NFL_Draft_Analyze.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import pickle\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "with open(\"../data/2015_draft\",'r') as f:\n",
    "    player_data = pickle.load(f)\n",
    "df = pd.DataFrame(player_data[1:], columns=player_data[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = df.convert_objects(convert_numeric=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Rnd</th>\n",
       "      <th>Pick</th>\n",
       "      <th>Tm</th>\n",
       "      <th>Name</th>\n",
       "      <th>Pos</th>\n",
       "      <th>Age</th>\n",
       "      <th>To</th>\n",
       "      <th>AP1</th>\n",
       "      <th>PB</th>\n",
       "      <th>St</th>\n",
       "      <th>...</th>\n",
       "      <th>TD</th>\n",
       "      <th>Rec</th>\n",
       "      <th>Yds</th>\n",
       "      <th>TD</th>\n",
       "      <th>Tkl</th>\n",
       "      <th>Int</th>\n",
       "      <th>Sk</th>\n",
       "      <th>College/Univ</th>\n",
       "      <th>College_Stats</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>CLE</td>\n",
       "      <td>Courtney Brown</td>\n",
       "      <td>DE</td>\n",
       "      <td>22</td>\n",
       "      <td>2005</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>155</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19.0</td>\n",
       "      <td>Penn St.</td>\n",
       "      <td>College Stats</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>WAS</td>\n",
       "      <td>LaVar Arrington</td>\n",
       "      <td>LB</td>\n",
       "      <td>22</td>\n",
       "      <td>2006</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>331</td>\n",
       "      <td>3</td>\n",
       "      <td>23.5</td>\n",
       "      <td>Penn St.</td>\n",
       "      <td>College Stats</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>WAS</td>\n",
       "      <td>Chris Samuels</td>\n",
       "      <td>T</td>\n",
       "      <td>23</td>\n",
       "      <td>2009</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>College Stats</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>CIN</td>\n",
       "      <td>Peter Warrick</td>\n",
       "      <td>WR</td>\n",
       "      <td>23</td>\n",
       "      <td>2005</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>275</td>\n",
       "      <td>2991</td>\n",
       "      <td>18</td>\n",
       "      <td>12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Florida St.</td>\n",
       "      <td>College Stats</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>BAL</td>\n",
       "      <td>Jamal Lewis</td>\n",
       "      <td>RB</td>\n",
       "      <td>21</td>\n",
       "      <td>2009</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>...</td>\n",
       "      <td>58</td>\n",
       "      <td>221</td>\n",
       "      <td>1879</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tennessee</td>\n",
       "      <td>College Stats</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Rnd  Pick   Tm             Name Pos  Age    To  AP1  PB  St  ...   TD  Rec  \\\n",
       "0    1     1  CLE   Courtney Brown  DE   22  2005    0   0   4  ...  NaN  NaN   \n",
       "1    1     2  WAS  LaVar Arrington  LB   22  2006    0   3   5  ...  NaN  NaN   \n",
       "2    1     3  WAS    Chris Samuels   T   23  2009    0   6   9  ...  NaN  NaN   \n",
       "3    1     4  CIN    Peter Warrick  WR   23  2005    0   0   4  ...    2  275   \n",
       "4    1     5  BAL      Jamal Lewis  RB   21  2009    1   1   9  ...   58  221   \n",
       "\n",
       "    Yds  TD  Tkl  Int    Sk  College/Univ  College_Stats  year  \n",
       "0   NaN NaN  155  NaN  19.0      Penn St.  College Stats  2000  \n",
       "1   NaN NaN  331    3  23.5      Penn St.  College Stats  2000  \n",
       "2   NaN NaN    1  NaN   NaN       Alabama  College Stats  2000  \n",
       "3  2991  18   12  NaN   NaN   Florida St.  College Stats  2000  \n",
       "4  1879   4    8  NaN   NaN     Tennessee  College Stats  2000  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11becc590>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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fB/7bSnS8XSx2xA8QrpzSmdJ8PSKyBPXS5lrgQQBr7V5ge03bxcBBa23cWpsHHgF2WGu/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\nQMgN6ZAN+w4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11bec17d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df.CarAV.dropna())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3709, 30)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean = df[pd.notnull(df.CarAV)]\n",
    "df_clean.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "top_10_pos = df_clean.Pos.value_counts()[:10].index\n",
    "df_pos = df_clean[df_clean.Pos.isin(top_10_pos)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3415, 30)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pos.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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QuB14BfDh5u8nAM+IiNNonEXjXOC5wA2ZOQ1MR8TtwHHATd2PLEmSJEmSJEmS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\nYJC/9Sj2FUczQQMAAAAAAAAAYBbmNEdq10BLswAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x121342490>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.FacetGrid(df_pos, col=\"Pos\")\n",
    "g.map(plt.hist, \"CarAV\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Rnd\n",
       "1    34.477228\n",
       "2    22.810379\n",
       "3    15.054511\n",
       "4    12.107527\n",
       "5     8.713178\n",
       "6     7.211650\n",
       "7     6.280069\n",
       "Name: CarAV, dtype: float64"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean.groupby('Rnd').CarAV.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "pos_car = df_clean.groupby('Pos').CarAV.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pos\n",
       "NT     22.222222\n",
       "T      21.215328\n",
       "C      20.228916\n",
       "QB     18.553571\n",
       "G      18.355330\n",
       "DE     17.868657\n",
       "LB     16.454139\n",
       "DT     15.182724\n",
       "RB     14.769231\n",
       "DB     13.488732\n",
       "WR     13.396552\n",
       "P      11.321429\n",
       "K      11.125000\n",
       "TE      8.654545\n",
       "FS      4.000000\n",
       "FB      3.075000\n",
       "ILB     2.818182\n",
       "OLB     2.388889\n",
       "SS      2.285714\n",
       "OL      2.194444\n",
       "CB      1.909091\n",
       "LS      1.000000\n",
       "DL      0.000000\n",
       "Name: CarAV, dtype: float64"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pos_car.order(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Pos</th>\n",
       "      <th>Rnd</th>\n",
       "      <th>Pick</th>\n",
       "      <th>CarAV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>208</th>\n",
       "      <td>Tom Brady</td>\n",
       "      <td>QB</td>\n",
       "      <td>6</td>\n",
       "      <td>199</td>\n",
       "      <td>160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>Drew Brees</td>\n",
       "      <td>QB</td>\n",
       "      <td>2</td>\n",
       "      <td>32</td>\n",
       "      <td>147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>270</th>\n",
       "      <td>LaDainian Tomlinson</td>\n",
       "      <td>RB</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>522</th>\n",
       "      <td>Julius Peppers</td>\n",
       "      <td>DE</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1071</th>\n",
       "      <td>Philip Rivers</td>\n",
       "      <td>QB</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Brian Urlacher</td>\n",
       "      <td>LB</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>295</th>\n",
       "      <td>Reggie Wayne</td>\n",
       "      <td>WR</td>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1355</th>\n",
       "      <td>Aaron Rodgers</td>\n",
       "      <td>QB</td>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1078</th>\n",
       "      <td>Ben Roethlisberger</td>\n",
       "      <td>QB</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>544</th>\n",
       "      <td>Ed Reed</td>\n",
       "      <td>DB</td>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>106</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Name Pos  Rnd  Pick  CarAV\n",
       "208             Tom Brady  QB    6   199    160\n",
       "299            Drew Brees  QB    2    32    147\n",
       "270   LaDainian Tomlinson  RB    1     5    128\n",
       "522        Julius Peppers  DE    1     2    126\n",
       "1071        Philip Rivers  QB    1     4    121\n",
       "8          Brian Urlacher  LB    1     9    118\n",
       "295          Reggie Wayne  WR    1    30    117\n",
       "1355        Aaron Rodgers  QB    1    24    112\n",
       "1078   Ben Roethlisberger  QB    1    11    108\n",
       "544               Ed Reed  DB    1    24    106"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean.sort(['CarAV'], ascending=False)[['Name', 'Pos', 'Rnd', 'Pick', 'CarAV']].head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.cross_validation import train_test_split\n",
    "\n",
    "df_prediction = df[['Pos', 'Rnd', 'Pick', 'CarAV']]\n",
    "df_prediction = df_prediction.dropna()\n",
    "features = ['Pos', 'Rnd', 'Pick']\n",
    "X = df_prediction[features]\n",
    "X = pd.get_dummies(X)\n",
    "y = df_prediction['CarAV']\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test RMSE: 19.0394556784\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "import numpy as np\n",
    "from sklearn.metrics import mean_squared_error\n",
    "clf = RandomForestRegressor(n_estimators=100, n_jobs=-1)\n",
    "clf.fit(X_train, y_train)\n",
    "pred = clf.predict(X_test)\n",
    "print(\"Test RMSE: {0}\".format(np.sqrt(mean_squared_error(y_test, pred))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x12148b850>"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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v3RNnQ1+WH75xjv/y/ZMMTJT45Id349WYf6lT8MtVm2sagVPnRzl4qsDp4RQA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\n/x8CKAO+75k4tQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12137da90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(y_test - pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(u'Pick', 0.74057355030955418),\n",
       " (u'Pos_WR', 0.027251325026239556),\n",
       " (u'Pos_QB', 0.026284907953210087),\n",
       " (u'Rnd', 0.024608358747377927),\n",
       " (u'Pos_LB', 0.023325271548009431),\n",
       " (u'Pos_DB', 0.023244069808998812),\n",
       " (u'Pos_DE', 0.022416329004330184),\n",
       " (u'Pos_RB', 0.020809612266325495),\n",
       " (u'Pos_T', 0.019328648430449551),\n",
       " (u'Pos_DT', 0.016179252959674748),\n",
       " (u'Pos_G', 0.015783498291433298),\n",
       " (u'Pos_C', 0.010605214872418485),\n",
       " (u'Pos_TE', 0.0098016266470714788),\n",
       " (u'Pos_NT', 0.0061282718000366231),\n",
       " (u'Pos_CB', 0.0033879088823145376),\n",
       " (u'Pos_OLB', 0.0022238058553690535),\n",
       " (u'Pos_K', 0.0021160134638889883),\n",
       " (u'Pos_ILB', 0.0013145299160797032),\n",
       " (u'Pos_P', 0.0012959567575557965),\n",
       " (u'Pos_FB', 0.001160939197053856),\n",
       " (u'Pos_OL', 0.00097489972575304262),\n",
       " (u'Pos_SS', 0.00068067392576227556),\n",
       " (u'Pos_FS', 0.00043987713249530035),\n",
       " (u'Pos_LS', 3.2781030621886093e-05),\n",
       " (u'Pos_DL', 3.2676447975885517e-05)]"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fi = zip(X.columns, clf.feature_importances_)\n",
    "fi = sorted(fi, key=lambda x:x[1], reverse=True)\n",
    "fi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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