early-rushes-charts.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pulling url https://raw.githubusercontent.com/saiemgilani/cfbscrapR-data/master/data/parquet/pbp_players_pos_2014.parquet\n",
      "Pulling url https://raw.githubusercontent.com/saiemgilani/cfbscrapR-data/master/data/parquet/pbp_players_pos_2015.parquet\n",
      "Pulling url https://raw.githubusercontent.com/saiemgilani/cfbscrapR-data/master/data/parquet/pbp_players_pos_2016.parquet\n",
      "Pulling url https://raw.githubusercontent.com/saiemgilani/cfbscrapR-data/master/data/parquet/pbp_players_pos_2017.parquet\n",
      "Pulling url https://raw.githubusercontent.com/saiemgilani/cfbscrapR-data/master/data/parquet/pbp_players_pos_2018.parquet\n",
      "Pulling url https://raw.githubusercontent.com/saiemgilani/cfbscrapR-data/master/data/parquet/pbp_players_pos_2019.parquet\n",
      "Pulling url https://raw.githubusercontent.com/saiemgilani/cfbscrapR-data/master/data/parquet/pbp_players_pos_2020.parquet\n"
     ]
    },
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>EPA</th>\n",
       "      <th>ExpScoreDiff</th>\n",
       "      <th>ExpScoreDiff_Time_Ratio</th>\n",
       "      <th>FG_after</th>\n",
       "      <th>FG_before</th>\n",
       "      <th>Goal_To_Go</th>\n",
       "      <th>Goal_To_Go_end</th>\n",
       "      <th>No_Score_after</th>\n",
       "      <th>No_Score_before</th>\n",
       "      <th>Opp_FG_after</th>\n",
       "      <th>...</th>\n",
       "      <th>yds_kickoff</th>\n",
       "      <th>yds_kickoff_return</th>\n",
       "      <th>yds_penalty</th>\n",
       "      <th>yds_punt_gained</th>\n",
       "      <th>yds_punt_return</th>\n",
       "      <th>yds_punted</th>\n",
       "      <th>yds_receiving</th>\n",
       "      <th>yds_rushed</th>\n",
       "      <th>yds_sacked</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.002218</td>\n",
       "      <td>0.001810</td>\n",
       "      <td>0.123708</td>\n",
       "      <td>...</td>\n",
       "      <td>64.0</td>\n",
       "      <td>18.0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2014.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.469578</td>\n",
       "      <td>0.270464</td>\n",
       "      <td>0.000075</td>\n",
       "      <td>0.120739</td>\n",
       "      <td>0.132497</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.002423</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2014.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.484325</td>\n",
       "      <td>-0.488417</td>\n",
       "      <td>-0.000136</td>\n",
       "      <td>0.111865</td>\n",
       "      <td>0.139679</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.002728</td>\n",
       "      <td>0.002383</td>\n",
       "      <td>0.149630</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2014.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.214516</td>\n",
       "      <td>-0.972742</td>\n",
       "      <td>-0.000273</td>\n",
       "      <td>0.093482</td>\n",
       "      <td>0.111865</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.002888</td>\n",
       "      <td>0.002728</td>\n",
       "      <td>0.155241</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2014.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.529815</td>\n",
       "      <td>-1.187259</td>\n",
       "      <td>-0.000336</td>\n",
       "      <td>0.158063</td>\n",
       "      <td>0.093482</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.002756</td>\n",
       "      <td>0.002888</td>\n",
       "      <td>0.097974</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2014.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 331 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        EPA  ExpScoreDiff  ExpScoreDiff_Time_Ratio  FG_after  FG_before  \\\n",
       "0 -0.565384      0.835848                 0.000232  0.132497   0.170311   \n",
       "1 -0.469578      0.270464                 0.000075  0.120739   0.132497   \n",
       "2 -0.484325     -0.488417                -0.000136  0.111865   0.139679   \n",
       "3 -0.214516     -0.972742                -0.000273  0.093482   0.111865   \n",
       "4  2.529815     -1.187259                -0.000336  0.158063   0.093482   \n",
       "\n",
       "  Goal_To_Go Goal_To_Go_end  No_Score_after  No_Score_before  Opp_FG_after  \\\n",
       "0      False          False        0.002218         0.001810      0.123708   \n",
       "1      False          False        0.002423         0.002218      0.134912   \n",
       "2      False          False        0.002728         0.002383      0.149630   \n",
       "3      False          False        0.002888         0.002728      0.155241   \n",
       "4      False          False        0.002756         0.002888      0.097974   \n",
       "\n",
       "   ...  yds_kickoff  yds_kickoff_return  yds_penalty  yds_punt_gained  \\\n",
       "0  ...         64.0                18.0          NaN              NaN   \n",
       "1  ...          NaN                 NaN         -5.0              NaN   \n",
       "2  ...          NaN                 NaN          NaN              NaN   \n",
       "3  ...          NaN                 NaN          NaN              NaN   \n",
       "4  ...          NaN                 NaN          NaN              NaN   \n",
       "\n",
       "   yds_punt_return  yds_punted  yds_receiving  yds_rushed  yds_sacked    year  \n",
       "0              NaN         NaN            NaN         NaN         NaN  2014.0  \n",
       "1              NaN         NaN            NaN         NaN         NaN  2014.0  \n",
       "2              NaN         NaN            3.0         NaN         NaN  2014.0  \n",
       "3              NaN         NaN            NaN         5.0         NaN  2014.0  \n",
       "4              NaN         NaN            8.0         NaN         NaN  2014.0  \n",
       "\n",
       "[5 rows x 331 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "pbp_data = pd.DataFrame()\n",
    "years = range(2014, 2021)\n",
    "for i in years:\n",
    "    url = f\"https://raw.githubusercontent.com/saiemgilani/cfbscrapR-data/master/data/parquet/pbp_players_pos_{i}.parquet\"\n",
    "    print(f\"Pulling url {url}\")\n",
    "    i_data = pd.read_parquet(url, engine='pyarrow')\n",
    "\n",
    "    #sort=True eliminates a warning and alphabetically sorts columns\n",
    "    pbp_data = pbp_data.append(i_data, sort=True)\n",
    "\n",
    "#Give each row a unique index\n",
    "pbp_data.reset_index(drop=True, inplace=True)\n",
    "pbp_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>EPA</th>\n",
       "      <th>ExpScoreDiff</th>\n",
       "      <th>ExpScoreDiff_Time_Ratio</th>\n",
       "      <th>FG_after</th>\n",
       "      <th>FG_before</th>\n",
       "      <th>Goal_To_Go</th>\n",
       "      <th>Goal_To_Go_end</th>\n",
       "      <th>No_Score_after</th>\n",
       "      <th>No_Score_before</th>\n",
       "      <th>Opp_FG_after</th>\n",
       "      <th>...</th>\n",
       "      <th>yds_kickoff</th>\n",
       "      <th>yds_kickoff_return</th>\n",
       "      <th>yds_penalty</th>\n",
       "      <th>yds_punt_gained</th>\n",
       "      <th>yds_punt_return</th>\n",
       "      <th>yds_punted</th>\n",
       "      <th>yds_receiving</th>\n",
       "      <th>yds_rushed</th>\n",
       "      <th>yds_sacked</th>\n",
       "      <th>year</th>\n",
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       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>-0.557756</td>\n",
       "      <td>-4.815587</td>\n",
       "      <td>-0.001698</td>\n",
       "      <td>0.174596</td>\n",
       "      <td>0.186889</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.034174</td>\n",
       "      <td>0.028832</td>\n",
       "      <td>0.077807</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2014.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>-0.146644</td>\n",
       "      <td>-5.562323</td>\n",
       "      <td>-0.001975</td>\n",
       "      <td>0.190542</td>\n",
       "      <td>0.177796</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.039403</td>\n",
       "      <td>0.034791</td>\n",
       "      <td>0.081771</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2014.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>0.002317</td>\n",
       "      <td>6.736423</td>\n",
       "      <td>0.002494</td>\n",
       "      <td>0.102043</td>\n",
       "      <td>0.102174</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.079531</td>\n",
       "      <td>0.078393</td>\n",
       "      <td>0.124172</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2014.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>-0.985344</td>\n",
       "      <td>6.738740</td>\n",
       "      <td>0.002499</td>\n",
       "      <td>0.072068</td>\n",
       "      <td>0.102043</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.079118</td>\n",
       "      <td>0.079531</td>\n",
       "      <td>0.145861</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2014.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>-1.015121</td>\n",
       "      <td>-4.442430</td>\n",
       "      <td>-0.001731</td>\n",
       "      <td>0.201113</td>\n",
       "      <td>0.189612</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.083976</td>\n",
       "      <td>0.071651</td>\n",
       "      <td>0.069481</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2014.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 331 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         EPA  ExpScoreDiff  ExpScoreDiff_Time_Ratio  FG_after  FG_before  \\\n",
       "37 -0.557756     -4.815587                -0.001698  0.174596   0.186889   \n",
       "38 -0.146644     -5.562323                -0.001975  0.190542   0.177796   \n",
       "45  0.002317      6.736423                 0.002494  0.102043   0.102174   \n",
       "46 -0.985344      6.738740                 0.002499  0.072068   0.102043   \n",
       "51 -1.015121     -4.442430                -0.001731  0.201113   0.189612   \n",
       "\n",
       "   Goal_To_Go Goal_To_Go_end  No_Score_after  No_Score_before  Opp_FG_after  \\\n",
       "37      False          False        0.034174         0.028832      0.077807   \n",
       "38      False          False        0.039403         0.034791      0.081771   \n",
       "45      False          False        0.079531         0.078393      0.124172   \n",
       "46      False          False        0.079118         0.079531      0.145861   \n",
       "51      False          False        0.083976         0.071651      0.069481   \n",
       "\n",
       "    ...  yds_kickoff  yds_kickoff_return  yds_penalty  yds_punt_gained  \\\n",
       "37  ...          NaN                 NaN         -5.0              NaN   \n",
       "38  ...          NaN                 NaN          NaN              NaN   \n",
       "45  ...          NaN                 NaN          NaN              NaN   \n",
       "46  ...          NaN                 NaN          NaN              NaN   \n",
       "51  ...          NaN                 NaN          NaN              NaN   \n",
       "\n",
       "    yds_punt_return  yds_punted  yds_receiving  yds_rushed  yds_sacked    year  \n",
       "37              NaN         NaN            NaN         NaN         NaN  2014.0  \n",
       "38              NaN         NaN            6.0         NaN         NaN  2014.0  \n",
       "45              NaN         NaN            NaN         NaN         NaN  2014.0  \n",
       "46              NaN         NaN            NaN         NaN         NaN  2014.0  \n",
       "51              NaN         NaN            NaN         NaN         NaN  2014.0  \n",
       "\n",
       "[5 rows x 331 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "early_rush = pbp_data[\n",
    "                        (pbp_data.down == 2) # change to <= for early downs\n",
    "                      & (pbp_data.down > 0) \n",
    "                      & (pbp_data.distance >= 7) # comment out for early downs\n",
    "                      & (pbp_data.wp_before >= 0.20)\n",
    "                      & (pbp_data.wp_before <= 0.80)\n",
    "                      & (pbp_data.half_play_number > 30) # neutral game script?\n",
    "                     ]\n",
    "\n",
    "early_rush.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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>pos_team</th>\n",
       "      <th>year</th>\n",
       "      <th>early_rush_rate</th>\n",
       "      <th>avgEPA</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Abilene Christian</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>0.214286</td>\n",
       "      <td>0.054035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Abilene Christian</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.596664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Abilene Christian</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>0.101451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Abilene Christian</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>-0.246311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Abilene Christian</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.565962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1220</th>\n",
       "      <td>Wyoming</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>-0.147096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1221</th>\n",
       "      <td>Yale</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>0.200276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1222</th>\n",
       "      <td>Youngstown State</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.121460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1223</th>\n",
       "      <td>Youngstown State</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.004012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1224</th>\n",
       "      <td>Youngstown State</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.042066</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1225 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               pos_team    year  early_rush_rate    avgEPA\n",
       "0     Abilene Christian  2014.0         0.214286  0.054035\n",
       "1     Abilene Christian  2015.0         1.000000 -0.596664\n",
       "2     Abilene Christian  2016.0         0.285714  0.101451\n",
       "3     Abilene Christian  2017.0         0.200000 -0.246311\n",
       "4     Abilene Christian  2019.0         0.000000 -0.565962\n",
       "...                 ...     ...              ...       ...\n",
       "1220            Wyoming  2020.0         0.500000 -0.147096\n",
       "1221               Yale  2014.0         0.300000  0.200276\n",
       "1222   Youngstown State  2014.0         0.727273  0.121460\n",
       "1223   Youngstown State  2015.0         0.000000 -0.004012\n",
       "1224   Youngstown State  2016.0         0.666667  0.042066\n",
       "\n",
       "[1225 rows x 4 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from numpy.core.fromnumeric import mean\n",
    "team_early_rush = early_rush.groupby(by=[\"pos_team\", \"year\"], as_index=False).agg(\n",
    "    early_rush_rate = (\"rush\", mean),\n",
    "    avgEPA = (\"EPA\", mean)\n",
    ")\n",
    "team_early_rush"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 700x500 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "team_early_rush[\"color\"] = np.where(\n",
    "    team_early_rush.pos_team.str.contains(\"Georgia Tech\"),\n",
    "    \"#B3A369FF\",\n",
    "    \"#00000011\"\n",
    ")\n",
    "\n",
    "selected_year = 2020\n",
    "selected_data = team_early_rush[(team_early_rush.year == selected_year)]\n",
    "\n",
    "fig, ax1 = plt.subplots()\n",
    "\n",
    "ax1.set_ylabel(\"EPA/Rush\")\n",
    "ax1.set_xlabel(\"2nd and Long Rush Rate\")\n",
    "ax1.scatter(selected_data.early_rush_rate, selected_data.avgEPA, c = selected_data.color)\n",
    "plt.suptitle(\"EPA on 2nd and Long Rushes\", fontsize=18)\n",
    "plt.title(f\"{selected_year} season; WP between 20% and 80%, neutral game script\", fontsize=10)\n",
    "\n",
    "y_mean = [np.mean(selected_data.avgEPA)] * len(selected_data.early_rush_rate)\n",
    "x_mean = [np.mean(selected_data.early_rush_rate)] * len(selected_data.avgEPA)\n",
    "\n",
    "y_mean_line = ax1.plot(selected_data.early_rush_rate, y_mean, linestyle='--', color=\"blue\")\n",
    "x_mean_line = ax1.plot(x_mean, selected_data.avgEPA, linestyle='--', color=\"blue\")\n",
    "\n",
    "fig.set_size_inches(7,5)\n",
    "fig.set_dpi(100)\n",
    "\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}