Chapter04-CredibleIntervals.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import pybaseball\n",
    "import scipy.stats as stats\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.special import gammaln\n",
    "from scipy.optimize import minimize"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load data from Chapter 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load dataframe\n",
    "bd = pd.read_pickle(\"./data/batting_data_ebestimate.pkl\")\n",
    "\n",
    "# Load beta parameters\n",
    "with open(\"./data/eb_estimate_beta_params.npy\",\"rb\") as f:\n",
    "    alpha0, beta0 = np.load(f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Posterior distribution for each batter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "bd[\"alpha1\"] = bd.apply(lambda row: alpha0 + row.H,axis=1)\n",
    "bd[\"beta1\"] = bd.apply(lambda row: beta0 + row.AB - row.H,axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Figure 4.1 (color order does not match)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Which players\n",
    "players = [\"Bernie Williams\", \"Chuck Knoblauch\", \"Darryl Strawberry\", \"Derek Jeter\", \"Jorge Posada\", \"Scott Brosius\", \"Tino Martinez\"]\n",
    "\n",
    "# Make figure\n",
    "with sns.axes_style(\"whitegrid\"):\n",
    "    # Fig\n",
    "    fig = plt.figure(figsize=(7,5))\n",
    "    ax = fig.add_subplot()\n",
    "    clrs = sns.color_palette(\"colorblind\",n_colors=len(players))\n",
    "\n",
    "    # Make distributions for prior and each player\n",
    "    x = np.linspace(0,1,1000)\n",
    "    ax.plot(x, stats.beta.pdf(x,alpha0,beta0),color='k',linestyle='--')\n",
    "    for (row,clr) in zip(bd.loc[(bd.Name.isin(players)) & (bd.AB > 500),:].itertuples(),clrs):\n",
    "        ax.plot(x,stats.beta.pdf(x,row.alpha1,row.beta1),color=clr,lw=1,label=row.Name)\n",
    "    ax.set_xlim(0.175,0.325)\n",
    "\n",
    "    # Add legend and clean up\n",
    "    ax.legend(frameon=False,bbox_to_anchor=(1.05, .65),loc='upper left')\n",
    "    ax.get_legend().set_title(\"Player\",prop = {'size':12,'weight': \"bold\"})\n",
    "    ax.set_xlabel(\"Batting Average\",fontweight=\"bold\")\n",
    "    ax.set_ylabel(\"Density\",fontweight=\"bold\")\n",
    "    ax.set_xticks([.2,.25, .3])\n",
    "    ax.set_yticks([0,25,50,75])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Add the credible intervals for each player"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/justinbrantley/miniconda3/envs/bayesball/lib/python3.9/site-packages/scipy/stats/_continuous_distns.py:624: RuntimeWarning: overflow encountered in _beta_ppf\n",
      "  return _boost._beta_ppf(q, a, b)\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>playerID</th>\n",
       "      <th>AB</th>\n",
       "      <th>H</th>\n",
       "      <th>Name</th>\n",
       "      <th>Avg</th>\n",
       "      <th>eb estimate Avg</th>\n",
       "      <th>alpha1</th>\n",
       "      <th>beta1</th>\n",
       "      <th>ci_low</th>\n",
       "      <th>ci_high</th>\n",
       "      <th>ci_low_diff</th>\n",
       "      <th>ci_high_diff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1023</th>\n",
       "      <td>brosisc01</td>\n",
       "      <td>3889</td>\n",
       "      <td>1001</td>\n",
       "      <td>Scott Brosius</td>\n",
       "      <td>0.257393</td>\n",
       "      <td>0.257621</td>\n",
       "      <td>1090.733185</td>\n",
       "      <td>3143.130635</td>\n",
       "      <td>0.244559</td>\n",
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       "    <tr>\n",
       "      <th>8575</th>\n",
       "      <td>strawda01</td>\n",
       "      <td>5418</td>\n",
       "      <td>1401</td>\n",
       "      <td>Darryl Strawberry</td>\n",
       "      <td>0.258583</td>\n",
       "      <td>0.258679</td>\n",
       "      <td>1490.733185</td>\n",
       "      <td>4272.130635</td>\n",
       "      <td>0.247454</td>\n",
       "      <td>0.270063</td>\n",
       "      <td>0.011128</td>\n",
       "      <td>0.011481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5535</th>\n",
       "      <td>martiti02</td>\n",
       "      <td>7111</td>\n",
       "      <td>1925</td>\n",
       "      <td>Tino Martinez</td>\n",
       "      <td>0.270707</td>\n",
       "      <td>0.270221</td>\n",
       "      <td>2014.733185</td>\n",
       "      <td>5441.130635</td>\n",
       "      <td>0.260201</td>\n",
       "      <td>0.280358</td>\n",
       "      <td>0.010506</td>\n",
       "      <td>0.009651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7071</th>\n",
       "      <td>posadjo01</td>\n",
       "      <td>6092</td>\n",
       "      <td>1664</td>\n",
       "      <td>Jorge Posada</td>\n",
       "      <td>0.273145</td>\n",
       "      <td>0.272451</td>\n",
       "      <td>1753.733185</td>\n",
       "      <td>4683.130635</td>\n",
       "      <td>0.261643</td>\n",
       "      <td>0.283394</td>\n",
       "      <td>0.011502</td>\n",
       "      <td>0.010248</td>\n",
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       "    <tr>\n",
       "      <th>4758</th>\n",
       "      <td>knoblch01</td>\n",
       "      <td>6366</td>\n",
       "      <td>1839</td>\n",
       "      <td>Chuck Knoblauch</td>\n",
       "      <td>0.288878</td>\n",
       "      <td>0.287405</td>\n",
       "      <td>1928.733185</td>\n",
       "      <td>4782.130635</td>\n",
       "      <td>0.276638</td>\n",
       "      <td>0.298291</td>\n",
       "      <td>0.012240</td>\n",
       "      <td>0.009412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9494</th>\n",
       "      <td>willibe02</td>\n",
       "      <td>7869</td>\n",
       "      <td>2336</td>\n",
       "      <td>Bernie Williams</td>\n",
       "      <td>0.296861</td>\n",
       "      <td>0.295322</td>\n",
       "      <td>2425.733185</td>\n",
       "      <td>5788.130635</td>\n",
       "      <td>0.285504</td>\n",
       "      <td>0.305234</td>\n",
       "      <td>0.011357</td>\n",
       "      <td>0.008372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4345</th>\n",
       "      <td>jeterde01</td>\n",
       "      <td>11195</td>\n",
       "      <td>3465</td>\n",
       "      <td>Derek Jeter</td>\n",
       "      <td>0.309513</td>\n",
       "      <td>0.308039</td>\n",
       "      <td>3554.733185</td>\n",
       "      <td>7985.130635</td>\n",
       "      <td>0.299648</td>\n",
       "      <td>0.316494</td>\n",
       "      <td>0.009865</td>\n",
       "      <td>0.006981</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       playerID     AB     H               Name       Avg  eb estimate Avg  \\\n",
       "1023  brosisc01   3889  1001      Scott Brosius  0.257393         0.257621   \n",
       "8575  strawda01   5418  1401  Darryl Strawberry  0.258583         0.258679   \n",
       "5535  martiti02   7111  1925      Tino Martinez  0.270707         0.270221   \n",
       "7071  posadjo01   6092  1664       Jorge Posada  0.273145         0.272451   \n",
       "4758  knoblch01   6366  1839    Chuck Knoblauch  0.288878         0.287405   \n",
       "9494  willibe02   7869  2336    Bernie Williams  0.296861         0.295322   \n",
       "4345  jeterde01  11195  3465        Derek Jeter  0.309513         0.308039   \n",
       "\n",
       "           alpha1        beta1    ci_low   ci_high  ci_low_diff  ci_high_diff  \n",
       "1023  1090.733185  3143.130635  0.244559  0.270900     0.012833      0.013507  \n",
       "8575  1490.733185  4272.130635  0.247454  0.270063     0.011128      0.011481  \n",
       "5535  2014.733185  5441.130635  0.260201  0.280358     0.010506      0.009651  \n",
       "7071  1753.733185  4683.130635  0.261643  0.283394     0.011502      0.010248  \n",
       "4758  1928.733185  4782.130635  0.276638  0.298291     0.012240      0.009412  \n",
       "9494  2425.733185  5788.130635  0.285504  0.305234     0.011357      0.008372  \n",
       "4345  3554.733185  7985.130635  0.299648  0.316494     0.009865      0.006981  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def get_credible_intervals(data,ci=[0.025,0.975]):\n",
    "        # Get explicit credible intervals\n",
    "        data[\"ci_low\"], data[\"ci_high\"] = stats.beta.ppf(ci,data[\"alpha1\"],data[\"beta1\"])\n",
    "        # Get difference between mean and CIs since that is what matplotlib.pyplot.errorbar wants\n",
    "        data[\"ci_low_diff\"], data[\"ci_high_diff\"] = np.absolute(data[\"Avg\"]-data[\"ci_low\"]), np.absolute(data[\"Avg\"]-data[\"ci_high\"])\n",
    "        \n",
    "        return data\n",
    "\n",
    "# bd = bd.append([\"ci_low\",\"ci_high\"])\n",
    "bd = bd.apply(get_credible_intervals,axis=1)\n",
    "\n",
    "# Get trimed dataframe\n",
    "bd_trim = bd.loc[(bd.Name.isin(players)) & (bd.AB > 500),:].sort_values(by=\"Avg\",ascending=True).copy()\n",
    "bd_trim"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Figure 4.2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Make figure\n",
    "with sns.axes_style(\"whitegrid\"):\n",
    "    # Fig\n",
    "    fig = plt.figure(figsize=(7,5))\n",
    "    ax = fig.add_subplot()\n",
    "    clrs = sns.color_palette(\"colorblind\",n_colors=len(players))\n",
    "\n",
    "    # Make distributions for prior and each player\n",
    "    x = np.linspace(0,1,1000)\n",
    "    ax.plot(x, stats.beta.pdf(x,alpha0,beta0),color='k',linestyle='--',label=\"Prior\")\n",
    "\n",
    "    aa,bb = bd_trim.loc[bd_trim.Name.isin([\"Derek Jeter\"]),[\"alpha1\",\"beta1\"]].to_numpy()[0]\n",
    "    djbeta = stats.beta.pdf(x,aa,bb)\n",
    "    ax.plot(x,djbeta,color='k',lw=1.5,label=\"Derek Jeter\")\n",
    "\n",
    "    low, high = bd_trim.loc[bd_trim.Name.isin([\"Derek Jeter\"]),[\"ci_low\",\"ci_high\"]].to_numpy()[0]\n",
    "    x_idx = list(np.where(np.logical_and(x > low,x < high)))[0]\n",
    "    ax.fill_between(x[x_idx],djbeta[x_idx],len(x_idx)*[0],color='r',alpha=0.2,label=\"95% Credible Interval\")\n",
    "    ax.plot(x[x_idx],len(x_idx)*[0],color='r')\n",
    "    ax.plot(50*[x[x_idx[0]]],np.linspace(-2.5,2.5,50),color='r')\n",
    "    ax.plot(50*[x[x_idx[-1]]],np.linspace(-2.5,2.5,50),color='r')\n",
    "    ax.set_xlim(0.175,0.35)\n",
    "\n",
    "    # Add legend and clean up\n",
    "    ax.legend(frameon=False)\n",
    "    ax.set_title(\"Posterior beta distribution for Derek Jeter (3465 H / 11195 AB)\",fontweight='bold')\n",
    "    ax.set_xlabel(\"Batting Average\",fontweight=\"bold\")\n",
    "    ax.set_ylabel(\"Density\",fontweight=\"bold\")\n",
    "    ax.set_xticks([.2,.25, .3])\n",
    "    ax.set_yticks([0,25,50,75])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get credible intervals as list of tuples\n",
    "credible_intervals = bd_trim.loc[:,[\"ci_low_diff\",\"ci_high_diff\"]].transpose().to_numpy()\n",
    "\n",
    "# Make figure\n",
    "with sns.axes_style(\"whitegrid\"):\n",
    "    fig = plt.figure(figsize=(7,5))\n",
    "    ax = fig.add_subplot()\n",
    "    ax.errorbar(x=bd_trim.loc[:,\"eb estimate Avg\"],y=np.arange(len(players)),xerr=credible_intervals,color='k',fmt='o',)\n",
    "    ax.axvline(x=stats.beta(alpha0,beta0).mean(),color='r',linestyle='--',lw=.75,label=\"Prior mean\")\n",
    "    # Clean up\n",
    "    ax.set_xticks([0.26,0.28,0.30])\n",
    "    ax.set_yticks(np.arange(len(players)))\n",
    "    ax.set_yticklabels(bd_trim.Name.tolist())\n",
    "    ax.set_xlabel(\"Estimated batting average (w/ 95% interval)\",fontweight=\"bold\")\n",
    "    ax.set_ylabel(\"Player\",fontweight=\"bold\")\n",
    "    ax.legend(frameon=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we will compute the confidence intervals."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/justinbrantley/miniconda3/envs/bayesball/lib/python3.9/site-packages/scipy/stats/_continuous_distns.py:621: RuntimeWarning: overflow encountered in _beta_isf\n",
      "  return _boost._beta_isf(x, a, b)\n",
      "/Users/justinbrantley/miniconda3/envs/bayesball/lib/python3.9/site-packages/scipy/stats/_continuous_distns.py:624: RuntimeWarning: overflow encountered in _beta_ppf\n",
      "  return _boost._beta_ppf(q, a, b)\n"
     ]
    },
    {
     "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>playerID</th>\n",
       "      <th>AB</th>\n",
       "      <th>H</th>\n",
       "      <th>Name</th>\n",
       "      <th>Avg</th>\n",
       "      <th>eb estimate Avg</th>\n",
       "      <th>alpha1</th>\n",
       "      <th>beta1</th>\n",
       "      <th>ci_low</th>\n",
       "      <th>ci_high</th>\n",
       "      <th>ci_low_diff</th>\n",
       "      <th>ci_high_diff</th>\n",
       "      <th>CI_Clopper_low</th>\n",
       "      <th>CI_Clopper_high</th>\n",
       "      <th>CI_Jeffreys_low</th>\n",
       "      <th>CI_Jeffreys_high</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>aaronha01</td>\n",
       "      <td>12364</td>\n",
       "      <td>3771</td>\n",
       "      <td>Hank Aaron</td>\n",
       "      <td>0.304998</td>\n",
       "      <td>0.303783</td>\n",
       "      <td>3860.733185</td>\n",
       "      <td>8848.130635</td>\n",
       "      <td>0.295817</td>\n",
       "      <td>0.311807</td>\n",
       "      <td>0.009181</td>\n",
       "      <td>0.006809</td>\n",
       "      <td>0.296889</td>\n",
       "      <td>0.313199</td>\n",
       "      <td>0.296929</td>\n",
       "      <td>0.313159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>aaronto01</td>\n",
       "      <td>944</td>\n",
       "      <td>216</td>\n",
       "      <td>Tommie Aaron</td>\n",
       "      <td>0.228814</td>\n",
       "      <td>0.237211</td>\n",
       "      <td>305.733185</td>\n",
       "      <td>983.130635</td>\n",
       "      <td>0.214390</td>\n",
       "      <td>0.260805</td>\n",
       "      <td>0.014424</td>\n",
       "      <td>0.031992</td>\n",
       "      <td>0.202367</td>\n",
       "      <td>0.256963</td>\n",
       "      <td>0.202873</td>\n",
       "      <td>0.256414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>abadan01</td>\n",
       "      <td>21</td>\n",
       "      <td>2</td>\n",
       "      <td>Andy Abad</td>\n",
       "      <td>0.095238</td>\n",
       "      <td>0.250730</td>\n",
       "      <td>91.733185</td>\n",
       "      <td>274.130635</td>\n",
       "      <td>0.207708</td>\n",
       "      <td>0.296334</td>\n",
       "      <td>0.112470</td>\n",
       "      <td>0.201096</td>\n",
       "      <td>0.011749</td>\n",
       "      <td>0.303774</td>\n",
       "      <td>0.020327</td>\n",
       "      <td>0.271911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>abadijo01</td>\n",
       "      <td>49</td>\n",
       "      <td>11</td>\n",
       "      <td>John Abadie</td>\n",
       "      <td>0.224490</td>\n",
       "      <td>0.255756</td>\n",
       "      <td>100.733185</td>\n",
       "      <td>293.130635</td>\n",
       "      <td>0.213934</td>\n",
       "      <td>0.299928</td>\n",
       "      <td>0.010556</td>\n",
       "      <td>0.075438</td>\n",
       "      <td>0.117743</td>\n",
       "      <td>0.366243</td>\n",
       "      <td>0.125540</td>\n",
       "      <td>0.354884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>abbated01</td>\n",
       "      <td>3044</td>\n",
       "      <td>772</td>\n",
       "      <td>Ed Abbaticchio</td>\n",
       "      <td>0.253614</td>\n",
       "      <td>0.254284</td>\n",
       "      <td>861.733185</td>\n",
       "      <td>2527.130635</td>\n",
       "      <td>0.239764</td>\n",
       "      <td>0.269079</td>\n",
       "      <td>0.013850</td>\n",
       "      <td>0.015465</td>\n",
       "      <td>0.238236</td>\n",
       "      <td>0.269466</td>\n",
       "      <td>0.238397</td>\n",
       "      <td>0.269299</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9797</th>\n",
       "      <td>zuninmi01</td>\n",
       "      <td>2226</td>\n",
       "      <td>446</td>\n",
       "      <td>Mike Zunino</td>\n",
       "      <td>0.200359</td>\n",
       "      <td>0.208386</td>\n",
       "      <td>535.733185</td>\n",
       "      <td>2035.130635</td>\n",
       "      <td>0.192907</td>\n",
       "      <td>0.224296</td>\n",
       "      <td>0.007453</td>\n",
       "      <td>0.023936</td>\n",
       "      <td>0.183910</td>\n",
       "      <td>0.217602</td>\n",
       "      <td>0.184127</td>\n",
       "      <td>0.217370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9798</th>\n",
       "      <td>zupcibo01</td>\n",
       "      <td>795</td>\n",
       "      <td>199</td>\n",
       "      <td>Bob Zupcic</td>\n",
       "      <td>0.250314</td>\n",
       "      <td>0.253305</td>\n",
       "      <td>288.733185</td>\n",
       "      <td>851.130635</td>\n",
       "      <td>0.228486</td>\n",
       "      <td>0.278944</td>\n",
       "      <td>0.021829</td>\n",
       "      <td>0.028630</td>\n",
       "      <td>0.220545</td>\n",
       "      <td>0.281948</td>\n",
       "      <td>0.221147</td>\n",
       "      <td>0.281296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9799</th>\n",
       "      <td>zupofr01</td>\n",
       "      <td>18</td>\n",
       "      <td>3</td>\n",
       "      <td>Frank Zupo</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.255559</td>\n",
       "      <td>92.733185</td>\n",
       "      <td>270.130635</td>\n",
       "      <td>0.212058</td>\n",
       "      <td>0.301612</td>\n",
       "      <td>0.045392</td>\n",
       "      <td>0.134945</td>\n",
       "      <td>0.035785</td>\n",
       "      <td>0.414177</td>\n",
       "      <td>0.049288</td>\n",
       "      <td>0.381357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9800</th>\n",
       "      <td>zuvelpa01</td>\n",
       "      <td>491</td>\n",
       "      <td>109</td>\n",
       "      <td>Paul Zuvella</td>\n",
       "      <td>0.221996</td>\n",
       "      <td>0.237758</td>\n",
       "      <td>198.733185</td>\n",
       "      <td>637.130635</td>\n",
       "      <td>0.209521</td>\n",
       "      <td>0.267183</td>\n",
       "      <td>0.012475</td>\n",
       "      <td>0.045187</td>\n",
       "      <td>0.185986</td>\n",
       "      <td>0.261392</td>\n",
       "      <td>0.186938</td>\n",
       "      <td>0.260320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9801</th>\n",
       "      <td>zwilldu01</td>\n",
       "      <td>1280</td>\n",
       "      <td>364</td>\n",
       "      <td>Dutch Zwilling</td>\n",
       "      <td>0.284375</td>\n",
       "      <td>0.279244</td>\n",
       "      <td>453.733185</td>\n",
       "      <td>1171.130635</td>\n",
       "      <td>0.257698</td>\n",
       "      <td>0.301304</td>\n",
       "      <td>0.026677</td>\n",
       "      <td>0.016929</td>\n",
       "      <td>0.259790</td>\n",
       "      <td>0.309955</td>\n",
       "      <td>0.260169</td>\n",
       "      <td>0.309555</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9802 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       playerID     AB     H            Name       Avg  eb estimate Avg  \\\n",
       "0     aaronha01  12364  3771      Hank Aaron  0.304998         0.303783   \n",
       "1     aaronto01    944   216    Tommie Aaron  0.228814         0.237211   \n",
       "2      abadan01     21     2       Andy Abad  0.095238         0.250730   \n",
       "3     abadijo01     49    11     John Abadie  0.224490         0.255756   \n",
       "4     abbated01   3044   772  Ed Abbaticchio  0.253614         0.254284   \n",
       "...         ...    ...   ...             ...       ...              ...   \n",
       "9797  zuninmi01   2226   446     Mike Zunino  0.200359         0.208386   \n",
       "9798  zupcibo01    795   199      Bob Zupcic  0.250314         0.253305   \n",
       "9799   zupofr01     18     3      Frank Zupo  0.166667         0.255559   \n",
       "9800  zuvelpa01    491   109    Paul Zuvella  0.221996         0.237758   \n",
       "9801  zwilldu01   1280   364  Dutch Zwilling  0.284375         0.279244   \n",
       "\n",
       "           alpha1        beta1    ci_low   ci_high  ci_low_diff  ci_high_diff  \\\n",
       "0     3860.733185  8848.130635  0.295817  0.311807     0.009181      0.006809   \n",
       "1      305.733185   983.130635  0.214390  0.260805     0.014424      0.031992   \n",
       "2       91.733185   274.130635  0.207708  0.296334     0.112470      0.201096   \n",
       "3      100.733185   293.130635  0.213934  0.299928     0.010556      0.075438   \n",
       "4      861.733185  2527.130635  0.239764  0.269079     0.013850      0.015465   \n",
       "...           ...          ...       ...       ...          ...           ...   \n",
       "9797   535.733185  2035.130635  0.192907  0.224296     0.007453      0.023936   \n",
       "9798   288.733185   851.130635  0.228486  0.278944     0.021829      0.028630   \n",
       "9799    92.733185   270.130635  0.212058  0.301612     0.045392      0.134945   \n",
       "9800   198.733185   637.130635  0.209521  0.267183     0.012475      0.045187   \n",
       "9801   453.733185  1171.130635  0.257698  0.301304     0.026677      0.016929   \n",
       "\n",
       "      CI_Clopper_low  CI_Clopper_high  CI_Jeffreys_low  CI_Jeffreys_high  \n",
       "0           0.296889         0.313199         0.296929          0.313159  \n",
       "1           0.202367         0.256963         0.202873          0.256414  \n",
       "2           0.011749         0.303774         0.020327          0.271911  \n",
       "3           0.117743         0.366243         0.125540          0.354884  \n",
       "4           0.238236         0.269466         0.238397          0.269299  \n",
       "...              ...              ...              ...               ...  \n",
       "9797        0.183910         0.217602         0.184127          0.217370  \n",
       "9798        0.220545         0.281948         0.221147          0.281296  \n",
       "9799        0.035785         0.414177         0.049288          0.381357  \n",
       "9800        0.185986         0.261392         0.186938          0.260320  \n",
       "9801        0.259790         0.309955         0.260169          0.309555  \n",
       "\n",
       "[9802 rows x 16 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use statsmodels for the confidence intervals\n",
    "import statsmodels.api as sm\n",
    "from statsmodels.stats.proportion import proportion_confint   \n",
    "\n",
    "\n",
    "def get_confidence_intervals(data):\n",
    "    # Clopper-Pearson\n",
    "    xx, yy = proportion_confint(count=data[\"H\"], nobs=data[\"AB\"], alpha=0.05, method='beta')\n",
    "    data[\"CI_Clopper_low\"], data[\"CI_Clopper_high\"] = xx, yy\n",
    "    # Jeffreys\n",
    "    xx, yy = proportion_confint(count=data[\"H\"], nobs=data[\"AB\"], alpha=0.05, method='jeffreys')\n",
    "    data[\"CI_Jeffreys_low\"], data[\"CI_Jeffreys_high\"] = xx, yy\n",
    "    return data\n",
    "    \n",
    "bd = bd.apply(get_confidence_intervals,axis=1)\n",
    "bd\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Figure  4.4\n",
    "\n",
    "Frequentist confidence intervals vs Bayesian credible intervals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 504x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get credible intervals as list of tuples\n",
    "bd_rand = bd.sample(20).copy()\n",
    "credible_intervals = bd_rand.loc[:,[\"ci_low_diff\",\"ci_high_diff\"]].transpose().to_numpy()\n",
    "confidence_intervals = np.vstack((np.absolute(bd_rand.loc[:,\"CI_Clopper_low\"] - bd_rand.loc[:,\"Avg\"]),np.absolute(bd_rand.loc[:,\"CI_Clopper_high\"] - bd_rand.loc[:,\"Avg\"])))\n",
    "\n",
    "# Make figure\n",
    "with sns.axes_style(\"whitegrid\"):\n",
    "    fig = plt.figure(figsize=(7,10))\n",
    "    ax = fig.add_subplot()\n",
    "    ax.errorbar(x=bd_rand.loc[:,\"Avg\"],y=np.arange(len(bd_rand)),xerr=confidence_intervals,color='coral',fmt='o',label=\"Credible\")\n",
    "    ax.errorbar(x=bd_rand.loc[:,\"eb estimate Avg\"],y=np.arange(len(bd_rand)),xerr=credible_intervals,color='mediumturquoise',fmt='o',label=\"Confidence\")\n",
    "    ax.axvline(x=stats.beta(alpha0,beta0).mean(),color='r',linestyle='--',lw=.75,label=\"Prior mean\")\n",
    "    # Clean up\n",
    "    ax.set_xticks([0, 0.2, 0.4, 0.6, 0.8, 1])\n",
    "    ax.set_yticks(np.arange(len(bd_rand)))\n",
    "   \n",
    "    def make_name_with_H_AB(zipped_up):\n",
    "        name, AB, H = zipped_up\n",
    "        return name + \" \" + \"(\" + str(H) + \"/\" + str(AB) + \")\"\n",
    "\n",
    "    yticklabs = [make_name_with_H_AB(ii) for ii in zip(bd_rand.Name.to_list(), bd_rand.AB.to_list(), bd_rand.H.to_list())]\n",
    "    ax.set_yticklabels(yticklabs)\n",
    "    ax.set_xlabel(\"Estimated batting average (w/ 95% interval)\",fontweight=\"bold\")\n",
    "    ax.set_ylabel(\"Player\",fontweight=\"bold\")\n",
    "    ax.legend(frameon=False,bbox_to_anchor=(1.05, .65),loc='upper left')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save dataframe\n",
    "bd.to_pickle(\"./data/batting_data_ch4.pkl\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.10.5 ('pymc_env')",
   "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": "3.10.5"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "855c483787daa00e7d5444023bffcad8757ed7571daa62fa67a31368d5ce2535"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}