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   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "I recently made a visualization that I thought was interesting and posted it on two great subreddits: [/r/soccer](http://www.reddit.com/r/soccer/) and [/r/dataisbeautiful](http://www.reddit.com/r/dataisbeautiful/).\n",
      "\n",
      "![final](http://i.imgur.com/OsrdIic.png)\n",
      "\n",
      "Since lots of people requested more information about the data and the process used to generate the charts, I decided to create a github repository to share this (and any future) findings.\n",
      "\n",
      "### Regarding the dataset\n",
      "\n",
      "The data was collected by me from UEFA's website, specifically from the Post-match timeline ([an example](http://www.uefa.com/uefachampionsleague/season=2014/matches/round=2000479/match=2011793/index.html)). The process is far from refined, so some degree of error is to be expected. In fact, a [bored engineer on reddit](http://www.reddit.com/r/soccer/comments/1r8ou2/how_likely_is_it_to_score_from_45_meters_an/cdl4r7m) proved that the goal marked as 45 meters was actually scored at 42 meters, by analyzing the video and the pitch patterns. It is unclear if the error comes from the source or from my calculations, but assume the latter rather than the former.\n",
      "\n",
      "The dataset consists of all shots marked on UEFA's website as an event. This introduces a major classification bias in the analysis, but, unfortunately, I don't believe a more complete dataset is available to the public.\n",
      "\n",
      "tl;dr: don't treat this as a scientific study.\n",
      "\n",
      "### Regarding the tools\n",
      "\n",
      "This analysis was made as a result of a fun exercise in web scraping, statistical modelling, and visualization tools.\n",
      "\n",
      "The tools used (because [python is awesome](http://www.talyarkoni.org/blog/2013/11/18/the-homogenization-of-scientific-computing-or-why-python-is-steadily-eating-other-languages-lunch/)) were:\n",
      "\n",
      "* beautifulsoup (for web scraping)\n",
      "* pandas (for data clean up and analysis)\n",
      "* statsmodels (for modelling)\n",
      "* matplotlib (for visualizations)\n",
      "\n",
      "All of it was done (and shared) using iPython Notebooks. If you don't know about this wonderful tool, check out [the website](http://ipython.org/notebook.html) and this gallery of [interesting Notebooks](https://github.com/ipython/ipython/wiki/A-gallery-of-interesting-IPython-Notebooks).\n",
      "\n",
      "### Update\n",
      "\n",
      "It was pointed out on /r/soccer that a particular goal was missing: [this stunner](http://www.youtube.com/watch?v=7zwf9G-ODeQ) from Chelsea's \u00d3scar. When investigating the data to understand why, I noticed I had only scrapped half of the competitions' games (only the first match day for each week).\n",
      "\n",
      "This notebook and the dataset were updated to reflect the new data, but the above image (the original post on reddit) was kept. The conclusions remained the same.\n",
      "\n",
      "----------------\n",
      "Enough talk already, let's get down to business:"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Checking out the data\n",
      "\n",
      "Let's start by importing pandas, loading the dataset and inspecting the data."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pandas as pd\n",
      "import numpy as np\n",
      "from matplotlib import pyplot as plt\n",
      "%matplotlib inline\n",
      "\n",
      "dfShots = pd.read_csv('..\\datasets\\cl-shots-2012.csv', index_col=0, na_values='N/A')\n",
      "\n",
      "dfShots.head()"
     ],
     "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>dist</th>\n",
        "      <th>dx</th>\n",
        "      <th>dy</th>\n",
        "      <th>event_id</th>\n",
        "      <th>goal</th>\n",
        "      <th>play_id</th>\n",
        "      <th>player</th>\n",
        "      <th>shot</th>\n",
        "      <th>team</th>\n",
        "      <th>x</th>\n",
        "      <th>y</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0 </th>\n",
        "      <td> 28.989564</td>\n",
        "      <td> 28.245</td>\n",
        "      <td>  6.528</td>\n",
        "      <td> 4fc58cdd-544b-4c05-8b2f-1cdc3d5a8164</td>\n",
        "      <td> False</td>\n",
        "      <td> 699caaab-f4d6-411c-b7ef-737ec6d23196</td>\n",
        "      <td> James Rodr\u00edguez</td>\n",
        "      <td> True</td>\n",
        "      <td>         Porto</td>\n",
        "      <td> 76.755</td>\n",
        "      <td> 28.832</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2 </th>\n",
        "      <td> 19.254175</td>\n",
        "      <td>-18.585</td>\n",
        "      <td>  5.032</td>\n",
        "      <td> 764b87c7-426b-45da-9921-9e0556b90922</td>\n",
        "      <td> False</td>\n",
        "      <td> 373c02f2-81ff-4b46-b2df-16629800c8fb</td>\n",
        "      <td>          Sammir</td>\n",
        "      <td> True</td>\n",
        "      <td> Dinamo Zagreb</td>\n",
        "      <td> 18.585</td>\n",
        "      <td> 27.404</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8 </th>\n",
        "      <td> 25.772814</td>\n",
        "      <td>-20.790</td>\n",
        "      <td>-15.232</td>\n",
        "      <td> a276c188-492e-4ada-8168-5bee7e998cc7</td>\n",
        "      <td> False</td>\n",
        "      <td> e190760e-9912-4d36-99f7-a1ceb696b5da</td>\n",
        "      <td>        Duje \u010cop</td>\n",
        "      <td> True</td>\n",
        "      <td> Dinamo Zagreb</td>\n",
        "      <td> 20.790</td>\n",
        "      <td> 48.280</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10</th>\n",
        "      <td> 35.180567</td>\n",
        "      <td> 33.810</td>\n",
        "      <td> -9.724</td>\n",
        "      <td> b5f9dd79-14ff-4671-ad3a-a198abfa24a8</td>\n",
        "      <td> False</td>\n",
        "      <td> 1204aee1-6925-48b1-92af-d70fa87b888f</td>\n",
        "      <td>          Maicon</td>\n",
        "      <td> True</td>\n",
        "      <td>         Porto</td>\n",
        "      <td> 71.190</td>\n",
        "      <td> 44.200</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12</th>\n",
        "      <td> 13.755000</td>\n",
        "      <td> 13.755</td>\n",
        "      <td>  0.000</td>\n",
        "      <td> 3a0178d9-67c8-4018-b18e-cceb3fbbdf48</td>\n",
        "      <td> False</td>\n",
        "      <td> a968c9e5-6f17-48ab-9081-7210ce10b3ed</td>\n",
        "      <td>    Miguel Lopes</td>\n",
        "      <td> True</td>\n",
        "      <td>         Porto</td>\n",
        "      <td> 91.245</td>\n",
        "      <td> 38.012</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 1,
       "text": [
        "         dist      dx      dy                              event_id   goal  \\\n",
        "0   28.989564  28.245   6.528  4fc58cdd-544b-4c05-8b2f-1cdc3d5a8164  False   \n",
        "2   19.254175 -18.585   5.032  764b87c7-426b-45da-9921-9e0556b90922  False   \n",
        "8   25.772814 -20.790 -15.232  a276c188-492e-4ada-8168-5bee7e998cc7  False   \n",
        "10  35.180567  33.810  -9.724  b5f9dd79-14ff-4671-ad3a-a198abfa24a8  False   \n",
        "12  13.755000  13.755   0.000  3a0178d9-67c8-4018-b18e-cceb3fbbdf48  False   \n",
        "\n",
        "                                 play_id           player  shot  \\\n",
        "0   699caaab-f4d6-411c-b7ef-737ec6d23196  James Rodr\u00edguez  True   \n",
        "2   373c02f2-81ff-4b46-b2df-16629800c8fb           Sammir  True   \n",
        "8   e190760e-9912-4d36-99f7-a1ceb696b5da         Duje \u010cop  True   \n",
        "10  1204aee1-6925-48b1-92af-d70fa87b888f           Maicon  True   \n",
        "12  a968c9e5-6f17-48ab-9081-7210ce10b3ed     Miguel Lopes  True   \n",
        "\n",
        "             team       x       y  \n",
        "0           Porto  76.755  28.832  \n",
        "2   Dinamo Zagreb  18.585  27.404  \n",
        "8   Dinamo Zagreb  20.790  48.280  \n",
        "10          Porto  71.190  44.200  \n",
        "12          Porto  91.245  38.012  "
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dfShots.describe()"
     ],
     "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>dist</th>\n",
        "      <th>dx</th>\n",
        "      <th>dy</th>\n",
        "      <th>goal</th>\n",
        "      <th>shot</th>\n",
        "      <th>x</th>\n",
        "      <th>y</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>count</th>\n",
        "      <td> 2273.000000</td>\n",
        "      <td> 2271.000000</td>\n",
        "      <td> 2271.000000</td>\n",
        "      <td>      2273</td>\n",
        "      <td> 2273</td>\n",
        "      <td> 2273.000000</td>\n",
        "      <td> 2273.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>mean</th>\n",
        "      <td>   18.387649</td>\n",
        "      <td>    0.124696</td>\n",
        "      <td>    0.842978</td>\n",
        "      <td> 0.1451826</td>\n",
        "      <td>    1</td>\n",
        "      <td>   51.402604</td>\n",
        "      <td>   33.157404</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>std</th>\n",
        "      <td>    8.120749</td>\n",
        "      <td>   17.499210</td>\n",
        "      <td>    9.879692</td>\n",
        "      <td> 0.3523623</td>\n",
        "      <td>    0</td>\n",
        "      <td>   38.015387</td>\n",
        "      <td>    9.182643</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>min</th>\n",
        "      <td>    0.000000</td>\n",
        "      <td>  -47.145000</td>\n",
        "      <td>  -42.160000</td>\n",
        "      <td>     False</td>\n",
        "      <td> True</td>\n",
        "      <td>    0.000000</td>\n",
        "      <td>    2.856000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>25%</th>\n",
        "      <td>   11.872038</td>\n",
        "      <td>  -13.387500</td>\n",
        "      <td>   -6.154000</td>\n",
        "      <td>     False</td>\n",
        "      <td> True</td>\n",
        "      <td>   13.020000</td>\n",
        "      <td>   26.384000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>50%</th>\n",
        "      <td>   17.511139</td>\n",
        "      <td>   -2.205000</td>\n",
        "      <td>    0.680000</td>\n",
        "      <td>         0</td>\n",
        "      <td>    1</td>\n",
        "      <td>   33.075000</td>\n",
        "      <td>   33.320000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>75%</th>\n",
        "      <td>   24.382473</td>\n",
        "      <td>   14.280000</td>\n",
        "      <td>    8.092000</td>\n",
        "      <td>     False</td>\n",
        "      <td> True</td>\n",
        "      <td>   90.300000</td>\n",
        "      <td>   40.324000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>max</th>\n",
        "      <td>   54.922115</td>\n",
        "      <td>   54.915000</td>\n",
        "      <td>   34.204000</td>\n",
        "      <td>      True</td>\n",
        "      <td> True</td>\n",
        "      <td>  105.000000</td>\n",
        "      <td>   63.444000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 2,
       "text": [
        "              dist           dx           dy       goal  shot            x            y\n",
        "count  2273.000000  2271.000000  2271.000000       2273  2273  2273.000000  2273.000000\n",
        "mean     18.387649     0.124696     0.842978  0.1451826     1    51.402604    33.157404\n",
        "std       8.120749    17.499210     9.879692  0.3523623     0    38.015387     9.182643\n",
        "min       0.000000   -47.145000   -42.160000      False  True     0.000000     2.856000\n",
        "25%      11.872038   -13.387500    -6.154000      False  True    13.020000    26.384000\n",
        "50%      17.511139    -2.205000     0.680000          0     1    33.075000    33.320000\n",
        "75%      24.382473    14.280000     8.092000      False  True    90.300000    40.324000\n",
        "max      54.922115    54.915000    34.204000       True  True   105.000000    63.444000"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.scatter(dfShots.dist, dfShots.goal, alpha=0.01)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 3,
       "text": [
        "<matplotlib.collections.PathCollection at 0xacceba8>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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OPVw3IklK5HItRFFIGJar9wJC2toijIEzZwqEYUBLS+VTam1Cd3fMoUOHgZBy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\nkiIKfRGRFFHoi4ikyP8HTw89APG7YycAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x5f3a710>"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "A simple scatter plot isn't enough to get a real sense of the relationship between distance and goal scoring, especially because goalscoring is a binary variable\n",
      "\n",
      "To get a better intuition on the data, let's aggregate shot in bins of equal distance, and check the accuracy in each bin."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "bins = np.linspace(dfShots.dist.min(), dfShots.dist.max(), 20)\n",
      "groups = dfShots.groupby(np.digitize(dfShots.dist, bins))\n",
      "\n",
      "chart = groups[['dist','goal']].mean()\n",
      "\n",
      "plt.plot(chart.dist, chart.goal, 'bo-')\n",
      "plt.ylim(0,1)\n",
      "plt.ylabel('probability')\n",
      "plt.xlabel('distance (m)')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "<matplotlib.text.Text at 0xacf3748>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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8vNMR1d6771qP9993OhLfpllGYosnnoBu3azuhpMnPXfejIxskpMnkJiYRnKy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\n",
       "text": [
        "<matplotlib.figure.Figure at 0xae0fa20>"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The data behaves mostly as antecipated (higher accuracy in close-distance shots), but **why the exceptions between 35m and 45m?**\n",
      "\n",
      "Let's investigate."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dfShots[dfShots.dist > 40]"
     ],
     "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>dist</th>\n",
        "      <th>dx</th>\n",
        "      <th>dy</th>\n",
        "      <th>event_id</th>\n",
        "      <th>goal</th>\n",
        "      <th>play_id</th>\n",
        "      <th>player</th>\n",
        "      <th>shot</th>\n",
        "      <th>team</th>\n",
        "      <th>x</th>\n",
        "      <th>y</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>499 </th>\n",
        "      <td> 40.115236</td>\n",
        "      <td>-37.590</td>\n",
        "      <td>-14.008</td>\n",
        "      <td> 3687319d-c4a1-4724-992f-48307e801454</td>\n",
        "      <td> False</td>\n",
        "      <td> c13c694c-cc1b-451d-9464-099c20b3d553</td>\n",
        "      <td>         Marco Reus</td>\n",
        "      <td> True</td>\n",
        "      <td>         Dortmund</td>\n",
        "      <td> 37.590</td>\n",
        "      <td> 50.796</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>648 </th>\n",
        "      <td> 45.980092</td>\n",
        "      <td> 34.440</td>\n",
        "      <td>-30.464</td>\n",
        "      <td> 54ffa7b6-3b48-4977-a0b1-32eb652467d0</td>\n",
        "      <td>  True</td>\n",
        "      <td> c0e7998a-4b29-41fc-833f-f44267a8ebe1</td>\n",
        "      <td> Aleksandar Kolarov</td>\n",
        "      <td> True</td>\n",
        "      <td>        Man. City</td>\n",
        "      <td> 70.560</td>\n",
        "      <td> 61.064</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>710 </th>\n",
        "      <td> 46.603368</td>\n",
        "      <td> 44.835</td>\n",
        "      <td> 12.716</td>\n",
        "      <td> 12162818-0fc0-4322-8438-86b20bdba953</td>\n",
        "      <td> False</td>\n",
        "      <td> 5e6ed4bb-e729-44f4-a42e-5420993e5d88</td>\n",
        "      <td> Yaroslav Rakitskiy</td>\n",
        "      <td> True</td>\n",
        "      <td> Shakhtar Donetsk</td>\n",
        "      <td> 60.165</td>\n",
        "      <td> 25.704</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1058</th>\n",
        "      <td> 48.384144</td>\n",
        "      <td>-47.145</td>\n",
        "      <td>-10.880</td>\n",
        "      <td> a1189bf9-9f19-457c-8dab-1c5309e00ed0</td>\n",
        "      <td> False</td>\n",
        "      <td> ad37626f-bd1e-411e-8a1c-0e68d24776b4</td>\n",
        "      <td>       Kris Commons</td>\n",
        "      <td> True</td>\n",
        "      <td>           Celtic</td>\n",
        "      <td> 47.145</td>\n",
        "      <td> 44.948</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3073</th>\n",
        "      <td> 54.922115</td>\n",
        "      <td> 54.915</td>\n",
        "      <td> -0.884</td>\n",
        "      <td> 70ab8f99-3fe5-4c08-9f0c-0ef4aa5ec9e2</td>\n",
        "      <td> False</td>\n",
        "      <td> 4fff0d62-c221-4235-b7cd-e5b6ef19c6d0</td>\n",
        "      <td>       Lu\u00eds Alberto</td>\n",
        "      <td> True</td>\n",
        "      <td>         CFR Cluj</td>\n",
        "      <td> 50.085</td>\n",
        "      <td> 39.236</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5003</th>\n",
        "      <td> 40.794737</td>\n",
        "      <td>-39.795</td>\n",
        "      <td>  8.976</td>\n",
        "      <td> 518a3a8f-7644-4544-9f6c-e785d5979384</td>\n",
        "      <td>  True</td>\n",
        "      <td> 0e4cd067-0b81-4ae5-8b71-e2c325ea895f</td>\n",
        "      <td>              Oscar</td>\n",
        "      <td> True</td>\n",
        "      <td>          Chelsea</td>\n",
        "      <td> 39.795</td>\n",
        "      <td> 28.152</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5120</th>\n",
        "      <td> 43.293600</td>\n",
        "      <td> 36.120</td>\n",
        "      <td> 23.868</td>\n",
        "      <td> c81ab84d-9012-4455-9e72-73f32fc367b4</td>\n",
        "      <td> False</td>\n",
        "      <td> d911c680-f5cf-4ce4-85e8-a625fb482660</td>\n",
        "      <td>      Renan Bressan</td>\n",
        "      <td> True</td>\n",
        "      <td>             BATE</td>\n",
        "      <td> 68.880</td>\n",
        "      <td>  9.860</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6187</th>\n",
        "      <td> 50.630296</td>\n",
        "      <td> 28.035</td>\n",
        "      <td>-42.160</td>\n",
        "      <td> 029ce092-2de2-418b-ba55-efddf6031b32</td>\n",
        "      <td> False</td>\n",
        "      <td> ddebe83c-a603-4e67-8f91-1610d36d7bef</td>\n",
        "      <td>      Douglas Costa</td>\n",
        "      <td> True</td>\n",
        "      <td> Shakhtar Donetsk</td>\n",
        "      <td> 76.965</td>\n",
        "      <td> 49.640</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6884</th>\n",
        "      <td> 40.664864</td>\n",
        "      <td>-34.650</td>\n",
        "      <td>-21.284</td>\n",
        "      <td> 8c78b5ea-80f1-47c7-857f-7465b641d4c8</td>\n",
        "      <td> False</td>\n",
        "      <td> 412a8ca1-e014-4218-8a4f-dfcb455d96cb</td>\n",
        "      <td>      Marco Estrada</td>\n",
        "      <td> True</td>\n",
        "      <td>      Montpellier</td>\n",
        "      <td> 34.650</td>\n",
        "      <td> 50.932</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7230</th>\n",
        "      <td> 41.844881</td>\n",
        "      <td> 34.650</td>\n",
        "      <td> 23.460</td>\n",
        "      <td> 5afb8868-c477-44d1-8c5e-c979316604e5</td>\n",
        "      <td> False</td>\n",
        "      <td> 5b82592c-712b-404a-9a10-9279ab3d7985</td>\n",
        "      <td>      Marco Estrada</td>\n",
        "      <td> True</td>\n",
        "      <td>      Montpellier</td>\n",
        "      <td> 70.350</td>\n",
        "      <td> 15.844</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7478</th>\n",
        "      <td> 41.040176</td>\n",
        "      <td> 22.680</td>\n",
        "      <td> 34.204</td>\n",
        "      <td> b53b142e-8d18-468a-a0d2-96b57e22b70b</td>\n",
        "      <td> False</td>\n",
        "      <td> e09a2304-6d12-443f-bde1-1253bdda9c55</td>\n",
        "      <td>         Marco Reus</td>\n",
        "      <td> True</td>\n",
        "      <td>         Dortmund</td>\n",
        "      <td> 82.320</td>\n",
        "      <td>  2.856</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8343</th>\n",
        "      <td> 47.573214</td>\n",
        "      <td> 47.565</td>\n",
        "      <td> -0.884</td>\n",
        "      <td> 22ab1483-735a-4b28-a80f-01d8c079ce1d</td>\n",
        "      <td> False</td>\n",
        "      <td> f6af7187-7367-4731-8c48-9e378f40e8f5</td>\n",
        "      <td>       Lu\u00eds Alberto</td>\n",
        "      <td> True</td>\n",
        "      <td>         CFR Cluj</td>\n",
        "      <td> 57.435</td>\n",
        "      <td> 30.124</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9337</th>\n",
        "      <td> 44.005908</td>\n",
        "      <td> 37.485</td>\n",
        "      <td> 23.052</td>\n",
        "      <td> 4f5e648c-45be-4cb5-abf1-eec40c2a9df4</td>\n",
        "      <td> False</td>\n",
        "      <td> 6e828e65-e7b4-41c2-bf30-1a9e86a8c3d5</td>\n",
        "      <td>      Franck Rib\u00e9ry</td>\n",
        "      <td> True</td>\n",
        "      <td>           Bayern</td>\n",
        "      <td> 67.515</td>\n",
        "      <td> 15.572</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9717</th>\n",
        "      <td> 41.683173</td>\n",
        "      <td> 41.370</td>\n",
        "      <td>  5.100</td>\n",
        "      <td> 74562657-1088-4f48-9685-11f07add45d2</td>\n",
        "      <td> False</td>\n",
        "      <td> 9203f147-0d5e-4aba-aa77-c7ed6b56f10e</td>\n",
        "      <td>     Andr\u00e9s Iniesta</td>\n",
        "      <td> True</td>\n",
        "      <td>        Barcelona</td>\n",
        "      <td> 63.630</td>\n",
        "      <td> 25.296</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "           dist      dx      dy                              event_id   goal  \\\n",
        "499   40.115236 -37.590 -14.008  3687319d-c4a1-4724-992f-48307e801454  False   \n",
        "648   45.980092  34.440 -30.464  54ffa7b6-3b48-4977-a0b1-32eb652467d0   True   \n",
        "710   46.603368  44.835  12.716  12162818-0fc0-4322-8438-86b20bdba953  False   \n",
        "1058  48.384144 -47.145 -10.880  a1189bf9-9f19-457c-8dab-1c5309e00ed0  False   \n",
        "3073  54.922115  54.915  -0.884  70ab8f99-3fe5-4c08-9f0c-0ef4aa5ec9e2  False   \n",
        "5003  40.794737 -39.795   8.976  518a3a8f-7644-4544-9f6c-e785d5979384   True   \n",
        "5120  43.293600  36.120  23.868  c81ab84d-9012-4455-9e72-73f32fc367b4  False   \n",
        "6187  50.630296  28.035 -42.160  029ce092-2de2-418b-ba55-efddf6031b32  False   \n",
        "6884  40.664864 -34.650 -21.284  8c78b5ea-80f1-47c7-857f-7465b641d4c8  False   \n",
        "7230  41.844881  34.650  23.460  5afb8868-c477-44d1-8c5e-c979316604e5  False   \n",
        "7478  41.040176  22.680  34.204  b53b142e-8d18-468a-a0d2-96b57e22b70b  False   \n",
        "8343  47.573214  47.565  -0.884  22ab1483-735a-4b28-a80f-01d8c079ce1d  False   \n",
        "9337  44.005908  37.485  23.052  4f5e648c-45be-4cb5-abf1-eec40c2a9df4  False   \n",
        "9717  41.683173  41.370   5.100  74562657-1088-4f48-9685-11f07add45d2  False   \n",
        "\n",
        "                                   play_id              player  shot  \\\n",
        "499   c13c694c-cc1b-451d-9464-099c20b3d553          Marco Reus  True   \n",
        "648   c0e7998a-4b29-41fc-833f-f44267a8ebe1  Aleksandar Kolarov  True   \n",
        "710   5e6ed4bb-e729-44f4-a42e-5420993e5d88  Yaroslav Rakitskiy  True   \n",
        "1058  ad37626f-bd1e-411e-8a1c-0e68d24776b4        Kris Commons  True   \n",
        "3073  4fff0d62-c221-4235-b7cd-e5b6ef19c6d0        Lu\u00eds Alberto  True   \n",
        "5003  0e4cd067-0b81-4ae5-8b71-e2c325ea895f               Oscar  True   \n",
        "5120  d911c680-f5cf-4ce4-85e8-a625fb482660       Renan Bressan  True   \n",
        "6187  ddebe83c-a603-4e67-8f91-1610d36d7bef       Douglas Costa  True   \n",
        "6884  412a8ca1-e014-4218-8a4f-dfcb455d96cb       Marco Estrada  True   \n",
        "7230  5b82592c-712b-404a-9a10-9279ab3d7985       Marco Estrada  True   \n",
        "7478  e09a2304-6d12-443f-bde1-1253bdda9c55          Marco Reus  True   \n",
        "8343  f6af7187-7367-4731-8c48-9e378f40e8f5        Lu\u00eds Alberto  True   \n",
        "9337  6e828e65-e7b4-41c2-bf30-1a9e86a8c3d5       Franck Rib\u00e9ry  True   \n",
        "9717  9203f147-0d5e-4aba-aa77-c7ed6b56f10e      Andr\u00e9s Iniesta  True   \n",
        "\n",
        "                  team       x       y  \n",
        "499           Dortmund  37.590  50.796  \n",
        "648          Man. City  70.560  61.064  \n",
        "710   Shakhtar Donetsk  60.165  25.704  \n",
        "1058            Celtic  47.145  44.948  \n",
        "3073          CFR Cluj  50.085  39.236  \n",
        "5003           Chelsea  39.795  28.152  \n",
        "5120              BATE  68.880   9.860  \n",
        "6187  Shakhtar Donetsk  76.965  49.640  \n",
        "6884       Montpellier  34.650  50.932  \n",
        "7230       Montpellier  70.350  15.844  \n",
        "7478          Dortmund  82.320   2.856  \n",
        "8343          CFR Cluj  57.435  30.124  \n",
        "9337            Bayern  67.515  15.572  \n",
        "9717         Barcelona  63.630  25.296  "
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "There are less than 20 shots from a distance longer than 40 meters, and only 2 goals from those shots. The fact that those goals were scored from 46m meters (by Kolarov), and 41 meters (by \u00d3scar) is probably due to pure chance, and not because those distances are particularly meaningful.\n",
      "\n",
      "Note that this conclusion is only possible because of our previous knowledge of the problem. Nothing in the data suggests that the 46 meters goal is just an accident: in fact, half the shots from 46 meters ended in goals! However, a sample of two is never significant.\n",
      "\n",
      "Lets look at the goal itself:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.display import YouTubeVideo\n",
      "YouTubeVideo('HhJ84p9KLKY')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "\n",
        "        <iframe\n",
        "            width=\"400\"\n",
        "            height=300\"\n",
        "            src=\"http://www.youtube.com/embed/HhJ84p9KLKY\"\n",
        "            frameborder=\"0\"\n",
        "            allowfullscreen\n",
        "        ></iframe>\n",
        "        "
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "<IPython.lib.display.YouTubeVideo at 0x5f3a1d0>"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Hardly a trend-setter. Quite lucky in fact...\n",
      "\n",
      "\n",
      "## Moddeling\n",
      "\n",
      "What we want is a model in which:\n",
      "\n",
      "* the dependent variable is binary (\"goal\" or \"not a goal\")\n",
      "\n",
      "* the output is a probability of the dependent variable being True (i.e. of scoring a goal)\n",
      "\n",
      "* the independent variable (distance) is continuous\n",
      "\n",
      "The Logistic Regression satisfies all of these conditions: \n",
      "\n",
      "![Logistic curve](http://upload.wikimedia.org/wikipedia/commons/thumb/8/88/Logistic-curve.svg/320px-Logistic-curve.svg.png)\n",
      "\n",
      "Python has a great module to handle regressions: [Statsmodels](http://statsmodels.sourceforge.net/). So let's use that."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import statsmodels.api as sm\n",
      "\n",
      "# adding a constant column to represent the intercept\n",
      "dfShots['intercept']=1.0\n",
      "\n",
      "# identify the independent and dependent variables \n",
      "ind_cols=['dist','intercept']\n",
      "dep_cols=['goal']\n",
      "\n",
      "#training the model\n",
      "logit = sm.Logit(dfShots[dep_cols], dfShots[ind_cols])\n",
      "result=logit.fit()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Optimization terminated successfully.\n",
        "         Current function value: 0.371002\n",
        "         Iterations 7\n"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# get the fitted coefficients from the results\n",
      "coeff = result.params\n",
      "print coeff"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "dist        -0.122124\n",
        "intercept    0.165303\n",
        "dtype: float64\n"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now we have the parameters. How do we use them?\n",
      "\n",
      "This is the basic logistic function:\n",
      "\n",
      "![logitic formula](http://upload.wikimedia.org/math/8/a/9/8a9c21e683de89ddb61f15262ee9fd3a.png)\n",
      "\n",
      "In our case, \u03b20 is the *intercept* parameter and \u03b21 the *dist* parameter. Let's define a function to return the probability based on a distance and these parameters of the model, and then get probabilities for shots from 1 to 60 meters."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def prob(dist,coeff):\n",
      "  z = coeff[0]*dist + coeff[1]\n",
      "  return 1/(1+np.exp(-1*z))\n",
      "\n",
      "lf = pd.DataFrame(range(1,60), columns=[\"dist\"])\n",
      "lf['prob']=prob(lf['dist'], coeff)\n",
      "\n",
      "lf.head()"
     ],
     "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>dist</th>\n",
        "      <th>prob</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 1</td>\n",
        "      <td> 0.510793</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> 2</td>\n",
        "      <td> 0.480274</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 3</td>\n",
        "      <td> 0.449902</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> 4</td>\n",
        "      <td> 0.419898</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> 5</td>\n",
        "      <td> 0.390475</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "   dist      prob\n",
        "0     1  0.510793\n",
        "1     2  0.480274\n",
        "2     3  0.449902\n",
        "3     4  0.419898\n",
        "4     5  0.390475"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Let's plot the logistic curve on top of the scatter chart we made earlier, in order to see how well it fits."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.scatter(chart.dist, chart.goal, label=\"frequency\")\n",
      "plt.plot(lf['dist'],lf['prob'], label=\"regression\")\n",
      "plt.xlim(0, 60)\n",
      "plt.xlabel(\"Distance (m)\")\n",
      "plt.ylim(0, 0.8)\n",
      "plt.ylabel(\"Probability\")\n",
      "plt.legend()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 10,
       "text": [
        "<matplotlib.legend.Legend at 0x14944cf8>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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0On777Tf27dsHwDPPPMPMmTNJTk7m0qVLfPrpp/kSiq+vLytWrECn0xEdHc2vv/5qeG/s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\n169f57nnnqNz586EhYUVeqxWq2Xu3Lm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       "text": [
        "<matplotlib.figure.Figure at 0x14921710>"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The next figure is an attempt at a football visualization using matplotlib (the goal is to have a generic template for these visualizations; for now it's just a green box with arrows :)\n",
      "\n",
      "Contributions and suggestions (via github) are welcome."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x_size = 105.0\n",
      "y_size = 68.0\n",
      "\n",
      "#set up field    \n",
      "fig = plt.figure()\n",
      "fig.patch.set_facecolor('green')\n",
      "\n",
      "axes = fig.add_subplot(1, 1, 1, axisbg='green')\n",
      "\n",
      "axes.xaxis.set_visible(False)\n",
      "axes.yaxis.set_visible(False)\n",
      "\n",
      "plt.xlim([0,x_size])\n",
      "plt.ylim([0,y_size])\n",
      "\n",
      "#draw shots\n",
      "for i, row in enumerate(dfShots.values):\n",
      "    size = 1.0\n",
      "    if row[4]:\n",
      "        color = 'red'\n",
      "        alpha = 0.4\n",
      "    else:\n",
      "        color = 'white'\n",
      "        alpha = 0.1\n",
      "       \n",
      "    \n",
      "    plt.arrow(row[9],row[10],row[1],row[2],fc=color, ec=color, head_width=size, head_length=size, alpha=alpha)\n",
      "    \n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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CQlRJpWbVWC4sU9AL2L7Nu0fv0pq00qRYykNTMkpJfPJ+OvqO3UkqwgzFQJM1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\n5qxZAtP37+CU9Kx+u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c3/6V/hUusOk3BCSk+xll2Ll3Bu4MYSNU3WqI/qlAdl9tv7HHYP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\nGI2uJFspKeIHTwExA/jy70aemsOZGKqMR3dck9b20lKu3sU+GoxiIbKAL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       "text": [
        "<matplotlib.figure.Figure at 0x149319e8>"
       ]
      }
     ],
     "prompt_number": 11
    }
   ],
   "metadata": {}
  }
 ]
}