{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "(item_response_nba)=\n", "# NBA Foul Analysis with Item Response Theory\n", "\n", ":::{post} Apr 17, 2022\n", ":tags: hierarchical model, case study, generalized linear model \n", ":category: intermediate, tutorial\n", ":author: Austin Rochford, Lorenzo Toniazzi\n", ":::" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Running on PyMC v5.28.0+58.gf58491a3\n" ] } ], "source": [ "import os\n", "\n", "import arviz as az\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", "import pymc as pm\n", "\n", "%matplotlib inline\n", "print(f\"Running on PyMC v{pm.__version__}\")" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "RANDOM_SEED = 8927\n", "rng = np.random.default_rng(RANDOM_SEED)\n", "az.style.use(\"arviz-variat\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Introduction\n", "This tutorial shows an application of Bayesian Item Response Theory {cite:p}`fox2010bayesian` to NBA basketball foul calls data using PyMC. Based on Austin Rochford's blogpost [NBA Foul Calls and Bayesian Item Response Theory](https://www.austinrochford.com/posts/2017-04-04-nba-irt.html).\n", "\n", "### Motivation\n", "Our scenario is that we observe a binary outcome (a foul being called or not) from an interaction (a basketball play) of two agents with two different roles (the player committing the alleged foul and the player disadvantaged in the play). Moreover, each committing or disadvantaged agent is an individual which might be observed several times (say LeBron James observed committing a foul in more than one play). Then it might be that not only the agent's role, but also the abilities of the single individual player contribute to the observed outcome. And so we'd like to __estimate the contribution to the observed outcome of each individual's (latent) ability as a committing or disadvantaged agent.__ This would allow us, for example, to rank players from more to less effective, quantify uncertainty in this ranking and discover extra hierarchical structures involved in foul calls. All pretty useful stuff! \n", " \n", "\n", "So how can we study this common and complex __multi-agent interaction__ scenario, with __hierarchical__ structures between more than a thousand individuals? \n", "\n", "Despite the scenario's overwhelming complexity, Bayesian Item Response Theory combined with modern powerful statistical software allows for quite elegant and effective modeling options. One of these options employs a {term}`Generalized Linear Model` called [Rasch model](https://en.wikipedia.org/wiki/Rasch_model), which we now discuss in more detail.\n", "\n", "\n", "### Rasch Model\n", "We sourced our data from the official [NBA Last Two Minutes Reports](https://official.nba.com/2020-21-nba-officiating-last-two-minute-reports/) with game data between 2015 to 2021. In this dataset, each row `k` is one play involving two players (the committing and the disadvantaged) where a foul has been either called or not. So we model the probability `p_k` that a referee calls a foul in play `k` as a function of the players involved. Hence we define two latent variables for each player, namely:\n", "- `theta`: which estimates the player's ability to have a foul called when disadvantaged, and\n", "- `b`: which estimates the player's ability to have a foul not called when committing.\n", "\n", "Note that the higher these player's parameters, the better the outcome for the player's team. These two parameters are then estimated using a standard Rasch model, by assuming the log-odds-ratio of `p_k` equals `theta-b` for the corresponding players involved in play `k`. Also, we place hierarchical hyperpriors on all `theta`'s and all `b`'s to account for shared abilities between players and largely different numbers of observations for different players.\n", "\n", "\n", "### Discussion \n", "Our analysis gives an estimate of the latent skills `theta` and `b` for each player in terms of posterior distributions. We analyze this outcome in three ways. \n", "\n", "We first display the role of shared hyperpriors, by showing how posteriors of players with little observations are drawn to the league average.\n", "\n", "Secondly, we rank the posteriors by their mean to view best and worst committing and disadvantaged players, and observe that several players still rank in the top 10 of the same model estimated in [Austin Rochford blogpost](https://www.austinrochford.com/posts/2017-04-04-nba-irt.html) on different data.\n", "\n", "Thirdly, we show how we spot that grouping payers by their position is likely to be an informative extra hierarchical layer to introduce in our model, and leave this as an exercise for the interested reader. Let us conclude by mentioning that this opportunity of easily adding informed hierarchical structure to a model is one of the features that makes Bayesian modelling very flexible and powerful for quantifying uncertainty in scenarios where introducing (or discovering) problem-specific knowledge is crucial.\n", "\n", "\n", "The analysis in this notebook is performed in four main steps: \n", "\n", "1. Data collection and processing.\n", "2. Definition and instantiation of the Rasch model. \n", "3. Posterior sampling and convergence checks.\n", "4. Analysis of the posterior results.\n", "\n", "## Data collection and processing\n", "We first import data from the original data set, which can be found at [this URL](https://raw.githubusercontent.com/polygraph-cool/last-two-minute-report/32f1c43dfa06c2e7652cc51ea65758007f2a1a01/output/all_games.csv). Each row corresponds to a play between the NBA seasons 2015-16 and 2020-21. We imported only five columns, namely\n", "- `committing`: the name of the committing player in the play.\n", "- `disadvantaged`: the name of the disadvantaged player in the play.\n", "- `decision`: the reviewed decision of the play, which can take four values, namely:\n", " - `CNC`: correct noncall, `INC`: incorrect noncall, `IC`: incorrect call, `CC`: correct call.\n", "- `committing_position`: the position of the committing player which can take values\n", " - `G`: guard, `F`: forward, `C`: center, `G-F`, `F-G`, `F-C`, `C-F`.\n", "- `disadvantaged_position`: the position of the disadvantaged player, with possible values as above.\n", "\n", "We note that we already removed from the original dataset the plays where less than two players are involved (for example travel calls or clock violations). Also, the original dataset does not contain information on the players' position, which we added ourselves." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "tags": [] }, "outputs": [ { "data": { "text/html": [ "
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committingdisadvantageddecisioncommitting_positiondisadvantaged_position
play_id
1Avery BradleyStephen CurryCNCGG
3Isaiah ThomasAndre IguodalaCNCGG-F
4Jae CrowderHarrison BarnesINCFF
6Draymond GreenIsaiah ThomasCCFG
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" ], "text/plain": [ " committing disadvantaged decision committing_position \\\n", "play_id \n", "1 Avery Bradley Stephen Curry CNC G \n", "3 Isaiah Thomas Andre Iguodala CNC G \n", "4 Jae Crowder Harrison Barnes INC F \n", "6 Draymond Green Isaiah Thomas CC F \n", "7 Avery Bradley Stephen Curry CC G \n", "\n", " disadvantaged_position \n", "play_id \n", "1 G \n", "3 G-F \n", "4 F \n", "6 G \n", "7 G " ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "try:\n", " df_orig = pd.read_csv(os.path.join(\"..\", \"data\", \"item_response_nba.csv\"), index_col=0)\n", "except FileNotFoundError:\n", " df_orig = pd.read_csv(pm.get_data(\"item_response_nba.csv\"), index_col=0)\n", "df_orig.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We now process our data in three steps:\n", " 1. We create a dataframe `df` by removing the position information from `df_orig`, and we create a dataframe `df_position` collecting all players with the respective position. (This last dataframe will not be used until the very end of the notebook.)\n", " 2. We add a column to `df`, called `foul_called`, that assigns 1 to a play if a foul was called, and 0 otherwise.\n", " 3. We assign IDs to committing and disadvantaged players and use this indexing to identify the respective players in each observed play.\n", "\n", "Finally, we display the head of our main dataframe `df` along with some basic statistics." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of observed plays: 46861\n", "Number of disadvantaged players: 770\n", "Number of committing players: 789\n", "Global probability of a foul being called: 23.3%\n", "\n", "\n" ] }, { "data": { "text/html": [ "
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committingdisadvantageddecisionfoul_called
play_id
1Avery BradleyStephen CurryCNC0
3Isaiah ThomasAndre IguodalaCNC0
4Jae CrowderHarrison BarnesINC0
6Draymond GreenIsaiah ThomasCC1
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" ], "text/plain": [ " committing disadvantaged decision foul_called\n", "play_id \n", "1 Avery Bradley Stephen Curry CNC 0\n", "3 Isaiah Thomas Andre Iguodala CNC 0\n", "4 Jae Crowder Harrison Barnes INC 0\n", "6 Draymond Green Isaiah Thomas CC 1\n", "7 Avery Bradley Stephen Curry CC 1" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 1. Construct df and df_position\n", "df = df_orig[[\"committing\", \"disadvantaged\", \"decision\"]]\n", "\n", "df_position = pd.concat(\n", " [\n", " df_orig.groupby(\"committing\").committing_position.first(),\n", " df_orig.groupby(\"disadvantaged\").disadvantaged_position.first(),\n", " ]\n", ").to_frame()\n", "df_position = df_position[~df_position.index.duplicated(keep=\"first\")]\n", "df_position.index.name = \"player\"\n", "df_position.columns = [\"position\"]\n", "\n", "\n", "# 2. Create the binary foul_called variable\n", "def foul_called(decision):\n", " \"\"\"Correct and incorrect noncalls (CNC and INC) take value 0.\n", " Correct and incorrect calls (CC and IC) take value 1.\n", " \"\"\"\n", " out = 0\n", " if (decision == \"CC\") | (decision == \"IC\"):\n", " out = 1\n", " return out\n", "\n", "\n", "df = df.assign(foul_called=lambda df: df[\"decision\"].apply(foul_called))\n", "\n", "# 3 We index observed calls by committing and disadvantaged players\n", "committing_observed, committing = pd.factorize(df.committing, sort=True)\n", "disadvantaged_observed, disadvantaged = pd.factorize(df.disadvantaged, sort=True)\n", "df.index.name = \"play_id\"\n", "\n", "# Display of main dataframe with some statistics\n", "print(f\"Number of observed plays: {len(df)}\")\n", "print(f\"Number of disadvantaged players: {len(disadvantaged)}\")\n", "print(f\"Number of committing players: {len(committing)}\")\n", "print(f\"Global probability of a foul being called: \" f\"{100*round(df.foul_called.mean(),3)}%\\n\\n\")\n", "df.head()" ] }, { "cell_type": "markdown", "metadata": { "tags": [] }, "source": [ "## Item Response Model\n", "\n", "### Model definition\n", "\n", "We denote by:\n", "- $N_d$ and $N_c$ the number of disadvantaged and committing players, respectively, \n", "- $K$ the number of plays,\n", "- $k$ a play, \n", "- $y_k$ the observed call/noncall in play $k$,\n", "- $p_k$ the probability of a foul being called in play $k$,\n", "- $i(k)$ the disadvantaged player in play $k$, and by\n", "- $j(k)$ the committing player in play $k$.\n", "\n", "We assume that each disadvantaged player is described by the latent variable: \n", "- $\\theta_i$ for $i=1,2,...,N_d$,\n", "\n", "and each committing player is described by the latent variable: \n", "- $b_j$ for $j=1,2,...,N_c$.\n", "\n", "Then we model each observation $y_k$ as the result of an independent Bernoulli trial with probability $p_k$, where\n", "\n", "$$\n", "p_k =\\text{sigmoid}(\\eta_k)=\\left(1+e^{-\\eta_k}\\right)^{-1},\\quad\\text{with}\\quad \\eta_k=\\theta_{i(k)}-b_{j(k)},\n", "$$\n", "\n", "for $k=1,2,...,K$, by defining (via a [non-centered parametrisation](https://twiecki.io/blog/2017/02/08/bayesian-hierchical-non-centered/))\n", "\n", "\\begin{align*}\n", "\\theta_{i}&= \\sigma_\\theta\\Delta_{\\theta,i}+\\mu_\\theta\\sim \\text{Normal}(\\mu_\\theta,\\sigma_\\theta^2), &i=1,2,...,N_d,\\\\\n", "b_{j}&= \\sigma_b\\Delta_{b,j}\\sim \\text{Normal}(0,\\sigma_b^2), &j=1,2,...,N_c,\n", "\\end{align*}\n", "\n", "with priors/hyperpriors\n", "\n", "\\begin{align*}\n", "\\Delta_{\\theta,i}&\\sim \\text{Normal}(0,1), &i=1,2,...,N_d,\\\\\n", "\\Delta_{b,j}&\\sim \\text{Normal}(0,1), &j=1,2,...,N_c,\\\\\n", "\\mu_\\theta&\\sim \\text{Normal}(0,100),\\\\\n", "\\sigma_\\theta &\\sim \\text{HalfCauchy}(2.5),\\\\\n", "\\sigma_b &\\sim \\text{HalfCauchy}(2.5).\n", "\\end{align*}\n", "\n", "Note that $p_k$ is always dependent on $\\mu_\\theta,\\,\\sigma_\\theta$ and $\\sigma_b$ (\"pooled priors\") and also depends on the actual players involved in the play due to $\\Delta_{\\theta,i}$ and $\\Delta_{b,j}$ (\"unpooled priors\"). This means our model features partial pooling. Morover, note that we do not pool $\\theta$'s with $b$'s, hence assuming these skills are independent even for the same player. Also, note that we normalised the mean of $b_{j}$ to zero. \n", "\n", "Finally, notice how we worked backwards from our data to construct this model. This is a very natural way to construct a model, allowing us to quickly see how each variable connects to others and their intuition. Meanwhile, when instantiating the model below, the construction goes in the opposite direction, i.e. starting from priors and moving up to the observations.\n", "\n", "### PyMC implementation\n", "We now implement the model above in PyMC. Note that, to easily keep track of the players (as we have hundreds of them being both committing and disadvantaged), we make use of the `coords` argument for {class}`pymc.Model`. (For tutorials on this functionality see the notebook {ref}`data_container` or [this blogpost](https://oriolabrilpla.cat/python/arviz/pymc3/xarray/2020/09/22/pymc3-arviz.html).) We choose our priors to be the same as in [Austin Rochford's post](https://www.austinrochford.com/posts/2017-04-04-nba-irt.html), to make the comparison consistent." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "coords = {\"disadvantaged\": disadvantaged, \"committing\": committing}\n", "\n", "with pm.Model(coords=coords) as model:\n", " # Data\n", " foul_called_observed = pm.Data(\"foul_called_observed\", df.foul_called)\n", "\n", " # Hyperpriors\n", " mu_theta = pm.Normal(\"mu_theta\", 0.0, 100.0)\n", " sigma_theta = pm.HalfCauchy(\"sigma_theta\", 2.5)\n", " sigma_b = pm.HalfCauchy(\"sigma_b\", 2.5)\n", "\n", " # Priors\n", " Delta_theta = pm.Normal(\"Delta_theta\", 0.0, 1.0, dims=\"disadvantaged\")\n", " Delta_b = pm.Normal(\"Delta_b\", 0.0, 1.0, dims=\"committing\")\n", "\n", " # Deterministic\n", " theta = pm.Deterministic(\"theta\", Delta_theta * sigma_theta + mu_theta, dims=\"disadvantaged\")\n", " b = pm.Deterministic(\"b\", Delta_b * sigma_b, dims=\"committing\")\n", " eta = pm.Deterministic(\"eta\", theta[disadvantaged_observed] - b[committing_observed])\n", "\n", " # Likelihood\n", " y = pm.Bernoulli(\"y\", logit_p=eta, observed=foul_called_observed)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We now plot our model to show the hierarchical structure (and the non-centered parametrisation) on the variables `theta` and `b`." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "tags": [] }, "outputs": [ { "data": { "image/svg+xml": [ "\n", "\n", "\n", "\n", "\n", "\n", "%3\n", "\n", "\n", "cluster46861\n", "\n", "46861\n", "\n", "\n", "clusterdisadvantaged (770)\n", "\n", "disadvantaged (770)\n", "\n", "\n", "clustercommitting (789)\n", "\n", "committing (789)\n", "\n", "\n", "\n", "foul_called_observed\n", "\n", "foul_called_observed\n", "~\n", "Data\n", "\n", "\n", "\n", "eta\n", "\n", "eta\n", "~\n", "Deterministic\n", "\n", "\n", "\n", "y\n", "\n", "y\n", "~\n", "Bernoulli\n", "\n", "\n", "\n", "eta->y\n", "\n", "\n", "\n", "\n", "\n", "y->foul_called_observed\n", "\n", "\n", "\n", "\n", "\n", "mu_theta\n", "\n", "mu_theta\n", "~\n", "Normal\n", "\n", "\n", "\n", "theta\n", "\n", "theta\n", "~\n", "Deterministic\n", "\n", "\n", "\n", "mu_theta->theta\n", "\n", "\n", "\n", "\n", "\n", "sigma_b\n", "\n", "sigma_b\n", "~\n", "Halfcauchy\n", "\n", "\n", "\n", "b\n", "\n", "b\n", "~\n", "Deterministic\n", "\n", "\n", "\n", "sigma_b->b\n", "\n", "\n", "\n", "\n", "\n", "sigma_theta\n", "\n", "sigma_theta\n", "~\n", "Halfcauchy\n", "\n", "\n", "\n", "sigma_theta->theta\n", "\n", "\n", "\n", "\n", "\n", "Delta_theta\n", "\n", "Delta_theta\n", "~\n", "Normal\n", "\n", "\n", "\n", "Delta_theta->theta\n", "\n", "\n", "\n", "\n", "\n", "theta->eta\n", "\n", "\n", "\n", "\n", "\n", "b->eta\n", "\n", "\n", "\n", "\n", "\n", "Delta_b\n", "\n", "Delta_b\n", "~\n", "Normal\n", "\n", "\n", "\n", "Delta_b->b\n", "\n", "\n", "\n", "\n", "\n" ], "text/plain": [ "" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pm.model_to_graphviz(model)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Sampling and convergence\n", "\n", "We now sample from our Rasch model." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Initializing NUTS using jitter+adapt_diag...\n", "Multiprocess sampling (4 chains in 4 jobs)\n", "NUTS: [mu_theta, sigma_theta, sigma_b, Delta_theta, Delta_b]\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "4fc8ba428900406fa7b680145a30b611", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Output()" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
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      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Sampling 4 chains for 1_500 tune and 1_000 draw iterations (6_000 + 4_000 draws total) took 221 seconds.\n"
     ]
    }
   ],
   "source": [
    "with model:\n",
    "    trace = pm.sample(1000, tune=1500, random_seed=RANDOM_SEED)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We plot below the energy difference of the obtained trace. Also, we assume our sampler has converged as it passed all automatic PyMC convergence checks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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yiK6//nr16dNHV111lW6++WZNnjxZq1atCsv5AQBNo6CgQM8995xGjx6tkydP1lu/sLDQVk5PTw/pfMH1T58+HVJ7oCbBcRRqXAZ/mRMc57VtD+VLzaSkJLVq1coqnz17VoFAIIReAvVbs2aNfvzjH2vJkiX11nXrugGcIB6B0MbljMkRS9yI53Cfg88ZNDXG/QAAhIaZtQAHmkOW//HjxzVz5kyrPGHChJAHuGi5ojXGt2/fft55cnJyNHHiRO3cudO2r7S0VAUFBdq1a5fee+899e/fX9OnT7cSvAAATa9bt24aMmSIevXqpYsvvlgpKSkqKirSwYMHtXbtWi1evNj2JdCaNWs0ZswYvfLKK3Uug3v27FlbOSEhIaR+VU9WkaQzZ86E1B6oSXAcNTYug+O8tu0NOU9xcbHteCkpKSEdAy1PUlKSrr76an33u99V9+7dlZ6erri4OOXn5+vvf/+7li5dqrVr11r1i4qKNHHiRKWmpuqaa66p9bhuXTeAE8QjYlkkxuWMyRFL3IjncJ+DzxlEAuN+AAAih2QtwIHmkOX/zDPPWDdR+vfvrx//+MdhPwdiV7TG+NGjR23l3NxcPfTQQ45u2G3atEl33XWX/vCHP7AcKAA0sWuvvVYjR46sdYndnj17aujQoRozZoymTZum//qv/7L2rV27Vi+88ILGjh1b6/GLiops5VBv8sXHx9d5PKAhguMoOM7qExzHtcVluOOfZC3UJSkpSZMnT9bw4cOVlJR03v4uXbpowIABGjlypNasWaOJEyfq+PHjkiS/36/HH39cS5cuVVpaWo3Hd+u6AZwgHhGLIjkuZ0yOWOJGPPM5g2jGuB8AgMgjWQtwINqz/L/44gt98sknkiSv16upU6fKMIywngOxLRpjvLi4WGVlZbZt48ePt/ratWtX3XPPPerXr5+Sk5N16NAhLV++XO+++67VrrCwUI899pg++ugjZWRkNLpPABBue/fujfgT4126dGnyxIubbrrJUb2UlBT97ne/U0JCgt577z1r+x//+Efde++9jpd3C3UcFFzfNM2Q2qNhWkr8V3ErLon/2NbU1016erruueceR8cZNGiQ3njjDf3kJz+xHuY4efKkXn75ZU2YMMHRMYhnRBPiEbHAzXE51wxiiRvxzDWDaMK4HwCAyCNZC3AgmrP8i4uLNXXqVKs8cuRIZWdnh+34aBmiMcZrmp3r0KFDkqTbbrtN06dPt/Wza9euGjRokO644w49+OCD1pM8BQUF+u1vf6t58+Y1uk8AEG5PPfWUNm7cGNFzLFiwQNddd11EzxFuTz/9tL744gsdOHBAUkVS8ZIlS2q9UZiYmGgrV1/SzYmSkhJbuaanRhF+sR7/wXEZHGf1CY7j2uIyMTHRluBeXFys5ORkx+cJ7lcobeG+5nbdXHLJJfrlL3+pyZMnW9sWLlyoxx9/vMYvZNy6bgAniEcgtHE5Y3LEEjfimc8ZxBLG/QAAhM7T1B0AmqNoyvKfO3eu9u/fL0m64IIL6lwiCHAqGmI8EAjUuL13796aMWNGrQllPXv21OzZs219WrZsmfbt29foPgEA3BEfH6+RI0fatq1Zs6bW+o29yUeyCiIh+GZxY+OyrmQtN84DNNTtt9+uNm3aWOXjx49r586dNdZ167oBnCAegdDG5YzJEUvciGc+ZxBrGPcDABAakrUAB6I1y3/nzp169dVXrfKTTz4ZNcu8oHmJxhiv7RgTJkyQ1+uts+3AgQM1ZMgQq2yappYsWdLoPgEA3DNo0CBbOScnp9a6rVu3tpXz8/NDOteJEydsZcZTCIfgOGpsXAbHeW3bQzlPUVGRbUbUpKQkeTzcJkB4+Xw+XXnllbZttX1p49Z1AzhBPAIVnI7LGZMjlrgRz3zOINYw7gcAIDQsgwg4EI1Z/oFAQJMnT1Z5ebkk6ZprrtEPf/jDRh8XLVM0xnhNx0hPT9f3vvc9R+1vvvlmrVixwipHerkYAGiI//iP/wj5b26o2rdvH9HjR0qnTp1s5bpu3F100UW2ctWyuU6UlpbabvKlpqaqbdu2jtuj4WI9/oPjMi8vL6T2wXGclZVVY72srCzt3r3b1u6SSy5xdI7gPgX3GdGnuV43Tv+mu3XdAE4Qj0CFhv4NZ0yO5syNeA735wxjeUQDxv0AADhHshbgQDRm+b/zzjvatGmTJCkhIUFTpkxp9DHRckVjjHu9XiUlJens2bPWtl69ejleorFPnz62MssgAohGHTp0aOouRK2EhARbOXgWx+q6dOliK1ctEe1EcN3gYyFyYj3+09LSlJ6ebo2Tjh07pqKiovNmNK1Nbm6urdy1a9ca63Xt2tWWoB5K/Ds9B6JHc71uWrVqZSvXlnDm1nUDOEE8AhWcjssZkyOWuBHPXbp0kWEYMk1T0vmfG/UJrs91g2jAuB8AAOdY3wBwIBqz/P/whz9Y74cPHy6Px6Pc3Nw6f4IHxocOHbLtr74EClqWaIxxSbr44ott5VC+nAqeEaCgoCAcXQIAuCQ4cTgtLa3Wut27d7eVN2/e7Pg8W7ZssZWdzkgEONGtWzfrfSAQ0LZt2xy3DY7j6seqjvhHcxDK33Q3rhvAKeIRcP43nDEJYokb8ZyUlKSOHTta5aNHj+rgwYOOzlFaWqrt27dbZZ/Px8xaiAqM+wEAcI5kLcCBqiz/KlVZ/k5FIsu/+lNs7777rq6//vp6f4IHsPfcc49t/+rVqxvdLzRP0Rjj0vk3M3w+n+O28fHxtnJpaWlY+gQAcMfWrVtt5QsuuKDWuv3797ctn7t161bHf/c3bNhgK19zzTUh9BKo26BBg2zl4HirTV5eng4cOGCVu3TpYvsip65zfPnll477R/zDLaH8TXfjugGcIh4B53/DGZMjlrgVzw39nNmyZYutP1dddVVI902BSGHcDwCAcyRrAQ6R5Y9YF40xHvwU2+nTpx23LSwstJXreooHABB9PvvsM1v5yiuvrLVufHy87Qb42bNn9ec//7nec5SUlGjp0qVW2efz6brrrmtAb4GaDR061Fb++OOPHbULrhd8nOoyMjLUq1cvq/zNN984evL/8OHDWrt2rVVu3769+vXr56h/QCj27NmjHTt2WGWv16sBAwbUWt+N6wZwingEnI/LGZMjlrgVz8GfDx999JGj/gXX43MG0YBxPwAAoSFZC3CILH/EumiM8eAnz/bs2eO4bXDdup7iAQBEly1btmjJkiW2bUOGDKmzzc0332wrv/baazJNs842H374oU6dOmWVBw8erNatW4fWWaAOl156qXr06GGV9+zZo5UrV9bZpri4WO+++65t2y233FJnm+D4f/XVV+vt22uvvaZAIGCVb7rpJhmGUW87IBSmaWrmzJm2v8f9+/dXmzZtam3j1nUDOEE8oqULdVzOmByxxI14HjRokG21g9WrVysnJ6fOcxw9elSffvqpVU5ISNCNN95YZxsg0hj3AwAQOpK1AIeiLct/w4YN2rlzZ0g/AwcOtB1j2bJltv3Dhg0LS9/QPEVbjEtS7969bYlfu3bt0tGjRx21XbNmja1c11M8AIDIef/990OaGXH37t36+c9/bksi6d+/v773ve/V2e7GG2+03eTbtGmTXn/99Vrr5+bm6ve//71VNgxDjz32mON+Ak4Fx9W0adN08uTJWus/99xztkT4YcOGKTs7u85z3HXXXWrfvr1V/uyzz+p88n/Lli167bXXrHKrVq3005/+tM5zAJs3bw5pmc1AIKDf/e53WrFihW37I488Um9bN64bwCniEbHCjXE5Y3LEEjfiOXgcHggE9Otf/7rWJRcDgYB+85vf2K7lO++8Ux06dKj39wGcYtwPAIA7SNYCHCLLH7EuWmP81ltvtd77/X69/fbb9bY5e/asPvzwQ9s2ptAHgJqdOnVKubm5Nf5UV15eXmu9kpKSWo//4osv6vrrr9f06dO1ceNGlZeX11jv5MmTmj9/vkaMGKEjR45Y2+Pj4/XUU0/V+3sYhqFf/vKXtpmBZsyYofnz55/Xv/Xr1+uee+6xLZl7yy23cIOvBYp0/EsVX/JcdtllVnn//v0aOXKkdu7caatXWFioadOm2b4ASkhI0Lhx4+r9PRITEzVmzBjbtvHjx+uDDz6Q3++3tpmmqc8//1wPPvig7Vp84IEH+IIH9dqzZ4/uvvtu3XPPPXr//fd17NixGuuZpqn169dr1KhR583ydsMNNzgal7tx3QBOEY+IFW6MyxmTw221jdGrz24lSfn5+TXWq+uhULfi+e6771ZWVpZV3rRpk0aPHq39+/fb6h09elTjx4/X559/bm1LS0vTww8/XO85EJsiFf+M+wEAcIdh1jdvKwDLn/70J40dO9YqZ2VlaeHChbVO5fpv//ZvtsHjsGHDNG/evFqPv2jRIk2aNMkqDxw4UG+88UYYel7h3nvv1bp166zysmXL1Llz57AdH81fNMb4yZMnNWzYMOsfmfHx8XrrrbfUt2/fWts8+eSTWrhwoVXu0aOHPvroI5b2AYAaPP/885o7d26jjvH666/rqquuqnHf0KFDbU89JiQkqHv37urQoYNSUlJUXFysgwcPaseOHbakEknyer2aNWuWfvSjHznuy9y5c/X888/btqWnp6t3795KTEzU3r17tWvXLtv+7Oxsvf3220pKSnJ8HsSGSMd/lcOHD+uOO+6wfeFpGIZ69eqlrKwsFRQUaMuWLTpz5oyt3cyZM22J6/WZNGmSFi1aZNuWmZmp7Oxseb1e5eTk6Ntvv7Xtv/baazV//nx5vV7H50HLFDyWl6QLL7xQXbp0UevWreXz+VRQUKC///3vOnHixHnt+/Xrp9dee02JiYmOzufWdQM4QTwiFrg5LmdMDrdceumljWrv5N6kG/Gck5Ojn/zkJzp79qy1LS4uTn379lVmZqaOHTumTZs22Wbc8vl8evnll+v9twhiV6Tin3E/AADuiGvqDgDNSVWW/1dffSXpXJb/rFmzbAPjwsJCzZ4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      "text/plain": [
       "
"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "az.plot_energy(trace);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Posterior analysis\n",
    "### Visualisation of partial pooling\n",
    "Our first check is to plot \n",
    "- y: the difference between the raw mean probability (from the data) and the posterior mean probability for each disadvantaged and committing player\n",
    "- x: as a function of the number of observations per disadvantaged and committing player.\n",
    "\n",
    "These plots show, as expected, that the hierarchical structure of our model tends to estimate posteriors towards the global mean for players with a low amount of observations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "
"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Global posterior means of μ_theta and μ_b\n",
    "mu_theta_mean, mu_b_mean = trace.posterior[\"mu_theta\"].mean(), 0\n",
    "# Raw mean from data of each disadvantaged player\n",
    "disadvantaged_raw_mean = df.groupby(\"disadvantaged\")[\"foul_called\"].mean()\n",
    "# Raw mean from data of each committing player\n",
    "committing_raw_mean = df.groupby(\"committing\")[\"foul_called\"].mean()\n",
    "# Posterior mean of each disadvantaged player\n",
    "disadvantaged_posterior_mean = (\n",
    "    1 / (1 + np.exp(-trace.posterior[\"theta\"].mean(dim=[\"chain\", \"draw\"]))).to_pandas()\n",
    ")\n",
    "# Posterior mean of each committing player\n",
    "committing_posterior_mean = (\n",
    "    1\n",
    "    / (1 + np.exp(-(mu_theta_mean - trace.posterior[\"b\"].mean(dim=[\"chain\", \"draw\"])))).to_pandas()\n",
    ")\n",
    "\n",
    "\n",
    "# Compute difference of raw and posterior mean for each\n",
    "# disadvantaged and committing player\n",
    "def diff(a, b):\n",
    "    return a - b\n",
    "\n",
    "\n",
    "df_disadvantaged = pd.DataFrame(\n",
    "    disadvantaged_raw_mean.combine(disadvantaged_posterior_mean, diff),\n",
    "    columns=[\"Raw - posterior mean\"],\n",
    ")\n",
    "df_committing = pd.DataFrame(\n",
    "    committing_raw_mean.combine(committing_posterior_mean, diff), columns=[\"Raw - posterior mean\"]\n",
    ")\n",
    "# Add the number of observations for each disadvantaged and committing player\n",
    "df_disadvantaged = df_disadvantaged.assign(obs_disadvantaged=df[\"disadvantaged\"].value_counts())\n",
    "df_committing = df_committing.assign(obs_committing=df[\"committing\"].value_counts())\n",
    "\n",
    "# Plot the difference between raw and posterior means as a function of\n",
    "# the number of observations\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, sharey=True, figsize=(12, 6))\n",
    "fig.suptitle(\n",
    "    \"Difference of raw and posterior mean of player's foul call probability as \"\n",
    "    \"\\na function of the player's number of observations\\n\",\n",
    ")\n",
    "ax1.scatter(data=df_disadvantaged, x=\"obs_disadvantaged\", y=\"Raw - posterior mean\", s=7, marker=\"o\")\n",
    "ax1.set_title(\"theta\")\n",
    "ax1.set_ylabel(\"Raw mean - posterior mean\")\n",
    "ax1.set_xlabel(\"obs_disadvantaged\")\n",
    "ax2.scatter(data=df_committing, x=\"obs_committing\", y=\"Raw - posterior mean\", s=7)\n",
    "ax2.set_title(\"b\")\n",
    "ax2.set_xlabel(\"obs_committing\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Top and bottom committing and disadvantaged players \n",
    "As we successfully estimated the skills of disadvantaged (`theta`) and committing (`b`) players, we can finally check which players perform better and worse in our model.\n",
    "So we now plot our posteriors using forest plots. We plot the 10 top and bottom players ranked with respect to the latent skill `theta`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "
"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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",
      "text/plain": [
       "
"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def order_posterior(inferencedata, var, bottom_bool):\n",
    "    xarray_ = inferencedata.posterior[var].mean(dim=[\"chain\", \"draw\"])\n",
    "    return xarray_.sortby(xarray_, ascending=bottom_bool)\n",
    "\n",
    "\n",
    "top_theta, bottom_theta = (\n",
    "    order_posterior(trace, \"theta\", False),\n",
    "    order_posterior(trace, \"theta\", True),\n",
    ")\n",
    "top_b, bottom_b = (order_posterior(trace, \"b\", False), order_posterior(trace, \"b\", True))\n",
    "\n",
    "amount = 10  # How many top players we want to display in each cathegory\n",
    "\n",
    "\n",
    "pc = az.plot_forest(\n",
    "    trace,\n",
    "    var_names=[\"theta\"],\n",
    "    combined=True,\n",
    "    coords={\"disadvantaged\": top_theta[\"disadvantaged\"][:amount]},\n",
    ")\n",
    "pc.coords = {\"column\": \"forest\"}\n",
    "az.add_lines(pc, mu_theta_mean.item())\n",
    "\n",
    "\n",
    "pc = az.plot_forest(\n",
    "    trace,\n",
    "    var_names=[\"theta\"],\n",
    "    combined=True,\n",
    "    coords={\"disadvantaged\": bottom_theta[\"disadvantaged\"][:amount]},\n",
    ")\n",
    "pc.coords = {\"column\": \"forest\"}\n",
    "az.add_lines(pc, mu_theta_mean.item());"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we plot with respect to the latent skill `b`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "
"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "
"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pc = az.plot_forest(\n",
    "    trace,\n",
    "    var_names=[\"b\"],\n",
    "    combined=True,\n",
    "    coords={\"committing\": top_b[\"committing\"][:amount]},\n",
    ")\n",
    "pc.coords = {\"column\": \"forest\"}\n",
    "az.add_lines(pc, 0)\n",
    "\n",
    "\n",
    "pc = az.plot_forest(\n",
    "    trace,\n",
    "    var_names=[\"b\"],\n",
    "    combined=True,\n",
    "    coords={\"committing\": bottom_b[\"committing\"][:amount]},\n",
    ")\n",
    "pc.coords = {\"column\": \"forest\"}\n",
    "az.add_lines(pc, 0);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By visiting [Austin Rochford post](https://www.austinrochford.com/posts/2017-04-04-nba-irt.html) and checking the analogous table for the Rasch model there (which uses data from the 2016-17 season),  the reader can see that several top players in both skills are still in the top 10 with our larger data set (covering seasons 2015-16 to 2020-21)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Discovering extra hierarchical structure\n",
    "\n",
    "A natural question to ask is whether players skilled as disadvantaged players (i.e. players with high `theta`) are also likely to be skilled as committing players  (i.e. with high `b`), and the other way around. So, the next two plots show the `theta` (resp. `b`) score for the top players with respect to `b` ( resp.`theta`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "
"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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      "text/plain": [
       "
"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "amount = 20  # How many top players we want to display\n",
    "top_theta_players = top_theta[\"disadvantaged\"][:amount].values\n",
    "top_b_players = top_b[\"committing\"][:amount].values\n",
    "\n",
    "top_theta_in_committing = set(committing).intersection(set(top_theta_players))\n",
    "top_b_in_disadvantaged = set(disadvantaged).intersection(set(top_b_players))\n",
    "\n",
    "pc = az.plot_forest(\n",
    "    trace,\n",
    "    var_names=[\"b\"],\n",
    "    combined=True,\n",
    "    coords={\"committing\": top_theta_players},\n",
    ")\n",
    "pc.coords = {\"column\": \"forest\"}\n",
    "az.add_lines(pc, mu_b_mean)\n",
    "\n",
    "pc = az.plot_forest(\n",
    "    trace,\n",
    "    var_names=[\"theta\"],\n",
    "    combined=True,\n",
    "    coords={\"disadvantaged\": top_b_players},\n",
    ")\n",
    "pc.coords = {\"column\": \"forest\"}\n",
    "az.add_lines(pc, mu_theta_mean.item());"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These plots suggest that scoring high in `theta` does not correlate with high or low scores in `b`. Moreover, with a little knowledge of NBA basketball, one can visually note that a higher score in `b` is expected from players playing center or forward rather than guards or point guards. \n",
    "Given the last observation, we decide to plot a histogram for the occurrence of different positions for top disadvantaged (`theta`) and committing (`b`) players. Interestingly, we see below that the largest share of best disadvantaged players are guards, meanwhile, the largest share of best committing players are centers (and at the same time a very small share of guards)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "amount = 50  # How many top players we want to display\n",
    "top_theta_players = top_theta[\"disadvantaged\"][:amount].values\n",
    "top_b_players = top_b[\"committing\"][:amount].values\n",
    "\n",
    "positions = [\"C\", \"C-F\", \"F-C\", \"F\", \"G-F\", \"G\"]\n",
    "\n",
    "# Histogram of positions of top disadvantaged players\n",
    "fig = plt.figure(figsize=(8, 6))\n",
    "top_theta_position = fig.add_subplot(121)\n",
    "df_position.loc[df_position.index.isin(top_theta_players)].position.value_counts().loc[\n",
    "    positions\n",
    "].plot.bar(ax=top_theta_position, color=\"C1\", label=\"theta\")\n",
    "top_theta_position.set_title(f\"Positions of top {amount} disadvantaged (theta)\\n\", fontsize=12)\n",
    "top_theta_position.legend(loc=\"upper left\")\n",
    "\n",
    "# Histogram of positions of top committing players\n",
    "top_b_position = fig.add_subplot(122, sharey=top_theta_position)\n",
    "df_position.loc[df_position.index.isin(top_b_players)].position.value_counts().loc[\n",
    "    positions\n",
    "].plot.bar(ax=top_b_position, label=\"b\")\n",
    "top_b_position.set_title(f\"Positions of top {amount} committing (b)\\n\", fontsize=12)\n",
    "top_b_position.legend(loc=\"upper right\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The histograms above suggest that it might be appropriate to add a hierarchical layer to our model. Namely, group disadvantaged and committing players by the respective positions to account for the role of position in evaluating the latent skills `theta` and `b`. This can be done in our Rasch model by imposing mean and variance hyperpriors for each player grouped by the positions, which is left as an exercise for the reader. To this end, notice that the dataframe `df_orig` is set up precisely to add this hierarchical structure. Have fun!\n",
    "\n",
    "A warm thank you goes to [Eric Ma](https://github.com/ericmjl) for many useful comments that improved this notebook."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Authors\n",
    " \n",
    "* Adapted from Austin Rochford's [blogpost on NBA Foul Calls and Bayesian Item Response Theory](https://www.austinrochford.com/posts/2017-04-04-nba-irt.html) by [Lorenzo Toniazzi](https://github.com/ltoniazzi) on  3 Jul 2021 ([PR181](https://github.com/pymc-devs/pymc-examples/pull/181))\n",
    "* Re-executed by [Michael Osthege](https://github.com/michaelosthege) on  10 Jan 2022 ([PR266](https://github.com/pymc-devs/pymc-examples/pull/266))\n",
    "* Updated by [Lorenzo Toniazzi](https://github.com/ltoniazzi) on  25 Apr 2022 ([PR309](https://github.com/pymc-devs/pymc-examples/pull/309))\n",
    "* Updated by Osvaldo Martin on Dec 2025\n",
    "* Updated by Osvaldo Martin on Apr 2026"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## References\n",
    "\n",
    ":::{bibliography}\n",
    ":filter: docname in docnames\n",
    ":::"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Watermark"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Last updated: Sat, 25 Apr 2026\n",
      "\n",
      "Python implementation: CPython\n",
      "Python version       : 3.14.4\n",
      "IPython version      : 9.12.0\n",
      "\n",
      "pytensor: 2.38.0+133.g80cc113b5\n",
      "aeppl   : unknown\n",
      "xarray  : 2026.4.0\n",
      "\n",
      "arviz     : 1.1.0\n",
      "matplotlib: 3.10.8\n",
      "numpy     : 2.4.4\n",
      "pandas    : 3.0.2\n",
      "pymc      : 5.28.0+58.gf58491a3\n",
      "\n",
      "Watermark: 2.6.0\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%load_ext watermark\n",
    "%watermark -n -u -v -iv -w -p pytensor,aeppl,xarray"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ":::{include} ../page_footer.md\n",
    ":::"
   ]
  }
 ],
 "metadata": {
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   "display_name": "arviz_1",
   "language": "python",
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   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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