muneebalam/scrapenhl2

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "The purpose of this script is to count penalties by zone for each team."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from pylab import *\n",
    "%matplotlib inline\n",
    "\n",
    "from scrapenhl2.scrape import team_info, schedules, autoupdate, teams\n",
    "import scrapenhl2.manipulate.manipulate as manip\n",
    "import scrapenhl2.manipulate.add_onice_players as onice\n",
    "# autoupdate.autoupdate()  # uncomment this to update data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can repurpose some code targeted at faceoffs for penalties. We'll have to also create a new category for penalties without a zone (because of human error, or because it doesn't apply). We'll also limit the search to counts of 5v5 penalties."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>Team2</th>\n",
       "      <th>Team</th>\n",
       "      <th>Zone</th>\n",
       "      <th>PDraw</th>\n",
       "      <th>PTake</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>D</td>\n",
       "      <td>26.0</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td>4.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>O</td>\n",
       "      <td>23.0</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>Zone N/A</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>D</td>\n",
       "      <td>25.0</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Team2  Team      Zone  PDraw  PTake\n",
       "0         1         D   26.0   31.0\n",
       "1         1         N    4.0   10.0\n",
       "2         1         O   23.0   20.0\n",
       "3         1  Zone N/A    4.0    4.0\n",
       "0         2         D   25.0   29.0"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dflst = []\n",
    "season = 2017\n",
    "for team in schedules.get_teams_in_season(season):\n",
    "    pbp = teams.get_team_pbp(season, team)\n",
    "\n",
    "    directions = manip.get_directions_for_xy_for_season(season, team)\n",
    "    penalties = manip.filter_for_five_on_five(manip.filter_for_event_types(pbp, 'Penalty'))\n",
    "    penalties = penalties[['Game', 'Team', 'Time', 'X', 'Y']].assign(Season=season)\n",
    "    pbp_zones = manip.infer_zones_for_events(penalties, directions, 'X', 'Y', 'Time', focus_team=team,\n",
    "                                             faceoffs=False) \\\n",
    "        .rename(columns={'EventLoc': 'Zone'})\n",
    "    zone_counts = pbp_zones.assign(Team2 = pbp_zones.Team.apply(lambda x: 'PTake' if x == team else 'PDraw'),\n",
    "                                  Team=team) \\\n",
    "        .drop({'Game', 'Time', 'X', 'Y', 'Season'}, axis=1) \\\n",
    "        .assign(Count=1) \\\n",
    "        .groupby(['Team', 'Zone', 'Team2'], as_index='False') \\\n",
    "        .count() \\\n",
    "        .pivot_table(index=['Team', 'Zone'], columns='Team2', values='Count') \\\n",
    "        .reset_index() \\\n",
    "        .fillna(0)\n",
    "    dflst.append(zone_counts)\n",
    "df = pd.concat(dflst)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>Team2</th>\n",
       "      <th>Team</th>\n",
       "      <th>Zone</th>\n",
       "      <th>PDraw</th>\n",
       "      <th>PTake</th>\n",
       "      <th>DrawX</th>\n",
       "      <th>TakeX</th>\n",
       "      <th>BottomDraw</th>\n",
       "      <th>BottomTake</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>D</td>\n",
       "      <td>26.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td>4.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>O</td>\n",
       "      <td>23.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>41.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>Zone N/A</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>61.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>D</td>\n",
       "      <td>25.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Team2  Team      Zone  PDraw  PTake  DrawX  TakeX  BottomDraw  BottomTake\n",
       "0         1         D   26.0   31.0    9.0   18.0         0.0         0.0\n",
       "1         1         N    4.0   10.0    9.0   18.0        26.0        31.0\n",
       "2         1         O   23.0   20.0    9.0   18.0        30.0        41.0\n",
       "3         1  Zone N/A    4.0    4.0    9.0   18.0        53.0        61.0\n",
       "4         2         D   25.0   29.0   14.0   12.0         0.0         0.0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get X coords for each graph\n",
    "xs = df[['Team', 'PDraw', 'PTake']].drop_duplicates() \\\n",
    "    .groupby('Team', as_index=False) \\\n",
    "    .sum()\n",
    "ranks = xs.drop('Team', axis=1) \\\n",
    "    .rank(method='first') \\\n",
    "    .rename(columns={'PDraw': 'DrawX', 'PTake': 'TakeX'})\n",
    "xs = pd.concat([xs, ranks], axis=1)\n",
    "xs.loc[:, 'TeamName'] = xs.Team.apply(lambda x: team_info.team_as_str(x))\n",
    "\n",
    "joined = df.merge(xs[['Team', 'DrawX', 'TakeX']], how='inner', on='Team')\n",
    "\n",
    "# Add bottoms for stacked bars\n",
    "joined = joined.sort_values(['Team', 'Zone'])\n",
    "joined.loc[:, 'BottomDraw'] = joined[['PDraw', 'Team']].groupby('Team').cumsum().PDraw\n",
    "joined.loc[:, 'BottomDraw'] = joined.BottomDraw - joined.PDraw\n",
    "joined.loc[:, 'BottomTake'] = joined[['PTake', 'Team']].groupby('Team').cumsum().PTake\n",
    "joined.loc[:, 'BottomTake'] = joined.BottomTake - joined.PTake\n",
    "\n",
    "joined = joined.fillna(0)\n",
    "joined.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x115265f60>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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eTCnd226ZlFICUpv1D4uIeyLinqeeemrwNZUkSRqGuslMbQG8OSIeBb4JbBsR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\nAABAwTdWIPOEnmHeYAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114d77da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = figure(figsize=(10, 8))\n",
    "\n",
    "ptype = 'Take'\n",
    "for zone in ['D', 'N', 'O', 'Zone N/A']:\n",
    "    temp = joined.query('Zone == \"{0:s}\"'.format(zone))\n",
    "    col1 = ptype + 'X'\n",
    "    col2 = 'P' + str(ptype)\n",
    "    col3 = 'Bottom' + str(ptype)\n",
    "    bar(left=temp[col1].values, height=temp[col2].values, bottom=temp[col3].values, label=zone)\n",
    "title('5v5 {0:s}n penalties by team and zone in {1:d}-{2:s}'.format(ptype.lower(), season, str(season+1)[2:]))\n",
    "\n",
    "xs = xs.sort_values(ptype + 'X')\n",
    "xcol = ptype + 'X'\n",
    "\n",
    "xticks(xs[xcol].values, xs.TeamName.values, rotation=45)\n",
    "xlim(xs[xcol].min() - 1, xs[xcol].max() + 1)\n",
    "legend(loc=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1163c3b00>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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kSZJqMJmSJEmqwWRKkiSpBpMpSZKkGkymJEmSajCZkiRJqsFkSpIkqQaTKUmS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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x115d68f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = figure(figsize=(10, 8))\n",
    "\n",
    "ptype = 'Draw'\n",
    "for zone in ['D', 'N', 'O', 'Zone N/A']:\n",
    "    temp = joined.query('Zone == \"{0:s}\"'.format(zone))\n",
    "    col1 = ptype + 'X'\n",
    "    col2 = 'P' + str(ptype)\n",
    "    col3 = 'Bottom' + str(ptype)\n",
    "    bar(left=temp[col1].values, height=temp[col2].values, bottom=temp[col3].values, label=zone)\n",
    "title('5v5 {0:s}n penalties by team and zone in {1:d}-{2:s}'.format(ptype.lower(), season, str(season+1)[2:]))\n",
    "\n",
    "xs = xs.sort_values(ptype + 'X')\n",
    "xcol = ptype + 'X'\n",
    "\n",
    "xticks(xs[xcol].values, xs.TeamName.values, rotation=45)\n",
    "xlim(xs[xcol].min() - 1, xs[xcol].max() + 1)\n",
    "legend(loc=2)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}