04-EDA.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import altair as alt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>Unnamed: 0</th>\n",
       "      <th>WinPercentage</th>\n",
       "      <th>PassYards</th>\n",
       "      <th>PassAttempts</th>\n",
       "      <th>PercentOnPaceRuns</th>\n",
       "      <th>RushYards</th>\n",
       "      <th>Sacks</th>\n",
       "      <th>ForcedFumbles</th>\n",
       "      <th>InterceptionThrown</th>\n",
       "      <th>EPAPerPlay</th>\n",
       "      <th>DefEPAPerPlay</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ARI2009</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>4222.0</td>\n",
       "      <td>587.0</td>\n",
       "      <td>0.462687</td>\n",
       "      <td>1484.0</td>\n",
       "      <td>42</td>\n",
       "      <td>15</td>\n",
       "      <td>20</td>\n",
       "      <td>0.013203</td>\n",
       "      <td>-0.044167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ARI2010</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>3161.0</td>\n",
       "      <td>558.0</td>\n",
       "      <td>0.424920</td>\n",
       "      <td>1367.0</td>\n",
       "      <td>34</td>\n",
       "      <td>24</td>\n",
       "      <td>19</td>\n",
       "      <td>-0.126148</td>\n",
       "      <td>0.022802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ARI2011</td>\n",
       "      <td>0.416667</td>\n",
       "      <td>3992.0</td>\n",
       "      <td>594.0</td>\n",
       "      <td>0.485175</td>\n",
       "      <td>1578.0</td>\n",
       "      <td>42</td>\n",
       "      <td>14</td>\n",
       "      <td>12</td>\n",
       "      <td>-0.090028</td>\n",
       "      <td>-0.041771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ARI2012</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>3791.0</td>\n",
       "      <td>674.0</td>\n",
       "      <td>0.372032</td>\n",
       "      <td>1344.0</td>\n",
       "      <td>42</td>\n",
       "      <td>17</td>\n",
       "      <td>24</td>\n",
       "      <td>-0.156911</td>\n",
       "      <td>-0.081527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ARI2013</td>\n",
       "      <td>0.571429</td>\n",
       "      <td>4400.0</td>\n",
       "      <td>608.0</td>\n",
       "      <td>0.417910</td>\n",
       "      <td>1567.0</td>\n",
       "      <td>47</td>\n",
       "      <td>20</td>\n",
       "      <td>21</td>\n",
       "      <td>-0.007928</td>\n",
       "      <td>-0.054623</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Unnamed: 0  WinPercentage  PassYards  PassAttempts  PercentOnPaceRuns  \\\n",
       "0    ARI2009       0.714286     4222.0         587.0           0.462687   \n",
       "1    ARI2010       0.100000     3161.0         558.0           0.424920   \n",
       "2    ARI2011       0.416667     3992.0         594.0           0.485175   \n",
       "3    ARI2012       0.400000     3791.0         674.0           0.372032   \n",
       "4    ARI2013       0.571429     4400.0         608.0           0.417910   \n",
       "\n",
       "   RushYards  Sacks  ForcedFumbles  InterceptionThrown  EPAPerPlay  \\\n",
       "0     1484.0     42             15                  20    0.013203   \n",
       "1     1367.0     34             24                  19   -0.126148   \n",
       "2     1578.0     42             14                  12   -0.090028   \n",
       "3     1344.0     42             17                  24   -0.156911   \n",
       "4     1567.0     47             20                  21   -0.007928   \n",
       "\n",
       "   DefEPAPerPlay  \n",
       "0      -0.044167  \n",
       "1       0.022802  \n",
       "2      -0.041771  \n",
       "3      -0.081527  \n",
       "4      -0.054623  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "team_df = pd.read_csv('/data/JakeOliver28/teams.csv')\n",
    "team_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3505593198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(team_df['WinPercentage'])\n",
    "plt.xlabel('Winning Percentage')\n",
    "plt.ylabel('Count')\n",
    "plt.title('Winning Percentage')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Passing Yards"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f350554d748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean: 4035.2980392156865\n",
      "Standard Deviation: 616.5033827778624\n",
      "Min: 2219.0\n",
      "Max: 5728.0\n"
     ]
    }
   ],
   "source": [
    "plt.scatter('PassYards', 'WinPercentage', data=team_df)\n",
    "plt.xlabel('PassYards')\n",
    "plt.ylabel('WinPercentage')\n",
    "plt.show()\n",
    "print(\"Mean: {}\".format(np.mean(team_df['PassYards'])))\n",
    "print(\"Standard Deviation: {}\".format(np.std(team_df['PassYards'])))\n",
    "print(\"Min: {}\".format(np.min(team_df['PassYards'])))\n",
    "print(\"Max: {}\".format(np.max(team_df['PassYards'])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Rush Yards"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3504c81630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean: 1824.7333333333333\n",
      "Standard Deviation: 346.6892028873885\n",
      "Min: 1242.0\n",
      "Max: 2891.0\n"
     ]
    }
   ],
   "source": [
    "plt.scatter('RushYards', 'WinPercentage', data=team_df)\n",
    "plt.xlabel('RushYards')\n",
    "plt.ylabel('WinPercentage')\n",
    "plt.show()\n",
    "print(\"Mean: {}\".format(np.mean(team_df['RushYards'])))\n",
    "print(\"Standard Deviation: {}\".format(np.std(team_df['RushYards'])))\n",
    "print(\"Min: {}\".format(np.min(team_df['RushYards'])))\n",
    "print(\"Max: {}\".format(np.max(team_df['RushYards'])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Percent On-Pace Runs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3504c52908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean: 0.4624666391782716\n",
      "Standard Deviation: 0.039520832266591324\n",
      "Min: 0.3550561797752809\n",
      "Max: 0.5809768637532133\n"
     ]
    }
   ],
   "source": [
    "plt.scatter('PercentOnPaceRuns', 'WinPercentage', data=team_df)\n",
    "plt.xlabel('PercentOnPaceRuns')\n",
    "plt.ylabel('WinPercentage')\n",
    "plt.show()\n",
    "print(\"Mean: {}\".format(np.mean(team_df['PercentOnPaceRuns'])))\n",
    "print(\"Standard Deviation: {}\".format(np.std(team_df['PercentOnPaceRuns'])))\n",
    "print(\"Min: {}\".format(np.min(team_df['PercentOnPaceRuns'])))\n",
    "print(\"Max: {}\".format(np.max(team_df['PercentOnPaceRuns'])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### EPA Per Play"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3504bcd780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean: 0.4624666391782716\n",
      "Standard Deviation: 0.039520832266591324\n",
      "Min: 0.3550561797752809\n",
      "Max: 0.5809768637532133\n"
     ]
    }
   ],
   "source": [
    "plt.scatter('EPAPerPlay', 'WinPercentage', data=team_df)\n",
    "plt.xlabel('EPAPerPlay')\n",
    "plt.ylabel('WinPercentage')\n",
    "plt.show()\n",
    "print(\"Mean: {}\".format(np.mean(team_df['PercentOnPaceRuns'])))\n",
    "print(\"Standard Deviation: {}\".format(np.std(team_df['PercentOnPaceRuns'])))\n",
    "print(\"Min: {}\".format(np.min(team_df['PercentOnPaceRuns'])))\n",
    "print(\"Max: {}\".format(np.max(team_df['PercentOnPaceRuns'])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Defensive EPA Per Play"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3504b52e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean: 0.003534920778510137\n",
      "Standard Deviation: 0.05123072163853087\n",
      "Min: -0.1415362446881135\n",
      "Max: 0.12178034013668874\n"
     ]
    }
   ],
   "source": [
    "plt.scatter('DefEPAPerPlay', 'WinPercentage', data=team_df)\n",
    "plt.xlabel('Defensive EPA Per Play')\n",
    "plt.ylabel('Winning Percentage')\n",
    "plt.show()\n",
    "print(\"Mean: {}\".format(np.mean(team_df['DefEPAPerPlay'])))\n",
    "print(\"Standard Deviation: {}\".format(np.std(team_df['DefEPAPerPlay'])))\n",
    "print(\"Min: {}\".format(np.min(team_df['DefEPAPerPlay'])))\n",
    "print(\"Max: {}\".format(np.max(team_df['DefEPAPerPlay'])))"
   ]
  },
  {
   "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": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}