  ...  tov%_max_opp_10_y  usg%_max_opp_10_y  \\\n",
       "0  0.269841  0.870478  ...           0.299895           0.152246   \n",
       "1  0.428571  0.750292  ...           0.467925           0.146598   \n",
       "2  0.285714  0.570595  ...           0.512788           0.162516   \n",
       "3  0.333333  0.735123  ...           0.430713           0.140436   \n",
       "4  0.301587  0.708285  ...           0.316771           0.121438   \n",
       "\n",
       "   ortg_max_opp_10_y  drtg_max_opp_10_y  total_opp_10_y  home_opp_10_y  \\\n",
       "0           0.384834           0.507059        0.436538            0.5   \n",
       "1           0.406161           0.374118        0.366346            0.5   \n",
       "2           0.488626           0.560000        0.437500            0.5   \n",
       "3           0.562559           0.548235        0.457692            0.7   \n",
       "4           0.396209           0.508235        0.443269            0.4   \n",
       "\n",
       "   won_10_y  season_10_y  team_opp_next_y  team_y  \n",
       "0       0.5       2017.0              DAL     HOU  \n",
       "1       0.4       2016.0              HOU     DAL  \n",
       "2       0.6       2018.0              OKC     MIN  \n",
       "3       0.7       2020.0              DAL     LAL  \n",
       "4       0.7       2018.0              ORL     LAC  \n",
       "\n",
       "[5 rows x 423 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "full = df.merge(df[rolling_cols + [\"team_opp_next\", \"date_next\", \"team\"]], left_on=[\"team\", \"date_next\"], right_on=[\"team_opp_next\", \"date_next\"])\n",
    "full.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "8cad5c59",
   "metadata": {},
   "outputs": [],
   "source": [
    "def modelstart(data, team, opposition, model, predictors, start=5, 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",
    "        train = data[data[\"season\"] < season]\n",
    "        test = data.loc[(data[\"team_x\"] == team) & (data[\"team_y\"]==opposition)& (data[\"season\"] == season)]\n",
    "        # print(test)\n",
    "        \n",
    "        model.fit(train[predictors], train[\"target\"])\n",
    "        \n",
    "        preds = model.predict(test[predictors])\n",
    "        preds = pd.Series(preds, index=test.index)\n",
    "        combined = pd.concat([test[\"target\"], preds], axis=1)\n",
    "        combined.columns = [\"actual\", \"prediction\"]\n",
    "        \n",
    "        all_predictions.append(combined)\n",
    "    predictions = pd.concat(all_predictions)\n",
    "    return predictions[\"prediction\"].mean(),predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "64413cf9-f844-4473-a0bc-6e067ecc4a77",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" 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=35, n_jobs=1)</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 sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SequentialFeatureSelector</label><div class=\"sk-toggleable__content\"><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=35, n_jobs=1)</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 sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: RidgeClassifier</label><div class=\"sk-toggleable__content\"><pre>RidgeClassifier(alpha=1)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RidgeClassifier</label><div class=\"sk-toggleable__content\"><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=35, n_jobs=1)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "removed_columns = list(full.columns[full.dtypes == \"object\"]) + removed_columns\n",
    "selected_columns = full.columns[~full.columns.isin(removed_columns)]\n",
    "sfs.fit(full[selected_columns], full[\"target\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "7ed4c74d-67f7-473e-8e67-2df99f130931",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['mp', 'drb%', 'usg%', 'fg%_max', 'blk_max', 'stl%_max', 'mp_opp', '3p%_opp', 'orb_opp', 'pf_opp', 'ftr_opp', 'usg%_opp', 'fg%_max_opp', 'trb_max_opp', 'blk_max_opp', 'ts%_max_opp', 'ftr_10_x', 'blk%_10_x', 'usg%_10_x', '3p_max_10_x', 'orb%_max_10_x', 'fg_opp_10_x', 'usg%_opp_10_x', 'pts_max_opp_10_x', 'ast%_max_opp_10_x', 'home_next', 'ast_10_y', 'tov_10_y', 'usg%_10_y', 'fg_max_10_y', 'blk_max_10_y', 'usg%_opp_10_y', 'fga_max_opp_10_y', '+/-_max_opp_10_y', 'tov%_max_opp_10_y'] \n",
      "\n",
      "No. of predicted columns = 35\n"
     ]
    }
   ],
   "source": [
    "predictors = list(selected_columns[sfs.get_support()])\n",
    "print(predictors,\"\\n\\nNo. of predicted columns =\",len(predictors))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1fa99170",
   "metadata": {},
   "source": [
    "## Model 2\n",
    "This is the final model<br>\n",
    "Run the below cell and enter the teams you want to preform predictions on:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "d17bc453",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "win %: 71.43\n",
      "accuracy : 94.29 % ( +/- 0.21 )\n"
     ]
    }
   ],
   "source": [
    "team = input(\"Enter team:\")\n",
    "opposition = input(\"Enter opposition:\")\n",
    "value,predictions = modelstart(full,team,opposition, rcl, predictors)\n",
    "print(\"win %:\",round(value*100,2))\n",
    "value = accuracy_score(predictions[\"actual\"], predictions[\"prediction\"])\n",
    "print(\"accuracy :\",round(accuracy_forupset(value)*100,2),\"%\",\"( +/-\",round(value*.3,2),\")\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "dc36013a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['DAL' 'HOU' 'OKC' 'ORL' 'WAS' 'NOP' 'MIN' 'NYK' 'CHI' 'LAL' 'TOR' 'GSW'\n",
      " 'DET' 'MIL' 'LAC' 'CHO' 'BRK' 'PHI' 'MEM' 'BOS' 'IND' 'DEN' 'ATL' 'UTA'\n",
      " 'CLE' 'SAS' 'MIA' 'PHO' 'SAC' 'POR']\n"
     ]
    }
   ],
   "source": [
    "dfnew = full.team_x.unique()\n",
    "print(dfnew)"
   ]
  }
 ],
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