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  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "An exploratory statistical analysis of the 2014 World Cup Final"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "As a fan of both data and soccer - from here on out referred to as football - I find the football fan's attitude toward statistics and data analysis perplexing, although understandable due to years and years of simple stats being the only thing that the media focuses on. Football is a complex team sport with deep interactions, therefore counting events (goals, assists, tackles, etc.) isn't enough.\n",
      "\n",
      "This notebook shows how you can use play-by-play data to analyse a football match, showing custom measures and visualizations to better understand the sport.\n",
      "\n",
      "Disclaimer: I'm a fan, not an expert. [Germany's National Team](http://www.telegraph.co.uk/technology/news/10959864/Germanys-World-Cup-tactics-shaped-by-data.html) and [Manchester City](http://www.wired.co.uk/magazine/archive/2014/01/features/the-winning-formula) have whole teams dedicated to data analysis, and the state of the art is quite above what is being shown here. However, rarely does that analysis is made public, so I hope this is useful (or at least entertaining). I hope to keep playing with the data and share useful insights in the future. Feel free to star the GitHub repository, or drop me at email at rjtavares@gmail.com.\n",
      "\n",
      "###A note on the data used\n",
      "\n",
      "This play-by-play data was gathered from a public website, and I have no guarantee that it is consistent or correct. The process used to gather will be the theme of its own post, so stay tuned. On the other hand, all calculations based on the raw data are included in this notebook, and should be questioned. I would love to get some feedback. "
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Some preprocessing"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Module imports and data preparation. You can ignore this section unless you want to play with the data yourself."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pandas as pd\n",
      "import numpy as np\n",
      "import matplotlib.pyplot as plt\n",
      "\n",
      "from footyscripts.footyviz import draw_events, draw_pitch, type_names\n",
      "\n",
      "#plotting settings\n",
      "%matplotlib inline\n",
      "pd.options.display.mpl_style = 'default'"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df = pd.read_csv(\"../datasets/germany-vs-argentina-731830.csv\", encoding='utf-8', index_col=0)\n",
      "\n",
      "#standard dimensions\n",
      "x_size = 105.0\n",
      "y_size = 68.0\n",
      "box_height = 16.5*2 + 7.32\n",
      "box_width = 16.5\n",
      "y_box_start = y_size/2-box_height/2\n",
      "y_box_end = y_size/2+box_height/2\n",
      "\n",
      "#scale of dataset is 100 by 100. Normalizing for a standard soccer pitch size\n",
      "df['x']=df['x']/100*x_size \n",
      "df['y']=df['y']/100*y_size\n",
      "df['to_x']=df['to_x']/100*x_size\n",
      "df['to_y']=df['to_y']/100*y_size\n",
      "\n",
      "#creating some measures and classifiers from the original \n",
      "df['count'] = 1\n",
      "df['dx'] = df['to_x'] - df['x']\n",
      "df['dy'] = df['to_y'] - df['y']\n",
      "df['distance'] = np.sqrt(df['dx']**2+df['dy']**2)\n",
      "df['fivemin'] = np.floor(df['min']/5)*5\n",
      "df['type_name'] = df['type'].map(type_names.get)\n",
      "df['to_box'] = (df['to_x'] > x_size - box_width) & (y_box_start < df['to_y']) & (df['to_y'] < y_box_end)\n",
      "df['from_box'] = (df['x'] > x_size - box_width) & (y_box_start < df['y']) & (df['y'] < y_box_end)\n",
      "df['on_offense'] = df['x']>x_size/2"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#preslicing of the main DataFrame in smaller DFs that will be reused along the notebook\n",
      "dfPeriod1 = df[df['period']==1]\n",
      "dfP1Shots = dfPeriod1[dfPeriod1['type'].isin([13, 14, 15, 16])]\n",
      "dfPeriod2 = df[df['period']==2]\n",
      "dfP2Shots = dfPeriod2[dfPeriod2['type'].isin([13, 14, 15, 16])]\n",
      "dfExtraTime = df[df['period']>2]\n",
      "dfETShots = dfExtraTime[dfExtraTime['type'].isin([13, 14, 15, 16])]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "The first half"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Let's get a quick profile of the first half. The chart below shows where in the field most events took place (positive numbers correspond to Germany's offensive half, negative numbers to its defensive half), with each team's shots pointed out."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig = plt.figure(figsize=(12,4))\n",
      "\n",
      "avg_x = (dfPeriod1[dfPeriod1['team_name']=='Germany'].groupby('min').apply(np.mean)['x'] - \n",
      "         dfPeriod1[dfPeriod1['team_name']=='Argentina'].groupby('min').apply(np.mean)['x'])\n",
      "\n",
      "plt.stackplot(list(avg_x.index.values), list([x if x>0 else 0 for x in avg_x]))\n",
      "plt.stackplot(list(avg_x.index.values), list([x if x<0 else 0 for x in avg_x]))\n",
      "\n",
      "for i, shot in dfP1Shots.iterrows():\n",
      "    x = shot['min']\n",
      "    y = avg_x.ix[shot['min']]\n",
      "    signal = 1 if shot['team_name']=='Germany' else -1\n",
      "    plt.annotate(s=(shot['type_name']+' ('+shot['team_name'][0]+\")\"), xy=(x, y), xytext=(x-5,y+30*signal), arrowprops=dict(facecolor='black'))\n",
      "\n",
      "plt.gca().set_xlabel('minute')\n",
      "plt.title(\"First Half Profile\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "<matplotlib.text.Text at 0xc692c18>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
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E5Obm4uqrr8aECRMwatQovPPOO+A4DpMmTUK/fv0cv7Np0yYYjUZIJBJMnTrV7eNmZGS4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\n",
       "text": [
        "<matplotlib.figure.Figure at 0xbbb9550>"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The first 45' of the final were incredibly interesting. Germany dominated possession and pressured high, forcing Argentina to play in its own half. That is obvious once we look at Argentina's passes during the first half:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "draw_pitch()\n",
      "draw_events(dfPeriod1[(dfPeriod1['type']==1) & (dfPeriod1['outcome']==1) & (dfPeriod1['team_name']=='Argentina')], mirror_away=True)\n",
      "plt.text(x_size/4, -3, \"Germany's defense\", color='black', bbox=dict(facecolor='white', alpha=0.5), horizontalalignment='center')\n",
      "plt.text(x_size*3/4, -3, \"Argentina's defense\", color='black', bbox=dict(facecolor='white', alpha=0.5), horizontalalignment='center')\n",
      "plt.title(\"Argentina's passes during the first half\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "<matplotlib.text.Text at 0xc6be8d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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mkpkOR1VJFVv/7yxRfXwIaeNufF6vk9j03Wni9mZh52SJXieZHUp0cLM26Qm9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\nCBYmAixBEARBEAQL+3/zYOPk/DdSoQAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0xbbb7da0>"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dfPeriod1.groupby('team_name').agg({'x': np.mean, 'on_offense': np.mean})"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>x</th>\n",
        "      <th>on_offense</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>team_name</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>Argentina</th>\n",
        "      <td> 38.320645</td>\n",
        "      <td> 0.276498</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Germany</th>\n",
        "      <td> 55.838736</td>\n",
        "      <td> 0.610984</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>2 rows \u00d7 2 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "                   x  on_offense\n",
        "team_name                       \n",
        "Argentina  38.320645    0.276498\n",
        "Germany    55.838736    0.610984\n",
        "\n",
        "[2 rows x 2 columns]"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dfPeriod1[dfPeriod1['type']==1].groupby('team_name').agg({'outcome': np.mean})"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>outcome</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>team_name</th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>Argentina</th>\n",
        "      <td> 0.694805</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Germany</th>\n",
        "      <td> 0.835777</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>2 rows \u00d7 1 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "            outcome\n",
        "team_name          \n",
        "Argentina  0.694805\n",
        "Germany    0.835777\n",
        "\n",
        "[2 rows x 1 columns]"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Only 28% of Argentina's passes were made on its offensive half, versus 61% for Germany. Despite playing in the offensive half, Germany managed to get a much higher passing accuracy, a testament to its amazing midfield.\n",
      "\n",
      "However, that superiority didn't manifest itself in chances and shots. In fact, Germany had quite a difficult time trying to get inside Argentina's penalty box."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "draw_pitch()\n",
      "draw_events(df[(df['to_box']==True) & (df['type']==1) & (df['from_box']==False) & (df['period']==1) & (df['outcome']==1)], mirror_away=True)\n",
      "draw_events(df[(df['to_box']==True) & (df['type']==1) & (df['from_box']==False) & (df['period']==1) & (df['outcome']==0)], mirror_away=True, alpha=0.2)\n",
      "draw_events(dfP1Shots, mirror_away=True, base_color='#a93e3e')\n",
      "plt.text(x_size/4, -3, \"Germany's defense\", color='black', bbox=dict(facecolor='white', alpha=0.5), horizontalalignment='center')\n",
      "plt.text(x_size*3/4, -3, \"Argentina's defense\", color='black', bbox=dict(facecolor='white', alpha=0.5), horizontalalignment='center')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "<matplotlib.text.Text at 0xccdf630>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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cRCE5iKzVG/ZQSMytLBmWZhtYmm5PY9JmEFSDFb+A5XsCqt7+kMNiOEVLyE7u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\n",
       "text": [
        "<matplotlib.figure.Figure at 0xccc0a20>"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dfPeriod1[(dfPeriod1['to_box']==True) & (dfPeriod1['from_box']==False) & (df['type']==1)].groupby(['team_name']).agg({'outcome': np.mean,\n",
      "                                                                                                                      'count': np.sum})"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>count</th>\n",
        "      <th>outcome</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>team_name</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>Argentina</th>\n",
        "      <td>  6</td>\n",
        "      <td> 0.166667</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Germany</th>\n",
        "      <td> 16</td>\n",
        "      <td> 0.187500</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>2 rows \u00d7 2 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "           count   outcome\n",
        "team_name                 \n",
        "Argentina      6  0.166667\n",
        "Germany       16  0.187500\n",
        "\n",
        "[2 rows x 2 columns]"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Out of 16 German atempts to get in the box, only one resulted in a shot: a late corner, with Howedes hitting the post."
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "The curious case of Christoph Kramer"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Kramer suffered an injury on the 19th minute, but was only subsituted 12 minutes later. This included Germany's worst period in the first half, as the first half profile chart above shows.\n",
      "\n",
      "[Reports say that he acted confused](http://www.theguardian.com/football/2014/jul/17/christoph-kramer-germany-concussion-world-cup-final-2014), and data shows that Kramer was largely \"absent\" in the period between the injury and the subsitutions: his only actions were one succesful reception and pass, and one loss of possesion."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dfKramer = df[df['player_name']=='Christoph Kramer']\n",
      "pd.pivot_table(dfKramer, values='count', rows='type_name', cols='min', aggfunc=sum, fill_value=0)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th>min</th>\n",
        "      <th>0</th>\n",
        "      <th>1</th>\n",
        "      <th>4</th>\n",
        "      <th>6</th>\n",
        "      <th>7</th>\n",
        "      <th>8</th>\n",
        "      <th>11</th>\n",
        "      <th>19</th>\n",
        "      <th>24</th>\n",
        "      <th>25</th>\n",
        "      <th>31</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>type_name</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>CLEAR BALL (OUT OF PITCH)</th>\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</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>LINEUP</th>\n",
        "      <td> 1</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</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>LOST CONTROL OF BALL</th>\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</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>PASS</th>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 2</td>\n",
        "      <td> 1</td>\n",
        "      <td> 2</td>\n",
        "      <td> 1</td>\n",
        "      <td> 2</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>RECEPTION</th>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 2</td>\n",
        "      <td> 1</td>\n",
        "      <td> 2</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>RUN WITH BALL</th>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>SUBSTITUTION (OFF)</th>\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</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>TACKLE</th>\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> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>8 rows \u00d7 11 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 10,
       "text": [
        "min                        0   1   4   6   7   8   11  19  24  25  31\n",
        "type_name                                                            \n",
        "CLEAR BALL (OUT OF PITCH)   0   0   0   0   0   0   0   1   0   0   0\n",
        "LINEUP                      1   0   0   0   0   0   0   0   0   0   0\n",
        "LOST CONTROL OF BALL        0   0   0   0   0   0   0   0   0   1   0\n",
        "PASS                        0   1   2   1   2   1   2   0   1   0   0\n",
        "RECEPTION                   0   1   2   1   2   1   1   0   1   0   0\n",
        "RUN WITH BALL               0   1   0   0   0   1   1   0   0   0   0\n",
        "SUBSTITUTION (OFF)          0   0   0   0   0   0   0   0   0   0   1\n",
        "TACKLE                      0   0   0   0   0   0   1   0   0   0   0\n",
        "\n",
        "[8 rows x 11 columns]"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dfKramer['action']=dfKramer['outcome'].map(str) + '-' + dfKramer['type_name']\n",
      "dfKramer['action'].unique()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 11,
       "text": [
        "array(['1-LINEUP', '1-RUN WITH BALL', '1-RECEPTION', '1-PASS', '0-PASS',\n",
        "       '0-TACKLE', '1-CLEAR BALL (OUT OF PITCH)', '0-LOST CONTROL OF BALL',\n",
        "       '1-SUBSTITUTION (OFF)'], dtype=object)"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "score = {'1-LINEUP': 0, '1-RUN WITH BALL': 0.5, '1-RECEPTION': 0, '1-PASS': 1, '0-PASS': -1,\n",
      "       '0-TACKLE (NO CONTROL)': 0, '1-CLEAR BALL (OUT OF PITCH)': 0.5,\n",
      "       '0-LOST CONTROL OF BALL': -1, '1-SUBSTITUTION (OFF)': 0}\n",
      "\n",
      "dfKramer['score'] = dfKramer['action'].map(score.get)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dfKramer.groupby('min')['score'].sum().reindex(range(32), fill_value=0).plot(kind='bar')\n",
      "plt.annotate('Injury', (19,0.5), (14,1.1), arrowprops=dict(facecolor='black'))\n",
      "plt.annotate('Substitution', (31,0), (22,1.6), arrowprops=dict(facecolor='black'))\n",
      "plt.gca().set_xlabel('minute')\n",
      "plt.gca().set_ylabel('no. events')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 13,
       "text": [
        "<matplotlib.text.Text at 0xcfe63c8>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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JQQ/rlmrE1hLXlb07thwcjzL1lGozOjpaUnb3cFbmHIiIDKznvR3q6upUi2X4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\nRK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0xccc0438>"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "The second half"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The second half was much more balanced. We reproduce the same charts as the first half, which confirm this perception."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig = plt.figure(figsize=(12,4))\n",
      "\n",
      "avg_x = (dfPeriod2[dfPeriod2['team_name']=='Germany'].groupby('min').apply(np.mean)['x'] - \n",
      "         dfPeriod2[dfPeriod2['team_name']=='Argentina'].groupby('min').apply(np.mean)['x'])\n",
      "\n",
      "plt.stackplot(list(avg_x.index.values), list([x if x>0 else 0 for x in avg_x]))\n",
      "plt.stackplot(list(avg_x.index.values), list([x if x<0 else 0 for x in avg_x]))\n",
      "\n",
      "for i, shot in dfP2Shots.iterrows():\n",
      "    x = shot['min']\n",
      "    y = avg_x.ix[shot['min']]\n",
      "    signal = 1 if shot['team_name']=='Germany' else -1\n",
      "    plt.annotate(s=(shot['type_name']+' ('+shot['team_name'][0]+\")\"), xy=(x, y), xytext=(x-5,y+30*signal), arrowprops=dict(facecolor='black'))\n",
      "\n",
      "plt.gca().set_xlabel('minute')\n",
      "plt.title(\"Second Half Profile\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 14,
       "text": [
        "<matplotlib.text.Text at 0xd4b01d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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6euzYsQNqtRqrVq3CbbfdJvi8WiwWfPnll3j66ad99mfv3r2YP39+Fwm3SKE2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\nCCGEEEJICP5/MpxT3ONpW9gAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0xd3ea400>"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dfPeriod2.groupby('team_name').agg({'x': np.mean, 'on_offense': np.mean})"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>x</th>\n",
        "      <th>on_offense</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>team_name</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>Argentina</th>\n",
        "      <td> 48.916711</td>\n",
        "      <td> 0.415625</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Germany</th>\n",
        "      <td> 54.551298</td>\n",
        "      <td> 0.598582</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>2 rows \u00d7 2 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 15,
       "text": [
        "                   x  on_offense\n",
        "team_name                       \n",
        "Argentina  48.916711    0.415625\n",
        "Germany    54.551298    0.598582\n",
        "\n",
        "[2 rows x 2 columns]"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dfPeriod2[dfPeriod2['type']==1].groupby('team_name').agg({'outcome': np.mean})"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>outcome</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>team_name</th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>Argentina</th>\n",
        "      <td> 0.817427</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Germany</th>\n",
        "      <td> 0.839695</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>2 rows \u00d7 1 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 16,
       "text": [
        "            outcome\n",
        "team_name          \n",
        "Argentina  0.817427\n",
        "Germany    0.839695\n",
        "\n",
        "[2 rows x 1 columns]"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "draw_pitch()\n",
      "draw_events(df[(df['to_box']==True) & (df['type']==1) & (df['from_box']==False) & (df['period']==2) & (df['outcome']==1)], mirror_away=True)\n",
      "draw_events(df[(df['to_box']==True) & (df['type']==1) & (df['from_box']==False) & (df['period']==2) & (df['outcome']==0)], mirror_away=True, alpha=0.2)\n",
      "draw_events(dfP2Shots, mirror_away=True, base_color='#a93e3e')\n",
      "plt.text(x_size/4, -3, \"Germany's defense\", color='black', bbox=dict(facecolor='white', alpha=0.5), horizontalalignment='center')\n",
      "plt.text(x_size*3/4, -3, \"Argentina's defense\", color='black', bbox=dict(facecolor='white', alpha=0.5), horizontalalignment='center')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 17,
       "text": [
        "<matplotlib.text.Text at 0xd25df28>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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CYl8a8UaOTnxzkBnVcOdXX8Jf3X7BjD2tbItBSRlLYr9BU7eQSR4u94HKfYRUhKqmADTF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6awdU9b3eDqlR+ex8e/GJ//2mIV4PTYrgYq1gVBzPVfe7s9tb9b+H1PLdhxum2wO62my8G13PB+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\nZAywiIiIiJrs/wMji1dNZZpDkQAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0xd4cf5f8>"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Even though Germany had much more success getting inside the box on the second half, only only pass resulted in a German shot from inside the box."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dfPeriod2[(dfPeriod2['to_box']==True) & (dfPeriod2['from_box']==False) & (df['type']==1)].groupby(['team_name']).agg({'outcome': np.mean,\n",
      "                                                                                                                      'count': np.sum})"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>count</th>\n",
        "      <th>outcome</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>team_name</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>Argentina</th>\n",
        "      <td> 12</td>\n",
        "      <td> 0.166667</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Germany</th>\n",
        "      <td> 16</td>\n",
        "      <td> 0.375000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>2 rows \u00d7 2 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 18,
       "text": [
        "           count   outcome\n",
        "team_name                 \n",
        "Argentina     12  0.166667\n",
        "Germany       16  0.375000\n",
        "\n",
        "[2 rows x 2 columns]"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "The extra time"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig = plt.figure(figsize=(12,4))\n",
      "\n",
      "avg_x = (dfExtraTime[dfExtraTime['team_name']=='Germany'].groupby('min').apply(np.mean)['x'] - \n",
      "         dfExtraTime[dfExtraTime['team_name']=='Argentina'].groupby('min').apply(np.mean)['x'].reindex(dfExtraTime['min'].unique(), fill_value=0))\n",
      "\n",
      "plt.stackplot(list(avg_x.index.values), list([x if x>0 else 0 for x in avg_x]))\n",
      "plt.stackplot(list(avg_x.index.values), list([x if x<0 else 0 for x in avg_x]))\n",
      "\n",
      "for i, shot in dfETShots.iterrows():\n",
      "    x = shot['min']\n",
      "    y = avg_x.ix[shot['min']]\n",
      "    signal = 1 if shot['team_name']=='Germany' else -1\n",
      "    plt.annotate(s=(shot['type_name']+' ('+shot['team_name'][0]+\")\"), xy=(x, y), xytext=(x-5,y+20*signal), arrowprops=dict(facecolor='black'))\n",
      "\n",
      "plt.gca().set_xlabel('minute')\n",
      "plt.title(\"Extra Time Profile\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 19,
       "text": [
        "<matplotlib.text.Text at 0xdc213c8>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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v9r9EZcAX5yqh9fCaLK3R7NCgnyt9L1To0dYjnJNF7M4cuwtX+t9fUf97JxpM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\np9iwYcN973N33FX/V69ehd1ux9q1a3Hy5EmkpKQMet66ujq0tPSU3Wt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sAAAD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\n/H8bOf+ZR0sO9gAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0xd286588>"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.groupby(['team_name', 'period']).agg({'count': np.sum, 'x': np.mean, 'on_offense': np.mean})"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th>count</th>\n",
        "      <th>x</th>\n",
        "      <th>on_offense</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>team_name</th>\n",
        "      <th>period</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th rowspan=\"4\" valign=\"top\">Argentina</th>\n",
        "      <th>1</th>\n",
        "      <td> 434</td>\n",
        "      <td> 38.320645</td>\n",
        "      <td> 0.276498</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 640</td>\n",
        "      <td> 48.916711</td>\n",
        "      <td> 0.415625</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> 105</td>\n",
        "      <td> 46.100000</td>\n",
        "      <td> 0.400000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> 152</td>\n",
        "      <td> 46.255263</td>\n",
        "      <td> 0.361842</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"4\" valign=\"top\">Germany</th>\n",
        "      <th>1</th>\n",
        "      <td> 874</td>\n",
        "      <td> 55.838736</td>\n",
        "      <td> 0.610984</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 705</td>\n",
        "      <td> 54.551298</td>\n",
        "      <td> 0.598582</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> 376</td>\n",
        "      <td> 60.176729</td>\n",
        "      <td> 0.659574</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> 209</td>\n",
        "      <td> 45.543876</td>\n",
        "      <td> 0.392344</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>8 rows \u00d7 3 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 20,
       "text": [
        "                  count          x  on_offense\n",
        "team_name period                              \n",
        "Argentina 1         434  38.320645    0.276498\n",
        "          2         640  48.916711    0.415625\n",
        "          3         105  46.100000    0.400000\n",
        "          4         152  46.255263    0.361842\n",
        "Germany   1         874  55.838736    0.610984\n",
        "          2         705  54.551298    0.598582\n",
        "          3         376  60.176729    0.659574\n",
        "          4         209  45.543876    0.392344\n",
        "\n",
        "[8 rows x 3 columns]"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We can see that Germany's 4th period was quite different from the rest of the match. Germany played a lot less high on the field, and decreased the number of passes significantly: showing that it tried to control the game, slow it down, dropping the defensive line. This is even more evident if we look at the time interval after the goal:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "goal_ix = df[df['type']==16].index[0]\n",
      "df.ix[goal_ix+1:].groupby(['team_name', 'period']).agg({'count': np.sum, 'x': np.mean, 'on_offense': np.mean})"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th>count</th>\n",
        "      <th>x</th>\n",
        "      <th>on_offense</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>team_name</th>\n",
        "      <th>period</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>Argentina</th>\n",
        "      <th>4</th>\n",
        "      <td> 90</td>\n",
        "      <td> 52.444000</td>\n",
        "      <td> 0.433333</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Germany</th>\n",
        "      <th>4</th>\n",
        "      <td> 64</td>\n",
        "      <td> 35.009297</td>\n",
        "      <td> 0.343750</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>2 rows \u00d7 3 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 21,
       "text": [
        "                  count          x  on_offense\n",
        "team_name period                              \n",
        "Argentina 4          90  52.444000    0.433333\n",
        "Germany   4          64  35.009297    0.343750\n",
        "\n",
        "[2 rows x 3 columns]"
       ]
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "draw_pitch()\n",
      "draw_events(df.ix[goal_ix+1:][(df['to_box']==True) & (df['type']==1) & (df['from_box']==False) & (df['outcome']==1)], mirror_away=True)\n",
      "draw_events(df.ix[goal_ix+1:][(df['to_box']==True) & (df['type']==1) & (df['from_box']==False) & (df['outcome']==0)], mirror_away=True, alpha=0.2)\n",
      "draw_events(df.ix[goal_ix+1:][df['type'].isin([13,14,15,16])], mirror_away=True, base_color='#a93e3e')\n",
      "plt.text(x_size/4, -3, \"Germany's defense\", color='black', bbox=dict(facecolor='white', alpha=0.5), horizontalalignment='center')\n",
      "plt.text(x_size*3/4, -3, \"Argentina's defense\", color='black', bbox=dict(facecolor='white', alpha=0.5), horizontalalignment='center')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "C:\\Anaconda\\lib\\site-packages\\pandas\\core\\frame.py:1663: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
        "  \"DataFrame index.\", UserWarning)\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 22,
       "text": [
        "<matplotlib.text.Text at 0xe211630>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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FKmL9FQ3YcEXjvHdCppNFtEHREmaXQe/pHTuKGt+SvAhYhmFguCsEycJGdqIT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\nAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0xd287fd0>"
       ]
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.ix[goal_ix+1:][df.ix[goal_ix+1:]['type'].isin([13,14,15,16])][['min', 'player_name', 'team_name', 'type_name']]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>min</th>\n",
        "      <th>player_name</th>\n",
        "      <th>team_name</th>\n",
        "      <th>type_name</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>3420</th>\n",
        "      <td> 122</td>\n",
        "      <td> Lionel Messi</td>\n",
        "      <td> Argentina</td>\n",
        "      <td> SHOT OFF GOAL</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3481</th>\n",
        "      <td> 127</td>\n",
        "      <td> Lionel Messi</td>\n",
        "      <td> Argentina</td>\n",
        "      <td> SHOT OFF GOAL</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>2 rows \u00d7 4 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 23,
       "text": [
        "      min   player_name  team_name      type_name\n",
        "3420  122  Lionel Messi  Argentina  SHOT OFF GOAL\n",
        "3481  127  Lionel Messi  Argentina  SHOT OFF GOAL\n",
        "\n",
        "[2 rows x 4 columns]"
       ]
      }
     ],
     "prompt_number": 23
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Germany completely gave up trying to score another goal, with only one attempt at a pass to the box. However, its defensive strategy was successful, with Argentina barely entering Germany's penalty box. Its only 2 shots came from outside the box, both by Messi, who at this point was probably feeling somewhat desperate."
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "The goal"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "goal = df[df['type']==16].index[0]\n",
      "dfGoal = df.ix[goal-30:goal]\n",
      "\n",
      "draw_pitch()\n",
      "draw_events(dfGoal[dfGoal.team_name=='Germany'], base_color='white')\n",
      "draw_events(dfGoal[dfGoal.team_name=='Argentina'], base_color='cyan')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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vLPxfZSTn679W3cMP1jNkkUVTRy5QcdY4bTqwUhv3P2860mnxB716s/YpuRyp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\niDAKFgAAQIRRsAAAACLs/wORBFBa5F65bgAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0xd281978>"
       ]
      }
     ],
     "prompt_number": 24
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Germany's players involved in the play\n",
      "dfGoal['progression']=dfGoal['to_x']-dfGoal['x']\n",
      "dfGoal[dfGoal['type'].isin([1, 101, 16])][['player_name', 'type_name', 'progression']]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>player_name</th>\n",
        "      <th>type_name</th>\n",
        "      <th>progression</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>3311</th>\n",
        "      <td>           Philipp Lahm</td>\n",
        "      <td>          PASS</td>\n",
        "      <td>  1.995</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3313</th>\n",
        "      <td> Bastian Schweinsteiger</td>\n",
        "      <td>          PASS</td>\n",
        "      <td> -1.365</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3317</th>\n",
        "      <td>           Philipp Lahm</td>\n",
        "      <td>          PASS</td>\n",
        "      <td>  0.525</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3319</th>\n",
        "      <td> Bastian Schweinsteiger</td>\n",
        "      <td> RUN WITH BALL</td>\n",
        "      <td>  0.315</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3320</th>\n",
        "      <td> Bastian Schweinsteiger</td>\n",
        "      <td>          PASS</td>\n",
        "      <td>-12.180</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3321</th>\n",
        "      <td>         Jerome Boateng</td>\n",
        "      <td> RUN WITH BALL</td>\n",
        "      <td> -0.735</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3323</th>\n",
        "      <td>         Jerome Boateng</td>\n",
        "      <td>          PASS</td>\n",
        "      <td>  2.310</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3325</th>\n",
        "      <td>           Mats Hummels</td>\n",
        "      <td> RUN WITH BALL</td>\n",
        "      <td>  0.000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3326</th>\n",
        "      <td>           Mats Hummels</td>\n",
        "      <td>          PASS</td>\n",
        "      <td> 11.340</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3327</th>\n",
        "      <td>             Toni Kroos</td>\n",
        "      <td> RUN WITH BALL</td>\n",
        "      <td>  2.940</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3329</th>\n",
        "      <td>             Toni Kroos</td>\n",
        "      <td>          PASS</td>\n",
        "      <td> 25.410</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3331</th>\n",
        "      <td>         Andr\u00e9 Sch\u00fcrrle</td>\n",
        "      <td> RUN WITH BALL</td>\n",
        "      <td>  0.105</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3332</th>\n",
        "      <td>         Andr\u00e9 Sch\u00fcrrle</td>\n",
        "      <td>          PASS</td>\n",
        "      <td>-11.760</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3334</th>\n",
        "      <td>             Toni Kroos</td>\n",
        "      <td>          PASS</td>\n",
        "      <td>  9.555</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3336</th>\n",
        "      <td>         Andr\u00e9 Sch\u00fcrrle</td>\n",
        "      <td> RUN WITH BALL</td>\n",
        "      <td> 24.675</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3337</th>\n",
        "      <td>         Andr\u00e9 Sch\u00fcrrle</td>\n",
        "      <td>          PASS</td>\n",
        "      <td>  5.460</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3339</th>\n",
        "      <td>            Mario G\u00f6tze</td>\n",
        "      <td> RUN WITH BALL</td>\n",
        "      <td>  1.995</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3340</th>\n",
        "      <td>            Mario G\u00f6tze</td>\n",
        "      <td>          GOAL</td>\n",
        "      <td>  3.780</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>18 rows \u00d7 3 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 25,
       "text": [
        "                 player_name      type_name  progression\n",
        "3311            Philipp Lahm           PASS        1.995\n",
        "3313  Bastian Schweinsteiger           PASS       -1.365\n",
        "3317            Philipp Lahm           PASS        0.525\n",
        "3319  Bastian Schweinsteiger  RUN WITH BALL        0.315\n",
        "3320  Bastian Schweinsteiger           PASS      -12.180\n",
        "3321          Jerome Boateng  RUN WITH BALL       -0.735\n",
        "3323          Jerome Boateng           PASS        2.310\n",
        "3325            Mats Hummels  RUN WITH BALL        0.000\n",
        "3326            Mats Hummels           PASS       11.340\n",
        "3327              Toni Kroos  RUN WITH BALL        2.940\n",
        "3329              Toni Kroos           PASS       25.410\n",
        "3331          Andr\u00e9 Sch\u00fcrrle  RUN WITH BALL        0.105\n",
        "3332          Andr\u00e9 Sch\u00fcrrle           PASS      -11.760\n",
        "3334              Toni Kroos           PASS        9.555\n",
        "3336          Andr\u00e9 Sch\u00fcrrle  RUN WITH BALL       24.675\n",
        "3337          Andr\u00e9 Sch\u00fcrrle           PASS        5.460\n",
        "3339             Mario G\u00f6tze  RUN WITH BALL        1.995\n",
        "3340             Mario G\u00f6tze           GOAL        3.780\n",
        "\n",
        "[18 rows x 3 columns]"
       ]
      }
     ],
     "prompt_number": 25
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Some basic stats"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#passing accuracy\n",
      "df.groupby(['player_name', 'team_name']).agg({'count': np.sum, 'outcome': np.mean}).sort('count', ascending=False)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th>count</th>\n",
        "      <th>outcome</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>player_name</th>\n",
        "      <th>team_name</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>Philipp Lahm</th>\n",
        "      <th>Germany</th>\n",
        "      <td> 296</td>\n",
        "      <td> 0.925676</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Toni Kroos</th>\n",
        "      <th>Germany</th>\n",
        "      <td> 287</td>\n",
        "      <td> 0.885017</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Bastian Schweinsteiger</th>\n",
        "      <th>Germany</th>\n",
        "      <td> 271</td>\n",
        "      <td> 0.926199</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Jerome Boateng</th>\n",
        "      <th>Germany</th>\n",
        "      <td> 211</td>\n",
        "      <td> 0.909953</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Mesut \u00d6zil</th>\n",
        "      <th>Germany</th>\n",
        "      <td> 196</td>\n",
        "      <td> 0.867347</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Javier Mascherano</th>\n",
        "      <th>Argentina</th>\n",
        "      <td> 173</td>\n",
        "      <td> 0.878613</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Andr\u00e9 Sch\u00fcrrle</th>\n",
        "      <th>Germany</th>\n",
        "      <td> 172</td>\n",
        "      <td> 0.825581</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Benedikt H\u00f6wedes</th>\n",
        "      <th>Germany</th>\n",
        "      <td> 167</td>\n",
        "      <td> 0.856287</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Mats Hummels</th>\n",
        "      <th>Germany</th>\n",
        "      <td> 165</td>\n",
        "      <td> 0.909091</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Thomas M\u00fcller</th>\n",
        "      <th>Germany</th>\n",
        "      <td> 155</td>\n",
        "      <td> 0.812903</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Lucas Biglia</th>\n",
        "      <th>Argentina</th>\n",
        "      <td> 136</td>\n",
        "      <td> 0.860294</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Ezequiel Garay</th>\n",
        "      <th>Argentina</th>\n",
        "      <td> 131</td>\n",
        "      <td> 0.893130</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Mart\u00edn Demichelis</th>\n",
        "      <th>Argentina</th>\n",
        "      <td> 126</td>\n",
        "      <td> 0.888889</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Pablo Zabaleta</th>\n",
        "      <th>Argentina</th>\n",
        "      <td> 122</td>\n",
        "      <td> 0.786885</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Lionel Messi</th>\n",
        "      <th>Argentina</th>\n",
        "      <td> 122</td>\n",
        "      <td> 0.770492</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Marcos Rojo</th>\n",
        "      <th>Argentina</th>\n",
        "      <td> 119</td>\n",
        "      <td> 0.798319</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Manuel Neuer</th>\n",
        "      <th>Germany</th>\n",
        "      <td>  98</td>\n",
        "      <td> 0.897959</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Enzo P\u00e9rez</th>\n",
        "      <th>Argentina</th>\n",
        "      <td>  89</td>\n",
        "      <td> 0.876404</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Sergio Ag\u00fcero</th>\n",
        "      <th>Argentina</th>\n",
        "      <td>  69</td>\n",
        "      <td> 0.753623</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Sergio Romero</th>\n",
        "      <th>Argentina</th>\n",
        "      <td>  69</td>\n",
        "      <td> 0.681159</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Miroslav Klose</th>\n",
        "      <th>Germany</th>\n",
        "      <td>  58</td>\n",
        "      <td> 0.724138</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Fernando Gago</th>\n",
        "      <th>Argentina</th>\n",
        "      <td>  57</td>\n",
        "      <td> 0.859649</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Mario G\u00f6tze</th>\n",
        "      <th>Germany</th>\n",
        "      <td>  57</td>\n",
        "      <td> 0.894737</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Gonzalo Higua\u00edn</th>\n",
        "      <th>Argentina</th>\n",
        "      <td>  53</td>\n",
        "      <td> 0.792453</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Ezequiel Lavezzi</th>\n",
        "      <th>Argentina</th>\n",
        "      <td>  34</td>\n",
        "      <td> 0.764706</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Rodrigo Palacio</th>\n",
        "      <th>Argentina</th>\n",
        "      <td>  31</td>\n",
        "      <td> 0.612903</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Christoph Kramer</th>\n",
        "      <th>Germany</th>\n",
        "      <td>  27</td>\n",
        "      <td> 0.888889</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Per Mertesacker</th>\n",
        "      <th>Germany</th>\n",
        "      <td>   4</td>\n",
        "      <td> 1.000000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>28 rows \u00d7 2 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 26,
       "text": [
        "                                  count   outcome\n",
        "player_name            team_name                 \n",
        "Philipp Lahm           Germany      296  0.925676\n",
        "Toni Kroos             Germany      287  0.885017\n",
        "Bastian Schweinsteiger Germany      271  0.926199\n",
        "Jerome Boateng         Germany      211  0.909953\n",
        "Mesut \u00d6zil             Germany      196  0.867347\n",
        "Javier Mascherano      Argentina    173  0.878613\n",
        "Andr\u00e9 Sch\u00fcrrle         Germany      172  0.825581\n",
        "Benedikt H\u00f6wedes       Germany      167  0.856287\n",
        "Mats Hummels           Germany      165  0.909091\n",
        "Thomas M\u00fcller          Germany      155  0.812903\n",
        "Lucas Biglia           Argentina    136  0.860294\n",
        "Ezequiel Garay         Argentina    131  0.893130\n",
        "Mart\u00edn Demichelis      Argentina    126  0.888889\n",
        "Pablo Zabaleta         Argentina    122  0.786885\n",
        "Lionel Messi           Argentina    122  0.770492\n",
        "Marcos Rojo            Argentina    119  0.798319\n",
        "Manuel Neuer           Germany       98  0.897959\n",
        "Enzo P\u00e9rez             Argentina     89  0.876404\n",
        "Sergio Ag\u00fcero          Argentina     69  0.753623\n",
        "Sergio Romero          Argentina     69  0.681159\n",
        "Miroslav Klose         Germany       58  0.724138\n",
        "Fernando Gago          Argentina     57  0.859649\n",
        "Mario G\u00f6tze            Germany       57  0.894737\n",
        "Gonzalo Higua\u00edn        Argentina     53  0.792453\n",
        "Ezequiel Lavezzi       Argentina     34  0.764706\n",
        "Rodrigo Palacio        Argentina     31  0.612903\n",
        "Christoph Kramer       Germany       27  0.888889\n",
        "Per Mertesacker        Germany        4  1.000000\n",
        "\n",
        "[28 rows x 2 columns]"
       ]
      }
     ],
     "prompt_number": 26
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#shots\n",
      "pd.pivot_table(df[df['type'].isin([13,14,15,16])],\n",
      "               values='count',\n",
      "               aggfunc=sum,\n",
      "               rows=['player_name', 'team_name'], \n",
      "               cols='type_name',\n",
      "               fill_value=0,\n",
      "               margins=True).sort('All', ascending=False)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>type_name</th>\n",
        "      <th>GOAL</th>\n",
        "      <th>SHOT HIT POST</th>\n",
        "      <th>SHOT OFF GOAL</th>\n",
        "      <th>SHOT ON GOAL</th>\n",
        "      <th>All</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>player_name</th>\n",
        "      <th>team_name</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>All</th>\n",
        "      <th></th>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 11</td>\n",
        "      <td> 7</td>\n",
        "      <td> 20</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Lionel Messi</th>\n",
        "      <th>Argentina</th>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td>  4</td>\n",
        "      <td> 0</td>\n",
        "      <td>  4</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Toni Kroos</th>\n",
        "      <th>Germany</th>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td>  1</td>\n",
        "      <td> 2</td>\n",
        "      <td>  3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Sergio Ag\u00fcero</th>\n",
        "      <th>Argentina</th>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td>  2</td>\n",
        "      <td> 0</td>\n",
        "      <td>  2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Mario G\u00f6tze</th>\n",
        "      <th>Germany</th>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>  0</td>\n",
        "      <td> 1</td>\n",
        "      <td>  2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Gonzalo Higua\u00edn</th>\n",
        "      <th>Argentina</th>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td>  2</td>\n",
        "      <td> 0</td>\n",
        "      <td>  2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Andr\u00e9 Sch\u00fcrrle</th>\n",
        "      <th>Germany</th>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td>  1</td>\n",
        "      <td> 1</td>\n",
        "      <td>  2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Rodrigo Palacio</th>\n",
        "      <th>Argentina</th>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td>  1</td>\n",
        "      <td> 0</td>\n",
        "      <td>  1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Miroslav Klose</th>\n",
        "      <th>Germany</th>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td>  0</td>\n",
        "      <td> 1</td>\n",
        "      <td>  1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Mesut \u00d6zil</th>\n",
        "      <th>Germany</th>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td>  0</td>\n",
        "      <td> 1</td>\n",
        "      <td>  1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Lucas Biglia</th>\n",
        "      <th>Argentina</th>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td>  0</td>\n",
        "      <td> 1</td>\n",
        "      <td>  1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Benedikt H\u00f6wedes</th>\n",
        "      <th>Germany</th>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td>  0</td>\n",
        "      <td> 0</td>\n",
        "      <td>  1</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>12 rows \u00d7 5 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 27,
       "text": [
        "type_name                   GOAL  SHOT HIT POST  SHOT OFF GOAL  SHOT ON GOAL  \\\n",
        "player_name      team_name                                                     \n",
        "All                            1              1             11             7   \n",
        "Lionel Messi     Argentina     0              0              4             0   \n",
        "Toni Kroos       Germany       0              0              1             2   \n",
        "Sergio Ag\u00fcero    Argentina     0              0              2             0   \n",
        "Mario G\u00f6tze      Germany       1              0              0             1   \n",
        "Gonzalo Higua\u00edn  Argentina     0              0              2             0   \n",
        "Andr\u00e9 Sch\u00fcrrle   Germany       0              0              1             1   \n",
        "Rodrigo Palacio  Argentina     0              0              1             0   \n",
        "Miroslav Klose   Germany       0              0              0             1   \n",
        "Mesut \u00d6zil       Germany       0              0              0             1   \n",
        "Lucas Biglia     Argentina     0              0              0             1   \n",
        "Benedikt H\u00f6wedes Germany       0              1              0             0   \n",
        "\n",
        "type_name                   All  \n",
        "player_name      team_name       \n",
        "All                          20  \n",
        "Lionel Messi     Argentina    4  \n",
        "Toni Kroos       Germany      3  \n",
        "Sergio Ag\u00fcero    Argentina    2  \n",
        "Mario G\u00f6tze      Germany      2  \n",
        "Gonzalo Higua\u00edn  Argentina    2  \n",
        "Andr\u00e9 Sch\u00fcrrle   Germany      2  \n",
        "Rodrigo Palacio  Argentina    1  \n",
        "Miroslav Klose   Germany      1  \n",
        "Mesut \u00d6zil       Germany      1  \n",
        "Lucas Biglia     Argentina    1  \n",
        "Benedikt H\u00f6wedes Germany      1  \n",
        "\n",
        "[12 rows x 5 columns]"
       ]
      }
     ],
     "prompt_number": 27
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#defensive play\n",
      "pd.pivot_table(df[df['type'].isin([7, 8, 49])],\n",
      "               values='count',\n",
      "               aggfunc=np.sum,\n",
      "               rows=['player_name', 'team_name'], \n",
      "               cols='type_name',\n",
      "               fill_value=0,\n",
      "               margins=True).sort('All', ascending=False)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>type_name</th>\n",
        "      <th>INTERCEPTION</th>\n",
        "      <th>TACKLE</th>\n",
        "      <th>WON CONTROL OF BALL</th>\n",
        "      <th>All</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>player_name</th>\n",
        "      <th>team_name</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>All</th>\n",
        "      <th></th>\n",
        "      <td> 35</td>\n",
        "      <td> 51</td>\n",
        "      <td> 109</td>\n",
        "      <td> 195</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Mart\u00edn Demichelis</th>\n",
        "      <th>Argentina</th>\n",
        "      <td>  5</td>\n",
        "      <td>  2</td>\n",
        "      <td>   9</td>\n",
        "      <td>  16</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Toni Kroos</th>\n",
        "      <th>Germany</th>\n",
        "      <td>  2</td>\n",
        "      <td>  3</td>\n",
        "      <td>  11</td>\n",
        "      <td>  16</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Bastian Schweinsteiger</th>\n",
        "      <th>Germany</th>\n",
        "      <td>  0</td>\n",
        "      <td>  4</td>\n",
        "      <td>  11</td>\n",
        "      <td>  15</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Mats Hummels</th>\n",
        "      <th>Germany</th>\n",
        "      <td>  3</td>\n",
        "      <td>  6</td>\n",
        "      <td>   6</td>\n",
        "      <td>  15</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Jerome Boateng</th>\n",
        "      <th>Germany</th>\n",
        "      <td>  3</td>\n",
        "      <td>  6</td>\n",
        "      <td>   5</td>\n",
        "      <td>  14</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Pablo Zabaleta</th>\n",
        "      <th>Argentina</th>\n",
        "      <td>  1</td>\n",
        "      <td>  8</td>\n",
        "      <td>   4</td>\n",
        "      <td>  13</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Enzo P\u00e9rez</th>\n",
        "      <th>Argentina</th>\n",
        "      <td>  2</td>\n",
        "      <td>  1</td>\n",
        "      <td>   7</td>\n",
        "      <td>  10</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Javier Mascherano</th>\n",
        "      <th>Argentina</th>\n",
        "      <td>  3</td>\n",
        "      <td>  2</td>\n",
        "      <td>   5</td>\n",
        "      <td>  10</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Philipp Lahm</th>\n",
        "      <th>Germany</th>\n",
        "      <td>  2</td>\n",
        "      <td>  0</td>\n",
        "      <td>   7</td>\n",
        "      <td>   9</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Marcos Rojo</th>\n",
        "      <th>Argentina</th>\n",
        "      <td>  3</td>\n",
        "      <td>  3</td>\n",
        "      <td>   3</td>\n",
        "      <td>   9</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Thomas M\u00fcller</th>\n",
        "      <th>Germany</th>\n",
        "      <td>  2</td>\n",
        "      <td>  1</td>\n",
        "      <td>   5</td>\n",
        "      <td>   8</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Miroslav Klose</th>\n",
        "      <th>Germany</th>\n",
        "      <td>  2</td>\n",
        "      <td>  3</td>\n",
        "      <td>   3</td>\n",
        "      <td>   8</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Ezequiel Garay</th>\n",
        "      <th>Argentina</th>\n",
        "      <td>  3</td>\n",
        "      <td>  2</td>\n",
        "      <td>   3</td>\n",
        "      <td>   8</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Benedikt H\u00f6wedes</th>\n",
        "      <th>Germany</th>\n",
        "      <td>  1</td>\n",
        "      <td>  2</td>\n",
        "      <td>   4</td>\n",
        "      <td>   7</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Andr\u00e9 Sch\u00fcrrle</th>\n",
        "      <th>Germany</th>\n",
        "      <td>  0</td>\n",
        "      <td>  3</td>\n",
        "      <td>   2</td>\n",
        "      <td>   5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Lucas Biglia</th>\n",
        "      <th>Argentina</th>\n",
        "      <td>  2</td>\n",
        "      <td>  0</td>\n",
        "      <td>   3</td>\n",
        "      <td>   5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Gonzalo Higua\u00edn</th>\n",
        "      <th>Argentina</th>\n",
        "      <td>  0</td>\n",
        "      <td>  0</td>\n",
        "      <td>   4</td>\n",
        "      <td>   4</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Mesut \u00d6zil</th>\n",
        "      <th>Germany</th>\n",
        "      <td>  0</td>\n",
        "      <td>  1</td>\n",
        "      <td>   3</td>\n",
        "      <td>   4</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Lionel Messi</th>\n",
        "      <th>Argentina</th>\n",
        "      <td>  0</td>\n",
        "      <td>  1</td>\n",
        "      <td>   2</td>\n",
        "      <td>   3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Ezequiel Lavezzi</th>\n",
        "      <th>Argentina</th>\n",
        "      <td>  0</td>\n",
        "      <td>  1</td>\n",
        "      <td>   2</td>\n",
        "      <td>   3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Sergio Ag\u00fcero</th>\n",
        "      <th>Argentina</th>\n",
        "      <td>  1</td>\n",
        "      <td>  1</td>\n",
        "      <td>   1</td>\n",
        "      <td>   3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Sergio Romero</th>\n",
        "      <th>Argentina</th>\n",
        "      <td>  0</td>\n",
        "      <td>  0</td>\n",
        "      <td>   3</td>\n",
        "      <td>   3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Fernando Gago</th>\n",
        "      <th>Argentina</th>\n",
        "      <td>  0</td>\n",
        "      <td>  0</td>\n",
        "      <td>   2</td>\n",
        "      <td>   2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Rodrigo Palacio</th>\n",
        "      <th>Argentina</th>\n",
        "      <td>  0</td>\n",
        "      <td>  0</td>\n",
        "      <td>   2</td>\n",
        "      <td>   2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Mario G\u00f6tze</th>\n",
        "      <th>Germany</th>\n",
        "      <td>  0</td>\n",
        "      <td>  0</td>\n",
        "      <td>   1</td>\n",
        "      <td>   1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Christoph Kramer</th>\n",
        "      <th>Germany</th>\n",
        "      <td>  0</td>\n",
        "      <td>  1</td>\n",
        "      <td>   0</td>\n",
        "      <td>   1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Manuel Neuer</th>\n",
        "      <th>Germany</th>\n",
        "      <td>  0</td>\n",
        "      <td>  0</td>\n",
        "      <td>   1</td>\n",
        "      <td>   1</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>28 rows \u00d7 4 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 28,
       "text": [
        "type_name                         INTERCEPTION  TACKLE  WON CONTROL OF BALL  \\\n",
        "player_name            team_name                                              \n",
        "All                                         35      51                  109   \n",
        "Mart\u00edn Demichelis      Argentina             5       2                    9   \n",
        "Toni Kroos             Germany               2       3                   11   \n",
        "Bastian Schweinsteiger Germany               0       4                   11   \n",
        "Mats Hummels           Germany               3       6                    6   \n",
        "Jerome Boateng         Germany               3       6                    5   \n",
        "Pablo Zabaleta         Argentina             1       8                    4   \n",
        "Enzo P\u00e9rez             Argentina             2       1                    7   \n",
        "Javier Mascherano      Argentina             3       2                    5   \n",
        "Philipp Lahm           Germany               2       0                    7   \n",
        "Marcos Rojo            Argentina             3       3                    3   \n",
        "Thomas M\u00fcller          Germany               2       1                    5   \n",
        "Miroslav Klose         Germany               2       3                    3   \n",
        "Ezequiel Garay         Argentina             3       2                    3   \n",
        "Benedikt H\u00f6wedes       Germany               1       2                    4   \n",
        "Andr\u00e9 Sch\u00fcrrle         Germany               0       3                    2   \n",
        "Lucas Biglia           Argentina             2       0                    3   \n",
        "Gonzalo Higua\u00edn        Argentina             0       0                    4   \n",
        "Mesut \u00d6zil             Germany               0       1                    3   \n",
        "Lionel Messi           Argentina             0       1                    2   \n",
        "Ezequiel Lavezzi       Argentina             0       1                    2   \n",
        "Sergio Ag\u00fcero          Argentina             1       1                    1   \n",
        "Sergio Romero          Argentina             0       0                    3   \n",
        "Fernando Gago          Argentina             0       0                    2   \n",
        "Rodrigo Palacio        Argentina             0       0                    2   \n",
        "Mario G\u00f6tze            Germany               0       0                    1   \n",
        "Christoph Kramer       Germany               0       1                    0   \n",
        "Manuel Neuer           Germany               0       0                    1   \n",
        "\n",
        "type_name                         All  \n",
        "player_name            team_name       \n",
        "All                               195  \n",
        "Mart\u00edn Demichelis      Argentina   16  \n",
        "Toni Kroos             Germany     16  \n",
        "Bastian Schweinsteiger Germany     15  \n",
        "Mats Hummels           Germany     15  \n",
        "Jerome Boateng         Germany     14  \n",
        "Pablo Zabaleta         Argentina   13  \n",
        "Enzo P\u00e9rez             Argentina   10  \n",
        "Javier Mascherano      Argentina   10  \n",
        "Philipp Lahm           Germany      9  \n",
        "Marcos Rojo            Argentina    9  \n",
        "Thomas M\u00fcller          Germany      8  \n",
        "Miroslav Klose         Germany      8  \n",
        "Ezequiel Garay         Argentina    8  \n",
        "Benedikt H\u00f6wedes       Germany      7  \n",
        "Andr\u00e9 Sch\u00fcrrle         Germany      5  \n",
        "Lucas Biglia           Argentina    5  \n",
        "Gonzalo Higua\u00edn        Argentina    4  \n",
        "Mesut \u00d6zil             Germany      4  \n",
        "Lionel Messi           Argentina    3  \n",
        "Ezequiel Lavezzi       Argentina    3  \n",
        "Sergio Ag\u00fcero          Argentina    3  \n",
        "Sergio Romero          Argentina    3  \n",
        "Fernando Gago          Argentina    2  \n",
        "Rodrigo Palacio        Argentina    2  \n",
        "Mario G\u00f6tze            Germany      1  \n",
        "Christoph Kramer       Germany      1  \n",
        "Manuel Neuer           Germany      1  \n",
        "\n",
        "[28 rows x 4 columns]"
       ]
      }
     ],
     "prompt_number": 28
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "In short: Kroos and Schweinsteiger were imense in Germany's midield. Kroos had the most defensive actions, the second most shots (behind Messi only due to his desperate late atempts), and the second most passes. He was also responsible for most of the progression in Germany's goal-leading play. He was objectively the man of the match. Why FIFA decided to give that award to Goetze is beyond me (and most football fans)."
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "What's missing?"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Football is a game of space. That's why [parking the bus can actually allow to win a match](http://www.mirror.co.uk/sport/football/news/liverpool-vs-chelsea-jose-mourinho-3469108). The dataset used only includes positions for players active offensive and defensive actions. Defensive positioning is therefore completely ignored, and even attacking play lacks important information: a player's run without the ball can be much more important than a pass to score a goal.\n",
      "\n",
      "After all, without the position of the defenders, what's so special about an assist like this one:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.display import Image\n",
      "Image(url='http://i.minus.com/ibpQVB7fHa5NDj.gif')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<img src=\"http://i.minus.com/ibpQVB7fHa5NDj.gif\"/>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 29,
       "text": [
        "<IPython.core.display.Image at 0xd3ecf28>"
       ]
      }
     ],
     "prompt_number": 29
    }
   ],
   "metadata": {}
  }
 ]
}