Item-Response-NBA.ipynb

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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running on PyMC3 v3.11.2\n"
     ]
    }
   ],
   "source": [
    "import arviz as az\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import pymc3 as pm\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "print(f\"Running on PyMC3 v{pm.__version__}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
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   "source": [
    "RANDOM_SEED = 8927\n",
    "rng = np.random.default_rng(RANDOM_SEED)\n",
    "az.style.use(\"arviz-darkgrid\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# NBA Foul Analysis with Item Response Theory\n",
    "This tutorial shows an application of [Bayesian Item Response Theory](https://www.springer.com/us/book/9781441907417) to NBA basketball foul calls data using [PyMC3](https://docs.pymc.io/). Based on Austin Rochford's blog post [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 softwares allows for quite elegant and effective modeling options. One of these options employs the [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",
    "- `θ`: 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 `θ-b` for the corresponding players involved in play `k`. Also, we place hierarchical hyperpriors on all `θ`'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 `θ` 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 post](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",
    "A warm thank you goes to [Eric Ma](https://github.com/ericmjl) for many useful comments that improved this notebook."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 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."
   ]
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>committing</th>\n",
       "      <th>disadvantaged</th>\n",
       "      <th>decision</th>\n",
       "      <th>committing_position</th>\n",
       "      <th>disadvantaged_position</th>\n",
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       "      <th>play_id</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Avery Bradley</td>\n",
       "      <td>Stephen Curry</td>\n",
       "      <td>CNC</td>\n",
       "      <td>G</td>\n",
       "      <td>G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Isaiah Thomas</td>\n",
       "      <td>Andre Iguodala</td>\n",
       "      <td>CNC</td>\n",
       "      <td>G</td>\n",
       "      <td>G-F</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Jae Crowder</td>\n",
       "      <td>Harrison Barnes</td>\n",
       "      <td>INC</td>\n",
       "      <td>F</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Draymond Green</td>\n",
       "      <td>Isaiah Thomas</td>\n",
       "      <td>CC</td>\n",
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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  "
      ]
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     "execution_count": 3,
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   "source": [
    "df_orig = pd.read_csv(\"./data/item_response_nba.csv\",index_col=0)\n",
    "df_orig.head()"
   ]
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    "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."
   ]
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     "text": [
      "Number of observed plays: 46861\n",
      "Number of disadvanteged players: 770\n",
      "Number of committing players: 789\n",
      "Global probability of a foul being called: 23.3%\n",
      "\n",
      "\n"
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>committing</th>\n",
       "      <th>disadvantaged</th>\n",
       "      <th>decision</th>\n",
       "      <th>foul_called</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>play_id</th>\n",
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       "      <th>1</th>\n",
       "      <td>Avery Bradley</td>\n",
       "      <td>Stephen Curry</td>\n",
       "      <td>CNC</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Isaiah Thomas</td>\n",
       "      <td>Andre Iguodala</td>\n",
       "      <td>CNC</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Jae Crowder</td>\n",
       "      <td>Harrison Barnes</td>\n",
       "      <td>INC</td>\n",
       "      <td>0</td>\n",
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       "      <th>6</th>\n",
       "      <td>Draymond Green</td>\n",
       "      <td>Isaiah Thomas</td>\n",
       "      <td>CC</td>\n",
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       "             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"
      ]
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     "execution_count": 4,
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   "source": [
    "#1. Construct df and df_position\n",
    "df = df_orig[[\"committing\", \"disadvantaged\", \"decision\"]]\n",
    "\n",
    "df_position = pd.concat([df_orig.groupby(\"committing\").committing_position.first()\n",
    "                         ,df_orig.groupby(\"disadvantaged\").disadvantaged_position.first()]\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",
    "#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",
    "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 disadvanteged players: {len(disadvantaged)}\")\n",
    "print(f\"Number of committing players: {len(committing)}\")\n",
    "print(f\"Global probability of a foul being called: \"\n",
    "      f\"{100*round(df.foul_called.mean(),3)}%\\n\\n\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Item Response (Rasch) Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## 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."
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PyMC3 implementation\n",
    "We now implement the model above in PyMC3. 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 `pm.Model`. (For tutorials on this functionality see [this blog post](https://oriolabril.github.io/oriol_unraveled/python/arviz/pymc3/xarray/2020/09/22/pymc3-arviz.html) or  [this PyMC3 example](https://docs.pymc.io/notebooks/data_container.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",
    "\n",
    "    # Data\n",
    "    foul_called_observed = pm.Data(\"foul_called_observed\", df.foul_called)\n",
    "\n",
    "    # Hyperpriors\n",
    "    μ_θ = pm.Normal(\"μ_θ\", 0.0, 100.0)\n",
    "    σ_θ = pm.HalfCauchy(\"σ_θ\", 2.5)\n",
    "    σ_b = pm.HalfCauchy(\"σ_b\", 2.5)\n",
    "\n",
    "    # Priors\n",
    "    Δ_θ = pm.Normal(\"Δ_θ\", 0.0, 1.0, dims=\"disadvantaged\")\n",
    "    Δ_b = pm.Normal(\"Δ_b\", 0.0, 1.0, dims=\"committing\")\n",
    "\n",
    "    # Deterministic\n",
    "    θ = pm.Deterministic(\"θ\", Δ_θ * σ_θ + μ_θ, dims=\"disadvantaged\")\n",
    "    b = pm.Deterministic(\"b\", Δ_b * σ_b, dims=\"committing\")\n",
    "    η = pm.Deterministic(\"η\", θ[disadvantaged_observed] - b[committing_observed])\n",
    "\n",
    "    # Likelihood\n",
    "    y = pm.Bernoulli(\"y\", logit_p=η, observed=foul_called_observed)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We use the method `model_to_graphviz` to plot our model. This shows the hierarchical structure (and the non-centered parametrisation) on the variables `θ` and `b`."
   ]
  },
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   "cell_type": "code",
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   "source": [
    "pm.model_to_graphviz(model)"
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   "source": [
    "## Sampling and convergence\n",
    "\n",
    "We now sample from our Rasch model and then plot the energy difference of the obtained trace. Also, we assume our sampler has converged as it passed all automatic PyMC3 convergence checks."
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   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
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      "Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "NUTS: [Δ_b, Δ_θ, σ_b, σ_θ, μ_θ]\n"
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       "      100.00% [10000/10000 06:13<00:00 Sampling 4 chains, 0 divergences]\n",
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      "Sampling 4 chains for 1_500 tune and 1_000 draw iterations (6_000 + 4_000 draws total) took 374 seconds.\n"
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\n",
      "text/plain": [
       "<Figure size 720x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with model:\n",
    "    trace = pm.sample(1000, tune=1500, return_inferencedata=True, random_seed=RANDOM_SEED)\n",
    "az.plot_energy(trace);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Posterior analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 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": 8,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Global posterior means of μ_θ and μ_b\n",
    "μ_θ_mean, μ_b_mean = trace.posterior[\"μ_θ\"].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[\"θ\"].mean(dim=[\"chain\", \"draw\"]))).to_pandas()\n",
    ")\n",
    "# Posterior mean of each committing player\n",
    "committing_posterior_mean = (\n",
    "    1 / (1 + np.exp(-(μ_θ_mean - trace.posterior[\"b\"].mean(dim=[\"chain\", \"draw\"])))).to_pandas()\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 funciton of\n",
    "# the number of observations\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, sharey=True)\n",
    "fig.suptitle(\n",
    "    \"Difference of raw and posterior player foul call probability as \"\n",
    "    \"\\na function of the number of observations per player\\n\",\n",
    "    fontsize=15,\n",
    ")\n",
    "sns.scatterplot(ax=ax1, data=df_disadvantaged, x=\"obs_disadvantaged\", y=\"Raw - posterior mean\")\n",
    "ax1.set_title(\"θ\")\n",
    "ax1.set_ylabel(\"Raw - posterior\")\n",
    "sns.scatterplot(ax=ax2, data=df_committing, x=\"obs_committing\", y=\"Raw - posterior mean\")\n",
    "ax2.set_title(\"b\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Top and bottom  committing and disadvantaged players \n",
    "As we successfully estimated the skills of disadvantaged (`θ`) 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 `θ` and `b`, respectively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1700x1400 with 4 Axes>"
      ]
     },
     "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_θ, bottom_θ = (order_posterior(trace, \"θ\", False), \n",
    "                   order_posterior(trace, \"θ\", True))\n",
    "top_b, bottom_b = (order_posterior(trace, \"b\", False), \n",
    "                   order_posterior(trace, \"b\", True))\n",
    "\n",
    "amount = 10  # How many top players we want to display in each cathegory\n",
    "\n",
    "fig = plt.figure(figsize=(17, 14))\n",
    "fig.suptitle(\n",
    "    \"\\nPosterior estimates for top and bottom disadvantaged (θ) and \"\n",
    "    \"committing (b) players \\n(94% HDI)\\n\",\n",
    "    fontsize=25,\n",
    ")\n",
    "θ_top_ax = fig.add_subplot(221)\n",
    "b_top_ax = fig.add_subplot(222)\n",
    "θ_bottom_ax = fig.add_subplot(223, sharex=θ_top_ax)\n",
    "b_bottom_ax = fig.add_subplot(224, sharex=b_top_ax)\n",
    "\n",
    "# θ: plot top\n",
    "az.plot_forest(\n",
    "    trace,\n",
    "    var_names=[\"θ\"],\n",
    "    combined=True,\n",
    "    coords={\"disadvantaged\": top_θ[:amount].coords[\"disadvantaged\"]},\n",
    "    ax=θ_top_ax,\n",
    ")\n",
    "θ_top_ax.set_title(f\"θ: top {amount}\")\n",
    "θ_top_ax.set_xlabel(\"θ\\n\")\n",
    "θ_top_ax.set_xlim(xmin=-2.5, xmax=0.1)\n",
    "θ_top_ax.vlines(μ_θ_mean, -1, amount, \"k\", \"--\", label=(\"League average\"))\n",
    "θ_top_ax.legend(loc=2)\n",
    "\n",
    "\n",
    "# θ: plot bottom\n",
    "az.plot_forest(\n",
    "    trace,\n",
    "    var_names=[\"θ\"],\n",
    "    colors=\"blue\",\n",
    "    combined=True,\n",
    "    coords={\"disadvantaged\": bottom_θ[:amount].coords[\"disadvantaged\"]},\n",
    "    ax=θ_bottom_ax,\n",
    ")\n",
    "θ_bottom_ax.set_title(f\"θ: bottom {amount}\")\n",
    "θ_bottom_ax.set_xlabel(\"θ\")\n",
    "θ_bottom_ax.vlines(μ_θ_mean, -1, amount, \"k\", \"--\", label=(\"League average\"))\n",
    "θ_bottom_ax.legend(loc=2)\n",
    "\n",
    "# b: plot top\n",
    "az.plot_forest(\n",
    "    trace,\n",
    "    var_names=[\"b\"],\n",
    "    colors=\"blue\",\n",
    "    combined=True,\n",
    "    coords={\"committing\": top_b[:amount].coords[\"committing\"]},\n",
    "    ax=b_top_ax,\n",
    ")\n",
    "b_top_ax.set_title(f\"b: top {amount}\")\n",
    "b_top_ax.set_xlabel(\"b\\n\")\n",
    "b_top_ax.set_xlim(xmin=-1.5, xmax=1.5)\n",
    "b_top_ax.vlines(0, -1, amount, \"k\", \"--\", label=\"League average\")\n",
    "b_top_ax.legend(loc=2)\n",
    "\n",
    "# b: plot bottom\n",
    "az.plot_forest(\n",
    "    trace,\n",
    "    var_names=[\"b\"],\n",
    "    colors=\"blue\",\n",
    "    combined=True,\n",
    "    coords={\"committing\": bottom_b[:amount].coords[\"committing\"]},\n",
    "    ax=b_bottom_ax,\n",
    ")\n",
    "b_bottom_ax.set_title(f\"b: bottom {amount}\")\n",
    "b_bottom_ax.set_xlabel(\"b\")\n",
    "b_bottom_ax.vlines(0, -1, amount, \"k\", \"--\", label=\"League average\")\n",
    "b_bottom_ax.legend(loc=2)\n",
    "plt.show();"
   ]
  },
  {
   "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": {},
   "source": [
    "### Discovering extra hierarchical structure\n",
    "\n",
    "A natural question to ask is whether players skilled as disadvantaged players (i.e. players with high `θ`) 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 `θ` (resp. `b`) score for the top players with respect to `b` ( resp.`θ`). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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sqFq1aiktLa3Y64qOjtZbb70lT09PhYSEyMHBQevXr9fo0aM1Y8YM9enTx4KRw1auXr2qQYMG6dKlS+rWrZv8/Px06NAh04Xn+fPny9Gx8A/m1qlTRyEhIbmmP/7445YMG6Vg3759GjVqlFxcXNSzZ09VrlxZmzZt0qRJk5SYmKhXXnmlUOt57733tHLlSjVq1EjPP/+8kpOTtW7dOu3atUsrVqxQo0aNrLwnKE2WajeSFBQUpKCgoFzTa9asacmQYQe++OILJSYmytPTUz4+PkpMTCzWeuhvgLLj4MGDmjx5sqpWrao2bdroD3/4g5KTk/XDDz/ozTff1JEjRzRt2rRCr6+ouYulz4HtiaVywqCgIL322mt5zgsLC1NSUpLat2+f5/zhw4erSpUqZtM8PT2LFYe9sGSuLRUtb6K93t8XX3yhdevWyd/fX127dpWHh4fi4uK0evVqbdy4UQsWLNATTzyR52dprwWjfzVnqf2jjzVny2tT5bnNWmrf7KaPNQAo044fP26Ii4szZGdnG44cOWLw9/c3TJkypcjruXHjhuGJJ54wtG7d2nDx4kXT9KSkJEO7du0MTzzxhOHGjRuWDB02MnnyZIO/v7/hn//8p2laTk6OYcqUKQZ/f39DeHh4odfl7+9veP75560RJkpZVlaWoVu3boZmzZoZTp06ZZp+8+ZNQ8+ePQ1NmjQxnDt37r7r2bNnj8Hf398wbNgww61bt0zTd+/ebQgICDA899xz1ggfNmKpdrN3716Dv7+/4csvv7RitLAnu3btMly4cMFgMBgM8+bNM/j7+xsiIiKKtA76G6BsOX36tCE6Otpw+/Zts+lXr141dO7c2eDv7284duxYodZVnNzFkufA9sZSOWF+rl69amjSpIkhKCjIrL81GAym45eQkGCx7dkLSx7XouZNtNf7i4iIMJw+fTrX9DVr1hj8/f0NPXr0yDWP9np/9K+5WXv/HtQ+1pbXpspzm7XUvtlLH1s2S4gATB577DE98sgjcnJyKtF6NmzYoNTUVD3//POqVauWabqPj4+GDx+u1NTUMv84MqS0tDStW7dOvr6+Gjp0qGm6g4ODJk6cKEdHR4WFhdkwQtjK3r17df78efXq1UtNmjQxTffw8NC4ceOUnZ2tyMjI+67H2H7eeOMNs8fR27Rpo/bt2+vAgQM6d+6c5XcANmGpdoMHT9u2bVWnTp0SrYP+BihbGjdurN69e8vFxcVserVq1TR48GBJd4e9Koyi5i7l/RzYUjlhfiIjI5Wdna3g4OAyP5RSUVj7uOaH9lo4/fr1U+PGjXNN79mzp/z8/PTTTz/p+vXrJdpGWWKra0Plvb2Wxv49iH2sLdtNeW6zltw3e+ljGTYQgCRp//79kpTnI8odOnTQZ599pv3795sSS5RNR48e1e3bt9W2bVs5ODiYzfPx8ZG/v7+OHTumW7duyc3NrVDrTE1NVVhYmJKTk+Xl5aWgoCDeM1IGFdQHtGvXzmyZguzbt0/u7u55Pprfvn177dixQwcOHFCDBg1KGDHsgaXajdEvv/yipUuX6rffflPt2rXVtm1beXl5WSZYlDv0N0D54ex899JEYS+6FjV3scY58IMkIiJCkjRw4MB8l9m2bZvS09Pl6uqqgIAABQUFlXrRx94VNm+ivZacsUhu7Ft+j/aaP/pXc6Wxfw9iH2vLa1Pluc2W1r6VZh9L8QqApLsXDCWpfv36ueYZp8XHx5dmSLAC43eYX3Gpfv36OnPmjBISEgr9npAzZ87o3XffNf3u4OCg3r1764MPPlDFihVLHDNKR0F9wEMPPSRPT8/79gEZGRm6evWq/P398zwxMbY747ZQ9lmi3dxrzZo1WrNmjen3ChUqaPz48Ro9enSJY0X5Qn8DlB937tzRqlWr5ODgoLZt2xbqM0XNXaxxDvygOHjwoH755RcFBgbqkUceyXe5Dz74wOx3Pz8/zZw5U02bNrV2iGVGYfMm2mvJHD9+XP/617/02GOP5XrnihHtNX/0r+asvX8Pah9ry2tT5bnNlsa+lXYfS/EKgCSZXuZZuXLlXPPc3d3l5OSkmzdvlnZYsDDjd5jX9yzdHerr3uXuZ+TIkerevbvq168vBwcHxcbG6u9//7uio6N1584dzZw50zKBw+oK6gOku23j8uXLBa7D2G6M7Sivddy7LZR9lmg3kuTl5aXJkyerU6dOql27tlJTU7Vv3z59+umnmjFjhjw8PDRkyBCLxo6yjf4GKD+++OILxcXFqX///vL39y/UZ4qau1j6HPhBEh4eLkkaMGBAnvOffPJJdenSRc2bN5enp6cuXLig0NBQLVu2TCNHjlR0dLRq1KhRmiHbpaLkTbTX4rt586amTJkiR0dHvfXWW7nm017vj/7VnLX370HtY215bao8t1lr75st+liKV4AdaN26tW7cuFHo5ZcuXarWrVtbLyDYPXtpM1OmTDH7/amnntLixYsVHBystWvXauzYsQXePQQAkvTII4+Y9RUVK1ZUnz591LhxY/Xr10+zZs3SoEGD5OjI61oBwNYseR4aGhqqefPmqUmTJnrnnXcsFGHZZC/n9/dKS0vThg0b5O7urh49euS5TP/+/c1+b9iwof73f/9XFStW1Ny5c7V48eJcOUNpspfjWt7yJns5rve6deuWXnvtNf373//WhAkT8twe7fXBZY/Hlj7Wcuhjy28fS/EKsAO9evVSenp6oZevVq2axWO4t/ru6elpNi8jI0N37tzJt3KP0lfcNmP8DvO7y+J+T1EURsWKFdWzZ0/NmTNHhw8fLlMnCA+y+92Bk5aWdt92YZyf35MOxun5PSmBsscS7aYg/v7+atGihQ4ePKj4+HjeXQQT+hvAdiyVu0REROi9996Tv7+/Fi5cqEqVKhV6nUXNXUrjHLik7CEn/L01a9YoMzNTAwYMKNL3I919imDu3Lk6cuSIlaIrHHs8rkb55U2016K7ffu2Xn31Ve3du1d//OMf9corrxTp87TX/yqP/atkn9dQHuQ+1pbXpspCm7W342rLPpbiFWAHpk2bZusQ5Ofnp5MnTyo+Pj7XCYpxzNS8xjyGbRS3zRi/w/zeARIfHy9HR0f5+voWNzRJMrWhzMzMEq0Hpcc4JnJ8fLyaNWtmNu/XX39VSkqKWrZsWeA63N3dVb16dV24cEF37tzJ9R4aY7vLb/xllD2WaDf3Y+xPfvvttxKtB+UL/Q1gO5bIXcLDwzVt2jQ1atRIS5YsyZV/3E9Rc5fSOgcuCXvICX/POJzVwIEDi/xZe8kH7PG43iuv40R7LZpbt25p3Lhx2rlzp0aPHq2JEycWeR201/8qj/2rZJ/XUB7kPtaW16bKQpu1p+Nq6z6WsVcASLo7Lqkk7dy5M9e8HTt2SJKCgoJKNSZYXmBgoFxdXbV7924ZDAazeVeuXFFcXJxatGghNze3Em3n2LFjkqS6deuWaD0oPQX1Abt27ZJUuD4gKChIGRkZOnz4cK55xnUbt4Wyz1LtJj/Z2dmKjY2Vg4ODatWqVez1oHyivwHKpvDwcL377rt6+OGHtWTJEnl5eRV5HUXNXUrrHLg8OXv2rE6cOKFHHnlEgYGBRf68MR+oU6eOhSMrX/LKm2ivhXfvRdWRI0fm+Q6WwqC9/hf9qzlr7d+D3sfa8tpUeW6zlt43e+hjKV4BD5jMzEz9/PPPunjxotn07t27q3Llylq2bJkuXbpkmn7lyhUtXbpUVapU0bPPPlva4cLCPDw81KNHDyUkJGj58uWm6QaDQTNnzlROTk6uu37yazOxsbF53jWxfv16rV27Vp6enmrTpo11dgQW16ZNG/n6+mrNmjU6ffq0aXpaWprmzJkjZ2dnhYSEmKZfv35dP//8s65fv262nkGDBkmSPv/8c92+fds0fc+ePdq5c6eefPJJhn4rRyzVbo4cOZLr5Do7O1uffPKJEhMT1b59e1WtWtWq+wL7RX8DlB9hYWFmhStvb+8Cl7dU7lKcc+DyLL/jei/jEwEDBgzId5mrV6/q/PnzuaYnJSVp+vTpku4OffSgsFTeRHs1l99xvfei6ksvvXTf96jQXs3RvxaOJa+h3OtB72NteW2qPLdZSx5Xe+ljHQy/v1IAoEw5ePCg6R+969eva9u2bapXr55atWolSXr44Yc1ZswY0/L79u3T8OHDFRQUpG+//dZsXatXr9bkyZPl6empnj17ysHBQevXr9e1a9f0ySefKDg4uPR2DFZz5coVDRo0SJcvX9YzzzwjPz8/HTx4UIcPH1b79u319ddfy9Hxv/c25Ndmpk6dqh9//FFt2rRRrVq1ZDAYFBsbq4MHD8rNzU2zZs1Sx44dbbGLKKa9e/dq9OjRcnFxUa9eveTh4aFNmzbpwoULeuONNzR27FjTsrNmzdLs2bP12muvafz48WbreffddxUWFqZGjRqpY8eOSk5O1rp16+Tm5qYVK1aoUaNGpb1rsCJLtJsuXbpIklq2bKkaNWro5s2bOnDggM6dO6fatWtr2bJlZfauQuQtLCxMhw4dkiTFxcXp1KlTevzxx01DXXTr1k3dunWTRH8DlBd79uzRSy+9JIPBoMGDB6t69eq5lnn00UdNf/uSZXOXop4DlyWWzAmlu++26NChg9LT07V9+/Z8n47bt2+fRowYoVatWunhhx/WQw89pMTERG3dulUZGRkKCQnRRx99JAcHByvstfVZ6rgWJ2+ivRbuuEZFRal69eoaPHhwntsKCQkxPXFBe7XetaHy3F4ly11DMaKPvcuW16bKc5u15HG1hz6Wd14BZdz58+cVFRWVa5qx2h0UFGR2glKQ4OBgeXp6av78+YqMjJQkNW3aVB9//LE6dOhg2cBhMz4+Plq5cqU+//xzbdu2TVu2bFHt2rU1fvx4jRkzptD/QHft2lWpqak6deqUduzYoezsbNWoUUMDBgzQyJEj1bBhQyvvCSztqaee0nfffacvv/xS69evV1ZWlho1aqTXX39dffr0KfR6Pvj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      "text/plain": [
       "<Figure size 1700x1400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "amount = 20  # How many top players we want to display\n",
    "top_θ_list = top_θ[:amount].coords[\"disadvantaged\"].to_dict()[\"data\"]\n",
    "top_b_list = top_b[:amount].coords[\"committing\"].to_dict()[\"data\"]\n",
    "\n",
    "top_θ_in_committing = set(committing).intersection(\n",
    "                      set(top_θ_list)\n",
    ")\n",
    "top_b_in_disadvantaged = set(disadvantaged).intersection(\n",
    "                         set(top_b_list)\n",
    ")\n",
    "if (len(top_θ_in_committing) < amount) | (len(top_b_in_disadvantaged) < amount):\n",
    "    print(\n",
    "        f\"Some players in the top {amount} for θ (or b) do not have observations for b (or θ).\\n\",\n",
    "        \"Plot not shown\",\n",
    "    )\n",
    "else:\n",
    "    fig = plt.figure(figsize=(17, 14))\n",
    "    fig.suptitle(\"\\nScores as committing (b) for best disadvantaged (θ) players\"\n",
    "                 \" and vice versa\"\n",
    "                 \"\\n(94% HDI)\\n\", \n",
    "                 fontsize=25,\n",
    "    )\n",
    "    b_top_θ = fig.add_subplot(121)\n",
    "    θ_top_b = fig.add_subplot(122)\n",
    "\n",
    "    az.plot_forest(\n",
    "        trace,\n",
    "        var_names=[\"b\"],\n",
    "        colors=\"blue\",\n",
    "        combined=True,\n",
    "        coords={\"committing\": top_θ[:amount].coords[\"disadvantaged\"].to_dict()[\"data\"]},\n",
    "        figsize=(7, 7),\n",
    "        ax= b_top_θ)\n",
    "    b_top_θ.set_title(f\"\\nb score for top {amount} in θ\\n (94% HDI)\\n\\n\", fontsize=17)\n",
    "    b_top_θ.set_xlabel(\"b\")\n",
    "    b_top_θ.vlines(μ_b_mean, -1, amount, color=\"k\", ls=\"--\", label=\"League average\")\n",
    "    b_top_θ.legend(loc=\"upper right\", bbox_to_anchor=(0.46, 1.05));\n",
    "    \n",
    "    az.plot_forest(\n",
    "        trace,\n",
    "        var_names=[\"θ\"],\n",
    "        colors=\"blue\",\n",
    "        combined=True,\n",
    "        coords={\"disadvantaged\": top_b[:amount].coords[\"committing\"].to_dict()[\"data\"]},\n",
    "        figsize=(7, 7),\n",
    "        ax= θ_top_b)\n",
    "    θ_top_b.set_title(f\"\\nθ score for top {amount} in b\\n (94% HDI)\\n\\n\", fontsize=17)\n",
    "    θ_top_b.set_xlabel(\"θ\")\n",
    "    θ_top_b.vlines(μ_θ_mean, -1, amount, color=\"k\", ls=\"--\", label=\"League average\")\n",
    "    θ_top_b.legend(loc=\"upper right\", bbox_to_anchor=(0.46, 1.05));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These plots suggest that scoring high in `θ` 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 occurence of different positions for top disadvantaged (`θ`) 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": 11,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "amount = 50 # How many top players we want to display\n",
    "top_θ_list = top_θ[:amount].coords[\"disadvantaged\"].to_dict()[\"data\"]\n",
    "top_b_list = top_b[:amount].coords[\"committing\"].to_dict()[\"data\"]\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_θ_position = fig.add_subplot(121)\n",
    "df_position.loc[df_position.index.isin(top_θ_list)].position.value_counts().loc[positions].plot.bar(ax = top_θ_position, color=\"orange\", label=\"θ\")\n",
    "top_θ_position.set_title(f\"Positions of top {amount} disadvantaged (θ)\\n\", fontsize =12)\n",
    "top_θ_position.legend(loc=\"upper left\");\n",
    "\n",
    "#Histogram of positions of top committing players\n",
    "top_b_position = fig.add_subplot(122, sharey=top_θ_position)\n",
    "df_position.loc[df_position.index.isin(top_b_list)].position.value_counts().loc[positions].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 `θ` 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",
    "\n",
    "\n",
    "Author: [Lorenzo Toniazzi](https://github.com/ltoniazzi)\n",
    "\n",
    "Email: lt6ga at protonmail.com"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Last updated: Mon Jun 21 2021\n",
      "\n",
      "Python implementation: CPython\n",
      "Python version       : 3.9.4\n",
      "IPython version      : 7.24.1\n",
      "\n",
      "theano: 1.0.5\n",
      "xarray: 0.18.2\n",
      "\n",
      "arviz     : 0.11.2\n",
      "seaborn   : 0.11.1\n",
      "pymc3     : 3.11.2\n",
      "numpy     : 1.20.3\n",
      "matplotlib: 3.4.2\n",
      "pandas    : 1.2.4\n",
      "\n",
      "Watermark: 2.2.0\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%load_ext watermark\n",
    "%watermark -n -u -v -iv -w -p theano,xarray"
   ]
  }
 ],
 "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": "3.9.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}