02_nfl_field_goals_data.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The watermark extension is already loaded. To reload it, use:\n",
      "  %reload_ext watermark\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import scipy.stats as stats\n",
    "\n",
    "from sklearn import preprocessing\n",
    "\n",
    "from sqlalchemy import create_engine\n",
    "\n",
    "from matplotlib import pylab as plt\n",
    "import seaborn as sns\n",
    "\n",
    "plt.style.use(\"bmh\")\n",
    "\n",
    "import arviz as az\n",
    "\n",
    "%load_ext watermark\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "matplotlib.pylab 1.17.3\n",
      "numpy            1.17.3\n",
      "scipy            1.3.1\n",
      "seaborn          0.9.0\n",
      "arviz            0.6.0\n",
      "sklearn          0.22\n",
      "pandas           0.25.2\n",
      "last updated: 2020-01-06 11:24:51 PST \n",
      "\n",
      "CPython 3.7.4\n",
      "IPython 7.9.0\n",
      "Git repo: git@github.com:clausherther/nfl-analysis.git\n"
     ]
    }
   ],
   "source": [
    "%watermark -v -d -t -z -u -r -iv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "url = \"bigquery://nfl\"\n",
    "key_file_path = \"/Users/claus/dev/bigquery-keys/nfl-pbp-bigquery-service-account.json\"\n",
    "engine = create_engine(url, credentials_path=key_file_path, echo=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2020-01-06 11:24:52,703 INFO sqlalchemy.engine.base.Engine select * from analysis.xa_field_goals\n",
      "2020-01-06 11:24:52,703 INFO sqlalchemy.engine.base.Engine {}\n"
     ]
    }
   ],
   "source": [
    "query = \"select * from analysis.xa_field_goals\"\n",
    "with engine.connect() as con:\n",
    "    df = pd.read_sql(query, con)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>play_key</th>\n",
       "      <th>game_date</th>\n",
       "      <th>game_id</th>\n",
       "      <th>play_id</th>\n",
       "      <th>season_nbr</th>\n",
       "      <th>season_type_code</th>\n",
       "      <th>season_code</th>\n",
       "      <th>quarter</th>\n",
       "      <th>down</th>\n",
       "      <th>play_type</th>\n",
       "      <th>...</th>\n",
       "      <th>kick_distance_yards</th>\n",
       "      <th>is_within_goal_line</th>\n",
       "      <th>field_goal_result</th>\n",
       "      <th>is_field_goal_success</th>\n",
       "      <th>field_goals</th>\n",
       "      <th>successful_field_goals</th>\n",
       "      <th>kick_angle_horizontal</th>\n",
       "      <th>kick_angle_vertical</th>\n",
       "      <th>kick_angle_horizontal_degrees</th>\n",
       "      <th>kick_angle_vertical_degrees</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>210b18c13f0bac908eddce02ed54331a</td>\n",
       "      <td>2015-09-10</td>\n",
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       "      <td>2015</td>\n",
       "      <td>REG</td>\n",
       "      <td>REG2015</td>\n",
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       "      <td>4</td>\n",
       "      <td>field_goal</td>\n",
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       "      <td>44.0</td>\n",
       "      <td>False</td>\n",
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       "      <td>2015-09-10</td>\n",
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       "      <td>4</td>\n",
       "      <td>field_goal</td>\n",
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       "      <td>46.0</td>\n",
       "      <td>False</td>\n",
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       "      <td>2015-09-10</td>\n",
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       "      <td>2015</td>\n",
       "      <td>REG</td>\n",
       "      <td>REG2015</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>field_goal</td>\n",
       "      <td>...</td>\n",
       "      <td>44.0</td>\n",
       "      <td>False</td>\n",
       "      <td>made</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.136807</td>\n",
       "      <td>0.259187</td>\n",
       "      <td>7.838475</td>\n",
       "      <td>14.850338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9947063607db959ca88d44812ab88363</td>\n",
       "      <td>2015-09-10</td>\n",
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       "      <td>2015</td>\n",
       "      <td>REG</td>\n",
       "      <td>REG2015</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>field_goal</td>\n",
       "      <td>...</td>\n",
       "      <td>24.0</td>\n",
       "      <td>True</td>\n",
       "      <td>made</td>\n",
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       "      <td>25.924902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>997a7e9ccc32cc4471a18b22f70490a2</td>\n",
       "      <td>2011-08-15</td>\n",
       "      <td>2011081551</td>\n",
       "      <td>301</td>\n",
       "      <td>2011</td>\n",
       "      <td>PRE</td>\n",
       "      <td>PRE2011</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>field_goal</td>\n",
       "      <td>...</td>\n",
       "      <td>33.0</td>\n",
       "      <td>False</td>\n",
       "      <td>made</td>\n",
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       "      <td>1</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                           play_key   game_date     game_id  play_id  \\\n",
       "0  210b18c13f0bac908eddce02ed54331a  2015-09-10  2015091000      285   \n",
       "1  972bc368c29d6c173827b5a3db7cd384  2015-09-10  2015091000     1208   \n",
       "2  be0fe604388ace9230c0d0e86a2f047f  2015-09-10  2015091000     1852   \n",
       "3  9947063607db959ca88d44812ab88363  2015-09-10  2015091000     2879   \n",
       "4  997a7e9ccc32cc4471a18b22f70490a2  2011-08-15  2011081551      301   \n",
       "\n",
       "   season_nbr season_type_code season_code  quarter  down   play_type  ...  \\\n",
       "0        2015              REG     REG2015        1     4  field_goal  ...   \n",
       "1        2015              REG     REG2015        2     4  field_goal  ...   \n",
       "2        2015              REG     REG2015        2     4  field_goal  ...   \n",
       "3        2015              REG     REG2015        4     4  field_goal  ...   \n",
       "4        2011              PRE     PRE2011        1     4  field_goal  ...   \n",
       "\n",
       "  kick_distance_yards is_within_goal_line field_goal_result  \\\n",
       "0                44.0               False            missed   \n",
       "1                46.0               False            missed   \n",
       "2                44.0               False              made   \n",
       "3                24.0                True              made   \n",
       "4                33.0               False              made   \n",
       "\n",
       "  is_field_goal_success field_goals successful_field_goals  \\\n",
       "0                 False           1                      0   \n",
       "1                 False           1                      0   \n",
       "2                  True           1                      1   \n",
       "3                  True           1                      1   \n",
       "4                  True           1                      1   \n",
       "\n",
       "   kick_angle_horizontal  kick_angle_vertical  kick_angle_horizontal_degrees  \\\n",
       "0               0.136807             0.259187                       7.838475   \n",
       "1               0.131124             0.248386                       7.512839   \n",
       "2               0.136807             0.259187                       7.838475   \n",
       "3               0.237672             0.452475                      13.617600   \n",
       "4               0.179119             0.339821                      10.262784   \n",
       "\n",
       "   kick_angle_vertical_degrees  \n",
       "0                    14.850338  \n",
       "1                    14.231458  \n",
       "2                    14.850338  \n",
       "3                    25.924902  \n",
       "4                    19.470302  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"game_date\"] = pd.to_datetime(df[\"game_date\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 13579 entries, 0 to 13578\n",
      "Data columns (total 28 columns):\n",
      "play_key                         13579 non-null object\n",
      "game_date                        13579 non-null datetime64[ns]\n",
      "game_id                          13579 non-null int64\n",
      "play_id                          13579 non-null int64\n",
      "season_nbr                       13579 non-null int64\n",
      "season_type_code                 13579 non-null object\n",
      "season_code                      13579 non-null object\n",
      "quarter                          13579 non-null int64\n",
      "down                             13579 non-null int64\n",
      "play_type                        13579 non-null object\n",
      "home_team_code                   13579 non-null object\n",
      "away_team_code                   13579 non-null object\n",
      "off_team_code                    13579 non-null object\n",
      "def_team_code                    13579 non-null object\n",
      "kicker_player_id                 13579 non-null object\n",
      "kicker_player_name               13576 non-null object\n",
      "yards_to_go                      13579 non-null int64\n",
      "yardline_100                     13579 non-null int64\n",
      "kick_distance_yards              13579 non-null float64\n",
      "is_within_goal_line              13579 non-null bool\n",
      "field_goal_result                13579 non-null object\n",
      "is_field_goal_success            13579 non-null bool\n",
      "field_goals                      13579 non-null int64\n",
      "successful_field_goals           13579 non-null int64\n",
      "kick_angle_horizontal            13579 non-null float64\n",
      "kick_angle_vertical              13579 non-null float64\n",
      "kick_angle_horizontal_degrees    13579 non-null float64\n",
      "kick_angle_vertical_degrees      13579 non-null float64\n",
      "dtypes: bool(2), datetime64[ns](1), float64(5), int64(9), object(11)\n",
      "memory usage: 2.7+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_hdf(\"data/field_goals.hd5\", \"field_goals\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Baseline Field Goal % is:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8338611090654687"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"successful_field_goals\"].sum()/df[\"field_goals\"].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df[\"kick_distance_yards\"], bins=30, hist=False, color=\"grey\", kde_kws={\"linestyle\":\"dotted\"})\n",
    "sns.distplot(df[df[\"is_field_goal_success\"]==False][\"kick_distance_yards\"], bins=30, hist=False, color=\"red\", label=\"Failed\");\n",
    "sns.distplot(df[df[\"is_field_goal_success\"]==True][\"kick_distance_yards\"], bins=30, hist=False, color=\"green\", label=\"Success\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_grp_yards = pd.DataFrame(\n",
    "    df\n",
    "#      .query(\"yardline_100 < 47\")\n",
    "     .groupby(\"yardline_100\")[[\"field_goals\", \"successful_field_goals\"]]\n",
    "     .sum()\n",
    "     .reset_index()\n",
    "    ,\n",
    "#     columns=[\"yardline_100\", \"field_goals_success_percent\"]\n",
    ")\n",
    "df_grp_yards[\"field_goals_success_percent\"] = df_grp_yards[\"successful_field_goals\"]/df_grp_yards[\"field_goals\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>yardline_100</th>\n",
       "      <th>field_goals</th>\n",
       "      <th>successful_field_goals</th>\n",
       "      <th>field_goals_success_percent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>144</td>\n",
       "      <td>144</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>261</td>\n",
       "      <td>259</td>\n",
       "      <td>0.992337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>302</td>\n",
       "      <td>296</td>\n",
       "      <td>0.980132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>321</td>\n",
       "      <td>315</td>\n",
       "      <td>0.981308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>373</td>\n",
       "      <td>367</td>\n",
       "      <td>0.983914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>298</td>\n",
       "      <td>285</td>\n",
       "      <td>0.956376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>351</td>\n",
       "      <td>347</td>\n",
       "      <td>0.988604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>334</td>\n",
       "      <td>322</td>\n",
       "      <td>0.964072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>360</td>\n",
       "      <td>347</td>\n",
       "      <td>0.963889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>404</td>\n",
       "      <td>386</td>\n",
       "      <td>0.955446</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   yardline_100  field_goals  successful_field_goals  \\\n",
       "0             1          144                     144   \n",
       "1             2          261                     259   \n",
       "2             3          302                     296   \n",
       "3             4          321                     315   \n",
       "4             5          373                     367   \n",
       "5             6          298                     285   \n",
       "6             7          351                     347   \n",
       "7             8          334                     322   \n",
       "8             9          360                     347   \n",
       "9            10          404                     386   \n",
       "\n",
       "   field_goals_success_percent  \n",
       "0                     1.000000  \n",
       "1                     0.992337  \n",
       "2                     0.980132  \n",
       "3                     0.981308  \n",
       "4                     0.983914  \n",
       "5                     0.956376  \n",
       "6                     0.988604  \n",
       "7                     0.964072  \n",
       "8                     0.963889  \n",
       "9                     0.955446  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_grp_yards.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8338611090654687"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_grp_yards[\"successful_field_goals\"].sum()/df_grp_yards[\"field_goals\"].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "yard_int = 5\n",
    "yard_lines_5 = list(range(yard_int, 100+yard_int, yard_int))\n",
    "yard_lines = list(range(1, 100+1, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_yards = df_grp_yards[\"yardline_100\"]\n",
    "y_probs = df_grp_yards[\"field_goals_success_percent\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_yard_lines = pd.DataFrame(yard_lines, columns=[\"yardline_100\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_grp_yards_expanded = df_yard_lines.merge(df_grp_yards, how=\"left\").fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100, 4)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_grp_yards_expanded.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1368x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_, ax = plt.subplots(1, 1, figsize=(19, 8))\n",
    "\n",
    "fmt=\".0%\"\n",
    "sns.heatmap(\n",
    "        df_grp_yards_expanded[[\"field_goals_success_percent\"]].T.values,\n",
    "        cmap=\"RdYlGn\", \n",
    "        cbar=True,\n",
    "#         vmin=0, vmax=1,\n",
    "        alpha=.8,\n",
    "        ax=ax\n",
    "    )\n",
    "# yards_10\n",
    "ax.set_xticks(yard_lines_5)\n",
    "ax.set_xticklabels(yard_lines_5)\n",
    "ax.set_yticklabels(\"\")\n",
    "ax.vlines(yard_lines_5, 0, 1, linestyles=\"solid\", alpha=1, color=\"white\")\n",
    "cbar = ax.collections[0].colorbar\n",
    "vals = np.arange(0, 1.1, 0.1)\n",
    "cbar.set_ticks(vals)\n",
    "cbar.set_ticklabels([f\"{x:,.0%}\" for x in vals])\n",
    "\n",
    "ax.set_xlim(ax.get_xlim()[1], 0)\n",
    "ax.set_title(\"Field Goal Success % by Yard Line\");\n",
    "# plt.tight_layout();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}