footywire-afl-fantasy-scoring-methods.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# AFL Fantasy Football Data Analysis\n",
    "## What is the best fantasy scoring method? Which is the strongest indication that a team will win?\n",
    "AFL fans often dismiss fantasy scores as irrelevant to the actual match result.\n",
    "\n",
    "This analysis delves into these results to determine how relevant these fantasy scores are historically, and also pits two of the most popular fantasy score methods (AFL Fantasy and AFL Supercoach) against each other.\n",
    "\n",
    "A winner will be crowned!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy import stats\n",
    "pd.set_option('display.max_columns', 999)\n",
    "pd.set_option('display.max_rows', 30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get Footywire Data\n",
    "This data was scraped using the fitzRoy R Package https://github.com/jimmyday12/fitzRoy.\n",
    "\n",
    "For this code, refer to fitzroy.R"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Season</th>\n",
       "      <th>Round</th>\n",
       "      <th>Venue</th>\n",
       "      <th>Player</th>\n",
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       "      <th>M</th>\n",
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       "      <th>AF</th>\n",
       "      <th>SC</th>\n",
       "      <th>CCL</th>\n",
       "      <th>SCL</th>\n",
       "      <th>SI</th>\n",
       "      <th>MG</th>\n",
       "      <th>TO</th>\n",
       "      <th>ITC</th>\n",
       "      <th>T5</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2010-03-25</td>\n",
       "      <td>2010</td>\n",
       "      <td>Round 1</td>\n",
       "      <td>MCG</td>\n",
       "      <td>Daniel Connors</td>\n",
       "      <td>Richmond</td>\n",
       "      <td>Carlton</td>\n",
       "      <td>Home</td>\n",
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       "      <td>8</td>\n",
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       "      <td>16</td>\n",
       "      <td>66.7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>14</td>\n",
       "      <td>10</td>\n",
       "      <td>24</td>\n",
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       "      <td>0</td>\n",
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       "      <td>4</td>\n",
       "      <td>6</td>\n",
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       "      <td>0</td>\n",
       "      <td>77</td>\n",
       "      <td>85</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2010-03-25</td>\n",
       "      <td>2010</td>\n",
       "      <td>Round 1</td>\n",
       "      <td>MCG</td>\n",
       "      <td>Daniel Jackson</td>\n",
       "      <td>Richmond</td>\n",
       "      <td>Carlton</td>\n",
       "      <td>Home</td>\n",
       "      <td>5089</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>14</td>\n",
       "      <td>60.9</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>80</td>\n",
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       "      <td>12</td>\n",
       "      <td>23</td>\n",
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       "      <td>0</td>\n",
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       "      <td>4</td>\n",
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       "      <td>0</td>\n",
       "      <td>85</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2010-03-25</td>\n",
       "      <td>2010</td>\n",
       "      <td>Round 1</td>\n",
       "      <td>MCG</td>\n",
       "      <td>Brett Deledio</td>\n",
       "      <td>Richmond</td>\n",
       "      <td>Carlton</td>\n",
       "      <td>Home</td>\n",
       "      <td>5089</td>\n",
       "      <td>7</td>\n",
       "      <td>14</td>\n",
       "      <td>16</td>\n",
       "      <td>76.2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>21</td>\n",
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       "      <td>0</td>\n",
       "      <td>6</td>\n",
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       "      <td>3</td>\n",
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       "      <td>2</td>\n",
       "      <td>94</td>\n",
       "      <td>93</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2010-03-25</td>\n",
       "      <td>2010</td>\n",
       "      <td>Round 1</td>\n",
       "      <td>MCG</td>\n",
       "      <td>Ben Cousins</td>\n",
       "      <td>Richmond</td>\n",
       "      <td>Carlton</td>\n",
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       "      <td>5089</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>57.9</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>69</td>\n",
       "      <td>13</td>\n",
       "      <td>6</td>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>65</td>\n",
       "      <td>70</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2010-03-25</td>\n",
       "      <td>2010</td>\n",
       "      <td>Round 1</td>\n",
       "      <td>MCG</td>\n",
       "      <td>Trent Cotchin</td>\n",
       "      <td>Richmond</td>\n",
       "      <td>Carlton</td>\n",
       "      <td>Home</td>\n",
       "      <td>5089</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "      <td>68.4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>77</td>\n",
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       "      <td>8</td>\n",
       "      <td>19</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>65</td>\n",
       "      <td>63</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>90416</th>\n",
       "      <td>2019-09-28</td>\n",
       "      <td>2019</td>\n",
       "      <td>Grand Final</td>\n",
       "      <td>MCG</td>\n",
       "      <td>Harrison Himmelberg</td>\n",
       "      <td>GWS</td>\n",
       "      <td>Richmond</td>\n",
       "      <td>Away</td>\n",
       "      <td>9927</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>58.3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>92</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
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       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>65</td>\n",
       "      <td>51</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>153.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90417</th>\n",
       "      <td>2019-09-28</td>\n",
       "      <td>2019</td>\n",
       "      <td>Grand Final</td>\n",
       "      <td>MCG</td>\n",
       "      <td>Samuel Reid</td>\n",
       "      <td>GWS</td>\n",
       "      <td>Richmond</td>\n",
       "      <td>Away</td>\n",
       "      <td>9927</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>66.7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>96</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>47</td>\n",
       "      <td>55</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>90418</th>\n",
       "      <td>2019-09-28</td>\n",
       "      <td>2019</td>\n",
       "      <td>Grand Final</td>\n",
       "      <td>MCG</td>\n",
       "      <td>Adam Kennedy</td>\n",
       "      <td>GWS</td>\n",
       "      <td>Richmond</td>\n",
       "      <td>Away</td>\n",
       "      <td>9927</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>81.8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>56</td>\n",
       "      <td>55</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90419</th>\n",
       "      <td>2019-09-28</td>\n",
       "      <td>2019</td>\n",
       "      <td>Grand Final</td>\n",
       "      <td>MCG</td>\n",
       "      <td>Shane Mumford</td>\n",
       "      <td>GWS</td>\n",
       "      <td>Richmond</td>\n",
       "      <td>Away</td>\n",
       "      <td>9927</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>70.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>75</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>42</td>\n",
       "      <td>62</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90420</th>\n",
       "      <td>2019-09-28</td>\n",
       "      <td>2019</td>\n",
       "      <td>Grand Final</td>\n",
       "      <td>MCG</td>\n",
       "      <td>Jeremy Finlayson</td>\n",
       "      <td>GWS</td>\n",
       "      <td>Richmond</td>\n",
       "      <td>Away</td>\n",
       "      <td>9927</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>63</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</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>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>89628 rows × 43 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Date  Season        Round Venue               Player      Team  \\\n",
       "1      2010-03-25    2010      Round 1   MCG       Daniel Connors  Richmond   \n",
       "2      2010-03-25    2010      Round 1   MCG       Daniel Jackson  Richmond   \n",
       "3      2010-03-25    2010      Round 1   MCG        Brett Deledio  Richmond   \n",
       "4      2010-03-25    2010      Round 1   MCG          Ben Cousins  Richmond   \n",
       "5      2010-03-25    2010      Round 1   MCG        Trent Cotchin  Richmond   \n",
       "...           ...     ...          ...   ...                  ...       ...   \n",
       "90416  2019-09-28    2019  Grand Final   MCG  Harrison Himmelberg       GWS   \n",
       "90417  2019-09-28    2019  Grand Final   MCG          Samuel Reid       GWS   \n",
       "90418  2019-09-28    2019  Grand Final   MCG         Adam Kennedy       GWS   \n",
       "90419  2019-09-28    2019  Grand Final   MCG        Shane Mumford       GWS   \n",
       "90420  2019-09-28    2019  Grand Final   MCG     Jeremy Finlayson       GWS   \n",
       "\n",
       "      Opposition Status  Match_id  CP  UP  ED     DE  CM  GA  MI5  \\\n",
       "1        Carlton   Home      5089   8  15  16   66.7   0   0    0   \n",
       "2        Carlton   Home      5089  11  10  14   60.9   1   0    0   \n",
       "3        Carlton   Home      5089   7  14  16   76.2   0   0    0   \n",
       "4        Carlton   Home      5089   9  10  11   57.9   0   1    0   \n",
       "5        Carlton   Home      5089   8  10  13   68.4   1   0    0   \n",
       "...          ...    ...       ...  ..  ..  ..    ...  ..  ..  ...   \n",
       "90416   Richmond   Away      9927   3   9   7   58.3   1   0    2   \n",
       "90417   Richmond   Away      9927   5   6   8   66.7   0   0    0   \n",
       "90418   Richmond   Away      9927   4   8   9   81.8   0   0    0   \n",
       "90419   Richmond   Away      9927   8   2   7   70.0   0   0    0   \n",
       "90420   Richmond   Away      9927   1   1   1  100.0   0   0    0   \n",
       "\n",
       "       One.Percenters  BO  TOG   K  HB   D  M  G  B  T  HO  GA1  I50  CL  CG  \\\n",
       "1                   1   0   69  14  10  24  3  0  0  1   0    0    2   2   4   \n",
       "2                   0   0   80  11  12  23  2  0  0  5   0    0    8   5   4   \n",
       "3                   0   0   89  12   9  21  5  1  0  6   0    0    4   3   4   \n",
       "4                   0   0   69  13   6  19  1  1  0  1   0    1    1   2   3   \n",
       "5                   0   1   77  11   8  19  6  0  0  1   0    0    2   3   3   \n",
       "...               ...  ..  ...  ..  ..  .. .. .. .. ..  ..  ...  ...  ..  ..   \n",
       "90416               4   0   92   9   3  12  7  1  1  1   2    0    2   0   3   \n",
       "90417               4   0   96   5   7  12  2  0  0  3   0    0    0   1   2   \n",
       "90418               1   0   71   5   6  11  4  0  0  4   0    0    2   1   0   \n",
       "90419               4   0   75   2   8  10  0  0  0  1  15    0    1   2   2   \n",
       "90420               3   0   63   1   0   1  1  0  0  1   1    0    0   1   1   \n",
       "\n",
       "       R50  FF  FA  AF  SC  CCL  SCL   SI     MG   TO  ITC   T5  \n",
       "1        6   2   0  77  85  NaN  NaN  NaN    NaN  NaN  NaN  NaN  \n",
       "2        1   2   0  85  89  NaN  NaN  NaN    NaN  NaN  NaN  NaN  \n",
       "3        3   1   2  94  93  NaN  NaN  NaN    NaN  NaN  NaN  NaN  \n",
       "4        4   1   0  65  70  NaN  NaN  NaN    NaN  NaN  NaN  NaN  \n",
       "5        2   0   2  65  63  NaN  NaN  NaN    NaN  NaN  NaN  NaN  \n",
       "...    ...  ..  ..  ..  ..  ...  ...  ...    ...  ...  ...  ...  \n",
       "90416    0   1   1  65  51  0.0  0.0  3.0  153.0  4.0  2.0  1.0  \n",
       "90417    1   0   0  47  55  0.0  1.0  1.0   97.0  2.0  5.0  0.0  \n",
       "90418    0   1   0  56  55  0.0  1.0  1.0  121.0  0.0  5.0  1.0  \n",
       "90419    1   1   0  42  62  1.0  1.0  0.0    0.0  1.0  2.0  0.0  \n",
       "90420    0   0   1   8   7  1.0  0.0  0.0   26.0  1.0  0.0  1.0  \n",
       "\n",
       "[89628 rows x 43 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get footywire match data with a row for each player\n",
    "df_footywire_data = pd.read_csv(r'inputs/footywire_player_matches.csv', index_col=0)\n",
    "# Drop duplicates\n",
    "df_footywire_data = df_footywire_data.drop_duplicates()\n",
    "df_footywire_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### EDA\n",
    "\n",
    "- Most Supercoach scores are between 0 and 170\n",
    "- Positively distributed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "ax = sns.scatterplot(x=\"Date\", y=\"SC\", data=df_footywire_data.loc[: ,['Date', 'SC']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1dfe3ed07c8>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df_footywire_data['SC'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get the total SuperCoach and AFL Fantasy scores for each team, for every match"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Match_id</th>\n",
       "      <th>AF_Away</th>\n",
       "      <th>AF_Home</th>\n",
       "      <th>SC_Away</th>\n",
       "      <th>SC_Home</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4961</td>\n",
       "      <td>1411</td>\n",
       "      <td>1610</td>\n",
       "      <td>1656</td>\n",
       "      <td>1661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4962</td>\n",
       "      <td>1393</td>\n",
       "      <td>1713</td>\n",
       "      <td>1359</td>\n",
       "      <td>1944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4963</td>\n",
       "      <td>1452</td>\n",
       "      <td>1900</td>\n",
       "      <td>1332</td>\n",
       "      <td>1973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4964</td>\n",
       "      <td>1699</td>\n",
       "      <td>1364</td>\n",
       "      <td>1776</td>\n",
       "      <td>1534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4965</td>\n",
       "      <td>1643</td>\n",
       "      <td>1503</td>\n",
       "      <td>1742</td>\n",
       "      <td>1556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2032</th>\n",
       "      <td>9923</td>\n",
       "      <td>1363</td>\n",
       "      <td>1544</td>\n",
       "      <td>1495</td>\n",
       "      <td>1802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2033</th>\n",
       "      <td>9924</td>\n",
       "      <td>1338</td>\n",
       "      <td>1415</td>\n",
       "      <td>1622</td>\n",
       "      <td>1678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2034</th>\n",
       "      <td>9925</td>\n",
       "      <td>1492</td>\n",
       "      <td>1385</td>\n",
       "      <td>1680</td>\n",
       "      <td>1616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2035</th>\n",
       "      <td>9926</td>\n",
       "      <td>1518</td>\n",
       "      <td>1580</td>\n",
       "      <td>1657</td>\n",
       "      <td>1641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2036</th>\n",
       "      <td>9927</td>\n",
       "      <td>1478</td>\n",
       "      <td>1632</td>\n",
       "      <td>1521</td>\n",
       "      <td>1773</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2037 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Match_id  AF_Away  AF_Home  SC_Away  SC_Home\n",
       "0         4961     1411     1610     1656     1661\n",
       "1         4962     1393     1713     1359     1944\n",
       "2         4963     1452     1900     1332     1973\n",
       "3         4964     1699     1364     1776     1534\n",
       "4         4965     1643     1503     1742     1556\n",
       "...        ...      ...      ...      ...      ...\n",
       "2032      9923     1363     1544     1495     1802\n",
       "2033      9924     1338     1415     1622     1678\n",
       "2034      9925     1492     1385     1680     1616\n",
       "2035      9926     1518     1580     1657     1641\n",
       "2036      9927     1478     1632     1521     1773\n",
       "\n",
       "[2037 rows x 5 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a summary to get the total SC and AF points for each match\n",
    "df_match_summary = pd.pivot_table(df_footywire_data, index=['Match_id'], values=['SC', 'AF'], columns=['Status'], aggfunc=np.sum)\n",
    "df_match_summary = df_match_summary.reset_index()\n",
    "df_match_summary.columns = df_match_summary.columns.to_series().str.join('_')\n",
    "df_match_summary = df_match_summary.rename(columns={'Match_id_': 'Match_id'})\n",
    "df_match_summary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get actual match information and results\n",
    "This data was not readily available using the fitzRoy package, to get this data it must be scraped from the footywire site directly.\n",
    "\n",
    "Refer to scrape_footywire.py for code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>date</th>\n",
       "      <th>venue</th>\n",
       "      <th>crowd</th>\n",
       "      <th>match_id</th>\n",
       "      <th>home_team</th>\n",
       "      <th>away_team</th>\n",
       "      <th>home_team_score</th>\n",
       "      <th>away_team_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2010</td>\n",
       "      <td>Thu 25 Mar 7:10pm</td>\n",
       "      <td>MCG</td>\n",
       "      <td>72010</td>\n",
       "      <td>5089</td>\n",
       "      <td>Richmond</td>\n",
       "      <td>Carlton</td>\n",
       "      <td>64</td>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2010</td>\n",
       "      <td>Fri 26 Mar 7:40pm</td>\n",
       "      <td>MCG</td>\n",
       "      <td>57772</td>\n",
       "      <td>5090</td>\n",
       "      <td>Geelong</td>\n",
       "      <td>Essendon</td>\n",
       "      <td>125</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2010</td>\n",
       "      <td>Sat 27 Mar 2:10pm</td>\n",
       "      <td>MCG</td>\n",
       "      <td>45615</td>\n",
       "      <td>5091</td>\n",
       "      <td>Melbourne</td>\n",
       "      <td>Hawthorn</td>\n",
       "      <td>61</td>\n",
       "      <td>117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2010</td>\n",
       "      <td>Sat 27 Mar 7:10pm</td>\n",
       "      <td>ANZ Stadium</td>\n",
       "      <td>31330</td>\n",
       "      <td>5093</td>\n",
       "      <td>Sydney</td>\n",
       "      <td>St Kilda</td>\n",
       "      <td>88</td>\n",
       "      <td>96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2010</td>\n",
       "      <td>Sat 27 Mar 7:30pm</td>\n",
       "      <td>Gabba</td>\n",
       "      <td>29201</td>\n",
       "      <td>5092</td>\n",
       "      <td>Brisbane</td>\n",
       "      <td>West Coast</td>\n",
       "      <td>114</td>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>2019</td>\n",
       "      <td>Fri 13 Sep 7:50pm</td>\n",
       "      <td>MCG</td>\n",
       "      <td>51813</td>\n",
       "      <td>9923</td>\n",
       "      <td>Geelong</td>\n",
       "      <td>West Coast</td>\n",
       "      <td>88</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>203</th>\n",
       "      <td>2019</td>\n",
       "      <td>Sat 14 Sep 7:25pm</td>\n",
       "      <td>Gabba</td>\n",
       "      <td>30034</td>\n",
       "      <td>9924</td>\n",
       "      <td>Brisbane</td>\n",
       "      <td>GWS</td>\n",
       "      <td>80</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>2019</td>\n",
       "      <td>Fri 20 Sep 7:50pm</td>\n",
       "      <td>MCG</td>\n",
       "      <td>94423</td>\n",
       "      <td>9925</td>\n",
       "      <td>Richmond</td>\n",
       "      <td>Geelong</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>205</th>\n",
       "      <td>2019</td>\n",
       "      <td>Sat 21 Sep 4:35pm</td>\n",
       "      <td>MCG</td>\n",
       "      <td>77828</td>\n",
       "      <td>9926</td>\n",
       "      <td>Collingwood</td>\n",
       "      <td>GWS</td>\n",
       "      <td>52</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206</th>\n",
       "      <td>2019</td>\n",
       "      <td>Sat 28 Sep 2:30pm</td>\n",
       "      <td>MCG</td>\n",
       "      <td>100014</td>\n",
       "      <td>9927</td>\n",
       "      <td>Richmond</td>\n",
       "      <td>GWS</td>\n",
       "      <td>114</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2037 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     year                date        venue   crowd  match_id    home_team  \\\n",
       "0    2010   Thu 25 Mar 7:10pm          MCG   72010      5089     Richmond   \n",
       "1    2010   Fri 26 Mar 7:40pm          MCG   57772      5090      Geelong   \n",
       "2    2010   Sat 27 Mar 2:10pm          MCG   45615      5091    Melbourne   \n",
       "3    2010   Sat 27 Mar 7:10pm  ANZ Stadium   31330      5093       Sydney   \n",
       "4    2010   Sat 27 Mar 7:30pm        Gabba   29201      5092     Brisbane   \n",
       "..    ...                 ...          ...     ...       ...          ...   \n",
       "202  2019   Fri 13 Sep 7:50pm          MCG   51813      9923      Geelong   \n",
       "203  2019   Sat 14 Sep 7:25pm        Gabba   30034      9924     Brisbane   \n",
       "204  2019   Fri 20 Sep 7:50pm          MCG   94423      9925     Richmond   \n",
       "205  2019   Sat 21 Sep 4:35pm          MCG   77828      9926  Collingwood   \n",
       "206  2019   Sat 28 Sep 2:30pm          MCG  100014      9927     Richmond   \n",
       "\n",
       "      away_team  home_team_score  away_team_score  \n",
       "0       Carlton               64              120  \n",
       "1      Essendon              125               94  \n",
       "2      Hawthorn               61              117  \n",
       "3      St Kilda               88               96  \n",
       "4    West Coast              114               82  \n",
       "..          ...              ...              ...  \n",
       "202  West Coast               88               68  \n",
       "203         GWS               80               83  \n",
       "204     Geelong               85               66  \n",
       "205         GWS               52               56  \n",
       "206         GWS              114               25  \n",
       "\n",
       "[2037 rows x 9 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get footywire match data with a single row for each match\n",
    "df_footywire_matches = pd.read_csv(r'Inputs/footywire_team_matches.csv', index_col=0)\n",
    "df_footywire_matches['away_team'] = df_footywire_matches['away_team'].str.strip()\n",
    "df_footywire_matches"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Combine the actual match results with fantasy scoring summaries "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>date</th>\n",
       "      <th>venue</th>\n",
       "      <th>crowd</th>\n",
       "      <th>match_id</th>\n",
       "      <th>home_team</th>\n",
       "      <th>away_team</th>\n",
       "      <th>home_team_score</th>\n",
       "      <th>away_team_score</th>\n",
       "      <th>Match_id</th>\n",
       "      <th>AF_Away</th>\n",
       "      <th>AF_Home</th>\n",
       "      <th>SC_Away</th>\n",
       "      <th>SC_Home</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2010</td>\n",
       "      <td>Thu 25 Mar 7:10pm</td>\n",
       "      <td>MCG</td>\n",
       "      <td>72010</td>\n",
       "      <td>5089</td>\n",
       "      <td>Richmond</td>\n",
       "      <td>Carlton</td>\n",
       "      <td>64</td>\n",
       "      <td>120</td>\n",
       "      <td>5089</td>\n",
       "      <td>1938</td>\n",
       "      <td>1250</td>\n",
       "      <td>1986</td>\n",
       "      <td>1321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2010</td>\n",
       "      <td>Fri 26 Mar 7:40pm</td>\n",
       "      <td>MCG</td>\n",
       "      <td>57772</td>\n",
       "      <td>5090</td>\n",
       "      <td>Geelong</td>\n",
       "      <td>Essendon</td>\n",
       "      <td>125</td>\n",
       "      <td>94</td>\n",
       "      <td>5090</td>\n",
       "      <td>1503</td>\n",
       "      <td>1774</td>\n",
       "      <td>1523</td>\n",
       "      <td>1775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2010</td>\n",
       "      <td>Sat 27 Mar 2:10pm</td>\n",
       "      <td>MCG</td>\n",
       "      <td>45615</td>\n",
       "      <td>5091</td>\n",
       "      <td>Melbourne</td>\n",
       "      <td>Hawthorn</td>\n",
       "      <td>61</td>\n",
       "      <td>117</td>\n",
       "      <td>5091</td>\n",
       "      <td>1863</td>\n",
       "      <td>1516</td>\n",
       "      <td>1829</td>\n",
       "      <td>1470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2010</td>\n",
       "      <td>Sat 27 Mar 7:10pm</td>\n",
       "      <td>ANZ Stadium</td>\n",
       "      <td>31330</td>\n",
       "      <td>5093</td>\n",
       "      <td>Sydney</td>\n",
       "      <td>St Kilda</td>\n",
       "      <td>88</td>\n",
       "      <td>96</td>\n",
       "      <td>5093</td>\n",
       "      <td>1490</td>\n",
       "      <td>1532</td>\n",
       "      <td>1637</td>\n",
       "      <td>1661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2010</td>\n",
       "      <td>Sat 27 Mar 7:30pm</td>\n",
       "      <td>Gabba</td>\n",
       "      <td>29201</td>\n",
       "      <td>5092</td>\n",
       "      <td>Brisbane</td>\n",
       "      <td>West Coast</td>\n",
       "      <td>114</td>\n",
       "      <td>82</td>\n",
       "      <td>5092</td>\n",
       "      <td>1543</td>\n",
       "      <td>1607</td>\n",
       "      <td>1609</td>\n",
       "      <td>1691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2032</th>\n",
       "      <td>2019</td>\n",
       "      <td>Fri 13 Sep 7:50pm</td>\n",
       "      <td>MCG</td>\n",
       "      <td>51813</td>\n",
       "      <td>9923</td>\n",
       "      <td>Geelong</td>\n",
       "      <td>West Coast</td>\n",
       "      <td>88</td>\n",
       "      <td>68</td>\n",
       "      <td>9923</td>\n",
       "      <td>1363</td>\n",
       "      <td>1544</td>\n",
       "      <td>1495</td>\n",
       "      <td>1802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2033</th>\n",
       "      <td>2019</td>\n",
       "      <td>Sat 14 Sep 7:25pm</td>\n",
       "      <td>Gabba</td>\n",
       "      <td>30034</td>\n",
       "      <td>9924</td>\n",
       "      <td>Brisbane</td>\n",
       "      <td>GWS</td>\n",
       "      <td>80</td>\n",
       "      <td>83</td>\n",
       "      <td>9924</td>\n",
       "      <td>1338</td>\n",
       "      <td>1415</td>\n",
       "      <td>1622</td>\n",
       "      <td>1678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2034</th>\n",
       "      <td>2019</td>\n",
       "      <td>Fri 20 Sep 7:50pm</td>\n",
       "      <td>MCG</td>\n",
       "      <td>94423</td>\n",
       "      <td>9925</td>\n",
       "      <td>Richmond</td>\n",
       "      <td>Geelong</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>9925</td>\n",
       "      <td>1492</td>\n",
       "      <td>1385</td>\n",
       "      <td>1680</td>\n",
       "      <td>1616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2035</th>\n",
       "      <td>2019</td>\n",
       "      <td>Sat 21 Sep 4:35pm</td>\n",
       "      <td>MCG</td>\n",
       "      <td>77828</td>\n",
       "      <td>9926</td>\n",
       "      <td>Collingwood</td>\n",
       "      <td>GWS</td>\n",
       "      <td>52</td>\n",
       "      <td>56</td>\n",
       "      <td>9926</td>\n",
       "      <td>1518</td>\n",
       "      <td>1580</td>\n",
       "      <td>1657</td>\n",
       "      <td>1641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2036</th>\n",
       "      <td>2019</td>\n",
       "      <td>Sat 28 Sep 2:30pm</td>\n",
       "      <td>MCG</td>\n",
       "      <td>100014</td>\n",
       "      <td>9927</td>\n",
       "      <td>Richmond</td>\n",
       "      <td>GWS</td>\n",
       "      <td>114</td>\n",
       "      <td>25</td>\n",
       "      <td>9927</td>\n",
       "      <td>1478</td>\n",
       "      <td>1632</td>\n",
       "      <td>1521</td>\n",
       "      <td>1773</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2037 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      year                date        venue   crowd  match_id    home_team  \\\n",
       "0     2010   Thu 25 Mar 7:10pm          MCG   72010      5089     Richmond   \n",
       "1     2010   Fri 26 Mar 7:40pm          MCG   57772      5090      Geelong   \n",
       "2     2010   Sat 27 Mar 2:10pm          MCG   45615      5091    Melbourne   \n",
       "3     2010   Sat 27 Mar 7:10pm  ANZ Stadium   31330      5093       Sydney   \n",
       "4     2010   Sat 27 Mar 7:30pm        Gabba   29201      5092     Brisbane   \n",
       "...    ...                 ...          ...     ...       ...          ...   \n",
       "2032  2019   Fri 13 Sep 7:50pm          MCG   51813      9923      Geelong   \n",
       "2033  2019   Sat 14 Sep 7:25pm        Gabba   30034      9924     Brisbane   \n",
       "2034  2019   Fri 20 Sep 7:50pm          MCG   94423      9925     Richmond   \n",
       "2035  2019   Sat 21 Sep 4:35pm          MCG   77828      9926  Collingwood   \n",
       "2036  2019   Sat 28 Sep 2:30pm          MCG  100014      9927     Richmond   \n",
       "\n",
       "       away_team  home_team_score  away_team_score  Match_id  AF_Away  \\\n",
       "0        Carlton               64              120      5089     1938   \n",
       "1       Essendon              125               94      5090     1503   \n",
       "2       Hawthorn               61              117      5091     1863   \n",
       "3       St Kilda               88               96      5093     1490   \n",
       "4     West Coast              114               82      5092     1543   \n",
       "...          ...              ...              ...       ...      ...   \n",
       "2032  West Coast               88               68      9923     1363   \n",
       "2033         GWS               80               83      9924     1338   \n",
       "2034     Geelong               85               66      9925     1492   \n",
       "2035         GWS               52               56      9926     1518   \n",
       "2036         GWS              114               25      9927     1478   \n",
       "\n",
       "      AF_Home  SC_Away  SC_Home  \n",
       "0        1250     1986     1321  \n",
       "1        1774     1523     1775  \n",
       "2        1516     1829     1470  \n",
       "3        1532     1637     1661  \n",
       "4        1607     1609     1691  \n",
       "...       ...      ...      ...  \n",
       "2032     1544     1495     1802  \n",
       "2033     1415     1622     1678  \n",
       "2034     1385     1680     1616  \n",
       "2035     1580     1657     1641  \n",
       "2036     1632     1521     1773  \n",
       "\n",
       "[2037 rows x 14 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Merge the fantasy summary and the match scores\n",
    "df_match_fantasy_summary = pd.merge(df_footywire_matches, df_match_summary,how='left', left_on='match_id', right_on='Match_id')\n",
    "# Drop bye and cancelled games\n",
    "df_match_fantasy_summary = df_match_fantasy_summary.drop(df_match_fantasy_summary.loc[df_match_fantasy_summary['venue'] == \"BYE\"].index)\n",
    "df_match_fantasy_summary = df_match_fantasy_summary.drop(df_match_fantasy_summary.loc[df_match_fantasy_summary['venue'] == \"MATCH CANCELLED\"].index)\n",
    "df_match_fantasy_summary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculate the result for each type of scoring (Actual scoring, AFL Fantasy Scoring, and Supercoach scoring)\n",
    "For example... the match of Richmond vs Carlton, Round 1 2010\n",
    "\n",
    "Actual result = Carlton won 120 to 62, therefore this is 'away_win'\n",
    "\n",
    "AFL Fantasy result = Carlton players scored 1938 points vs Richmond's 1250, therefore this is 'away_win'\n",
    "\n",
    "SC result = Carlton players scored 1986 points vs Richmond's 1321, therefore this is 'away_win'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>date</th>\n",
       "      <th>venue</th>\n",
       "      <th>crowd</th>\n",
       "      <th>match_id</th>\n",
       "      <th>home_team</th>\n",
       "      <th>away_team</th>\n",
       "      <th>home_team_score</th>\n",
       "      <th>away_team_score</th>\n",
       "      <th>Match_id</th>\n",
       "      <th>AF_Away</th>\n",
       "      <th>AF_Home</th>\n",
       "      <th>SC_Away</th>\n",
       "      <th>SC_Home</th>\n",
       "      <th>match_result</th>\n",
       "      <th>match_result_home_margin</th>\n",
       "      <th>match_result_away_margin</th>\n",
       "      <th>sc_result</th>\n",
       "      <th>sc_correct?</th>\n",
       "      <th>af_result</th>\n",
       "      <th>af_correct?</th>\n",
       "      <th>diff_fantasy_result</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2010</td>\n",
       "      <td>Thu 25 Mar 7:10pm</td>\n",
       "      <td>MCG</td>\n",
       "      <td>72010</td>\n",
       "      <td>5089</td>\n",
       "      <td>Richmond</td>\n",
       "      <td>Carlton</td>\n",
       "      <td>64</td>\n",
       "      <td>120</td>\n",
       "      <td>5089</td>\n",
       "      <td>1938</td>\n",
       "      <td>1250</td>\n",
       "      <td>1986</td>\n",
       "      <td>1321</td>\n",
       "      <td>away_win</td>\n",
       "      <td>-56</td>\n",
       "      <td>56</td>\n",
       "      <td>away_win</td>\n",
       "      <td>True</td>\n",
       "      <td>away_win</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2010</td>\n",
       "      <td>Fri 26 Mar 7:40pm</td>\n",
       "      <td>MCG</td>\n",
       "      <td>57772</td>\n",
       "      <td>5090</td>\n",
       "      <td>Geelong</td>\n",
       "      <td>Essendon</td>\n",
       "      <td>125</td>\n",
       "      <td>94</td>\n",
       "      <td>5090</td>\n",
       "      <td>1503</td>\n",
       "      <td>1774</td>\n",
       "      <td>1523</td>\n",
       "      <td>1775</td>\n",
       "      <td>home_win</td>\n",
       "      <td>31</td>\n",
       "      <td>-31</td>\n",
       "      <td>home_win</td>\n",
       "      <td>True</td>\n",
       "      <td>home_win</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2010</td>\n",
       "      <td>Sat 27 Mar 2:10pm</td>\n",
       "      <td>MCG</td>\n",
       "      <td>45615</td>\n",
       "      <td>5091</td>\n",
       "      <td>Melbourne</td>\n",
       "      <td>Hawthorn</td>\n",
       "      <td>61</td>\n",
       "      <td>117</td>\n",
       "      <td>5091</td>\n",
       "      <td>1863</td>\n",
       "      <td>1516</td>\n",
       "      <td>1829</td>\n",
       "      <td>1470</td>\n",
       "      <td>away_win</td>\n",
       "      <td>-56</td>\n",
       "      <td>56</td>\n",
       "      <td>away_win</td>\n",
       "      <td>True</td>\n",
       "      <td>away_win</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2010</td>\n",
       "      <td>Sat 27 Mar 7:10pm</td>\n",
       "      <td>ANZ Stadium</td>\n",
       "      <td>31330</td>\n",
       "      <td>5093</td>\n",
       "      <td>Sydney</td>\n",
       "      <td>St Kilda</td>\n",
       "      <td>88</td>\n",
       "      <td>96</td>\n",
       "      <td>5093</td>\n",
       "      <td>1490</td>\n",
       "      <td>1532</td>\n",
       "      <td>1637</td>\n",
       "      <td>1661</td>\n",
       "      <td>away_win</td>\n",
       "      <td>-8</td>\n",
       "      <td>8</td>\n",
       "      <td>home_win</td>\n",
       "      <td>False</td>\n",
       "      <td>home_win</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2010</td>\n",
       "      <td>Sat 27 Mar 7:30pm</td>\n",
       "      <td>Gabba</td>\n",
       "      <td>29201</td>\n",
       "      <td>5092</td>\n",
       "      <td>Brisbane</td>\n",
       "      <td>West Coast</td>\n",
       "      <td>114</td>\n",
       "      <td>82</td>\n",
       "      <td>5092</td>\n",
       "      <td>1543</td>\n",
       "      <td>1607</td>\n",
       "      <td>1609</td>\n",
       "      <td>1691</td>\n",
       "      <td>home_win</td>\n",
       "      <td>32</td>\n",
       "      <td>-32</td>\n",
       "      <td>home_win</td>\n",
       "      <td>True</td>\n",
       "      <td>home_win</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2032</th>\n",
       "      <td>2019</td>\n",
       "      <td>Fri 13 Sep 7:50pm</td>\n",
       "      <td>MCG</td>\n",
       "      <td>51813</td>\n",
       "      <td>9923</td>\n",
       "      <td>Geelong</td>\n",
       "      <td>West Coast</td>\n",
       "      <td>88</td>\n",
       "      <td>68</td>\n",
       "      <td>9923</td>\n",
       "      <td>1363</td>\n",
       "      <td>1544</td>\n",
       "      <td>1495</td>\n",
       "      <td>1802</td>\n",
       "      <td>home_win</td>\n",
       "      <td>20</td>\n",
       "      <td>-20</td>\n",
       "      <td>home_win</td>\n",
       "      <td>True</td>\n",
       "      <td>home_win</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2033</th>\n",
       "      <td>2019</td>\n",
       "      <td>Sat 14 Sep 7:25pm</td>\n",
       "      <td>Gabba</td>\n",
       "      <td>30034</td>\n",
       "      <td>9924</td>\n",
       "      <td>Brisbane</td>\n",
       "      <td>GWS</td>\n",
       "      <td>80</td>\n",
       "      <td>83</td>\n",
       "      <td>9924</td>\n",
       "      <td>1338</td>\n",
       "      <td>1415</td>\n",
       "      <td>1622</td>\n",
       "      <td>1678</td>\n",
       "      <td>away_win</td>\n",
       "      <td>-3</td>\n",
       "      <td>3</td>\n",
       "      <td>home_win</td>\n",
       "      <td>False</td>\n",
       "      <td>home_win</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2034</th>\n",
       "      <td>2019</td>\n",
       "      <td>Fri 20 Sep 7:50pm</td>\n",
       "      <td>MCG</td>\n",
       "      <td>94423</td>\n",
       "      <td>9925</td>\n",
       "      <td>Richmond</td>\n",
       "      <td>Geelong</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>9925</td>\n",
       "      <td>1492</td>\n",
       "      <td>1385</td>\n",
       "      <td>1680</td>\n",
       "      <td>1616</td>\n",
       "      <td>home_win</td>\n",
       "      <td>19</td>\n",
       "      <td>-19</td>\n",
       "      <td>away_win</td>\n",
       "      <td>False</td>\n",
       "      <td>away_win</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2035</th>\n",
       "      <td>2019</td>\n",
       "      <td>Sat 21 Sep 4:35pm</td>\n",
       "      <td>MCG</td>\n",
       "      <td>77828</td>\n",
       "      <td>9926</td>\n",
       "      <td>Collingwood</td>\n",
       "      <td>GWS</td>\n",
       "      <td>52</td>\n",
       "      <td>56</td>\n",
       "      <td>9926</td>\n",
       "      <td>1518</td>\n",
       "      <td>1580</td>\n",
       "      <td>1657</td>\n",
       "      <td>1641</td>\n",
       "      <td>away_win</td>\n",
       "      <td>-4</td>\n",
       "      <td>4</td>\n",
       "      <td>away_win</td>\n",
       "      <td>True</td>\n",
       "      <td>home_win</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2036</th>\n",
       "      <td>2019</td>\n",
       "      <td>Sat 28 Sep 2:30pm</td>\n",
       "      <td>MCG</td>\n",
       "      <td>100014</td>\n",
       "      <td>9927</td>\n",
       "      <td>Richmond</td>\n",
       "      <td>GWS</td>\n",
       "      <td>114</td>\n",
       "      <td>25</td>\n",
       "      <td>9927</td>\n",
       "      <td>1478</td>\n",
       "      <td>1632</td>\n",
       "      <td>1521</td>\n",
       "      <td>1773</td>\n",
       "      <td>home_win</td>\n",
       "      <td>89</td>\n",
       "      <td>-89</td>\n",
       "      <td>home_win</td>\n",
       "      <td>True</td>\n",
       "      <td>home_win</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2037 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      year                date        venue   crowd  match_id    home_team  \\\n",
       "0     2010   Thu 25 Mar 7:10pm          MCG   72010      5089     Richmond   \n",
       "1     2010   Fri 26 Mar 7:40pm          MCG   57772      5090      Geelong   \n",
       "2     2010   Sat 27 Mar 2:10pm          MCG   45615      5091    Melbourne   \n",
       "3     2010   Sat 27 Mar 7:10pm  ANZ Stadium   31330      5093       Sydney   \n",
       "4     2010   Sat 27 Mar 7:30pm        Gabba   29201      5092     Brisbane   \n",
       "...    ...                 ...          ...     ...       ...          ...   \n",
       "2032  2019   Fri 13 Sep 7:50pm          MCG   51813      9923      Geelong   \n",
       "2033  2019   Sat 14 Sep 7:25pm        Gabba   30034      9924     Brisbane   \n",
       "2034  2019   Fri 20 Sep 7:50pm          MCG   94423      9925     Richmond   \n",
       "2035  2019   Sat 21 Sep 4:35pm          MCG   77828      9926  Collingwood   \n",
       "2036  2019   Sat 28 Sep 2:30pm          MCG  100014      9927     Richmond   \n",
       "\n",
       "       away_team  home_team_score  away_team_score  Match_id  AF_Away  \\\n",
       "0        Carlton               64              120      5089     1938   \n",
       "1       Essendon              125               94      5090     1503   \n",
       "2       Hawthorn               61              117      5091     1863   \n",
       "3       St Kilda               88               96      5093     1490   \n",
       "4     West Coast              114               82      5092     1543   \n",
       "...          ...              ...              ...       ...      ...   \n",
       "2032  West Coast               88               68      9923     1363   \n",
       "2033         GWS               80               83      9924     1338   \n",
       "2034     Geelong               85               66      9925     1492   \n",
       "2035         GWS               52               56      9926     1518   \n",
       "2036         GWS              114               25      9927     1478   \n",
       "\n",
       "      AF_Home  SC_Away  SC_Home match_result  match_result_home_margin  \\\n",
       "0        1250     1986     1321     away_win                       -56   \n",
       "1        1774     1523     1775     home_win                        31   \n",
       "2        1516     1829     1470     away_win                       -56   \n",
       "3        1532     1637     1661     away_win                        -8   \n",
       "4        1607     1609     1691     home_win                        32   \n",
       "...       ...      ...      ...          ...                       ...   \n",
       "2032     1544     1495     1802     home_win                        20   \n",
       "2033     1415     1622     1678     away_win                        -3   \n",
       "2034     1385     1680     1616     home_win                        19   \n",
       "2035     1580     1657     1641     away_win                        -4   \n",
       "2036     1632     1521     1773     home_win                        89   \n",
       "\n",
       "      match_result_away_margin sc_result  sc_correct? af_result  af_correct?  \\\n",
       "0                           56  away_win         True  away_win         True   \n",
       "1                          -31  home_win         True  home_win         True   \n",
       "2                           56  away_win         True  away_win         True   \n",
       "3                            8  home_win        False  home_win        False   \n",
       "4                          -32  home_win         True  home_win         True   \n",
       "...                        ...       ...          ...       ...          ...   \n",
       "2032                       -20  home_win         True  home_win         True   \n",
       "2033                         3  home_win        False  home_win        False   \n",
       "2034                       -19  away_win        False  away_win        False   \n",
       "2035                         4  away_win         True  home_win        False   \n",
       "2036                       -89  home_win         True  home_win         True   \n",
       "\n",
       "      diff_fantasy_result  \n",
       "0                   False  \n",
       "1                   False  \n",
       "2                   False  \n",
       "3                   False  \n",
       "4                   False  \n",
       "...                   ...  \n",
       "2032                False  \n",
       "2033                False  \n",
       "2034                False  \n",
       "2035                 True  \n",
       "2036                False  \n",
       "\n",
       "[2037 rows x 22 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the match result, the SC result, and the AF result\n",
    "def get_match_result(home_score, away_score):\n",
    "    result = \"\"\n",
    "    if home_score > away_score:\n",
    "        result = \"home_win\"\n",
    "    elif home_score < away_score:\n",
    "        result = \"away_win\"\n",
    "    elif home_score == away_score:\n",
    "        result = \"tie\"\n",
    "    return result\n",
    "\n",
    "df_match_fantasy_summary['match_result'] = df_match_fantasy_summary.apply(lambda row: get_match_result(row['home_team_score'], row['away_team_score']), axis=1)\n",
    "df_match_fantasy_summary['match_result_home_margin'] = df_match_fantasy_summary['home_team_score'] - df_match_fantasy_summary['away_team_score']\n",
    "df_match_fantasy_summary['match_result_away_margin'] = df_match_fantasy_summary['away_team_score'] - df_match_fantasy_summary['home_team_score']\n",
    "df_match_fantasy_summary['sc_result'] = df_match_fantasy_summary.apply(lambda row: get_match_result(row['SC_Home'], row['SC_Away']), axis=1)\n",
    "df_match_fantasy_summary['sc_correct?'] = df_match_fantasy_summary.apply(lambda row: row['sc_result'] == row['match_result'], axis=1)\n",
    "df_match_fantasy_summary['af_result'] = df_match_fantasy_summary.apply(lambda row: get_match_result(row['AF_Home'], row['AF_Away']), axis=1)\n",
    "df_match_fantasy_summary['af_correct?'] = df_match_fantasy_summary.apply(lambda row: row['af_result'] == row['match_result'], axis=1)\n",
    "df_match_fantasy_summary['diff_fantasy_result'] = df_match_fantasy_summary.apply(lambda row: row['sc_result'] != row['af_result'], axis=1)\n",
    "df_match_fantasy_summary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### View the accuracy of each fantasy scoring method\n",
    "When a team's players score more supercoach points than the opposition, they win approx 87% of matches.\n",
    "\n",
    "When a team's players score more AFL Fantasy points than the opposition, they win approx 78% of matches."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>True</th>\n",
       "      <th>False</th>\n",
       "      <th>Total</th>\n",
       "      <th>% Correct</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>sc_correct?</th>\n",
       "      <td>1783</td>\n",
       "      <td>254</td>\n",
       "      <td>2037</td>\n",
       "      <td>87.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>af_correct?</th>\n",
       "      <td>1607</td>\n",
       "      <td>430</td>\n",
       "      <td>2037</td>\n",
       "      <td>78.89</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             True  False  Total  % Correct\n",
       "sc_correct?  1783    254   2037      87.53\n",
       "af_correct?  1607    430   2037      78.89"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sc_series = df_match_fantasy_summary['sc_correct?'].value_counts()\n",
    "af_series = df_match_fantasy_summary['af_correct?'].value_counts()\n",
    "data_dict = {\n",
    "    'sc_correct?': sc_series,\n",
    "    'af_correct?': af_series\n",
    "}\n",
    "df_fantasy_accuracy = pd.DataFrame(data_dict).transpose()\n",
    "df_fantasy_accuracy['Total'] = df_fantasy_accuracy[True] + df_fantasy_accuracy[False] \n",
    "df_fantasy_accuracy['% Correct'] = round((df_fantasy_accuracy[True] / df_fantasy_accuracy['Total']) * 100, 2)\n",
    "df_fantasy_accuracy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Supercoach is the most accurate measure of indicating wins\n",
    "Round 1 goes to Supercoach!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Now to measure how close each fantasy method is at predicting the margin for a match\n",
    "We can go further with this data, the \"best\" method should be more closely aligned with the actual margin of a match."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### First... plot the past results (total fantasy score & margin)\n",
    "There is a clear linear pattern here, teams that score higher are more likely to have a higher winner margin... and vice versa."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# SC Home team results\n",
    "plt.scatter(df_match_fantasy_summary['match_result_home_margin'], df_match_fantasy_summary['SC_Home'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# AF Home team results\n",
    "plt.scatter(df_match_fantasy_summary['match_result_home_margin'], df_match_fantasy_summary['AF_Home'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Build linear regression models for both Fantasy Scoring methods\n",
    "The method with the least mean squared error rate is the closest at predicting the actual match result."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### AFL Fantasy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1059.4548825861575"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import linear_model\n",
    "from sklearn import model_selection\n",
    "\n",
    "X = df_match_fantasy_summary['AF_Home']\n",
    "X = np.array(X).reshape(-1, 1)\n",
    "y = df_match_fantasy_summary['match_result_home_margin']\n",
    "\n",
    "X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y, test_size = 0.30)\n",
    "\n",
    "clr = linear_model.LinearRegression()\n",
    "\n",
    "cv_result = model_selection.cross_val_score(estimator=clr\n",
    "                                , X=X_train\n",
    "                                , y=y_train\n",
    "                                , cv=50\n",
    "                                , scoring=\"neg_mean_squared_error\"\n",
    "                                )\n",
    "\n",
    "cv_result.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import metrics\n",
    "\n",
    "clr.fit(X_train, y_train)\n",
    "pred = clr.predict(X_test)\n",
    "af_mse = metrics.mean_squared_error(pred, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Supercoach"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-466.8037183125851"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import linear_model\n",
    "from sklearn import model_selection\n",
    "\n",
    "X = df_match_fantasy_summary['SC_Home']\n",
    "X = np.array(X).reshape(-1, 1)\n",
    "y = df_match_fantasy_summary['match_result_home_margin']\n",
    "\n",
    "X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y, test_size = 0.30)\n",
    "\n",
    "clr = linear_model.LinearRegression()\n",
    "\n",
    "cv_result = model_selection.cross_val_score(estimator=clr\n",
    "                                , X=X_train\n",
    "                                , y=y_train\n",
    "                                , cv=50\n",
    "                                , scoring=\"neg_mean_squared_error\"\n",
    "                                )\n",
    "\n",
    "cv_result.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import metrics\n",
    "\n",
    "clr.fit(X_train, y_train)\n",
    "pred = clr.predict(X_test)\n",
    "sc_mse = metrics.mean_squared_error(pred, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Assess the MSE of both Fantasy Methods...\n",
    "Supercoach score is easily the more reliable indicator of the winning/losing margin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Supercoach Mean Squared Error': 520.0,\n",
       " 'AFL Fantasy Mean Squared Error': 1154.8}"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "{\n",
    "    \"Supercoach Mean Squared Error\": round(sc_mse, 1),\n",
    "    \"AFL Fantasy Mean Squared Error\": round(af_mse, 1)\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Supercoach Wins! 2-0"
   ]
  }
 ],
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