bayes_qb_tds_hacking.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import petl\n",
    "import psycopg2\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (20.0, 10.0)\n",
    "\n",
    "# suppress bullshit scipy warnings\n",
    "import warnings\n",
    "warnings.filterwarnings(action=\"ignore\", module=\"scipy\", message=\"^internal gelsd\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "conn = psycopg2.connect('dbname=nfldb')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "qb_agg = petl.fromdb(conn, 'select * from lombardi.qb_aggs')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class='petl'>\n",
       "<thead>\n",
       "<tr>\n",
       "<th>year</th>\n",
       "<th>full_name</th>\n",
       "<th>avg_yds</th>\n",
       "<th>avg_tds</th>\n",
       "<th>avg_int</th>\n",
       "<th>avg_cmp</th>\n",
       "<th>avg_att</th>\n",
       "<th>std_yds</th>\n",
       "<th>std_tds</th>\n",
       "<th>std_int</th>\n",
       "<th>std_cmp</th>\n",
       "<th>std_att</th>\n",
       "<th>num_games</th>\n",
       "</tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "<tr>\n",
       "<td style='text-align: right'>2009</td>\n",
       "<td>Aaron Rodgers</td>\n",
       "<td>277.13</td>\n",
       "<td>1.88</td>\n",
       "<td>0.44</td>\n",
       "<td>21.88</td>\n",
       "<td>33.81</td>\n",
       "<td>68.14</td>\n",
       "<td>0.96</td>\n",
       "<td>0.89</td>\n",
       "<td>6.20</td>\n",
       "<td>8.61</td>\n",
       "<td style='text-align: right'>16</td>\n",
       "</tr>\n",
       "<tr>\n",
       "<td style='text-align: right'>2009</td>\n",
       "<td>Alex Smith</td>\n",
       "<td>213.64</td>\n",
       "<td>1.64</td>\n",
       "<td>1.09</td>\n",
       "<td>20.45</td>\n",
       "<td>33.82</td>\n",
       "<td>55.69</td>\n",
       "<td>0.92</td>\n",
       "<td>1.14</td>\n",
       "<td>4.95</td>\n",
       "<td>7.85</td>\n",
       "<td style='text-align: right'>11</td>\n",
       "</tr>\n",
       "<tr>\n",
       "<td style='text-align: right'>2009</td>\n",
       "<td>Ben Roethlisberger</td>\n",
       "<td>288.53</td>\n",
       "<td>1.73</td>\n",
       "<td>0.80</td>\n",
       "<td>22.47</td>\n",
       "<td>33.73</td>\n",
       "<td>96.10</td>\n",
       "<td>1.10</td>\n",
       "<td>0.68</td>\n",
       "<td>5.53</td>\n",
       "<td>6.52</td>\n",
       "<td style='text-align: right'>15</td>\n",
       "</tr>\n",
       "<tr>\n",
       "<td style='text-align: right'>2009</td>\n",
       "<td>Brian Hoyer</td>\n",
       "<td>47.33</td>\n",
       "<td>0.00</td>\n",
       "<td>0.00</td>\n",
       "<td>6.33</td>\n",
       "<td>9.00</td>\n",
       "<td>26.31</td>\n",
       "<td>0.00</td>\n",
       "<td>0.00</td>\n",
       "<td>3.79</td>\n",
       "<td>4.36</td>\n",
       "<td style='text-align: right'>3</td>\n",
       "</tr>\n",
       "<tr>\n",
       "<td style='text-align: right'>2009</td>\n",
       "<td>Carson Palmer</td>\n",
       "<td>193.38</td>\n",
       "<td>1.31</td>\n",
       "<td>0.81</td>\n",
       "<td>17.63</td>\n",
       "<td>29.13</td>\n",
       "<td>77.50</td>\n",
       "<td>1.30</td>\n",
       "<td>0.75</td>\n",
       "<td>5.77</td>\n",
       "<td>8.06</td>\n",
       "<td style='text-align: right'>16</td>\n",
       "</tr>\n",
       "</tbody>\n",
       "</table>\n",
       "<p><strong>...</strong></p>"
      ],
      "text/plain": [
       "+------+----------------------+-------------------+-----------------+-----------------+------------------+------------------+------------------+-----------------+-----------------+-----------------+-----------------+-----------+\n",
       "| year | full_name            | avg_yds           | avg_tds         | avg_int         | avg_cmp          | avg_att          | std_yds          | std_tds         | std_int         | std_cmp         | std_att         | num_games |\n",
       "+======+======================+===================+=================+=================+==================+==================+==================+=================+=================+=================+=================+===========+\n",
       "| 2009 | 'Aaron Rodgers'      | Decimal('277.13') | Decimal('1.88') | Decimal('0.44') | Decimal('21.88') | Decimal('33.81') | Decimal('68.14') | Decimal('0.96') | Decimal('0.89') | Decimal('6.20') | Decimal('8.61') |        16 |\n",
       "+------+----------------------+-------------------+-----------------+-----------------+------------------+------------------+------------------+-----------------+-----------------+-----------------+-----------------+-----------+\n",
       "| 2009 | 'Alex Smith'         | Decimal('213.64') | Decimal('1.64') | Decimal('1.09') | Decimal('20.45') | Decimal('33.82') | Decimal('55.69') | Decimal('0.92') | Decimal('1.14') | Decimal('4.95') | Decimal('7.85') |        11 |\n",
       "+------+----------------------+-------------------+-----------------+-----------------+------------------+------------------+------------------+-----------------+-----------------+-----------------+-----------------+-----------+\n",
       "| 2009 | 'Ben Roethlisberger' | Decimal('288.53') | Decimal('1.73') | Decimal('0.80') | Decimal('22.47') | Decimal('33.73') | Decimal('96.10') | Decimal('1.10') | Decimal('0.68') | Decimal('5.53') | Decimal('6.52') |        15 |\n",
       "+------+----------------------+-------------------+-----------------+-----------------+------------------+------------------+------------------+-----------------+-----------------+-----------------+-----------------+-----------+\n",
       "| 2009 | 'Brian Hoyer'        | Decimal('47.33')  | Decimal('0.00') | Decimal('0.00') | Decimal('6.33')  | Decimal('9.00')  | Decimal('26.31') | Decimal('0.00') | Decimal('0.00') | Decimal('3.79') | Decimal('4.36') |         3 |\n",
       "+------+----------------------+-------------------+-----------------+-----------------+------------------+------------------+------------------+-----------------+-----------------+-----------------+-----------------+-----------+\n",
       "| 2009 | 'Carson Palmer'      | Decimal('193.38') | Decimal('1.31') | Decimal('0.81') | Decimal('17.63') | Decimal('29.13') | Decimal('77.50') | Decimal('1.30') | Decimal('0.75') | Decimal('5.77') | Decimal('8.06') |        16 |\n",
       "+------+----------------------+-------------------+-----------------+-----------------+------------------+------------------+------------------+-----------------+-----------------+-----------------+-----------------+-----------+\n",
       "..."
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qb_agg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "qb_agg_full = qb_agg.selecteq('num_games', 16).todataframe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>full_name</th>\n",
       "      <th>avg_yds</th>\n",
       "      <th>avg_tds</th>\n",
       "      <th>avg_int</th>\n",
       "      <th>avg_cmp</th>\n",
       "      <th>avg_att</th>\n",
       "      <th>std_yds</th>\n",
       "      <th>std_tds</th>\n",
       "      <th>std_int</th>\n",
       "      <th>std_cmp</th>\n",
       "      <th>std_att</th>\n",
       "      <th>num_games</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2009</td>\n",
       "      <td>Aaron Rodgers</td>\n",
       "      <td>277.13</td>\n",
       "      <td>1.88</td>\n",
       "      <td>0.44</td>\n",
       "      <td>21.88</td>\n",
       "      <td>33.81</td>\n",
       "      <td>68.14</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.89</td>\n",
       "      <td>6.20</td>\n",
       "      <td>8.61</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2009</td>\n",
       "      <td>Carson Palmer</td>\n",
       "      <td>193.38</td>\n",
       "      <td>1.31</td>\n",
       "      <td>0.81</td>\n",
       "      <td>17.63</td>\n",
       "      <td>29.13</td>\n",
       "      <td>77.50</td>\n",
       "      <td>1.30</td>\n",
       "      <td>0.75</td>\n",
       "      <td>5.77</td>\n",
       "      <td>8.06</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2009</td>\n",
       "      <td>Eli Manning</td>\n",
       "      <td>251.31</td>\n",
       "      <td>1.69</td>\n",
       "      <td>0.88</td>\n",
       "      <td>19.81</td>\n",
       "      <td>31.81</td>\n",
       "      <td>73.89</td>\n",
       "      <td>1.01</td>\n",
       "      <td>0.89</td>\n",
       "      <td>5.94</td>\n",
       "      <td>8.60</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2009</td>\n",
       "      <td>Joe Flacco</td>\n",
       "      <td>225.81</td>\n",
       "      <td>1.31</td>\n",
       "      <td>0.75</td>\n",
       "      <td>19.69</td>\n",
       "      <td>31.19</td>\n",
       "      <td>77.50</td>\n",
       "      <td>1.14</td>\n",
       "      <td>0.93</td>\n",
       "      <td>5.55</td>\n",
       "      <td>8.77</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2009</td>\n",
       "      <td>Matt Schaub</td>\n",
       "      <td>298.13</td>\n",
       "      <td>1.81</td>\n",
       "      <td>0.94</td>\n",
       "      <td>24.75</td>\n",
       "      <td>36.44</td>\n",
       "      <td>63.62</td>\n",
       "      <td>1.17</td>\n",
       "      <td>0.68</td>\n",
       "      <td>6.06</td>\n",
       "      <td>6.82</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year      full_name avg_yds avg_tds avg_int avg_cmp avg_att std_yds  \\\n",
       "0  2009  Aaron Rodgers  277.13    1.88    0.44   21.88   33.81   68.14   \n",
       "1  2009  Carson Palmer  193.38    1.31    0.81   17.63   29.13   77.50   \n",
       "2  2009    Eli Manning  251.31    1.69    0.88   19.81   31.81   73.89   \n",
       "3  2009     Joe Flacco  225.81    1.31    0.75   19.69   31.19   77.50   \n",
       "4  2009    Matt Schaub  298.13    1.81    0.94   24.75   36.44   63.62   \n",
       "\n",
       "  std_tds std_int std_cmp std_att  num_games  \n",
       "0    0.96    0.89    6.20    8.61         16  \n",
       "1    1.30    0.75    5.77    8.06         16  \n",
       "2    1.01    0.89    5.94    8.60         16  \n",
       "3    1.14    0.93    5.55    8.77         16  \n",
       "4    1.17    0.68    6.06    6.82         16  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qb_agg_full.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x110be1e48>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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CIQAAAIDGCIQAAAAAGiMQAgAAAGiMQAgAAACgMQIhAAAAgMYIhAAAAAAa81TXBQAAADC+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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1146132b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(qb_agg_full['avg_tds'], qb_agg_full['std_tds'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tds = list(qb_agg_full['avg_tds'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x114c38898>]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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EE0IAAABQx2a1Ls/oPfrleROGV51CHTEIAQAAQJ1asWFbbly0NidNHZ2WFvcGsfsMQgAA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\nFEIAAAAAPaMQAgAAAOgZhRAAAABAz/wXSfARD9sEwkgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1149a1630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(range(len(yds)), sorted(tds))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "min_tds = 0\n",
    "max_tds = 3\n",
    "div = 0.5\n",
    "\n",
    "num_bins = int((max_tds - min_tds) / div)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AAAYQgQAAAAAG+L+LvYwO/wDqswAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1104b9400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x,y, = plt.hist(tds, bins=6, range=(0,3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'qb_aggs_full' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-52-bd2a8cbf0c55>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mqb_aggs_full\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'qb_aggs_full' is not defined"
     ]
    }
   ],
   "source": [
    "qb_aggs_full.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "qbs = petl.fromdataframe(qb_agg_full)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "qbs = qbs.addfield('bucket', lambda rec: int(float(rec['avg_tds']) // 0.5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "qb_buckets = qbs.sort(('bucket', 'year', 'full_name'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class='petl'>\n",
       "<thead>\n",
       "<tr>\n",
       "<th>year</th>\n",
       "<th>full_name</th>\n",
       "<th>avg_yds</th>\n",
       "<th>avg_tds</th>\n",
       "<th>avg_int</th>\n",
       "<th>avg_cmp</th>\n",
       "<th>avg_att</th>\n",
       "<th>std_yds</th>\n",
       "<th>std_tds</th>\n",
       "<th>std_int</th>\n",
       "<th>std_cmp</th>\n",
       "<th>std_att</th>\n",
       "<th>num_games</th>\n",
       "<th>bucket</th>\n",
       "</tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "<tr>\n",
       "<td style='text-align: right'>2012</td>\n",
       "<td>Ryan Tannehill</td>\n",
       "<td>205.88</td>\n",
       "<td>0.75</td>\n",
       "<td>0.81</td>\n",
       "<td>17.63</td>\n",
       "<td>30.25</td>\n",
       "<td>86.01</td>\n",
       "<td>0.77</td>\n",
       "<td>1.11</td>\n",
       "<td>5.56</td>\n",
       "<td>8.42</td>\n",
       "<td style='text-align: right'>16</td>\n",
       "<td style='text-align: right'>1</td>\n",
       "</tr>\n",
       "<tr>\n",
       "<td style='text-align: right'>2013</td>\n",
       "<td>Geno Smith</td>\n",
       "<td>190.38</td>\n",
       "<td>0.75</td>\n",
       "<td>1.31</td>\n",
       "<td>15.44</td>\n",
       "<td>27.69</td>\n",
       "<td>74.23</td>\n",
       "<td>0.93</td>\n",
       "<td>1.01</td>\n",
       "<td>5.62</td>\n",
       "<td>7.54</td>\n",
       "<td style='text-align: right'>16</td>\n",
       "<td style='text-align: right'>1</td>\n",
       "</tr>\n",
       "<tr>\n",
       "<td style='text-align: right'>2015</td>\n",
       "<td>Teddy Bridgewater</td>\n",
       "<td>201.94</td>\n",
       "<td>0.88</td>\n",
       "<td>0.56</td>\n",
       "<td>18.25</td>\n",
       "<td>27.94</td>\n",
       "<td>74.79</td>\n",
       "<td>1.02</td>\n",
       "<td>0.63</td>\n",
       "<td>5.26</td>\n",
       "<td>7.06</td>\n",
       "<td style='text-align: right'>16</td>\n",
       "<td style='text-align: right'>1</td>\n",
       "</tr>\n",
       "<tr>\n",
       "<td style='text-align: right'>2009</td>\n",
       "<td>Carson Palmer</td>\n",
       "<td>193.38</td>\n",
       "<td>1.31</td>\n",
       "<td>0.81</td>\n",
       "<td>17.63</td>\n",
       "<td>29.13</td>\n",
       "<td>77.50</td>\n",
       "<td>1.30</td>\n",
       "<td>0.75</td>\n",
       "<td>5.77</td>\n",
       "<td>8.06</td>\n",
       "<td style='text-align: right'>16</td>\n",
       "<td style='text-align: right'>2</td>\n",
       "</tr>\n",
       "<tr>\n",
       "<td style='text-align: right'>2009</td>\n",
       "<td>Joe Flacco</td>\n",
       "<td>225.81</td>\n",
       "<td>1.31</td>\n",
       "<td>0.75</td>\n",
       "<td>19.69</td>\n",
       "<td>31.19</td>\n",
       "<td>77.50</td>\n",
       "<td>1.14</td>\n",
       "<td>0.93</td>\n",
       "<td>5.55</td>\n",
       "<td>8.77</td>\n",
       "<td style='text-align: right'>16</td>\n",
       "<td style='text-align: right'>2</td>\n",
       "</tr>\n",
       "</tbody>\n",
       "</table>\n",
       "<p><strong>...</strong></p>"
      ],
      "text/plain": [
       "+------+---------------------+-------------------+-----------------+-----------------+------------------+------------------+------------------+-----------------+-----------------+-----------------+-----------------+-----------+--------+\n",
       "| year | full_name           | avg_yds           | avg_tds         | avg_int         | avg_cmp          | avg_att          | std_yds          | std_tds         | std_int         | std_cmp         | std_att         | num_games | bucket |\n",
       "+======+=====================+===================+=================+=================+==================+==================+==================+=================+=================+=================+=================+===========+========+\n",
       "| 2012 | 'Ryan Tannehill'    | Decimal('205.88') | Decimal('0.75') | Decimal('0.81') | Decimal('17.63') | Decimal('30.25') | Decimal('86.01') | Decimal('0.77') | Decimal('1.11') | Decimal('5.56') | Decimal('8.42') |        16 |      1 |\n",
       "+------+---------------------+-------------------+-----------------+-----------------+------------------+------------------+------------------+-----------------+-----------------+-----------------+-----------------+-----------+--------+\n",
       "| 2013 | 'Geno Smith'        | Decimal('190.38') | Decimal('0.75') | Decimal('1.31') | Decimal('15.44') | Decimal('27.69') | Decimal('74.23') | Decimal('0.93') | Decimal('1.01') | Decimal('5.62') | Decimal('7.54') |        16 |      1 |\n",
       "+------+---------------------+-------------------+-----------------+-----------------+------------------+------------------+------------------+-----------------+-----------------+-----------------+-----------------+-----------+--------+\n",
       "| 2015 | 'Teddy Bridgewater' | Decimal('201.94') | Decimal('0.88') | Decimal('0.56') | Decimal('18.25') | Decimal('27.94') | Decimal('74.79') | Decimal('1.02') | Decimal('0.63') | Decimal('5.26') | Decimal('7.06') |        16 |      1 |\n",
       "+------+---------------------+-------------------+-----------------+-----------------+------------------+------------------+------------------+-----------------+-----------------+-----------------+-----------------+-----------+--------+\n",
       "| 2009 | 'Carson Palmer'     | Decimal('193.38') | Decimal('1.31') | Decimal('0.81') | Decimal('17.63') | Decimal('29.13') | Decimal('77.50') | Decimal('1.30') | Decimal('0.75') | Decimal('5.77') | Decimal('8.06') |        16 |      2 |\n",
       "+------+---------------------+-------------------+-----------------+-----------------+------------------+------------------+------------------+-----------------+-----------------+-----------------+-----------------+-----------+--------+\n",
       "| 2009 | 'Joe Flacco'        | Decimal('225.81') | Decimal('1.31') | Decimal('0.75') | Decimal('19.69') | Decimal('31.19') | Decimal('77.50') | Decimal('1.14') | Decimal('0.93') | Decimal('5.55') | Decimal('8.77') |        16 |      2 |\n",
       "+------+---------------------+-------------------+-----------------+-----------------+------------------+------------------+------------------+-----------------+-----------------+-----------------+-----------------+-----------+--------+\n",
       "..."
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qb_buckets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "conn = psycopg2.connect('dbname=nfldb')\n",
    "qb_games = petl.fromdb(conn, '''\n",
    "    select\n",
    "        s.year,\n",
    "        s.week,\n",
    "        s.full_name,\n",
    "        s.touchdowns\n",
    "    from\n",
    "        lombardi.qb_stats s\n",
    "        join\n",
    "            lombardi.qb_aggs a\n",
    "            on s.year = a.year\n",
    "            and s.full_name = a.full_name\n",
    "    where\n",
    "        a.num_games = 16\n",
    "''')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "seasons = (\n",
    "    qb_buckets\n",
    "    .join(qb_games, key=('year', 'full_name'))\n",
    "    .cut(('year', 'full_name', 'bucket', 'week', 'touchdowns'))\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 21.,  19.,   6.,   1.,   1.,   0.,   0.]),\n",
       " array([ 0.,  1.,  2.,  3.,  4.,  5.,  6.,  7.]),\n",
       " <a list of 7 Patch objects>)"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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OS3JxVbktlpNSVS9Lcri7b1v1LOx6L+ju5yS5LMlrFrfqw8nYk+Q5SX63u5+d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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x117ac15c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(list(seasons.selecteq('bucket', 1)['touchdowns']), bins=7, range=(0,7))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([  2.,  11.,  17.,  17.,  11.,   6.,   0.]),\n",
       " array([ 0.,  1.,  2.,  3.,  4.,  5.,  6.,  7.]),\n",
       " <a list of 7 Patch objects>)"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AACIQAAAAwAAiEAAAAMAAIhAAAADAACIQAAAAwAAiEAAAAMAAIhAAAADAACIQAAAAwAAi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\nAAAGEIEAAAAABhCBAAAAAAb4v/c3YUalrvv1AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1175cb978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(list(seasons.selecteq('bucket', 5)['touchdowns']), bins=7, range=(0,7))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "records = []\n",
    "\n",
    "for bucket in set(seasons['bucket']):\n",
    "    \n",
    "    sub = seasons.selecteq('bucket', bucket)\n",
    "    \n",
    "    record = {}\n",
    "    record['bucket'] = bucket\n",
    "    \n",
    "    counter = Counter(sub['touchdowns'])\n",
    "    tot = sum(counter.values())\n",
    "    \n",
    "    for tds in range(7):\n",
    "        records.append(dict(\n",
    "            bucket=bucket,\n",
    "            tds=tds,\n",
    "            num=counter.get(tds, 0),\n",
    "            p=round(counter.get(tds,0)/tot, 2),\n",
    "        ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'bucket': 1, 'num': 21, 'p': 0.44, 'tds': 0},\n",
       " {'bucket': 1, 'num': 19, 'p': 0.4, 'tds': 1},\n",
       " {'bucket': 1, 'num': 6, 'p': 0.12, 'tds': 2},\n",
       " {'bucket': 1, 'num': 1, 'p': 0.02, 'tds': 3},\n",
       " {'bucket': 1, 'num': 1, 'p': 0.02, 'tds': 4},\n",
       " {'bucket': 1, 'num': 0, 'p': 0.0, 'tds': 5},\n",
       " {'bucket': 1, 'num': 0, 'p': 0.0, 'tds': 6},\n",
       " {'bucket': 2, 'num': 90, 'p': 0.23, 'tds': 0},\n",
       " {'bucket': 2, 'num': 147, 'p': 0.38, 'tds': 1},\n",
       " {'bucket': 2, 'num': 109, 'p': 0.28, 'tds': 2},\n",
       " {'bucket': 2, 'num': 29, 'p': 0.08, 'tds': 3},\n",
       " {'bucket': 2, 'num': 6, 'p': 0.02, 'tds': 4},\n",
       " {'bucket': 2, 'num': 3, 'p': 0.01, 'tds': 5},\n",
       " {'bucket': 2, 'num': 0, 'p': 0.0, 'tds': 6},\n",
       " {'bucket': 3, 'num': 78, 'p': 0.12, 'tds': 0},\n",
       " {'bucket': 3, 'num': 211, 'p': 0.34, 'tds': 1},\n",
       " {'bucket': 3, 'num': 196, 'p': 0.31, 'tds': 2},\n",
       " {'bucket': 3, 'num': 99, 'p': 0.16, 'tds': 3},\n",
       " {'bucket': 3, 'num': 34, 'p': 0.05, 'tds': 4},\n",
       " {'bucket': 3, 'num': 3, 'p': 0.0, 'tds': 5},\n",
       " {'bucket': 3, 'num': 3, 'p': 0.0, 'tds': 6},\n",
       " {'bucket': 4, 'num': 20, 'p': 0.06, 'tds': 0},\n",
       " {'bucket': 4, 'num': 83, 'p': 0.26, 'tds': 1},\n",
       " {'bucket': 4, 'num': 102, 'p': 0.32, 'tds': 2},\n",
       " {'bucket': 4, 'num': 69, 'p': 0.22, 'tds': 3},\n",
       " {'bucket': 4, 'num': 32, 'p': 0.1, 'tds': 4},\n",
       " {'bucket': 4, 'num': 11, 'p': 0.03, 'tds': 5},\n",
       " {'bucket': 4, 'num': 3, 'p': 0.01, 'tds': 6},\n",
       " {'bucket': 5, 'num': 2, 'p': 0.03, 'tds': 0},\n",
       " {'bucket': 5, 'num': 11, 'p': 0.17, 'tds': 1},\n",
       " {'bucket': 5, 'num': 17, 'p': 0.27, 'tds': 2},\n",
       " {'bucket': 5, 'num': 17, 'p': 0.27, 'tds': 3},\n",
       " {'bucket': 5, 'num': 11, 'p': 0.17, 'tds': 4},\n",
       " {'bucket': 5, 'num': 6, 'p': 0.09, 'tds': 5},\n",
       " {'bucket': 5, 'num': 0, 'p': 0.0, 'tds': 6}]"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "records"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [],
   "source": [
    "likelihoods = petl.fromdicts(records)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class='petl'>\n",
       "<thead>\n",
       "<tr>\n",
       "<th>year</th>\n",
       "<th>full_name</th>\n",
       "<th>avg_yds</th>\n",
       "<th>avg_tds</th>\n",
       "<th>avg_int</th>\n",
       "<th>avg_cmp</th>\n",
       "<th>avg_att</th>\n",
       "<th>std_yds</th>\n",
       "<th>std_tds</th>\n",
       "<th>std_int</th>\n",
       "<th>std_cmp</th>\n",
       "<th>std_att</th>\n",
       "<th>num_games</th>\n",
       "<th>bucket</th>\n",
       "</tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "<tr>\n",
       "<td style='text-align: right'>2012</td>\n",
       "<td>Ryan Tannehill</td>\n",
       "<td>205.88</td>\n",
       "<td>0.75</td>\n",
       "<td>0.81</td>\n",
       "<td>17.63</td>\n",
       "<td>30.25</td>\n",
       "<td>86.01</td>\n",
       "<td>0.77</td>\n",
       "<td>1.11</td>\n",
       "<td>5.56</td>\n",
       "<td>8.42</td>\n",
       "<td style='text-align: right'>16</td>\n",
       "<td style='text-align: right'>1</td>\n",
       "</tr>\n",
       "<tr>\n",
       "<td style='text-align: right'>2013</td>\n",
       "<td>Geno Smith</td>\n",
       "<td>190.38</td>\n",
       "<td>0.75</td>\n",
       "<td>1.31</td>\n",
       "<td>15.44</td>\n",
       "<td>27.69</td>\n",
       "<td>74.23</td>\n",
       "<td>0.93</td>\n",
       "<td>1.01</td>\n",
       "<td>5.62</td>\n",
       "<td>7.54</td>\n",
       "<td style='text-align: right'>16</td>\n",
       "<td style='text-align: right'>1</td>\n",
       "</tr>\n",
       "<tr>\n",
       "<td style='text-align: right'>2015</td>\n",
       "<td>Teddy Bridgewater</td>\n",
       "<td>201.94</td>\n",
       "<td>0.88</td>\n",
       "<td>0.56</td>\n",
       "<td>18.25</td>\n",
       "<td>27.94</td>\n",
       "<td>74.79</td>\n",
       "<td>1.02</td>\n",
       "<td>0.63</td>\n",
       "<td>5.26</td>\n",
       "<td>7.06</td>\n",
       "<td style='text-align: right'>16</td>\n",
       "<td style='text-align: right'>1</td>\n",
       "</tr>\n",
       "<tr>\n",
       "<td style='text-align: right'>2009</td>\n",
       "<td>Carson Palmer</td>\n",
       "<td>193.38</td>\n",
       "<td>1.31</td>\n",
       "<td>0.81</td>\n",
       "<td>17.63</td>\n",
       "<td>29.13</td>\n",
       "<td>77.50</td>\n",
       "<td>1.30</td>\n",
       "<td>0.75</td>\n",
       "<td>5.77</td>\n",
       "<td>8.06</td>\n",
       "<td style='text-align: right'>16</td>\n",
       "<td style='text-align: right'>2</td>\n",
       "</tr>\n",
       "<tr>\n",
       "<td style='text-align: right'>2009</td>\n",
       "<td>Joe Flacco</td>\n",
       "<td>225.81</td>\n",
       "<td>1.31</td>\n",
       "<td>0.75</td>\n",
       "<td>19.69</td>\n",
       "<td>31.19</td>\n",
       "<td>77.50</td>\n",
       "<td>1.14</td>\n",
       "<td>0.93</td>\n",
       "<td>5.55</td>\n",
       "<td>8.77</td>\n",
       "<td style='text-align: right'>16</td>\n",
       "<td style='text-align: right'>2</td>\n",
       "</tr>\n",
       "</tbody>\n",
       "</table>\n",
       "<p><strong>...</strong></p>"
      ],
      "text/plain": [
       "+------+---------------------+-------------------+-----------------+-----------------+------------------+------------------+------------------+-----------------+-----------------+-----------------+-----------------+-----------+--------+\n",
       "| year | full_name           | avg_yds           | avg_tds         | avg_int         | avg_cmp          | avg_att          | std_yds          | std_tds         | std_int         | std_cmp         | std_att         | num_games | bucket |\n",
       "+======+=====================+===================+=================+=================+==================+==================+==================+=================+=================+=================+=================+===========+========+\n",
       "| 2012 | 'Ryan Tannehill'    | Decimal('205.88') | Decimal('0.75') | Decimal('0.81') | Decimal('17.63') | Decimal('30.25') | Decimal('86.01') | Decimal('0.77') | Decimal('1.11') | Decimal('5.56') | Decimal('8.42') |        16 |      1 |\n",
       "+------+---------------------+-------------------+-----------------+-----------------+------------------+------------------+------------------+-----------------+-----------------+-----------------+-----------------+-----------+--------+\n",
       "| 2013 | 'Geno Smith'        | Decimal('190.38') | Decimal('0.75') | Decimal('1.31') | Decimal('15.44') | Decimal('27.69') | Decimal('74.23') | Decimal('0.93') | Decimal('1.01') | Decimal('5.62') | Decimal('7.54') |        16 |      1 |\n",
       "+------+---------------------+-------------------+-----------------+-----------------+------------------+------------------+------------------+-----------------+-----------------+-----------------+-----------------+-----------+--------+\n",
       "| 2015 | 'Teddy Bridgewater' | Decimal('201.94') | Decimal('0.88') | Decimal('0.56') | Decimal('18.25') | Decimal('27.94') | Decimal('74.79') | Decimal('1.02') | Decimal('0.63') | Decimal('5.26') | Decimal('7.06') |        16 |      1 |\n",
       "+------+---------------------+-------------------+-----------------+-----------------+------------------+------------------+------------------+-----------------+-----------------+-----------------+-----------------+-----------+--------+\n",
       "| 2009 | 'Carson Palmer'     | Decimal('193.38') | Decimal('1.31') | Decimal('0.81') | Decimal('17.63') | Decimal('29.13') | Decimal('77.50') | Decimal('1.30') | Decimal('0.75') | Decimal('5.77') | Decimal('8.06') |        16 |      2 |\n",
       "+------+---------------------+-------------------+-----------------+-----------------+------------------+------------------+------------------+-----------------+-----------------+-----------------+-----------------+-----------+--------+\n",
       "| 2009 | 'Joe Flacco'        | Decimal('225.81') | Decimal('1.31') | Decimal('0.75') | Decimal('19.69') | Decimal('31.19') | Decimal('77.50') | Decimal('1.14') | Decimal('0.93') | Decimal('5.55') | Decimal('8.77') |        16 |      2 |\n",
       "+------+---------------------+-------------------+-----------------+-----------------+------------------+------------------+------------------+-----------------+-----------------+-----------------+-----------------+-----------+--------+\n",
       "..."
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qb_buckets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "counter = Counter(qb_buckets['bucket'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({1: 3, 2: 24, 3: 39, 4: 20, 5: 4})"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "bucket_records = [dict(bucket=k, num=v, p=round(v/sum(counter.values()),2)) for k,v in counter.items()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'bucket': 1, 'num': 3, 'p': 0.03},\n",
       " {'bucket': 2, 'num': 24, 'p': 0.27},\n",
       " {'bucket': 3, 'num': 39, 'p': 0.43},\n",
       " {'bucket': 4, 'num': 20, 'p': 0.22},\n",
       " {'bucket': 5, 'num': 4, 'p': 0.04}]"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bucket_records"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "prior = petl.fromdicts(bucket_records)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class='petl'>\n",
       "<thead>\n",
       "<tr>\n",
       "<th>bucket</th>\n",
       "<th>num</th>\n",
       "<th>p</th>\n",
       "</tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "<tr>\n",
       "<td style='text-align: right'>1</td>\n",
       "<td style='text-align: right'>3</td>\n",
       "<td style='text-align: right'>0.03</td>\n",
       "</tr>\n",
       "<tr>\n",
       "<td style='text-align: right'>2</td>\n",
       "<td style='text-align: right'>24</td>\n",
       "<td style='text-align: right'>0.27</td>\n",
       "</tr>\n",
       "<tr>\n",
       "<td style='text-align: right'>3</td>\n",
       "<td style='text-align: right'>39</td>\n",
       "<td style='text-align: right'>0.43</td>\n",
       "</tr>\n",
       "<tr>\n",
       "<td style='text-align: right'>4</td>\n",
       "<td style='text-align: right'>20</td>\n",
       "<td style='text-align: right'>0.22</td>\n",
       "</tr>\n",
       "<tr>\n",
       "<td style='text-align: right'>5</td>\n",
       "<td style='text-align: right'>4</td>\n",
       "<td style='text-align: right'>0.04</td>\n",
       "</tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "+--------+-----+------+\n",
       "| bucket | num | p    |\n",
       "+========+=====+======+\n",
       "|      1 |   3 | 0.03 |\n",
       "+--------+-----+------+\n",
       "|      2 |  24 | 0.27 |\n",
       "+--------+-----+------+\n",
       "|      3 |  39 | 0.43 |\n",
       "+--------+-----+------+\n",
       "|      4 |  20 | 0.22 |\n",
       "+--------+-----+------+\n",
       "|      5 |   4 | 0.04 |\n",
       "+--------+-----+------+"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prior"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class='petl'>\n",
       "<thead>\n",
       "<tr>\n",
       "<th>bucket</th>\n",
       "<th>tds</th>\n",
       "<th>num</th>\n",
       "<th>p</th>\n",
       "</tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "<tr>\n",
       "<td style='text-align: right'>1</td>\n",
       "<td style='text-align: right'>0</td>\n",
       "<td style='text-align: right'>21</td>\n",
       "<td style='text-align: right'>0.44</td>\n",
       "</tr>\n",
       "<tr>\n",
       "<td style='text-align: right'>1</td>\n",
       "<td style='text-align: right'>1</td>\n",
       "<td style='text-align: right'>19</td>\n",
       "<td style='text-align: right'>0.4</td>\n",
       "</tr>\n",
       "<tr>\n",
       "<td style='text-align: right'>1</td>\n",
       "<td style='text-align: right'>2</td>\n",
       "<td style='text-align: right'>6</td>\n",
       "<td style='text-align: right'>0.12</td>\n",
       "</tr>\n",
       "<tr>\n",
       "<td style='text-align: right'>1</td>\n",
       "<td style='text-align: right'>3</td>\n",
       "<td style='text-align: right'>1</td>\n",
       "<td style='text-align: right'>0.02</td>\n",
       "</tr>\n",
       "<tr>\n",
       "<td style='text-align: right'>1</td>\n",
       "<td style='text-align: right'>4</td>\n",
       "<td style='text-align: right'>1</td>\n",
       "<td style='text-align: right'>0.02</td>\n",
       "</tr>\n",
       "</tbody>\n",
       "</table>\n",
       "<p><strong>...</strong></p>"
      ],
      "text/plain": [
       "+--------+-----+-----+------+\n",
       "| bucket | tds | num | p    |\n",
       "+========+=====+=====+======+\n",
       "|      1 |   0 |  21 | 0.44 |\n",
       "+--------+-----+-----+------+\n",
       "|      1 |   1 |  19 |  0.4 |\n",
       "+--------+-----+-----+------+\n",
       "|      1 |   2 |   6 | 0.12 |\n",
       "+--------+-----+-----+------+\n",
       "|      1 |   3 |   1 | 0.02 |\n",
       "+--------+-----+-----+------+\n",
       "|      1 |   4 |   1 | 0.02 |\n",
       "+--------+-----+-----+------+\n",
       "..."
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "likelihoods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [],
   "source": [
    "posterior = prior.join(likelihoods, key='bucket', lprefix='pr_', rprefix='lk_').selecteq('lk_tds', 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "+-----------+--------+------+--------+--------+------+\n",
       "| pr_bucket | pr_num | pr_p | lk_tds | lk_num | lk_p |\n",
       "+===========+========+======+========+========+======+\n",
       "|         1 |      3 | 0.03 |      0 |     21 | 0.44 |\n",
       "+-----------+--------+------+--------+--------+------+\n",
       "|         2 |     24 | 0.27 |      0 |     90 | 0.23 |\n",
       "+-----------+--------+------+--------+--------+------+\n",
       "|         3 |     39 | 0.43 |      0 |     78 | 0.12 |\n",
       "+-----------+--------+------+--------+--------+------+\n",
       "|         4 |     20 | 0.22 |      0 |     20 | 0.06 |\n",
       "+-----------+--------+------+--------+--------+------+\n",
       "|         5 |      4 | 0.04 |      0 |      2 | 0.03 |\n",
       "+-----------+--------+------+--------+--------+------+"
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "posterior.lookall()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [],
   "source": [
    "posterior = posterior.addfield('p_new', lambda r: r['pr_p']*r['lk_p'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class='petl'>\n",
       "<thead>\n",
       "<tr>\n",
       "<th>pr_bucket</th>\n",
       "<th>pr_num</th>\n",
       "<th>pr_p</th>\n",
       "<th>lk_tds</th>\n",
       "<th>lk_num</th>\n",
       "<th>lk_p</th>\n",
       "<th>p_new</th>\n",
       "<th>p_norm</th>\n",
       "</tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "<tr>\n",
       "<td style='text-align: right'>1</td>\n",
       "<td style='text-align: right'>3</td>\n",
       "<td style='text-align: right'>0.03</td>\n",
       "<td style='text-align: right'>0</td>\n",
       "<td style='text-align: right'>21</td>\n",
       "<td style='text-align: right'>0.44</td>\n",
       "<td style='text-align: right'>0.0132</td>\n",
       "<td style='text-align: right'>0.09341825902335456</td>\n",
       "</tr>\n",
       "<tr>\n",
       "<td style='text-align: right'>2</td>\n",
       "<td style='text-align: right'>24</td>\n",
       "<td style='text-align: right'>0.27</td>\n",
       "<td style='text-align: right'>0</td>\n",
       "<td style='text-align: right'>90</td>\n",
       "<td style='text-align: right'>0.23</td>\n",
       "<td style='text-align: right'>0.06210000000000001</td>\n",
       "<td style='text-align: right'>0.4394904458598726</td>\n",
       "</tr>\n",
       "<tr>\n",
       "<td style='text-align: right'>3</td>\n",
       "<td style='text-align: right'>39</td>\n",
       "<td style='text-align: right'>0.43</td>\n",
       "<td style='text-align: right'>0</td>\n",
       "<td style='text-align: right'>78</td>\n",
       "<td style='text-align: right'>0.12</td>\n",
       "<td style='text-align: right'>0.0516</td>\n",
       "<td style='text-align: right'>0.3651804670912951</td>\n",
       "</tr>\n",
       "<tr>\n",
       "<td style='text-align: right'>4</td>\n",
       "<td style='text-align: right'>20</td>\n",
       "<td style='text-align: right'>0.22</td>\n",
       "<td style='text-align: right'>0</td>\n",
       "<td style='text-align: right'>20</td>\n",
       "<td style='text-align: right'>0.06</td>\n",
       "<td style='text-align: right'>0.0132</td>\n",
       "<td style='text-align: right'>0.09341825902335456</td>\n",
       "</tr>\n",
       "<tr>\n",
       "<td style='text-align: right'>5</td>\n",
       "<td style='text-align: right'>4</td>\n",
       "<td style='text-align: right'>0.04</td>\n",
       "<td style='text-align: right'>0</td>\n",
       "<td style='text-align: right'>2</td>\n",
       "<td style='text-align: right'>0.03</td>\n",
       "<td style='text-align: right'>0.0012</td>\n",
       "<td style='text-align: right'>0.00849256900212314</td>\n",
       "</tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "+-----------+--------+------+--------+--------+------+---------------------+---------------------+\n",
       "| pr_bucket | pr_num | pr_p | lk_tds | lk_num | lk_p | p_new               | p_norm              |\n",
       "+===========+========+======+========+========+======+=====================+=====================+\n",
       "|         1 |      3 | 0.03 |      0 |     21 | 0.44 |              0.0132 | 0.09341825902335456 |\n",
       "+-----------+--------+------+--------+--------+------+---------------------+---------------------+\n",
       "|         2 |     24 | 0.27 |      0 |     90 | 0.23 | 0.06210000000000001 |  0.4394904458598726 |\n",
       "+-----------+--------+------+--------+--------+------+---------------------+---------------------+\n",
       "|         3 |     39 | 0.43 |      0 |     78 | 0.12 |              0.0516 |  0.3651804670912951 |\n",
       "+-----------+--------+------+--------+--------+------+---------------------+---------------------+\n",
       "|         4 |     20 | 0.22 |      0 |     20 | 0.06 |              0.0132 | 0.09341825902335456 |\n",
       "+-----------+--------+------+--------+--------+------+---------------------+---------------------+\n",
       "|         5 |      4 | 0.04 |      0 |      2 | 0.03 |              0.0012 | 0.00849256900212314 |\n",
       "+-----------+--------+------+--------+--------+------+---------------------+---------------------+"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "posterior.addfield('p_norm', lambda r: r['p_new'] / sum(posterior['p_new']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "posterior = (\n",
    "    prior\n",
    "    .join(likelihoods, key='bucket', lprefix='pr_', rprefix='lk_')\n",
    "    .selecteq('lk_tds', 0)\n",
    "    .addfield('p_new', lambda r: r['pr_p']*r['lk_p'])\n",
    ")\n",
    "posterior = posterior.addfield('p_norm', lambda r: r['p_new'] / sum(posterior['p_new']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "+--------+-----+------+\n",
       "| bucket | num | p    |\n",
       "+========+=====+======+\n",
       "|      1 |   3 | 0.03 |\n",
       "+--------+-----+------+\n",
       "|      2 |  24 | 0.27 |\n",
       "+--------+-----+------+\n",
       "|      3 |  39 | 0.43 |\n",
       "+--------+-----+------+\n",
       "|      4 |  20 | 0.22 |\n",
       "+--------+-----+------+\n",
       "|      5 |   4 | 0.04 |\n",
       "+--------+-----+------+"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prior.look()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "+--------+-----+-----+------+\n",
       "| bucket | tds | num | p    |\n",
       "+========+=====+=====+======+\n",
       "|      1 |   0 |  21 | 0.44 |\n",
       "+--------+-----+-----+------+\n",
       "|      1 |   1 |  19 |  0.4 |\n",
       "+--------+-----+-----+------+\n",
       "|      1 |   2 |   6 | 0.12 |\n",
       "+--------+-----+-----+------+\n",
       "|      1 |   3 |   1 | 0.02 |\n",
       "+--------+-----+-----+------+\n",
       "|      1 |   4 |   1 | 0.02 |\n",
       "+--------+-----+-----+------+\n",
       "..."
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "likelihoods.look()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def posterior(prior, likelihoods, event_tds):\n",
    "    \n",
    "    pst = (\n",
    "        prior\n",
    "        .join(likelihoods, key='bucket', lprefix='pr_', rprefix='lk_')\n",
    "        .selecteq('lk_tds', 0)\n",
    "        .addfield('p_new', lambda r: r['pr_p']*r['lk_p'])\n",
    "    )\n",
    "    tot = sum(pst['p_new'])\n",
    "    pst = pst.addfield('p_norm', lambda r: round(r['p_new']/tot, 2))\n",
    "    pst = pst.cut(('pr_bucket', 'p_norm'))\n",
    "    pst = pst.rename({'pr_bucket': 'bucket', 'p_norm': 'p'})\n",
    "    return pst"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "p = posterior(prior, likelihoods, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "+--------+------+\n",
       "| bucket | p    |\n",
       "+========+======+\n",
       "|      1 | 0.09 |\n",
       "+--------+------+\n",
       "|      2 | 0.44 |\n",
       "+--------+------+\n",
       "|      3 | 0.37 |\n",
       "+--------+------+\n",
       "|      4 | 0.09 |\n",
       "+--------+------+\n",
       "|      5 | 0.01 |\n",
       "+--------+------+"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p.lookall()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class='petl'>\n",
       "<thead>\n",
       "<tr>\n",
       "<th>bucket</th>\n",
       "<th>num</th>\n",
       "<th>p</th>\n",
       "</tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "<tr>\n",
       "<td style='text-align: right'>1</td>\n",
       "<td style='text-align: right'>3</td>\n",
       "<td style='text-align: right'>0.03</td>\n",
       "</tr>\n",
       "<tr>\n",
       "<td style='text-align: right'>2</td>\n",
       "<td style='text-align: right'>24</td>\n",
       "<td style='text-align: right'>0.27</td>\n",
       "</tr>\n",
       "<tr>\n",
       "<td style='text-align: right'>3</td>\n",
       "<td style='text-align: right'>39</td>\n",
       "<td style='text-align: right'>0.43</td>\n",
       "</tr>\n",
       "<tr>\n",
       "<td style='text-align: right'>4</td>\n",
       "<td style='text-align: right'>20</td>\n",
       "<td style='text-align: right'>0.22</td>\n",
       "</tr>\n",
       "<tr>\n",
       "<td style='text-align: right'>5</td>\n",
       "<td style='text-align: right'>4</td>\n",
       "<td style='text-align: right'>0.04</td>\n",
       "</tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "+--------+-----+------+\n",
       "| bucket | num | p    |\n",
       "+========+=====+======+\n",
       "|      1 |   3 | 0.03 |\n",
       "+--------+-----+------+\n",
       "|      2 |  24 | 0.27 |\n",
       "+--------+-----+------+\n",
       "|      3 |  39 | 0.43 |\n",
       "+--------+-----+------+\n",
       "|      4 |  20 | 0.22 |\n",
       "+--------+-----+------+\n",
       "|      5 |   4 | 0.04 |\n",
       "+--------+-----+------+"
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prior"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pst = (\n",
    "        prior\n",
    "        .join(likelihoods, key='bucket', lprefix='pr_', rprefix='lk_')\n",
    "        .addfield('p_new', lambda r: r['pr_p']*r['lk_p'])\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "+--------+----------------------+\n",
       "| lk_tds | value                |\n",
       "+========+======================+\n",
       "|      0 |               0.1413 |\n",
       "+--------+----------------------+\n",
       "|      1 |   0.3248000000000001 |\n",
       "+--------+----------------------+\n",
       "|      2 |               0.2937 |\n",
       "+--------+----------------------+\n",
       "|      3 |               0.1502 |\n",
       "+--------+----------------------+\n",
       "|      4 |               0.0563 |\n",
       "+--------+----------------------+\n",
       "|      5 | 0.012899999999999998 |\n",
       "+--------+----------------------+\n",
       "|      6 |               0.0022 |\n",
       "+--------+----------------------+"
      ]
     },
     "execution_count": 209,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pst.aggregate('lk_tds', sum, 'p_new').lookall()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def posterior_predictive(posterior, likelihoods):\n",
    "    pp = (\n",
    "        posterior\n",
    "        .join(likelihoods, key='bucket', lprefix='pr_', rprefix='lk_')\n",
    "        .addfield('p_new', lambda r: r['pr_p']*r['lk_p'])\n",
    "    )\n",
    "    tot = sum(pp['p_new'])\n",
    "    pp = pp.addfield('p_norm', lambda r: r['p_new']/tot)\n",
    "    pp = pp.cut(('lk_tds', 'p_norm'))\n",
    "    pp = pp.rename({'lk_tds': 'tds', 'p_norm': 'p'})\n",
    "    pp = pp.aggregate('tds', sum, 'p')\n",
    "    pp = pp.rename({'value': 'p'})\n",
    "    pp = pp.convert('p', lambda x: round(x,2))\n",
    "    return pp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {},
   "outputs": [],
   "source": [
    "pp = posterior_predictive(prior, likelihoods)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "+-----+------+\n",
       "| tds | p    |\n",
       "+=====+======+\n",
       "|   0 | 0.14 |\n",
       "+-----+------+\n",
       "|   1 | 0.33 |\n",
       "+-----+------+\n",
       "|   2 |  0.3 |\n",
       "+-----+------+\n",
       "|   3 | 0.15 |\n",
       "+-----+------+\n",
       "|   4 | 0.06 |\n",
       "+-----+------+\n",
       "|   5 | 0.01 |\n",
       "+-----+------+\n",
       "|   6 |  0.0 |\n",
       "+-----+------+"
      ]
     },
     "execution_count": 244,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pp.lookall()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "+--------+-----+------+\n",
       "| bucket | num | p    |\n",
       "+========+=====+======+\n",
       "|      1 |   3 | 0.03 |\n",
       "+--------+-----+------+\n",
       "|      2 |  24 | 0.27 |\n",
       "+--------+-----+------+\n",
       "|      3 |  39 | 0.43 |\n",
       "+--------+-----+------+\n",
       "|      4 |  20 | 0.22 |\n",
       "+--------+-----+------+\n",
       "|      5 |   4 | 0.04 |\n",
       "+--------+-----+------+"
      ]
     },
     "execution_count": 246,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prior.lookall()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {},
   "outputs": [],
   "source": [
    "from lombardi import bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "+--------+-----+-----+------+\n",
       "| bucket | tds | num | p    |\n",
       "+========+=====+=====+======+\n",
       "|      1 |   0 |  21 | 0.44 |\n",
       "+--------+-----+-----+------+\n",
       "|      1 |   1 |  19 |  0.4 |\n",
       "+--------+-----+-----+------+\n",
       "|      1 |   2 |   6 | 0.12 |\n",
       "+--------+-----+-----+------+\n",
       "|      1 |   3 |   1 | 0.02 |\n",
       "+--------+-----+-----+------+\n",
       "|      1 |   4 |   1 | 0.02 |\n",
       "+--------+-----+-----+------+\n",
       "..."
      ]
     },
     "execution_count": 249,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "likelihoods.look()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 369,
   "metadata": {},
   "outputs": [],
   "source": [
    "ld = dict()\n",
    "for b in set(likelihoods['bucket']):\n",
    "    \n",
    "    tmp = likelihoods.selecteq('bucket', b)\n",
    "    lk = petl.dicts(tmp.cut(('tds', 'p')))\n",
    "    ld[b] = {p['tds']:p['p'] for p in lk}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 370,
   "metadata": {},
   "outputs": [],
   "source": [
    "ld = {k: bayes.Pdf(v) for k,v in ld.items()}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 371,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pd = bayes.Pdf({\n",
    "    1: 0.03,\n",
    "    2: 0.27,\n",
    "    3: 0.43,\n",
    "    4: 0.22,\n",
    "    5: 0.04,\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 372,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dist = bayes.DiscreteBayes(likelihoods=ld, prior=pd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 373,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pps = dict()\n",
    "for i in range(5):\n",
    "    # simulate 5 5-touchdown games in a row\n",
    "    pps[i] = list(dist.posterior_predictive().probs.values())\n",
    "    dist.update_prior(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 374,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1198d7320>"
      ]
     },
     "execution_count": 374,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ig9W/piUK8vVmZOfWjOzcGsMwyCupqOt19MFP23l7yVZ8LF70TQhjWGokw9MiSYoMxGRS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\n7SP/j7dTtWoVrR95hNArf3fCsWXFVXw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mXXwRwR/+0XzbqrzuFYoSw+HiLHBtADVzb5gq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8nVgUcHOBF+gpG1RlLZrbLR5jFkZPBk7Oeg0+GzbCkaq7ytLNNl6jm54RaXD4VR+W1v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\neXA9Lo9TCyEKMmkWGbsrm+HOfnj/G3Dw0jrNP0QmRjL9zHSqO1dnQOUBB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d0V2RStlsc/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\nUVgkIiIiIiIiIiJt/j/dcJ7vlb/VKgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1198d7978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for k,v in pps.items():\n",
    "    plt.plot(range(7), v, label=k)\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 346,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pp = dist.posterior_predictive()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 347,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pdf(probs={0: 0.14397799062563685, 1: 0.33095577746077043, 2: 0.2992663541878948, 3: 0.15304666802527, 4: 0.05736702669655594, 5: 0.01314448746688404, 6: 0.0022416955369879764})"
      ]
     },
     "execution_count": 347,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 350,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "before = dist.posterior_predictive()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 351,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dist.update_prior(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 352,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "after = dist.posterior_predictive()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 357,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Container object of 7 artists>"
      ]
     },
     "execution_count": 357,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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t9x9IcmDds7CjvSjJDYt3zNyb5Mer6jfWOxI7XXc/uvjzc0l+OxuvkICzcSLJ\niU130747G/GIC4xYxNk6nOSqqrpy8XKzm5IcXPNMAN9i8TLityf5eHf/0rrnYeerqj1V9T2L5b+S\njS96+KP1TsVO1t1v6O693X1FNv576v7ufvWax2IHq6qnL77UIYvHhF6axDfNcla6+/8kOV5VVy82\n/UQSXxhyAdq97gHY2br7ZFXdluS+JLuS3NPdD615LHa4qvr3Sf5ekkuq6kSSN3b329c7FTvci5L8\n/SQfW7xjJkn+RXcfWuNM7GzPSfKOxbeCfleSd3W3rzoHzifPTvLbG39fkt1J3tnd/2W9I3GB+EdJ\nfnNxs8AjSf7BmudhG1S3L+4AAAAAYIPH0AAAAAAYYhEAAAAAQywCAAAAYIhFAAAAAAyxCAAAAIAh\nFgEAAAAwxCIAAAAAhlgEAAAAwPj/qf7EncSchNUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x119de39e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(range(7), before.probs.values(), alpha=0.3)\n",
    "plt.bar(range(7), after.probs.values(), alpha=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 362,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x11b19bcc0>"
      ]
     },
     "execution_count": 362,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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T2wRFRERERKTS04BrkSrqRHYePScuI7KON9Pu1FDryiS3oJCEPSeJT0lnWfJRUo5kARDi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sJ88gf2SjqedlVzMyyZ2WZUrU1xtHVz7J4Dkk4ppRXLIkmSJEl6Hw1NzTy5sJqSsgqefn0lAMce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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x119aa6e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(range(7), list(before.probs.values()), label='before')\n",
    "plt.plot(range(7), list(after.probs.values()), label='after')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 376,
   "metadata": {},
   "outputs": [],
   "source": [
    "expected_values = {k: sum(i*x for i,x in enumerate(v)) for k,v in pps.items()}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 377,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: 1.6972692072549418,\n",
       " 1: 2.1239534883720927,\n",
       " 2: 2.410983606557377,\n",
       " 3: 2.5591603053435117,\n",
       " 4: 2.6182194417709335}"
      ]
     },
     "execution_count": 377,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "expected_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "pps = dict()\n",
    "for i in range(5):\n",
    "    # simulate 5 5-touchdown games in a row\n",
    "    pps[i] = list(dist.posterior_predictive().probs.values())\n",
    "    dist.update_prior(5)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}