{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.rc_context at 0x1092c3898>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pylab import *\n",
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "from scipy.stats import linregress\n",
    "xkcd()"
   ]
  },
  {
   "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></th>\n",
       "      <th>GD</th>\n",
       "      <th>Pts</th>\n",
       "      <th>Team</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10</td>\n",
       "      <td>55</td>\n",
       "      <td>WSH</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6</td>\n",
       "      <td>52</td>\n",
       "      <td>NJD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13</td>\n",
       "      <td>49</td>\n",
       "      <td>NYR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-3</td>\n",
       "      <td>49</td>\n",
       "      <td>CBJ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-11</td>\n",
       "      <td>46</td>\n",
       "      <td>CAR</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   GD  Pts Team\n",
       "0  10   55  WSH\n",
       "1   6   52  NJD\n",
       "2  13   49  NYR\n",
       "3  -3   49  CBJ\n",
       "4 -11   46  CAR"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Manually copy data from NHL.com\n",
    "teams = ['WSH', 'NJD', 'NYR', 'CBJ', 'CAR', 'PHI', 'NYI', 'PIT',\n",
    "        'TBL', 'BOS', 'TOR', 'DET', 'FLA', 'MTL', 'OTT', 'BUF',\n",
    "        'WPG', 'STL', 'NSH', 'DAL', 'COL', 'MIN', 'CHI',\n",
    "        'VGK', 'LAK', 'SJS', 'ANA', 'CGY', 'EDM', 'VAN', 'ARI']\n",
    "pts = [55, 52, 49, 49, 46, 46, 46, 45,\n",
    "      61, 52, 52, 41, 39, 38, 37, 29,\n",
    "      57, 55, 54, 51, 47, 47, 46,\n",
    "      58, 53, 48, 47, 46, 39, 38, 26]\n",
    "gd = [12, 5, 12, -2, -11, 1, 12, 13,\n",
    "     49, 30, 17, -12, -19, -24, -27, -51,\n",
    "     27, 15, 16, 14, 11, 0, 11,\n",
    "     29, 27, 4, -3, -4, -23, -27, -52]\n",
    "sogd = [2, -1, -1, 1, 0, -3, 2, 1,\n",
    "       0, 0, 3, 2, 0, 1, -4, -1,\n",
    "       0, 3, -1, 2, 0, 1, -1,\n",
    "       0, 2, 0, -3, -1, 0, -3, -1]\n",
    "\n",
    "df = pd.DataFrame({'Team': teams, 'GD': gd, 'Pts': pts, 'SO': sogd})\n",
    "\n",
    "# subtract out shootout goal differential\n",
    "df = df.assign(GD = df.GD - df.SO).drop('SO', axis=1)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Annotation at 0x116858860>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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qqlJKKVVVVaXGjh2rALVz506llFInTpxQb731lkHvi5sqKirUvHnz1MSJE9WGDRt+txuo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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11367a9b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f = figure(figsize=[6, 6])\n",
    "\n",
    "scatter(df.GD, df.Pts, s=1, alpha=0)\n",
    "for i, diff, p, team in df.itertuples():\n",
    "    if team == 'WSH':\n",
    "        annotate(team, xy=(diff, p), fontsize=12, color='r')\n",
    "    else:\n",
    "        annotate(team, xy=(diff, p), fontsize=8, alpha=0.5)\n",
    "\n",
    "\n",
    "m, b, r, p, e = linregress(df.GD, df.Pts)\n",
    "xs = arange(-100, 100, 1)\n",
    "ys = m * xs + b\n",
    "\n",
    "xlimits = xlim()\n",
    "ylimits = ylim()\n",
    "\n",
    "plot(xs, ys, color='k', ls='--')\n",
    "xlim(-20, 50)\n",
    "xlabel('Goal differential')\n",
    "ylabel('Points')\n",
    "\n",
    "for direc in ['top', 'right', 'left', 'bottom']:\n",
    "    gca().spines[direc].set_visible(False)\n",
    "    \n",
    "annotate('Outperforming\\ngoal diff', xy=(-10, 70))\n",
    "annotate('Underperforming\\ngoal diff', xy=(40, 30))"
   ]
  }
 ],
 "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": 2
}