EPL_Prediction_Tuning.ipynb

{
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  {
   "cell_type": "code",
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   "source": [
    "import pandas as pd #  Loading the data, data wrangling and manipulation, feature engineering.\n",
    "import numpy as np\n",
    "import scipy.stats as scipy # Libraries for classifiers, model evaluation, metrics, cross-validation\n",
    "import matplotlib.pyplot as plt # Data visualization\n",
    "import urllib.request\n",
    "import sys\n",
    "from __future__ import division\n",
    "from IPython.display import HTML\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "<h1 align=\"center\"> FOOTBALL 369 : The Future is Today </h1>\n",
       "<div style=\"width:100%;height:0;padding-bottom:63%;position:relative;\"><iframe src=\"https://giphy.com/embed/1msyFvFnk3Nw0SDf0V\" width=\"100%\" height=\"100%\" style=\"position:absolute\" frameBorder=\"0\" class=\"giphy-embed\" allowFullScreen></iframe></div><p><a href=\"https://giphy.com/gifs/football369-1msyFvFnk3Nw0SDf0V\">via GIPHY</a></p>"
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       "<IPython.core.display.HTML object>"
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   "source": [
    "%%HTML\n",
    "\n",
    "<h1 align=\"center\"> FOOTBALL 369 : The Future is Today </h1>\n",
    "<div style=\"width:100%;height:0;padding-bottom:63%;position:relative;\"><iframe src=\"https://giphy.com/embed/1msyFvFnk3Nw0SDf0V\" width=\"100%\" height=\"100%\" style=\"position:absolute\" frameBorder=\"0\" class=\"giphy-embed\" allowFullScreen></iframe></div><p><a href=\"https://giphy.com/gifs/football369-1msyFvFnk3Nw0SDf0V\">via GIPHY</a></p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       " $('div.input').show();\n",
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       " code_show = !code_show\n",
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       "$( document ).ready(code_toggle);\n",
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       "The raw code for this IPython notebook is by default hidden for easier reading.\n",
       "To toggle on/off the raw code, click <a href=\"javascript:code_toggle()\">here</a>."
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       "<IPython.core.display.HTML object>"
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    "HTML('''<script>\n",
    "code_show=true; \n",
    "function code_toggle() {\n",
    " if (code_show){\n",
    " $('div.input').hide();\n",
    " } else {\n",
    " $('div.input').show();\n",
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    " code_show = !code_show\n",
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    "$( document ).ready(code_toggle);\n",
    "</script>\n",
    "The raw code for this IPython notebook is by default hidden for easier reading.\n",
    "To toggle on/off the raw code, click <a href=\"javascript:code_toggle()\">here</a>.''')"
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Short Keys to results data:</b>\n",
    "\n",
    "Div = League Division<br>\n",
    "Date = Match Date (dd/mm/yy)<br>\n",
    "HomeTeam = Home Team<br>\n",
    "AwayTeam = Away Team <br>\n",
    "FTHG = Full Time Home Team Goals<br>\n",
    "FTAG = Full Time Away Team Goals<br>\n",
    "FTR = Full Time Result (H=Home Win, D=Draw, A=Away Win)<br>\n",
    "HTHG = Half Time Home Team Goals<br>\n",
    "HTAG = Half Time Away Team Goals<br>\n",
    "HTR = Half Time Result (H=Home Win, D=Draw, A=Away Win)<br>\n",
    "\n",
    "Match Statistics (where available)\n",
    "Attendance = Crowd Attendance<br>\n",
    "Referee = Match Referee<br>\n",
    "HS = Home Team Shots<br>\n",
    "AS = Away Team Shots<br>\n",
    "HST = Home Team Shots on Target<br>\n",
    "AST = Away Team Shots on Target<br>\n",
    "HHW = Home Team Hit Woodwork<br>\n",
    "AHW = Away Team Hit Woodwork<br>\n",
    "HC = Home Team Corners<br>\n",
    "AC = Away Team Corners<br>\n",
    "HF = Home Team Fouls Committed<br>\n",
    "AF = Away Team Fouls Committed<br>\n",
    "HO = Home Team Offsides<br>\n",
    "AO = Away Team Offsides<br>\n",
    "HY = Home Team Yellow Cards<br>\n",
    "AY = Away Team Yellow Cards<br>\n",
    "HR = Home Team Red Cards<br>\n",
    "AR = Away Team Red Cards<br>\n",
    "HBP = Home Team Bookings Points (10 = yellow, 25 = red)<br>\n",
    "ABP = Away Team Bookings Points (10 = yellow, 25 = red)<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('./Data/England/E0_16.csv', <http.client.HTTPMessage at 0x20ea3d56320>)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#testfile = urllib.URLopener()\n",
    "testfile = urllib.request.URLopener()\n",
    "testfile.retrieve(\"http://www.football-data.co.uk/mmz4281/1617/E0.csv\", \"./Data/England/E0_16.csv\")\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"./Data/England/E0_16.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Div', 'Date', 'HomeTeam', 'AwayTeam', 'FTHG', 'FTAG', 'FTR', 'HTHG',\n",
       "       'HTAG', 'HTR', 'Referee', 'HS', 'AS', 'HST', 'AST', 'HF', 'AF', 'HC',\n",
       "       'AC', 'HY', 'AY', 'HR', 'AR', 'B365H', 'B365D', 'B365A', 'BWH', 'BWD',\n",
       "       'BWA', 'IWH', 'IWD', 'IWA', 'LBH', 'LBD', 'LBA', 'PSH', 'PSD', 'PSA',\n",
       "       'WHH', 'WHD', 'WHA', 'VCH', 'VCD', 'VCA', 'Bb1X2', 'BbMxH', 'BbAvH',\n",
       "       'BbMxD', 'BbAvD', 'BbMxA', 'BbAvA', 'BbOU', 'BbMx>2.5', 'BbAv>2.5',\n",
       "       'BbMx<2.5', 'BbAv<2.5', 'BbAH', 'BbAHh', 'BbMxAHH', 'BbAvAHH',\n",
       "       'BbMxAHA', 'BbAvAHA', 'PSCH', 'PSCD', 'PSCA'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cleaning and subsetting required data\n",
    "\n",
    "Here i'm cleaning the unwanted fields from the datasheet since i dont need it for prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "res_16 = df.iloc[:,:23]\n",
    "res_16 = res_16.drop(['Div','Date'],axis=1)\n",
    "table_features = df.iloc[:,:7]\n",
    "table_features = table_features.drop(['FTHG','FTAG','Div','Date'],axis=1)\n",
    "bet_16 = df.iloc[:,23:]\n",
    "\n",
    "#res_16 = df.ix[:,:23]  \n",
    "# res_14 = df_14.ix[:,:23]\n",
    "# res_14  =res_14.drop(['Div','Date'],axis=1)\n",
    "#table_features = df.ix[:,:7]\n",
    "#bet_16 = df.ix[:,23:]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "res_16.head()\n",
    "feature_table = df.iloc[:,:23]\n",
    "\n",
    "#feature_table = df.ix[:,:23]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>HomeTeam</th>\n",
       "      <th>AwayTeam</th>\n",
       "      <th>FTHG</th>\n",
       "      <th>FTAG</th>\n",
       "      <th>FTR</th>\n",
       "      <th>HTHG</th>\n",
       "      <th>HTAG</th>\n",
       "      <th>HTR</th>\n",
       "      <th>Referee</th>\n",
       "      <th>HS</th>\n",
       "      <th>...</th>\n",
       "      <th>HST</th>\n",
       "      <th>AST</th>\n",
       "      <th>HF</th>\n",
       "      <th>AF</th>\n",
       "      <th>HC</th>\n",
       "      <th>AC</th>\n",
       "      <th>HY</th>\n",
       "      <th>AY</th>\n",
       "      <th>HR</th>\n",
       "      <th>AR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>375</th>\n",
       "      <td>Liverpool</td>\n",
       "      <td>Middlesbrough</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>H</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>H</td>\n",
       "      <td>M Atkinson</td>\n",
       "      <td>25</td>\n",
       "      <td>...</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>376</th>\n",
       "      <td>Man United</td>\n",
       "      <td>Crystal Palace</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>H</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>H</td>\n",
       "      <td>A Taylor</td>\n",
       "      <td>9</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>377</th>\n",
       "      <td>Southampton</td>\n",
       "      <td>Stoke</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>D</td>\n",
       "      <td>L Probert</td>\n",
       "      <td>14</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>378</th>\n",
       "      <td>Swansea</td>\n",
       "      <td>West Brom</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>H</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>A</td>\n",
       "      <td>M Dean</td>\n",
       "      <td>12</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>379</th>\n",
       "      <td>Watford</td>\n",
       "      <td>Man City</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>A</td>\n",
       "      <td>J Moss</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        HomeTeam        AwayTeam  FTHG  FTAG FTR  HTHG  HTAG HTR     Referee  \\\n",
       "375    Liverpool   Middlesbrough     3     0   H     1     0   H  M Atkinson   \n",
       "376   Man United  Crystal Palace     2     0   H     2     0   H    A Taylor   \n",
       "377  Southampton           Stoke     0     1   A     0     0   D   L Probert   \n",
       "378      Swansea       West Brom     2     1   H     0     1   A      M Dean   \n",
       "379      Watford        Man City     0     5   A     0     4   A      J Moss   \n",
       "\n",
       "     HS ...  HST  AST  HF  AF  HC  AC  HY  AY  HR  AR  \n",
       "375  25 ...   10    3  13   8   3   3   0   1   0   0  \n",
       "376   9 ...    2    1  16  10   3   6   2   0   0   0  \n",
       "377  14 ...    6    3  12  10   4  10   2   4   0   0  \n",
       "378  12 ...    2    5   8  10   7   4   1   1   0   0  \n",
       "379   6 ...    3    9  12  10   4   5   1   0   0   0  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res_16.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Team, Home Goals Score, Away Goals Score, Attack Strength, Home Goals Conceded, Away Goals Conceded, Defensive Strength\n",
    "table_16 = pd.DataFrame(columns=('Team','HGS','AGS','HAS','AAS','HGC','AGC','HDS','ADS',))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "380"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res_16.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average number of goals at home 1.5973684210526315\n",
      "Average number of goals away 1.2026315789473685\n",
      "Average number of goals conceded at home 1.2026315789473685\n",
      "Average number of goals conceded away 1.5973684210526315\n"
     ]
    }
   ],
   "source": [
    "avg_home_scored_16 = res_16.FTHG.sum()*1.0 / res_16.shape[0]\n",
    "avg_away_scored_16 = res_16.FTAG.sum()*1.0 / res_16.shape[0]\n",
    "avg_home_conceded_16 = avg_away_scored_16\n",
    "avg_away_conceded_16 = avg_home_scored_16\n",
    "print (\"Average number of goals at home\",avg_home_scored_16);\n",
    "print (\"Average number of goals away\", avg_away_scored_16)\n",
    "print (\"Average number of goals conceded at home\",avg_home_conceded_16);\n",
    "print (\"Average number of goals conceded away\",avg_away_conceded_16);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "res_home = res_16.groupby('HomeTeam')\n",
    "res_away = res_16.groupby('AwayTeam')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# list(res_home)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Team</th>\n",
       "      <th>HGS</th>\n",
       "      <th>AGS</th>\n",
       "      <th>HAS</th>\n",
       "      <th>AAS</th>\n",
       "      <th>HGC</th>\n",
       "      <th>AGC</th>\n",
       "      <th>HDS</th>\n",
       "      <th>ADS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Arsenal</td>\n",
       "      <td>39</td>\n",
       "      <td>38</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16</td>\n",
       "      <td>28</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bournemouth</td>\n",
       "      <td>35</td>\n",
       "      <td>20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29</td>\n",
       "      <td>38</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Burnley</td>\n",
       "      <td>26</td>\n",
       "      <td>13</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20</td>\n",
       "      <td>35</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Chelsea</td>\n",
       "      <td>55</td>\n",
       "      <td>30</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17</td>\n",
       "      <td>16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Crystal Palace</td>\n",
       "      <td>24</td>\n",
       "      <td>26</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25</td>\n",
       "      <td>38</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Everton</td>\n",
       "      <td>42</td>\n",
       "      <td>20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16</td>\n",
       "      <td>28</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Hull</td>\n",
       "      <td>28</td>\n",
       "      <td>9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>35</td>\n",
       "      <td>45</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Leicester</td>\n",
       "      <td>31</td>\n",
       "      <td>17</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25</td>\n",
       "      <td>38</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Liverpool</td>\n",
       "      <td>45</td>\n",
       "      <td>33</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18</td>\n",
       "      <td>24</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Man City</td>\n",
       "      <td>37</td>\n",
       "      <td>43</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17</td>\n",
       "      <td>22</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Man United</td>\n",
       "      <td>26</td>\n",
       "      <td>28</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12</td>\n",
       "      <td>17</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Middlesbrough</td>\n",
       "      <td>17</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23</td>\n",
       "      <td>30</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Southampton</td>\n",
       "      <td>17</td>\n",
       "      <td>24</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Stoke</td>\n",
       "      <td>24</td>\n",
       "      <td>17</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24</td>\n",
       "      <td>32</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Sunderland</td>\n",
       "      <td>16</td>\n",
       "      <td>13</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>34</td>\n",
       "      <td>35</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Swansea</td>\n",
       "      <td>27</td>\n",
       "      <td>18</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>34</td>\n",
       "      <td>36</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Tottenham</td>\n",
       "      <td>47</td>\n",
       "      <td>39</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9</td>\n",
       "      <td>17</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Watford</td>\n",
       "      <td>25</td>\n",
       "      <td>15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29</td>\n",
       "      <td>39</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>West Brom</td>\n",
       "      <td>27</td>\n",
       "      <td>16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22</td>\n",
       "      <td>29</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>West Ham</td>\n",
       "      <td>19</td>\n",
       "      <td>28</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31</td>\n",
       "      <td>33</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Team  HGS  AGS  HAS  AAS  HGC  AGC  HDS  ADS\n",
       "0          Arsenal   39   38  NaN  NaN   16   28  NaN  NaN\n",
       "1      Bournemouth   35   20  NaN  NaN   29   38  NaN  NaN\n",
       "2          Burnley   26   13  NaN  NaN   20   35  NaN  NaN\n",
       "3          Chelsea   55   30  NaN  NaN   17   16  NaN  NaN\n",
       "4   Crystal Palace   24   26  NaN  NaN   25   38  NaN  NaN\n",
       "5          Everton   42   20  NaN  NaN   16   28  NaN  NaN\n",
       "6             Hull   28    9  NaN  NaN   35   45  NaN  NaN\n",
       "7        Leicester   31   17  NaN  NaN   25   38  NaN  NaN\n",
       "8        Liverpool   45   33  NaN  NaN   18   24  NaN  NaN\n",
       "9         Man City   37   43  NaN  NaN   17   22  NaN  NaN\n",
       "10      Man United   26   28  NaN  NaN   12   17  NaN  NaN\n",
       "11   Middlesbrough   17   10  NaN  NaN   23   30  NaN  NaN\n",
       "12     Southampton   17   24  NaN  NaN   21   27  NaN  NaN\n",
       "13           Stoke   24   17  NaN  NaN   24   32  NaN  NaN\n",
       "14      Sunderland   16   13  NaN  NaN   34   35  NaN  NaN\n",
       "15         Swansea   27   18  NaN  NaN   34   36  NaN  NaN\n",
       "16       Tottenham   47   39  NaN  NaN    9   17  NaN  NaN\n",
       "17         Watford   25   15  NaN  NaN   29   39  NaN  NaN\n",
       "18       West Brom   27   16  NaN  NaN   22   29  NaN  NaN\n",
       "19        West Ham   19   28  NaN  NaN   31   33  NaN  NaN"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table_16.Team = res_home.HomeTeam.all().values\n",
    "table_16.HGS = res_home.FTHG.sum().values\n",
    "table_16.HGC = res_home.FTAG.sum().values\n",
    "table_16.AGS = res_away.FTAG.sum().values\n",
    "table_16.AGC = res_away.FTHG.sum().values\n",
    "table_16"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Assuming number of home games = number of away games\n",
    "num_games = res_16.shape[0]/20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Team</th>\n",
       "      <th>HGS</th>\n",
       "      <th>AGS</th>\n",
       "      <th>HAS</th>\n",
       "      <th>AAS</th>\n",
       "      <th>HGC</th>\n",
       "      <th>AGC</th>\n",
       "      <th>HDS</th>\n",
       "      <th>ADS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Arsenal</td>\n",
       "      <td>39</td>\n",
       "      <td>38</td>\n",
       "      <td>1.285008</td>\n",
       "      <td>1.663020</td>\n",
       "      <td>16</td>\n",
       "      <td>28</td>\n",
       "      <td>0.700219</td>\n",
       "      <td>0.922570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bournemouth</td>\n",
       "      <td>35</td>\n",
       "      <td>20</td>\n",
       "      <td>1.153213</td>\n",
       "      <td>0.875274</td>\n",
       "      <td>29</td>\n",
       "      <td>38</td>\n",
       "      <td>1.269147</td>\n",
       "      <td>1.252059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Burnley</td>\n",
       "      <td>26</td>\n",
       "      <td>13</td>\n",
       "      <td>0.856672</td>\n",
       "      <td>0.568928</td>\n",
       "      <td>20</td>\n",
       "      <td>35</td>\n",
       "      <td>0.875274</td>\n",
       "      <td>1.153213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Chelsea</td>\n",
       "      <td>55</td>\n",
       "      <td>30</td>\n",
       "      <td>1.812191</td>\n",
       "      <td>1.312910</td>\n",
       "      <td>17</td>\n",
       "      <td>16</td>\n",
       "      <td>0.743982</td>\n",
       "      <td>0.527183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Crystal Palace</td>\n",
       "      <td>24</td>\n",
       "      <td>26</td>\n",
       "      <td>0.790774</td>\n",
       "      <td>1.137856</td>\n",
       "      <td>25</td>\n",
       "      <td>38</td>\n",
       "      <td>1.094092</td>\n",
       "      <td>1.252059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Everton</td>\n",
       "      <td>42</td>\n",
       "      <td>20</td>\n",
       "      <td>1.383855</td>\n",
       "      <td>0.875274</td>\n",
       "      <td>16</td>\n",
       "      <td>28</td>\n",
       "      <td>0.700219</td>\n",
       "      <td>0.922570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Hull</td>\n",
       "      <td>28</td>\n",
       "      <td>9</td>\n",
       "      <td>0.922570</td>\n",
       "      <td>0.393873</td>\n",
       "      <td>35</td>\n",
       "      <td>45</td>\n",
       "      <td>1.531729</td>\n",
       "      <td>1.482702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Leicester</td>\n",
       "      <td>31</td>\n",
       "      <td>17</td>\n",
       "      <td>1.021417</td>\n",
       "      <td>0.743982</td>\n",
       "      <td>25</td>\n",
       "      <td>38</td>\n",
       "      <td>1.094092</td>\n",
       "      <td>1.252059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Liverpool</td>\n",
       "      <td>45</td>\n",
       "      <td>33</td>\n",
       "      <td>1.482702</td>\n",
       "      <td>1.444201</td>\n",
       "      <td>18</td>\n",
       "      <td>24</td>\n",
       "      <td>0.787746</td>\n",
       "      <td>0.790774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Man City</td>\n",
       "      <td>37</td>\n",
       "      <td>43</td>\n",
       "      <td>1.219110</td>\n",
       "      <td>1.881838</td>\n",
       "      <td>17</td>\n",
       "      <td>22</td>\n",
       "      <td>0.743982</td>\n",
       "      <td>0.724876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Man United</td>\n",
       "      <td>26</td>\n",
       "      <td>28</td>\n",
       "      <td>0.856672</td>\n",
       "      <td>1.225383</td>\n",
       "      <td>12</td>\n",
       "      <td>17</td>\n",
       "      <td>0.525164</td>\n",
       "      <td>0.560132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Middlesbrough</td>\n",
       "      <td>17</td>\n",
       "      <td>10</td>\n",
       "      <td>0.560132</td>\n",
       "      <td>0.437637</td>\n",
       "      <td>23</td>\n",
       "      <td>30</td>\n",
       "      <td>1.006565</td>\n",
       "      <td>0.988468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Southampton</td>\n",
       "      <td>17</td>\n",
       "      <td>24</td>\n",
       "      <td>0.560132</td>\n",
       "      <td>1.050328</td>\n",
       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "      <td>0.919037</td>\n",
       "      <td>0.889621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Stoke</td>\n",
       "      <td>24</td>\n",
       "      <td>17</td>\n",
       "      <td>0.790774</td>\n",
       "      <td>0.743982</td>\n",
       "      <td>24</td>\n",
       "      <td>32</td>\n",
       "      <td>1.050328</td>\n",
       "      <td>1.054366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Sunderland</td>\n",
       "      <td>16</td>\n",
       "      <td>13</td>\n",
       "      <td>0.527183</td>\n",
       "      <td>0.568928</td>\n",
       "      <td>34</td>\n",
       "      <td>35</td>\n",
       "      <td>1.487965</td>\n",
       "      <td>1.153213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Swansea</td>\n",
       "      <td>27</td>\n",
       "      <td>18</td>\n",
       "      <td>0.889621</td>\n",
       "      <td>0.787746</td>\n",
       "      <td>34</td>\n",
       "      <td>36</td>\n",
       "      <td>1.487965</td>\n",
       "      <td>1.186161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Tottenham</td>\n",
       "      <td>47</td>\n",
       "      <td>39</td>\n",
       "      <td>1.548600</td>\n",
       "      <td>1.706783</td>\n",
       "      <td>9</td>\n",
       "      <td>17</td>\n",
       "      <td>0.393873</td>\n",
       "      <td>0.560132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Watford</td>\n",
       "      <td>25</td>\n",
       "      <td>15</td>\n",
       "      <td>0.823723</td>\n",
       "      <td>0.656455</td>\n",
       "      <td>29</td>\n",
       "      <td>39</td>\n",
       "      <td>1.269147</td>\n",
       "      <td>1.285008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>West Brom</td>\n",
       "      <td>27</td>\n",
       "      <td>16</td>\n",
       "      <td>0.889621</td>\n",
       "      <td>0.700219</td>\n",
       "      <td>22</td>\n",
       "      <td>29</td>\n",
       "      <td>0.962801</td>\n",
       "      <td>0.955519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>West Ham</td>\n",
       "      <td>19</td>\n",
       "      <td>28</td>\n",
       "      <td>0.626030</td>\n",
       "      <td>1.225383</td>\n",
       "      <td>31</td>\n",
       "      <td>33</td>\n",
       "      <td>1.356674</td>\n",
       "      <td>1.087315</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Team  HGS  AGS       HAS       AAS  HGC  AGC       HDS       ADS\n",
       "0          Arsenal   39   38  1.285008  1.663020   16   28  0.700219  0.922570\n",
       "1      Bournemouth   35   20  1.153213  0.875274   29   38  1.269147  1.252059\n",
       "2          Burnley   26   13  0.856672  0.568928   20   35  0.875274  1.153213\n",
       "3          Chelsea   55   30  1.812191  1.312910   17   16  0.743982  0.527183\n",
       "4   Crystal Palace   24   26  0.790774  1.137856   25   38  1.094092  1.252059\n",
       "5          Everton   42   20  1.383855  0.875274   16   28  0.700219  0.922570\n",
       "6             Hull   28    9  0.922570  0.393873   35   45  1.531729  1.482702\n",
       "7        Leicester   31   17  1.021417  0.743982   25   38  1.094092  1.252059\n",
       "8        Liverpool   45   33  1.482702  1.444201   18   24  0.787746  0.790774\n",
       "9         Man City   37   43  1.219110  1.881838   17   22  0.743982  0.724876\n",
       "10      Man United   26   28  0.856672  1.225383   12   17  0.525164  0.560132\n",
       "11   Middlesbrough   17   10  0.560132  0.437637   23   30  1.006565  0.988468\n",
       "12     Southampton   17   24  0.560132  1.050328   21   27  0.919037  0.889621\n",
       "13           Stoke   24   17  0.790774  0.743982   24   32  1.050328  1.054366\n",
       "14      Sunderland   16   13  0.527183  0.568928   34   35  1.487965  1.153213\n",
       "15         Swansea   27   18  0.889621  0.787746   34   36  1.487965  1.186161\n",
       "16       Tottenham   47   39  1.548600  1.706783    9   17  0.393873  0.560132\n",
       "17         Watford   25   15  0.823723  0.656455   29   39  1.269147  1.285008\n",
       "18       West Brom   27   16  0.889621  0.700219   22   29  0.962801  0.955519\n",
       "19        West Ham   19   28  0.626030  1.225383   31   33  1.356674  1.087315"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table_16.HAS = (table_16.HGS / num_games) / avg_home_scored_16\n",
    "table_16.AAS = (table_16.AGS / num_games) / avg_away_scored_16\n",
    "table_16.HDS = (table_16.HGC / num_games) / avg_home_conceded_16\n",
    "table_16.ADS = (table_16.AGC / num_games) / avg_away_conceded_16\n",
    "table_16"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Team</th>\n",
       "      <th>HGS</th>\n",
       "      <th>AGS</th>\n",
       "      <th>HAS</th>\n",
       "      <th>AAS</th>\n",
       "      <th>HGC</th>\n",
       "      <th>AGC</th>\n",
       "      <th>HDS</th>\n",
       "      <th>ADS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Swansea</td>\n",
       "      <td>27</td>\n",
       "      <td>18</td>\n",
       "      <td>0.889621</td>\n",
       "      <td>0.787746</td>\n",
       "      <td>34</td>\n",
       "      <td>36</td>\n",
       "      <td>1.487965</td>\n",
       "      <td>1.186161</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Team  HGS  AGS       HAS       AAS  HGC  AGC       HDS       ADS\n",
       "15  Swansea   27   18  0.889621  0.787746   34   36  1.487965  1.186161"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table_16[table_16.Team == \"Swansea\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "''' feature_table contains all the fixtures in the current season.\n",
    "ftr = full time result\n",
    "hst = home shots on target\n",
    "ast = away shots on target\n",
    "'''\n",
    "\n",
    "feature_table = feature_table[['HomeTeam','AwayTeam','FTR','HST','AST']]\n",
    "f_HAS = []\n",
    "f_HDS = []\n",
    "f_AAS = []\n",
    "f_ADS = []\n",
    "for index,row in feature_table.iterrows():\n",
    "    f_HAS.append(table_16[table_16['Team'] == row['HomeTeam']]['HAS'].values[0])\n",
    "    f_HDS.append(table_16[table_16['Team'] == row['HomeTeam']]['HDS'].values[0])\n",
    "    f_AAS.append(table_16[table_16['Team'] == row['AwayTeam']]['AAS'].values[0])\n",
    "    f_ADS.append(table_16[table_16['Team'] == row['AwayTeam']]['ADS'].values[0])\n",
    "    \n",
    "feature_table['HAS'] = f_HAS\n",
    "feature_table['HDS'] = f_HDS\n",
    "feature_table['AAS'] = f_AAS\n",
    "feature_table['ADS'] = f_ADS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>HomeTeam</th>\n",
       "      <th>AwayTeam</th>\n",
       "      <th>FTR</th>\n",
       "      <th>HST</th>\n",
       "      <th>AST</th>\n",
       "      <th>HAS</th>\n",
       "      <th>HDS</th>\n",
       "      <th>AAS</th>\n",
       "      <th>ADS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Burnley</td>\n",
       "      <td>Swansea</td>\n",
       "      <td>A</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>0.856672</td>\n",
       "      <td>0.875274</td>\n",
       "      <td>0.787746</td>\n",
       "      <td>1.186161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Crystal Palace</td>\n",
       "      <td>West Brom</td>\n",
       "      <td>A</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>0.790774</td>\n",
       "      <td>1.094092</td>\n",
       "      <td>0.700219</td>\n",
       "      <td>0.955519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Everton</td>\n",
       "      <td>Tottenham</td>\n",
       "      <td>D</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>1.383855</td>\n",
       "      <td>0.700219</td>\n",
       "      <td>1.706783</td>\n",
       "      <td>0.560132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Hull</td>\n",
       "      <td>Leicester</td>\n",
       "      <td>H</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>0.922570</td>\n",
       "      <td>1.531729</td>\n",
       "      <td>0.743982</td>\n",
       "      <td>1.252059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Man City</td>\n",
       "      <td>Sunderland</td>\n",
       "      <td>H</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>1.219110</td>\n",
       "      <td>0.743982</td>\n",
       "      <td>0.568928</td>\n",
       "      <td>1.153213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Middlesbrough</td>\n",
       "      <td>Stoke</td>\n",
       "      <td>D</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.560132</td>\n",
       "      <td>1.006565</td>\n",
       "      <td>0.743982</td>\n",
       "      <td>1.054366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Southampton</td>\n",
       "      <td>Watford</td>\n",
       "      <td>D</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0.560132</td>\n",
       "      <td>0.919037</td>\n",
       "      <td>0.656455</td>\n",
       "      <td>1.285008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Arsenal</td>\n",
       "      <td>Liverpool</td>\n",
       "      <td>A</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>1.285008</td>\n",
       "      <td>0.700219</td>\n",
       "      <td>1.444201</td>\n",
       "      <td>0.790774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Bournemouth</td>\n",
       "      <td>Man United</td>\n",
       "      <td>A</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>1.153213</td>\n",
       "      <td>1.269147</td>\n",
       "      <td>1.225383</td>\n",
       "      <td>0.560132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Chelsea</td>\n",
       "      <td>West Ham</td>\n",
       "      <td>H</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>1.812191</td>\n",
       "      <td>0.743982</td>\n",
       "      <td>1.225383</td>\n",
       "      <td>1.087315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Man United</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>H</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0.856672</td>\n",
       "      <td>0.525164</td>\n",
       "      <td>1.050328</td>\n",
       "      <td>0.889621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Burnley</td>\n",
       "      <td>Liverpool</td>\n",
       "      <td>H</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>0.856672</td>\n",
       "      <td>0.875274</td>\n",
       "      <td>1.444201</td>\n",
       "      <td>0.790774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Leicester</td>\n",
       "      <td>Arsenal</td>\n",
       "      <td>D</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1.021417</td>\n",
       "      <td>1.094092</td>\n",
       "      <td>1.663020</td>\n",
       "      <td>0.922570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Stoke</td>\n",
       "      <td>Man City</td>\n",
       "      <td>A</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>0.790774</td>\n",
       "      <td>1.050328</td>\n",
       "      <td>1.881838</td>\n",
       "      <td>0.724876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Swansea</td>\n",
       "      <td>Hull</td>\n",
       "      <td>A</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0.889621</td>\n",
       "      <td>1.487965</td>\n",
       "      <td>0.393873</td>\n",
       "      <td>1.482702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Tottenham</td>\n",
       "      <td>Crystal Palace</td>\n",
       "      <td>H</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>1.548600</td>\n",
       "      <td>0.393873</td>\n",
       "      <td>1.137856</td>\n",
       "      <td>1.252059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Watford</td>\n",
       "      <td>Chelsea</td>\n",
       "      <td>A</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>0.823723</td>\n",
       "      <td>1.269147</td>\n",
       "      <td>1.312910</td>\n",
       "      <td>0.527183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>West Brom</td>\n",
       "      <td>Everton</td>\n",
       "      <td>A</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>0.889621</td>\n",
       "      <td>0.962801</td>\n",
       "      <td>0.875274</td>\n",
       "      <td>0.922570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Sunderland</td>\n",
       "      <td>Middlesbrough</td>\n",
       "      <td>A</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>0.527183</td>\n",
       "      <td>1.487965</td>\n",
       "      <td>0.437637</td>\n",
       "      <td>0.988468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>West Ham</td>\n",
       "      <td>Bournemouth</td>\n",
       "      <td>H</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>0.626030</td>\n",
       "      <td>1.356674</td>\n",
       "      <td>0.875274</td>\n",
       "      <td>1.252059</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          HomeTeam        AwayTeam FTR  HST  AST       HAS       HDS  \\\n",
       "0          Burnley         Swansea   A    3    9  0.856672  0.875274   \n",
       "1   Crystal Palace       West Brom   A    4    3  0.790774  1.094092   \n",
       "2          Everton       Tottenham   D    6    4  1.383855  0.700219   \n",
       "3             Hull       Leicester   H    5    5  0.922570  1.531729   \n",
       "4         Man City      Sunderland   H    4    3  1.219110  0.743982   \n",
       "5    Middlesbrough           Stoke   D    2    1  0.560132  1.006565   \n",
       "6      Southampton         Watford   D    6    1  0.560132  0.919037   \n",
       "7          Arsenal       Liverpool   A    5    7  1.285008  0.700219   \n",
       "8      Bournemouth      Man United   A    3    7  1.153213  1.269147   \n",
       "9          Chelsea        West Ham   H    6    3  1.812191  0.743982   \n",
       "10      Man United     Southampton   H    5    1  0.856672  0.525164   \n",
       "11         Burnley       Liverpool   H    2    5  0.856672  0.875274   \n",
       "12       Leicester         Arsenal   D    1    4  1.021417  1.094092   \n",
       "13           Stoke        Man City   A    3    6  0.790774  1.050328   \n",
       "14         Swansea            Hull   A    3    4  0.889621  1.487965   \n",
       "15       Tottenham  Crystal Palace   H    5    2  1.548600  0.393873   \n",
       "16         Watford         Chelsea   A    2    4  0.823723  1.269147   \n",
       "17       West Brom         Everton   A    4    7  0.889621  0.962801   \n",
       "18      Sunderland   Middlesbrough   A    5    3  0.527183  1.487965   \n",
       "19        West Ham     Bournemouth   H    2    5  0.626030  1.356674   \n",
       "\n",
       "         AAS       ADS  \n",
       "0   0.787746  1.186161  \n",
       "1   0.700219  0.955519  \n",
       "2   1.706783  0.560132  \n",
       "3   0.743982  1.252059  \n",
       "4   0.568928  1.153213  \n",
       "5   0.743982  1.054366  \n",
       "6   0.656455  1.285008  \n",
       "7   1.444201  0.790774  \n",
       "8   1.225383  0.560132  \n",
       "9   1.225383  1.087315  \n",
       "10  1.050328  0.889621  \n",
       "11  1.444201  0.790774  \n",
       "12  1.663020  0.922570  \n",
       "13  1.881838  0.724876  \n",
       "14  0.393873  1.482702  \n",
       "15  1.137856  1.252059  \n",
       "16  1.312910  0.527183  \n",
       "17  0.875274  0.922570  \n",
       "18  0.437637  0.988468  \n",
       "19  0.875274  1.252059  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_table.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def transformResult(row):\n",
    "    '''Converts results (H,A or D) into numeric values'''\n",
    "    if(row.FTR == 'H'):\n",
    "        return 1\n",
    "    elif(row.FTR == 'A'):\n",
    "        return -1\n",
    "    else:\n",
    "        return 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "feature_table[\"Result\"] = feature_table.apply(lambda row: transformResult(row),axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
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       "    .dataframe thead th {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>HomeTeam</th>\n",
       "      <th>AwayTeam</th>\n",
       "      <th>FTR</th>\n",
       "      <th>HST</th>\n",
       "      <th>AST</th>\n",
       "      <th>HAS</th>\n",
       "      <th>HDS</th>\n",
       "      <th>AAS</th>\n",
       "      <th>ADS</th>\n",
       "      <th>Result</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>370</th>\n",
       "      <td>Arsenal</td>\n",
       "      <td>Everton</td>\n",
       "      <td>H</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>1.285008</td>\n",
       "      <td>0.700219</td>\n",
       "      <td>0.875274</td>\n",
       "      <td>0.922570</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>371</th>\n",
       "      <td>Burnley</td>\n",
       "      <td>West Ham</td>\n",
       "      <td>A</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0.856672</td>\n",
       "      <td>0.875274</td>\n",
       "      <td>1.225383</td>\n",
       "      <td>1.087315</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>372</th>\n",
       "      <td>Chelsea</td>\n",
       "      <td>Sunderland</td>\n",
       "      <td>H</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>1.812191</td>\n",
       "      <td>0.743982</td>\n",
       "      <td>0.568928</td>\n",
       "      <td>1.153213</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>373</th>\n",
       "      <td>Hull</td>\n",
       "      <td>Tottenham</td>\n",
       "      <td>A</td>\n",
       "      <td>4</td>\n",
       "      <td>14</td>\n",
       "      <td>0.922570</td>\n",
       "      <td>1.531729</td>\n",
       "      <td>1.706783</td>\n",
       "      <td>0.560132</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>374</th>\n",
       "      <td>Leicester</td>\n",
       "      <td>Bournemouth</td>\n",
       "      <td>D</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>1.021417</td>\n",
       "      <td>1.094092</td>\n",
       "      <td>0.875274</td>\n",
       "      <td>1.252059</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>375</th>\n",
       "      <td>Liverpool</td>\n",
       "      <td>Middlesbrough</td>\n",
       "      <td>H</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>1.482702</td>\n",
       "      <td>0.787746</td>\n",
       "      <td>0.437637</td>\n",
       "      <td>0.988468</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>376</th>\n",
       "      <td>Man United</td>\n",
       "      <td>Crystal Palace</td>\n",
       "      <td>H</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.856672</td>\n",
       "      <td>0.525164</td>\n",
       "      <td>1.137856</td>\n",
       "      <td>1.252059</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>377</th>\n",
       "      <td>Southampton</td>\n",
       "      <td>Stoke</td>\n",
       "      <td>A</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>0.560132</td>\n",
       "      <td>0.919037</td>\n",
       "      <td>0.743982</td>\n",
       "      <td>1.054366</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>378</th>\n",
       "      <td>Swansea</td>\n",
       "      <td>West Brom</td>\n",
       "      <td>H</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>0.889621</td>\n",
       "      <td>1.487965</td>\n",
       "      <td>0.700219</td>\n",
       "      <td>0.955519</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>379</th>\n",
       "      <td>Watford</td>\n",
       "      <td>Man City</td>\n",
       "      <td>A</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>0.823723</td>\n",
       "      <td>1.269147</td>\n",
       "      <td>1.881838</td>\n",
       "      <td>0.724876</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        HomeTeam        AwayTeam FTR  HST  AST       HAS       HDS       AAS  \\\n",
       "370      Arsenal         Everton   H    9    7  1.285008  0.700219  0.875274   \n",
       "371      Burnley        West Ham   A    1    4  0.856672  0.875274  1.225383   \n",
       "372      Chelsea      Sunderland   H    8    3  1.812191  0.743982  0.568928   \n",
       "373         Hull       Tottenham   A    4   14  0.922570  1.531729  1.706783   \n",
       "374    Leicester     Bournemouth   D    5    4  1.021417  1.094092  0.875274   \n",
       "375    Liverpool   Middlesbrough   H   10    3  1.482702  0.787746  0.437637   \n",
       "376   Man United  Crystal Palace   H    2    1  0.856672  0.525164  1.137856   \n",
       "377  Southampton           Stoke   A    6    3  0.560132  0.919037  0.743982   \n",
       "378      Swansea       West Brom   H    2    5  0.889621  1.487965  0.700219   \n",
       "379      Watford        Man City   A    3    9  0.823723  1.269147  1.881838   \n",
       "\n",
       "          ADS  Result  \n",
       "370  0.922570       1  \n",
       "371  1.087315      -1  \n",
       "372  1.153213       1  \n",
       "373  0.560132      -1  \n",
       "374  1.252059       0  \n",
       "375  0.988468       1  \n",
       "376  1.252059       1  \n",
       "377  1.054366      -1  \n",
       "378  0.955519       1  \n",
       "379  0.724876      -1  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_table.tail(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train = feature_table[['HST','AST','HAS','HDS','AAS','ADS',]]\n",
    "y_train = feature_table['Result']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from xgboost import XGBClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.ensemble import voting_classifier\n",
    "from sklearn.svm import LinearSVC\n",
    "import sys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "clf1 = RandomForestClassifier()\n",
    "clf2 = MultinomialNB()\n",
    "clf3 = XGBClassifier()\n",
    "clf4 = LinearSVC()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.53846154  0.53846154  0.51282051  0.43589744  0.47368421  0.55263158\n",
      "  0.60526316  0.64864865  0.56756757  0.63888889]\n",
      "0.551232507811\n"
     ]
    }
   ],
   "source": [
    "y_pred = clf4.fit(X_train,y_train).predict(X_train)\n",
    "accuracy_score(y_pred,y_train)\n",
    "scores = cross_val_score(clf1, X_train, y_train, cv=10)\n",
    "print (scores);\n",
    "print (scores.mean());"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.48717949  0.56410256  0.53846154  0.51282051  0.52631579  0.63157895\n",
      "  0.63157895  0.54054054  0.51351351  0.52777778]\n",
      "0.547386961861\n"
     ]
    }
   ],
   "source": [
    "y_pred = clf1.fit(X_train,y_train).predict(X_train)\n",
    "accuracy_score(y_pred,y_train)\n",
    "scores = cross_val_score(clf1, X_train, y_train, cv=10)\n",
    "print (scores);\n",
    "print (scores.mean());"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.61538462  0.61538462  0.58974359  0.61538462  0.71052632  0.60526316\n",
      "  0.71052632  0.56756757  0.62162162  0.66666667]\n",
      "0.631806908123\n"
     ]
    }
   ],
   "source": [
    "y_pred = clf2.fit(X_train,y_train).predict(X_train)\n",
    "accuracy_score(y_pred,y_train)\n",
    "scores = cross_val_score(clf2, X_train, y_train, cv=10)\n",
    "print (scores);\n",
    "print (scores.mean());"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.48717949  0.58974359  0.51282051  0.69230769  0.57894737  0.63157895\n",
      "  0.76315789  0.64864865  0.67567568  0.63888889]\n",
      "0.621894870579\n"
     ]
    }
   ],
   "source": [
    "y_pred = clf3.fit(X_train, y_train).predict(X_train)\n",
    "accuracy_score(y_pred,y_train)\n",
    "scores = cross_val_score(clf3, X_train, y_train, cv=10)\n",
    "print (scores);\n",
    "print (scores.mean());"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Adding Recent Performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#What is this for?\n",
    "ht = res_16.loc[40].HomeTeam\n",
    "at = res_16.loc[40].AwayTeam"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "feat_table = res_16.sort_index(ascending=False)\n",
    "feat_table = feat_table[['HomeTeam','AwayTeam','FTR','FTHG','FTAG','HS','AS','HC','AC']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Adding next week fixtures\n",
    "new_fixtures = pd.DataFrame( [['Chelsea','Man City','D',0,0,0,0,0,0],\n",
    "                             ['Liverpool','Bournemouth','D',0,0,0,0,0,0],\n",
    "                             ['Southampton','Crystal Palace','D',0,0,0,0,0,0],\n",
    "                             ['Hull','Middlesbrough','D',0,0,0,0,0,0],\n",
    "                             ['Arsenal','West Ham','D',0,0,0,0,0,0],\n",
    "                             ['Swansea','Tottenham','D',0,0,0,0,0,0],\n",
    "                             ['Man United','Everton','D',0,0,0,0,0,0],\n",
    "                             ['Leicester','Sunderland','D',0,0,0,0,0,0],\n",
    "                             ['Burnley','Stoke','D',0,0,0,0,0,0],\n",
    "                             ['Watford','West Brom','D',0,0,0,0,0,0]],columns=feat_table.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(390, 9)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_feat_table = new_fixtures.append(feat_table,ignore_index=True)\n",
    "new_feat_table = new_feat_table.sort_index(ascending=False)\n",
    "new_feat_table = new_feat_table.reset_index().drop(['index'], axis=1)\n",
    "new_feat_table = new_feat_table.sort_index(ascending=False)\n",
    "# feat_table = n\n",
    "feat_table = new_feat_table\n",
    "feat_table.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Adding k recent performance measures\n",
    "feat_table[\"pastHS\"] = 0.0\n",
    "feat_table[\"pastHC\"] = 0.0\n",
    "feat_table[\"pastAS\"] = 0.0\n",
    "feat_table[\"pastAC\"] = 0.0\n",
    "feat_table[\"pastHG\"] = 0.0\n",
    "feat_table[\"pastAG\"] = 0.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>HomeTeam</th>\n",
       "      <th>AwayTeam</th>\n",
       "      <th>FTR</th>\n",
       "      <th>FTHG</th>\n",
       "      <th>FTAG</th>\n",
       "      <th>HS</th>\n",
       "      <th>AS</th>\n",
       "      <th>HC</th>\n",
       "      <th>AC</th>\n",
       "      <th>pastHS</th>\n",
       "      <th>pastHC</th>\n",
       "      <th>pastAS</th>\n",
       "      <th>pastAC</th>\n",
       "      <th>pastHG</th>\n",
       "      <th>pastAG</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>389</th>\n",
       "      <td>Chelsea</td>\n",
       "      <td>Man City</td>\n",
       "      <td>D</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>388</th>\n",
       "      <td>Liverpool</td>\n",
       "      <td>Bournemouth</td>\n",
       "      <td>D</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>387</th>\n",
       "      <td>Southampton</td>\n",
       "      <td>Crystal Palace</td>\n",
       "      <td>D</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>386</th>\n",
       "      <td>Hull</td>\n",
       "      <td>Middlesbrough</td>\n",
       "      <td>D</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>385</th>\n",
       "      <td>Arsenal</td>\n",
       "      <td>West Ham</td>\n",
       "      <td>D</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>384</th>\n",
       "      <td>Swansea</td>\n",
       "      <td>Tottenham</td>\n",
       "      <td>D</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>383</th>\n",
       "      <td>Man United</td>\n",
       "      <td>Everton</td>\n",
       "      <td>D</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>382</th>\n",
       "      <td>Leicester</td>\n",
       "      <td>Sunderland</td>\n",
       "      <td>D</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>381</th>\n",
       "      <td>Burnley</td>\n",
       "      <td>Stoke</td>\n",
       "      <td>D</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>380</th>\n",
       "      <td>Watford</td>\n",
       "      <td>West Brom</td>\n",
       "      <td>D</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>379</th>\n",
       "      <td>Watford</td>\n",
       "      <td>Man City</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>15</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>378</th>\n",
       "      <td>Swansea</td>\n",
       "      <td>West Brom</td>\n",
       "      <td>H</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>16</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        HomeTeam        AwayTeam FTR  FTHG  FTAG  HS  AS  HC  AC  pastHS  \\\n",
       "389      Chelsea        Man City   D     0     0   0   0   0   0     0.0   \n",
       "388    Liverpool     Bournemouth   D     0     0   0   0   0   0     0.0   \n",
       "387  Southampton  Crystal Palace   D     0     0   0   0   0   0     0.0   \n",
       "386         Hull   Middlesbrough   D     0     0   0   0   0   0     0.0   \n",
       "385      Arsenal        West Ham   D     0     0   0   0   0   0     0.0   \n",
       "384      Swansea       Tottenham   D     0     0   0   0   0   0     0.0   \n",
       "383   Man United         Everton   D     0     0   0   0   0   0     0.0   \n",
       "382    Leicester      Sunderland   D     0     0   0   0   0   0     0.0   \n",
       "381      Burnley           Stoke   D     0     0   0   0   0   0     0.0   \n",
       "380      Watford       West Brom   D     0     0   0   0   0   0     0.0   \n",
       "379      Watford        Man City   A     0     5   6  15   4   5     0.0   \n",
       "378      Swansea       West Brom   H     2     1  12  16   7   4     0.0   \n",
       "\n",
       "     pastHC  pastAS  pastAC  pastHG  pastAG  \n",
       "389     0.0     0.0     0.0     0.0     0.0  \n",
       "388     0.0     0.0     0.0     0.0     0.0  \n",
       "387     0.0     0.0     0.0     0.0     0.0  \n",
       "386     0.0     0.0     0.0     0.0     0.0  \n",
       "385     0.0     0.0     0.0     0.0     0.0  \n",
       "384     0.0     0.0     0.0     0.0     0.0  \n",
       "383     0.0     0.0     0.0     0.0     0.0  \n",
       "382     0.0     0.0     0.0     0.0     0.0  \n",
       "381     0.0     0.0     0.0     0.0     0.0  \n",
       "380     0.0     0.0     0.0     0.0     0.0  \n",
       "379     0.0     0.0     0.0     0.0     0.0  \n",
       "378     0.0     0.0     0.0     0.0     0.0  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feat_table.head(12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# for index,row in feat_table.iterrows():\n",
    "#     #print row['HomeTeam'], table_16[table_16['Team'] == row['HomeTeam']]['HAS'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\AnverMAM\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:7: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  import sys\n",
      "C:\\Users\\AnverMAM\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:8: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "# Adding k recent performance metrics. Change value of k.\n",
    "k = 4\n",
    "for i in range(feat_table.shape[0]-1,-1,-1):\n",
    "    row = feat_table.loc[i]\n",
    "    ht = row.HomeTeam\n",
    "    at = row.AwayTeam\n",
    "    ht_stats = feat_table.loc[i-1:-1][(feat_table.HomeTeam == ht) | (feat_table.AwayTeam == ht)].head(k)\n",
    "    at_stats = feat_table.loc[i-1:-1][(feat_table.HomeTeam == at) | (feat_table.AwayTeam == at)].head(k)\n",
    "\n",
    "    feat_table.set_value(i, 'pastHC', (ht_stats[ht_stats[\"AwayTeam\"] == ht].sum().HC + ht_stats[ht_stats[\"HomeTeam\"] == ht].sum().HC)/k)\n",
    "    feat_table.set_value(i, 'pastAC', (at_stats[at_stats[\"AwayTeam\"] == at].sum().HC + at_stats[at_stats[\"HomeTeam\"] == at].sum().HC)/k)\n",
    "    feat_table.set_value(i, 'pastHS', (ht_stats[ht_stats[\"AwayTeam\"] == ht].sum().HS + ht_stats[ht_stats[\"HomeTeam\"] == ht].sum().AS)/k)\n",
    "    feat_table.set_value(i, 'pastAS', (at_stats[at_stats[\"AwayTeam\"] == at].sum().HS + at_stats[at_stats[\"HomeTeam\"] == at].sum().AS)/k)\n",
    "    feat_table.set_value(i, 'pastHG', (ht_stats[ht_stats[\"AwayTeam\"] == ht].sum().FTAG + ht_stats[ht_stats[\"HomeTeam\"] == ht].sum().FTHG)/k)\n",
    "    feat_table.set_value(i, 'pastAG', (at_stats[at_stats[\"AwayTeam\"] == at].sum().FTAG + at_stats[at_stats[\"HomeTeam\"] == at].sum().FTHG)/k)\n",
    "\n",
    "f_HAS = []\n",
    "f_HDS = []\n",
    "f_AAS = []\n",
    "f_ADS = []\n",
    "for index,row in feat_table.iterrows():\n",
    "    #print row\n",
    "    f_HAS.append(table_16[table_16['Team'] == row['HomeTeam']]['HAS'].values[0])\n",
    "    f_HDS.append(table_16[table_16['Team'] == row['HomeTeam']]['HDS'].values[0])\n",
    "    f_AAS.append(table_16[table_16['Team'] == row['HomeTeam']]['AAS'].values[0])\n",
    "    f_ADS.append(table_16[table_16['Team'] == row['HomeTeam']]['ADS'].values[0])\n",
    "    \n",
    "feat_table['HAS'] = f_HAS\n",
    "feat_table['HDS'] = f_HDS\n",
    "feat_table['AAS'] = f_AAS\n",
    "feat_table['ADS'] = f_ADS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "test_table = feat_table.drop(['FTHG','FTAG','HS','AS','HC','AC'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "test_table[\"Result\"] = test_table.apply(lambda row: transformResult(row),axis=1)\n",
    "test_table.sort_index(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(390, 19)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "380"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# num_games decides the train-test split\n",
    "print (feat_table.shape)\n",
    "num_games = feat_table.shape[0]-10\n",
    "num_games"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train = test_table[['pastHS','pastHC','pastAS','pastAC','pastHG','pastAG','HAS','HDS','AAS','ADS']].loc[0:num_games]\n",
    "y_train = test_table['Result'].loc[0:num_games]\n",
    "X_test = test_table[['pastHS','pastHC','pastAS','pastAC','pastHG','pastAG','HAS','HDS','AAS','ADS']].loc[num_games:]\n",
    "y_test = test_table['Result'].loc[num_games:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "### Try predictions here too."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Adding home advantage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "test_table[\"pastCornerDiff\"] = (test_table[\"pastHC\"] - test_table[\"pastAC\"])/k\n",
    "test_table[\"pastGoalDiff\"] = (test_table[\"pastHG\"] - test_table[\"pastAG\"])/k\n",
    "test_table[\"pastShotsDiff\"] = (test_table[\"pastHS\"] - test_table[\"pastAG\"])/k"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "380\n"
     ]
    }
   ],
   "source": [
    "''' number of games to exclude in the training set for validation\n",
    "For example, if 240 games have been played, test_table has 250 fixtures - the last 10 being the ones that haven't\n",
    "been played. So, we set aside 20 fixtures from the training set(240 fixtures) for validation.''' \n",
    "num_games = feat_table.shape[0]-10\n",
    "print (num_games)\n",
    "v_split = 20\n",
    "n_games = num_games - v_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "test_table.drop(['pastHC','pastAS','pastAC','pastHG','pastAG'],axis=1)\n",
    "X_train = test_table[['pastCornerDiff','pastGoalDiff','pastShotsDiff','HAS','HDS','AAS','ADS']].loc[0:n_games]\n",
    "y_train = test_table['Result'].loc[0:n_games]\n",
    "X_test = test_table[['pastCornerDiff','pastGoalDiff','pastShotsDiff','HAS','HDS','AAS','ADS']].loc[n_games:num_games-1]\n",
    "y_test = test_table['Result'].loc[n_games:num_games-1]\n",
    "X_predict = test_table[['pastCornerDiff','pastGoalDiff','pastShotsDiff','HAS','HDS','AAS','ADS']].loc[num_games:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pastCornerDiff</th>\n",
       "      <th>pastGoalDiff</th>\n",
       "      <th>pastShotsDiff</th>\n",
       "      <th>HAS</th>\n",
       "      <th>HDS</th>\n",
       "      <th>AAS</th>\n",
       "      <th>ADS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>380</th>\n",
       "      <td>0.4375</td>\n",
       "      <td>-0.0625</td>\n",
       "      <td>3.8125</td>\n",
       "      <td>0.823723</td>\n",
       "      <td>1.269147</td>\n",
       "      <td>0.656455</td>\n",
       "      <td>1.285008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>381</th>\n",
       "      <td>0.7500</td>\n",
       "      <td>0.1250</td>\n",
       "      <td>3.3750</td>\n",
       "      <td>0.856672</td>\n",
       "      <td>0.875274</td>\n",
       "      <td>0.568928</td>\n",
       "      <td>1.153213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>382</th>\n",
       "      <td>-0.6875</td>\n",
       "      <td>0.1875</td>\n",
       "      <td>4.0000</td>\n",
       "      <td>1.021417</td>\n",
       "      <td>1.094092</td>\n",
       "      <td>0.743982</td>\n",
       "      <td>1.252059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>383</th>\n",
       "      <td>-0.0625</td>\n",
       "      <td>0.0625</td>\n",
       "      <td>2.7500</td>\n",
       "      <td>0.856672</td>\n",
       "      <td>0.525164</td>\n",
       "      <td>1.225383</td>\n",
       "      <td>0.560132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>384</th>\n",
       "      <td>0.7500</td>\n",
       "      <td>-0.5625</td>\n",
       "      <td>2.1250</td>\n",
       "      <td>0.889621</td>\n",
       "      <td>1.487965</td>\n",
       "      <td>0.787746</td>\n",
       "      <td>1.186161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>385</th>\n",
       "      <td>1.3750</td>\n",
       "      <td>0.5000</td>\n",
       "      <td>3.0625</td>\n",
       "      <td>1.285008</td>\n",
       "      <td>0.700219</td>\n",
       "      <td>1.663020</td>\n",
       "      <td>0.922570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>386</th>\n",
       "      <td>-0.1875</td>\n",
       "      <td>-0.1250</td>\n",
       "      <td>3.1875</td>\n",
       "      <td>0.922570</td>\n",
       "      <td>1.531729</td>\n",
       "      <td>0.393873</td>\n",
       "      <td>1.482702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>387</th>\n",
       "      <td>-0.2500</td>\n",
       "      <td>-0.1250</td>\n",
       "      <td>2.8750</td>\n",
       "      <td>0.560132</td>\n",
       "      <td>0.919037</td>\n",
       "      <td>1.050328</td>\n",
       "      <td>0.889621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>388</th>\n",
       "      <td>-0.5625</td>\n",
       "      <td>0.1250</td>\n",
       "      <td>1.6250</td>\n",
       "      <td>1.482702</td>\n",
       "      <td>0.787746</td>\n",
       "      <td>1.444201</td>\n",
       "      <td>0.790774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389</th>\n",
       "      <td>0.1875</td>\n",
       "      <td>-0.1250</td>\n",
       "      <td>0.6250</td>\n",
       "      <td>1.812191</td>\n",
       "      <td>0.743982</td>\n",
       "      <td>1.312910</td>\n",
       "      <td>0.527183</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     pastCornerDiff  pastGoalDiff  pastShotsDiff       HAS       HDS  \\\n",
       "380          0.4375       -0.0625         3.8125  0.823723  1.269147   \n",
       "381          0.7500        0.1250         3.3750  0.856672  0.875274   \n",
       "382         -0.6875        0.1875         4.0000  1.021417  1.094092   \n",
       "383         -0.0625        0.0625         2.7500  0.856672  0.525164   \n",
       "384          0.7500       -0.5625         2.1250  0.889621  1.487965   \n",
       "385          1.3750        0.5000         3.0625  1.285008  0.700219   \n",
       "386         -0.1875       -0.1250         3.1875  0.922570  1.531729   \n",
       "387         -0.2500       -0.1250         2.8750  0.560132  0.919037   \n",
       "388         -0.5625        0.1250         1.6250  1.482702  0.787746   \n",
       "389          0.1875       -0.1250         0.6250  1.812191  0.743982   \n",
       "\n",
       "          AAS       ADS  \n",
       "380  0.656455  1.285008  \n",
       "381  0.568928  1.153213  \n",
       "382  0.743982  1.252059  \n",
       "383  1.225383  0.560132  \n",
       "384  0.787746  1.186161  \n",
       "385  1.663020  0.922570  \n",
       "386  0.393873  1.482702  \n",
       "387  1.050328  0.889621  \n",
       "388  1.444201  0.790774  \n",
       "389  1.312910  0.527183  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# clf1.fit(X_train,y_train)\n",
    "# #clf2.fit(X_train,y_train)\n",
    "# clf3.fit(X_train,y_train)\n",
    "# clf4.fit(X_train,y_train)\n",
    "# clf5.fit(X_train,y_train)\n",
    "# clf6.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#KNN\n",
    "plot_scores_knn = []\n",
    "for b in range(1,50): #xrange is changed to range in python 3\n",
    "    clf_knn = KNeighborsClassifier(n_neighbors=b)\n",
    "    clf_knn.fit(X_train,y_train)\n",
    "    scores = accuracy_score(y_test,clf_knn.predict(X_test))\n",
    "    plot_scores_knn.append(scores)\n",
    "\n",
    "#XGBClassifier\n",
    "plot_scores_XGB = []\n",
    "for i in range(1,100):\n",
    "    clf_XGB = XGBClassifier(n_estimators=i,max_depth=100)\n",
    "    clf_XGB.fit(X_train, y_train)\n",
    "    scores = accuracy_score(y_test,clf_XGB.predict(X_test))\n",
    "    plot_scores_XGB.append(scores)\n",
    "    \n",
    "#Logistic Regression\n",
    "plot_scores_logreg= []\n",
    "cs = [0.01,0.02,0.1,0.5,1,3,4,5,10]\n",
    "for c in cs:\n",
    "    clf_logreg = LogisticRegression(C=c,solver='lbfgs',multi_class='ovr')\n",
    "    clf_logreg.fit(X_train, y_train)\n",
    "    scores = accuracy_score(y_test,clf_logreg.predict(X_test))\n",
    "    plot_scores_logreg.append(scores)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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F5uKvIXe/NNr3De5+AuEC+D7CQxWoPiy3EZ+Ptr8s6tfeR4U+PfII4aYMKA4lXAg8XLLM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\nA5ATIngAA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      "text/plain": [
       "<matplotlib.figure.Figure at 0x20ea45b4f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (16,7))\n",
    "# plt.style.use('ggplot')\n",
    "\n",
    "ax1 = fig.add_subplot(1,3,1)\n",
    "ax1.plot(range(1,50),plot_scores_knn);\n",
    "ax1.set_title(\"KNN - Accuracy vs N\")\n",
    "ax1.set_xticks(range(1,50,5));\n",
    "\n",
    "ax2 = fig.add_subplot(1,3,2)\n",
    "ax2.plot(range(1,100),plot_scores_XGB);\n",
    "ax2.set_xticks(range(1,100,6));\n",
    "ax2.set_title(\"XGB - Accuracy vs n_estimators\")\n",
    "\n",
    "ax3 = fig.add_subplot(1,3,3)\n",
    "ax3.plot(range(1,10),plot_scores_logreg);\n",
    "ax3.set_xticks(range(1,10));\n",
    "ax3.set_title(\"Logistic Regression - Accuracy vs C\")\n",
    "\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.75 34\n",
      "0.7 27\n",
      "0.8 5\n"
     ]
    }
   ],
   "source": [
    "max_knn_n = max(plot_scores_knn)\n",
    "max_knn_ind = plot_scores_knn.index(max_knn_n)\n",
    "\n",
    "max_XGB_e = max(plot_scores_XGB)\n",
    "max_XGB_ind = plot_scores_XGB.index(max_XGB_e)\n",
    "\n",
    "max_logreg_c = max(plot_scores_logreg)\n",
    "max_logreg_ind = plot_scores_logreg.index(max_logreg_c)\n",
    "\n",
    "print (max_knn_n, max_knn_ind)\n",
    "print (max_XGB_e, max_XGB_ind)\n",
    "print (max_logreg_c, max_logreg_ind)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "clf_knn = KNeighborsClassifier(n_neighbors=max_knn_ind).fit(X_train,y_train)\n",
    "clf_XGB = XGBClassifier(n_estimators=max_XGB_ind).fit(X_train,y_train)\n",
    "clf_logreg = LogisticRegression(C=max_logreg_ind,solver='lbfgs',multi_class='ovr').fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y_pred_knn = clf_knn.predict(X_predict)\n",
    "y_pred_XGB = clf_XGB.predict(X_predict)\n",
    "y_pred_logreg = clf_logreg.predict(X_predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "this_week = test_table[['HomeTeam','AwayTeam']].loc[num_games:]\n",
    "this_week['Result_knn']=y_pred_knn\n",
    "this_week['Result_XGB']=y_pred_XGB\n",
    "this_week['Result_logreg']=y_pred_logreg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>HomeTeam</th>\n",
       "      <th>AwayTeam</th>\n",
       "      <th>Result_knn</th>\n",
       "      <th>Result_XGB</th>\n",
       "      <th>Result_logreg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>380</th>\n",
       "      <td>Watford</td>\n",
       "      <td>West Brom</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>381</th>\n",
       "      <td>Burnley</td>\n",
       "      <td>Stoke</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>382</th>\n",
       "      <td>Leicester</td>\n",
       "      <td>Sunderland</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>383</th>\n",
       "      <td>Man United</td>\n",
       "      <td>Everton</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>384</th>\n",
       "      <td>Swansea</td>\n",
       "      <td>Tottenham</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>385</th>\n",
       "      <td>Arsenal</td>\n",
       "      <td>West Ham</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>386</th>\n",
       "      <td>Hull</td>\n",
       "      <td>Middlesbrough</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>387</th>\n",
       "      <td>Southampton</td>\n",
       "      <td>Crystal Palace</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>388</th>\n",
       "      <td>Liverpool</td>\n",
       "      <td>Bournemouth</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389</th>\n",
       "      <td>Chelsea</td>\n",
       "      <td>Man City</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        HomeTeam        AwayTeam  Result_knn  Result_XGB  Result_logreg\n",
       "380      Watford       West Brom           1           1              1\n",
       "381      Burnley           Stoke           1           1              1\n",
       "382    Leicester      Sunderland           1           1              1\n",
       "383   Man United         Everton           1           1              1\n",
       "384      Swansea       Tottenham          -1          -1             -1\n",
       "385      Arsenal        West Ham           1           1              1\n",
       "386         Hull   Middlesbrough           1           1             -1\n",
       "387  Southampton  Crystal Palace          -1          -1             -1\n",
       "388    Liverpool     Bournemouth           1           1              1\n",
       "389      Chelsea        Man City           1           1              1"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "this_week"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def transformResultBack(row,col_name):\n",
    "    if(row[col_name] == 1):\n",
    "        return 'H'\n",
    "    elif(row[col_name] == -1):\n",
    "        return 'A'\n",
    "    else:\n",
    "        return 'D'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "this_week[\"Res_knn\"] = this_week.apply(lambda row: transformResultBack(row,\"Result_knn\"),axis=1)\n",
    "this_week[\"Res_XGB\"] = this_week.apply(lambda row: transformResultBack(row,\"Result_XGB\"),axis=1)\n",
    "this_week[\"Res_logreg\"] = this_week.apply(lambda row: transformResultBack(row,\"Result_logreg\"),axis=1)\n",
    "\n",
    "this_week.drop([\"Result_knn\", \"Result_XGB\",\"Result_logreg\"],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.75 0.7 0.8\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>HomeTeam</th>\n",
       "      <th>AwayTeam</th>\n",
       "      <th>Res_knn</th>\n",
       "      <th>Res_XGB</th>\n",
       "      <th>Res_logreg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>380</th>\n",
       "      <td>Watford</td>\n",
       "      <td>West Brom</td>\n",
       "      <td>H</td>\n",
       "      <td>H</td>\n",
       "      <td>H</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>381</th>\n",
       "      <td>Burnley</td>\n",
       "      <td>Stoke</td>\n",
       "      <td>H</td>\n",
       "      <td>H</td>\n",
       "      <td>H</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>382</th>\n",
       "      <td>Leicester</td>\n",
       "      <td>Sunderland</td>\n",
       "      <td>H</td>\n",
       "      <td>H</td>\n",
       "      <td>H</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>383</th>\n",
       "      <td>Man United</td>\n",
       "      <td>Everton</td>\n",
       "      <td>H</td>\n",
       "      <td>H</td>\n",
       "      <td>H</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>384</th>\n",
       "      <td>Swansea</td>\n",
       "      <td>Tottenham</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>385</th>\n",
       "      <td>Arsenal</td>\n",
       "      <td>West Ham</td>\n",
       "      <td>H</td>\n",
       "      <td>H</td>\n",
       "      <td>H</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>386</th>\n",
       "      <td>Hull</td>\n",
       "      <td>Middlesbrough</td>\n",
       "      <td>H</td>\n",
       "      <td>H</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>387</th>\n",
       "      <td>Southampton</td>\n",
       "      <td>Crystal Palace</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>388</th>\n",
       "      <td>Liverpool</td>\n",
       "      <td>Bournemouth</td>\n",
       "      <td>H</td>\n",
       "      <td>H</td>\n",
       "      <td>H</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389</th>\n",
       "      <td>Chelsea</td>\n",
       "      <td>Man City</td>\n",
       "      <td>H</td>\n",
       "      <td>H</td>\n",
       "      <td>H</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        HomeTeam        AwayTeam Res_knn Res_XGB Res_logreg\n",
       "380      Watford       West Brom       H       H          H\n",
       "381      Burnley           Stoke       H       H          H\n",
       "382    Leicester      Sunderland       H       H          H\n",
       "383   Man United         Everton       H       H          H\n",
       "384      Swansea       Tottenham       A       A          A\n",
       "385      Arsenal        West Ham       H       H          H\n",
       "386         Hull   Middlesbrough       H       H          A\n",
       "387  Southampton  Crystal Palace       A       A          A\n",
       "388    Liverpool     Bournemouth       H       H          H\n",
       "389      Chelsea        Man City       H       H          H"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print (max_knn_n, max_XGB_e, max_logreg_c)\n",
    "this_week"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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