{ "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 v5.26.1\n" ] } ], "source": [ "import os\n", "\n", "import arviz.preview 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", "\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", "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", "foul_called_observed\n", "\n", "foul_called_observed\n", "~\n", "Data\n", "\n", "\n", "\n", "y->foul_called_observed\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", "theta\n", "\n", "theta\n", "~\n", "Deterministic\n", "\n", "\n", "\n", "sigma_theta->theta\n", "\n", "\n", "\n", "\n", "\n", "mu_theta\n", "\n", "mu_theta\n", "~\n", "Normal\n", "\n", "\n", "\n", "mu_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": "06ef6cc4804749db9ea48f6bf7b78009", "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 212 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": [
    {
     "data": {
      "image/png": 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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"
    },
    {
     "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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h4eGBlStX4tixY7h27RqGDRtWJvHExsZix44dQtvExARBQUHo1auXkNgFAMbGxhg+fDjWrFkj6r9u3TpkZmaWWrxUfsltbHTu3Bl79uzBnj17MGzYMK2JXQDQsWNH7N69G82bNxe25eTkYOXKlaUVKlUAchsfBdm6dSsiIiIA5N6P9OrVq4wjovKIyV0laPny5aJJRSIiIiIiKj0bNmwQtRcuXIiqVavmu7+vr68oWeTkyZO4d++eXmMKDAwUtWfNmoW6devmu7+npydatWoltO/evYtTp07pNSaqmOQ2Pr755husW7cOjo6OkvavUaMGlixZItp2/PhxKBQKvcVEFZPcxkZBrl69in379gEAKleujPnz55fK51LFJcfxkZKSInrpxM7ODtu3b4e1tbWk/oaGhjA05CNyKj65jY8HDx6IKlrY2dlhwoQJBfYxNjbG559/LtqmvtQpUVFMnjwZN27cQHBwMObPn4+BAwfC0dFRlERV2rZs2SJKzvL09Cyw2lDv3r3Rt29foR0XF4fg4OASjZHKP7mNjblz5yIoKAgtW7aUtL+FhQVWrVoFIyMjYdu1a9dkV1GJ3k1yGx8Fefz4Mb799lsAufcXixYtEo0LIn3hnSsREREREZU7Dx48EC196OTkBDc3twL7VK5cGf/73/9E2w4dOqS3mFJSUnDhwgWhXbVqVfz3v/8tsI+BgQE8PT1LLCaqmOQ4PmrXrq1zn86dO4uSI1+/fl1qSTVUPslxbORHfcmHKVOmiJIEiPRNruPj559/RkJCgtD29fXVWvmRqCTJcXxER0eL2v/5z39gampaaD9nZ2fRMtfx8fHIyMjQW1xUMdnY2MDY2LiswxDk5OSIKj4aGRlhzJgxhfYbN26cqM17cyouuY2NotyXOzk5iarRK5VK3LhxQ59hUQUlt/FREH9/f+Flw48++khygiSRrt6NEUFERETvnOzsbNy9exf//PMPkpKSkJGRAQsLC9SpUwfOzs5Fmnz6559/cPfuXSQkJCAzMxPVqlVDrVq10LZtW1SuXFmv8d++fRuRkZGIi4uDsbExHB0d0aFDB1SqVKnAfg8ePMDt27eRmJgIMzMz1KlTB506dYKlpaVe4srJycHNmzfx9OlTxMfHw8zMDE2aNEG7du0KvNlRKpW4ffs2/v77byQnJ8Pc3BwNGjRAx44dYWZmppfYyquMjAxcu3YNL168QHJyMqytreHk5ITWrVsX6wYzJycHt27dQmRkJF6+fImqVasKP8/5LR9D0p09e1bUHjBggKR+AwYMwOrVq4X2mTNn8Mknn+glpt9//x1ZWVlCu0+fPpLGX69evVC5cmVhUuXixYtQKBSSJmeItJHj+CgqZ2dn0eRlXFxcGUZD77p3aWxs3rwZjx8/BgA0atRIY7KRSN/kOD6USiV+/fVXoV2tWjXJcRHpkxzHh3pClmrCVmHs7e0RExMjtFNTU/X+zIWoLIWGhgrLWgNAhw4d8l16TlXr1q1Rv359PHnyBABw584dxMXFoUaNGiUWK9G7wNnZGVeuXBHavC+nimTfvn3Cz7+trS1mz55dxhFReVZukrtiY2MRHh6Op0+f4tWrVwBy34S3t7eHq6srqlSpotfPUygUuHHjBl68eIGEhASYmJigffv2orWFiyMpKQkPHz5EVFQUUlNTkZOTAysrK9SoUQOurq6wsbHRy+fkUSgUuH79Op49e4aXL1/C2toaDRs2RJs2bUq9bGB6ejpu3ryJmJgYJCUlwdTUFLa2tmjRogUcHBxKNRYiItJdVFQUNm3ahFOnTgl/k7Vp0KAB+vbtixEjRsDe3j7f/bKzs7F7924EBQUhKipK6z5mZmZwc3PDzJkz4eTkJCnO9957D8+ePQMAtG/fHj/88AOUSiX27t2L7du3IzIyUqNP1apVMW3aNHh4eGh87ezZswgICBC9rZvH1NQUY8eOhY+Pj6REjqZNmwr/Hjx4MJYvX463b9/i+++/x08//YQXL15o9KlRowbmzZuHDz74QONr+/fvx/r164XzVWVhYQEfHx+MHTtW0nIk2mJTtX79eo3lKIDcZYNU+6o7ffo06tatCw8PD1y9elXj6xs2bNB6XACoU6cOzpw5I9q2b98+zJs3T2jv3LkTHTp00No/v33T0tIQEBCAkJAQpKWlafSztrbG5MmT4eHhodP1UnZ2Nn744Qds27YN8fHxGl83NzdH//794efnB2tra53Ohf7fpUuXRG3VtwgLYm9vjzp16gjj5fHjx3jx4kWBv6ekunz5cpFiMjMzg4uLizA20tLSEBYWJrk/kTo5jo+iUv/9+/bt2zKKhMqDd2VsREREYPPmzUL7iy++gImJSYl8FlEeOY6P69ev4+nTp0K7T58+78zb/VS+yHF8VK9eXdR+8+aN5L6q+xoYGMDKyqrY8RDJifq9eZs2bST3bdOmjZDcpVQqcfnyZQwaNEif4RG9c3hfThVVUlISVqxYIbQ/+eSTApflJiquErnbnTt3Lvbv3y+0Q0JC4OzsrNMx1q5di02bNgntb7/9Fj169BDaOTk5uH79Oo4cOYJLly6JbuTVGRoaomPHjpg4cSI6duwo6fOvXLkiKsO6bNkyDBkyBKmpqQgICMCRI0eQmpoq6jNmzBhRcpf69+HBgwcFfubt27dx5MgRXLhwAY8ePSpw35YtW8Lb2xu9e/eWdD75SUtLQ2BgIPbt24fk5GSNr9va2mLcuHHw8vIq8SSvsLAwBAYG4vLly0LpQnUNGzbExIkTMWjQIEkT0EREVHqUSiXWrVuHrVu3iirT5CcqKgqbN29GRkYGPv/8c637xMbGYuLEiYUusZSZmYkTJ07gzJkzmDVrFsaPH69z/FlZWZgzZ46oLLu6lJQULFmyBI8ePcKiRYuE7atWrcK2bdvy7adQKLBlyxbcu3cPGzdu1LnazuvXrzFp0iStSU954uLiMGvWLERHR8Pb21s4p08//RSHDx8u8NgrVqzAo0ePsHTpUlmuWV8WHj58iKlTpwoP7LRJTk7GsmXLcPPmTQQEBEiaSEpLS8OECRNw8+bNfPdJT0/Hnj17cPnyZdH1MOlG9Xra0NAQLVq0kNy3VatWomTI8PBwvUywhIeHi9ouLi46xaT6OyA8PJzJXVRkchwfRaX+LEB9IpNIF+/C2FAqlfD39xcmTAYPHox27drp/XOI1MlxfFy7dk3jc4jKghzHR4sWLWBiYiL8vZC6dPWbN29EL7s5ODiwaheVO+r35rosn9WqVSvRvF9hc3lEFQHvy6miWr58uZBf0b59eyb7UokrkewY9R/cAwcO6NRfqVTi4MGDQrt69ero2rWraJ99+/bBw8MDwcHBBSZ2AbmJYJcvX4anpyeWL1+O7OxsneLJc+/ePQwaNAi//PKLRmJXcf3xxx8YOnQogoKCJF0MhoWFwcfHB76+vjq9daPqxYsXGDp0KLZv3641sQsAEhMT8fXXX2PkyJEFVl8pjrdv32LBggUYNmwYzp07l29iF5C7HNe8efPg6emp9/8HRERUdNnZ2ZgxYwY2bdqkkdjl4OCA7t27w93dHT169ICDg4OkBKKYmBiMGDFC4wGknZ0d3Nzc8MEHH8DV1VWUfJyVlYVVq1ZhzZo1Op/D0qVLhcQuU1NTtGvXDv3790ePHj1gbW0t2jc4OFh4kLNx40YhscvIyAitWrVCv3790Lt3b42S7hcuXBBVWZBCqVRi9uzZQlKHubk5OnfuDHd3d3Tp0kVj+b6AgAChDLC/v7+Q2GViYoK2bdsK51StWjVRv19//VX0cKoiS0hIwIQJE4TELhsbG3Tt2jXf7/nx48exZcuWQo+rUCgwfvx4jcSuKlWqoEuXLnB3d8d//vMfYQnPZ8+eYcqUKVqrhlHBUlJSREss2Nra6jQhUbduXVE7b9mr4lI9joGBAerVq1fmMVHFI9fxURTPnj0TXScYGxvr/GIZUZ53ZWzs3btXSGipWrVqmS+NShWDXMfHnTt3RO3GjRsDyL3uPnr0KCZNmoSePXvCxcUFHTp0gLu7OxYuXIjff/9dL59PBMh3fFSpUkVU2fvatWsaCS3a7NmzB5mZmUKbS51SeaQ+znhvTlR0GRkZGhUsdUlyJnpX/fHHHwgJCQGQO/fyxRdflG1AVCGUSOWuDh06iMoJHzp0CH5+fpLLYl+5ckX0tsqAAQM0+iqVSlG7UqVKaNSoEezs7GBpaYnMzEw8f/4cDx48EJV/DAoKgrGxMfz8/HQ6p5cvX2Ly5MnCMkgWFhZo2bIlbGxskJKSonUJJl2on4+JiQmcnJxQq1YtWFpa4u3bt4iLi8P9+/eRkZEh7Hf48GEolUoEBATo9HmZmZnw9vYWLjxNTU3RunVr2NnZISUlBbdv30ZKSoqwf2hoKMaPH4+dO3dKWk5KlzgmTZqkUQbXwsICLVq0QPXq1aFQKBAZGYmIiAjh61evXsXo0aOxa9cuvjlERCQD33zzDY4fPy60DQwMMGTIEEyePFnrA5Lk5GScPHkSu3bt0no8pVKJTz/9VHQ9UL16dSxYsAB9+vQRVW9MSEjAypUrhQtpILfiZ5s2bdCtWzdJ8d+/fx9Xr16FgYEBvLy8MHnyZNGSzgqFAuvXrxcl8KxZswb16tXD+vXrAQBDhgzB7NmzYWdnJzqPnTt3YtmyZcLf+q1bt8LDw0MjYSw/Z86cQWpqKkxMTDBjxgyMGTNG9Lf49evXWLx4sZCYpVQqsXr1aowdOxZ79+6FgYEBPv74Y0yaNEl0Tm/fvhUqreUJCAjAwIEDi1Wt09PTE4MHDwYAjBw5ErGxsQBy32ws6HqlVq1aQgyZmZmIiYnBqFGjhK+PGTMGnp6eWvvqe+mVL7/8EsnJyahduzY+++wz9OzZU/Qzl5GRgdWrV+OHH34Qtn377bcYNWpUgWWXN23ahFu3bgntypUrw9fXF8OHDxdVc1MoFAgODkZAQACio6MRGBio1/OrCNQrrun65nvez2N+xyuKpKQkUaKejY2NTlX8SiImqpjkOD6K6scffxTdS7dp04bLBlGRvQtjIzExEatWrRLavr6+sLGx0fvnEKmT6/hQfxGoVq1aePjwIfz8/DRWUFAoFEhOTkZ4eDh27dqF1q1bY8mSJUJCGFFRyXV8AICfnx8uXbqEhIQE5OTkYMaMGQgKCtJ4ES3P5cuX8fXXXwvt2rVri1Y3ISov1AtG6DJu5XQ/RCQH+/btQ3p6utCuW7cur6+o3MvMzIS/v7/Q9vLygpOTUxlGRBVFiSR3GRgYYODAgdi4cSOA3EnXixcvonv37pL6q07OAhAmCNVVr14dQ4YMwXvvvYeWLVtqnYhMTU1FcHAwNm7cKCRFbdu2Db1799apVPeGDRuQnp4OS0tL+Pn54b///a9oMiY7OxtxcXGSj6dNlSpVMGjQIPTs2RNt27aFiYmJxj4ZGRk4ePAgAgIChGpbR44cQe/evdGvXz/Jn7Vr1y6kpqbCwMAAHh4emD59usYk9u7du/H1118L37fQ0FBs2LABvr6+xTpPVUuXLhUldtnb28PPzw99+/bVmKi9f/8+Fi1aJFS7ePDgAb766issXrxYb/EQEZHuwsLCRNWojI2NsXz58gLf7rS2tsawYcMwbNgwJCQkaHz94MGD+PPPP4W2jY0NfvzxRzg4OGjsW716daxcuRK2trbYvn27sN3f3x8nT56UlPiTVw3S398fI0aM0Pi6qakpfH198fTpUxw7dgxA7pKRkydPRk5ODiZNmoRZs2Zp9DMwMICnpydiYmKE2DIzM3H06FGMHDmy0LjyYjM0NERgYCDc3Nw0vm5hYYFly5bh8ePHCA0NBQD89ddfws3F4sWLMWzYMI1+JiYm8PPzw5MnT4TEvPj4eFy8eFHr50hlZWUlTKyrfu/NzMw03m7URjU5Tv24UvrrQ3JyMurXr4+ffvoJNWrU0Ph65cqVMX/+fLx8+VKojJaZmYnDhw+LEtJUxcbGihLpTExMsGnTJnTq1EljX1NTU4wZMwaNGjWCt7d3vhVWKX/q1c50nfhWr2ynjwq2xY1Jff+SqqpL5Z8cx0dRRERE4McffxRtyy8JmEiKd2FsfPXVV8KLeK1bt8ZHH32k988g0kau4yM+Pl7Ujo6Oxvjx4/H69etC+4aGhmLEiBHYtGkTlzalYpHr+ACAGjVqICgoCFOmTMHTp08RERGBAQMG4H//+x86d+6MmjVrIisrC1FRUfjtt99w9OhRYdUTW1tbfPvtt0JlaaLyIjs7W5SIYm5ujkqVKknuz3tzov+XmJiIb775RrSN9+VUEWzcuBFRUVEAgDp16mDy5MllHBFVFCWyLCOgmZAldZmf9PR0/Pbbb0K7WbNmaNq0qcZ+3bt3x9mzZ+Hr66uxJJMqKysreHt7IygoSEjGUiqVCAoKknoqQlzm5ubYsWMHRowYofGWvZGRkc5v5ahq1qwZzp8/j/nz56NTp05aE7uA3MnE4cOHIzg4WFTxQ3UyW4q8Sew5c+bg888/FyV2AbmTiqNHj8aGDRtEsWzfvl1vbyJcuHBBVLHF2dkZBw4cgLu7u9aJeGdnZ+zYsQNdunQRtu3evbvYVdOIiKh4Nm/eLKqaMWnSJJ3K9levXl1jm2pFJABYsGCB1sQuVX5+fvjXv/4ltJ8/f45Tp05JjqNHjx5aE7tUjRs3TtROTU1F8+bNMWPGjAL7jR07VrQUZd4Si1KNGjWqwISrvCQy9dh69eqlNbFLlZeXl6ita2zl1cqVK7UmdqmaOnWqqJ23RJI2e/bsEVWTHTNmjNbELlWdO3eGh4eHhGhJnfqEnq6VZ9Uf7Ko++C0q9WPoUrUL0DwHfcREFZMcx4euFAoFfH19oVAohG2dOnVCz549Sz0WKj/kPjYuXrwoJJUbGRnhiy++kLTUOZE+yHF8vHnzRnR9DQCzZs0SYnV0dMSCBQuwd+9eHDt2DEFBQfDw8BA953z16hV8fHyEasNERSHH8aGqSZMmCAkJwezZs9GwYUOkpKRg8+bN8PT0RN++feHu7o6pU6fi0KFDyM7OhpGREdzd3bF//36t8zJE7zremxPph1KpxLx580QvpTo6Ohb6fJ3oXRceHo7vvvtOaC9YsICrjFGpKbHkrvr166NNmzZC+8yZM6Jl/vJz4sQJ0cVQflW77OzsdLrocnV1FVVSOHXqlOhBsBTTp08vsXWCra2tYWFhIXl/BwcH+Pj4CO2wsDBER0fr9Jnt27fHxx9/XOA+Xbp0EU0qZmVlITg4WKfPyY9qlZdKlSohMDCw0CWqTE1NsXLlStEvSfUEACIiKj2JiYk4ffq00K5evTomTpxYrGM+fvwYt2/fFtqNGjXCBx98UGg/IyMj0d9GILcCmFTqSU7atGzZEubm5qJtY8aMES3Zp03NmjXh6OgotO/fvy85LiA3OawwHTt21NimnoymTatWrUTnpGts5VHbtm3h6upa6H6Ojo6oX7++0C7oe6e6bKmRkVGh12B5vLy8irVMZkWluow5UPyHterHKwr1B766TvrwATLpixzHh678/f1FS3FZWFiwojMVm5zHxps3b/DFF18I7dGjR4teaiAqaXIcH9oqpcTExAAABg4ciJCQEIwePRouLi5wdHRE586dMX/+fOzduxe2trZCn+TkZHz55ZfFjocqLjmOD3U5OTkACo/N0NAQo0aNwuzZs/NdupHoXcd7cyL9CAwMxPnz54W2sbExli1blm/xEqLyQKlUYsGCBcJLJr169UKPHj3KOCqqSEosuQsQJ2YpFAocPXq00D6qSzKamJjA3d1db/GovsX79u1b0cPgwpibm2P48OF6i0Uf1N9KDgsL06n/lClTJO3n7e0t+mN86NAhnT5Hm0ePHomqWwwdOlTyUku2traiSf5z584VOx4iIiqaq1eviqp2DRw4UOcHmepu3bolaktJ7Mrj5uYmLAmo7Vj5MTc3x7///e9C9zMwMND4e/Wf//xH0meoJgG9fPlSUh8AaNiwoaS/kTY2NqJEcXNzc0kJSurnpEts5VXXrl0l76uatJff9y4tLQ2PHj0S2m3atBFNKBXEzs5O0s8mFUzXyibq+6v+ntOX4sZEpC9yHB8F2bp1K/bt2yeKZ8mSJahXr16pxkHln5zGxoYNG/D06VMAuUtsTZ8+XW/HJioKOYyPvGQVdS1atMCyZcvyvS91dnbG2rVrRTGdPn0ajx8/LnZMRIA8xoeq8+fPo0+fPggICCh0BYycnBzs3LkTffr0weLFi/HmzRu9xkIkR7w3J9LdkSNHsGHDBtG2WbNmoXXr1mUTEFEpCQ4OFuaczM3N8fnnn5dxRFTRlGhyV79+/URlhQ8cOFDg/jExMfjzzz+Ftpubm85r1CuVSqSlpSE2NhbR0dGi/9RvjCIjIyUft2PHjhqVOkqDUqlEeno64uLiNM4nKytLtK8u52NjY4MOHTpI2rdatWqiaiBxcXF4/vy55M/S5sqVK6L2+++/r1P/tm3biuLJe8hJRESlSz2xWPX3c1HdvXtX1G7VqpXkviYmJmjWrJnQTkpKwosXLwrtV69ePa1LAmtjaWkp+rednZ3O/dLS0iT1AcTJQ7p8Rr169SRXfCpqbOWVk5OT5H2lfO/Cw8NFk0+qP6NS6Lo/QaMUdmZmpk791Scx9HEfoH4MXSdKSiImqpjkOD6kCgkJwerVq0Xb/Pz8dEoEJ8qPXMfGgwcPEBQUJLQ/++wz0fUHUWmQ4/jI7xi+vr6F3ge1b98e3bt3F9pKpVLSS8lE2shxfOQ5efIkJk+ejKSkJGGbi4sLVqxYgdOnT+P27du4efMmDh48iDlz5qBGjRoAclfv+PHHH+Hp6clnBFTu8N6cqHj++OMPfPrpp6I595EjR2L8+PFlGBVRyYuLixM9k5o6dSpq165dhhFRRSRtFrOILC0t0atXLxw+fBgAEBoaisePH8PBwUHr/iEhIaKJr/yWZFSVnZ2Ny5cv47fffsPt27cRGRkplMIrjJRlIvOUZrn7Gzdu4OjRowgNDcWjR48kX1zqcj7NmjUrdAkpVS4uLrhw4YLQvnv3brF+Yd28eVPUrlKlik7LSqq/HfHs2TO+qU1EVAbi4+NFbV2SYvKj+tARABo0aKBTfwcHB1GyeFJSEuzt7QvsU6VKFcnHV00CK2o/9QTtgsg5tvJKlwlT1eqm+X3vkpOTRe3Cfh7V6bo/aT5c1XWCRX1/fTysVZ/00XWJ+JKIiSomOY4PKc6fP4/PPvtM9AB5woQJfIBMeiPHsZGTk4OFCxcK1xhdunRBv379in1cIl3JcXxoO4aNjQ06deokqb+7uzvOnj0rtNWfVRJJJcfxAQCxsbGYN28esrOzhW3jx4+Hr6+vaF7A1NQUTZs2RdOmTfHRRx9h6tSpuHr1KoDc+Rx/f3+N5Hqid1lx783V9+e9OVUkd+7cwdSpU0Xz8O7u7liwYEEZRkVUOpYuXSosDd+kSROMHTu2bAOiCqlEk7sAYMiQIUJyF5BbvWvWrFla91Wt7FWtWjW4ubkVeOxbt25h4cKFhZYTzo8ub53oWkGsKCIiIrBw4UJcv369SP11OR/VpaGkUJ9YT0xM1Km/upiYGFF70KBBxTqeLoltRESkP+pJK7okFOUnNTVV1Na1MoF6DOrH00aXhGd99CuNzyiN2MorfX/v8m768qgunykFq3PoTv17putyo+pJpvr43aZ+DF1jUt9fHzFRxSTH8VGY69evY/r06aIk2o8++gh+fn4l/tlUcchxbPzyyy8IDQ0FAJiZmcHf37/YxyQqCjmODyMjI5ibmyM9PV3Y1rx5c8nLZbm4uIjaXJaRikqO4wMAvv/+e9G9qJubG+bMmVNgHysrKwQGBqJfv35ISEgAABw+fBienp5o2bKlXuIiKmvGxsaoXLkyMjIyAADp6el48+aNaBWigpTF/RCRHERGRmLChAl4/fq1sM3NzQ3Lly/nc2gq986dO4fffvsNQG4BGn9/f8krwRDpU4n/1HXq1Am1atUSknkOHjyImTNnatxoh4WFiZYVdHd3F1VCUPf7779j6tSpOmfVq9Jl/fqSzr6/e/cuxo0bV6wkJV3OR9dJQvX9pUyUF0TfyViqD3KIiKjsSH2QTlSRqF/TSq0ym6c417sVlfqLDFKWZ1Wl/iKCPirE2tjYwNLSUnghIzExEQqFAqamppL6q58Dq9ZSUclxfBTk77//xqRJk0QVrfv164dFixaV6OdSxSPHsbFp0ybh34MHD4ahoWGhVc/VK8Cox2Vra6tRsYKoMHIcH0Duy6j37t0T2nZ2dpL7Vq9eXdRWf3GJSCq5jo/jx4+L2t7e3pL6WVlZYeTIkfjmm2+EbQcPHmRyF5Ur9erVExWNiImJQcOGDSX15b05VUTPnz+Hl5eXKLmxbdu2+OabbwqcyycqL1Tvzbt3745atWoVem+unruQkJAg6mNtbc2XuklnJZ7cZWhoiIEDB2Lz5s0Acv8A/Pnnnxolsvfv3y9qF7QkY3JyMubMmSOa6KpTpw4GDhwIV1dX1KtXD9WrV4eZmZlosiQ6Oho9e/bUx2nplUKhwOzZs0UJT7a2thg4cCDatm2LBg0awM7ODpUqVYKZmZmob9OmTUs7XL3QdVKzMLokthERkf5UrVpV1H716hWqVatWrGNaWVmJ2mlpaTodU71KkvrxiEqb+jjRNUmeFUp1Z21tDRsbG+GhU0JCAjIyMiRPZqvfnDs6OuolLgcHB9y+fRtA7vVrdHS05GOXVExU8ch1fGgTGRmJjz/+WPS3vVu3bli1ahXfDCa9k+PYUE1qDA4ORnBwsM7HGDVqlKgdGBiIXr16FTs2qljkOD4AwMnJSZTcpcvkonqCPV+ooKKS4/h4/fo1nj17JrRNTU3h6uoquX+HDh1E7Tt37hQ7JiI5cXR0FCV3PX36VHJyF+/NqaJJSEjAuHHjRImNzZs3x+bNmyVXvCN616nem589e1a0vLtUs2fPFrXnzZvHpR1JZ6VSL27QoEFCcheQu/yianKXQqHA0aNHhXaTJk3QvHnzfI/3888/i96m6t+/P5YvX17oW++qpSLl5NixY/jnn3+Edvv27bFp06ZCszV1WYaxuH3V9y/uRLm1tbWo/ddff/EigIjoHaT+ZnRERITOS/+qU18K+cmTJzq9Baf6N1Xb8YhKm729vagdHh6uU/9Hjx7pM5wKo1GjRrh69SoAICcnB3fu3EG7du0k9f3rr780jqUPjRs3FpK7gNzqxVIfBJdUTFQxyXF8qMvvzeD169fzzWAqMe/C2CAqK3IcH40bNxa1dXneqf5SkPqzSiJdyG18qI8Fa2trGBkZSe5va2srauu61CSR3DVu3FhYXgvIHYddu3aV1DcsLEzU5jUflWepqanw8vISPW93dHTEtm3bWHGIiKgMlMqrro6OjmjVqpXQPnHihCjR6ty5c6JkrYKqdgHA+fPnhX9XqVIFS5YskbScSd468XKjej6GhoZYsWKFpD+KxTmfJ0+e6LR/VFSUqK1+g6cr9Yl23iASEb2bWrduLWpfv3692MdUT/BWf9BZkKysLNy9e1do29jYaCTW0LuhPC3x2bBhQ1Fi/PXr1yVXHVUqlbhx40ZJhVaude7cWdSW+vvpxYsXorfcHRwcULt2bb3EpF69WGpMmZmZorflLSwsRPdXRLqS4/hQlZiYyDeDqUzIfWwQlSU5jo8uXbqI2hEREZL7qu9bo0YNvcREFZPcxoeFhYWonZGRoVN/9f3Nzc2LHRORnKjfm+vy3EV1fBsYGGgci6i8yMjIgLe3Nx48eCBsq1OnDoKCgvgyNRFRGSm1dQxUE7bS09Nx4sQJoa26JKOxsTE+/PDDAo+lmmjUpk0byTcXoaGhEqMtXarn4+TkJPkGrjjnc/fuXeTk5EjeX7XCAKA58a4r9WQAXSbuiYhIPtq3by9aFikkJKTYS++qLxVw7NgxyX3Pnz8vWvJO/e8NlS7V5Htdfy7UE/f1vaRzaTIwMED79u2FdkxMDC5fviyp76VLlxATE1NSoZVr7733nqh96NAhSf3U91M/TnG4ubnB2Pj/iyefOHECmZmZhfY7deoU0tPThXbXrl0lvdxClB85jo882t4MdnJy4pvBVCrkNjauX7+OBw8e6PSf6jUHAJw+fVr0dS7JSEUlt/EBAC1atBA9Rw0PD0d8fLykvurX4//+97/1FhdVPHIbH5aWlqI5k1evXuH58+eS+6sudwoU/0VvIrlxdXUVJadcuXIFsbGxhfYLDQ0VFU5o0aIFatasWSIxEpUlhUIBHx8f3Lp1S9hmZ2eH77//HrVq1SrDyIjKRkhIiM735urFjHbu3Cn6OpdkpKIoteSu/v37iyYgDhw4AABISkrChQsXhO1dunRB9erVCzyWallhqQ93lUolDh8+rEPEpaco5wNIv0nU5uXLl7hy5Yrkff/880+hXaNGjWK/QaT+NoMuE/dERCQfNjY2ogmi+Ph4bNmypVjHdHBwECURP3z4ECdPniy0X05ODgIDA0XbCksYp5JVpUoV4d9SJ1m09S1Kf7kZNmyYqL1ixQooFIoC+ygUCqxYsaIkwyrXmjZtiiZNmgjtiIgIUcVcbd68eYPg4GDRtgEDBugtpqpVq4qWekhJScGvv/5aYB+lUokdO3aUWExUMclxfOR9xqRJk3D//n1hW926dflmMJUauY4NIjmQ6/hQvefLzs7Gzz//XGif9PR07N27V7StW7dueo2LKhY5jo+2bduK2vv27ZPcV31fJj9SeWNoaIgPPvhAaGdnZ2Pnzp2F9gsKChK1ec1H5VFOTg7mzJmDixcvCtusra2xfft21K9fvwwjIyKiUkvusrKyQs+ePYX2lStX8Pz5cxw+fFhUiaGwJRkB8WTf48ePJX1+SEgIIiMjdYi49KieT1RUlKSKWlevXsWlS5eK9bkbN26UtN+WLVtE/4/0ccHasmVL0cT9iRMnWL2LiOgd5e3tLVpCb+PGjTh69Kjk/tqWGR4zZoyovWjRIjx9+rTA46xevVq0JKO9vT169+4tOQ7SPwcHB+Hfz549Q3R0tOS+lSpVEi2pef36dWRnZ+s1vtLUrVs3ODs7C+0HDx5g1qxZompMqtLT0zFz5kw8fPiwtEIsl3x8fETtxYsXIyUlJd/9V69eLVoWpVevXvjXv/6V7/779u1D06ZNhf88PDwKjWnq1Kmi9po1a0SfqW7Hjh2i6+TmzZuL7quIikpu4+Pt27eYNm2aaEmUGjVq4Pvvv+fb8FSq5DY2iOREjuPDy8tLtAT6tm3bEBYWVmCfJUuWIC4uTmg3adJElIBPVBRyGx99+vQRtbdu3Vro2ABy7z/Ul53j/QfJ3ZUrV0TjQ0oVPG9vb1FBih07dmisYKPq1KlT+O2334S2nZ0dhg8fXrzAiUpYUcaGv7+/6GfdwsICW7duFSUxE5UHRRkfRGWt1JK7AHHillKpREhIiFDBC8jN/JUycFT/gNy9exdXr14tcP+wsDAsXrxY94BLier5JCUl4eDBgwXuHxUVhTlz5kCpVBbrc69evYrvvvuuwH0uXbqEH374QWgbGxvr7YJVdWIrJycHU6dOFa3dLEVUVJTkCmRERFQyXFxcMHHiRKGdlZWF2bNnY8GCBfkm8yQnJ2Pv3r0YOnQoNm/erPH1Dz/8ULSkTHx8PEaOHImTJ09q/P1LTEzEvHnzsG3bNtH2RYsWiZY/o9LXrl074d9KpRJTpkzBoUOH8PDhQ0RHR4v+y8rK0uiv+qbxkydPMH36dJw/fx6RkZGivu/CsoWGhob46quvRD+Tp06dgru7O7Zt24bQ0FA8fvwYt27dwpYtW+Du7o7Tp08DAN5///2yCvud16dPH9FSr0+fPsXo0aM1rjlfvXqFxYsXi97UNTMzw8yZM/Uek4uLC/r37y+0U1NTMXLkSNEECgBkZmZi06ZNWL58ubDNwMAAfn5+ooRaoqKS2/iYO3cufv/9d6FdqVIlLFmyBAYGBhp/Mwr6T3V5ZqKikNvYIJITOY6PqlWripJqFAoFPv74Y4SEhGi8QJuYmAhfX19R5VRDQ0PMmzeP11dUbHIbH4MGDRJVWHnz5g08PT3x008/aV0aPj4+HosWLcJXX30l2t63b180bdpUr7FRxST1+v3ly5da99N3RfeaNWuKlsR6+/YtvLy8cOrUKdF+WVlZ2L17t8YYnTFjBipVqqTXmKhiktPYWLNmDXbv3i20DQ0NsWDBAtjY2Oh0X56UlKS3mKhik9P4IJKDUp3x7NKlC+zs7ISBtGPHDrx8+VL4+gcffCDKlM/P+++/L1omcNq0afjqq6803iDJK228bt06pKeno1q1aqLPk4v3339f9FDB398f2dnZGDRoEIyMjITtb9++xZEjR7By5UokJiYW63ysrKyQmpqKVatWITY2FtOmTRNVEFMoFNizZw9WrVolqtrl5eWFBg0aFOkz1fXs2RPDhw/Hrl27AOTeQH700Ufw8vLCiBEjUKNGDa39EhMTcf78eRw/fhy///47Pv74Y3To0EEvMRERUdFMnz4djx8/xvHjxwHkJvLs3r0bu3fvhpOTE+rXrw9LS0ukpaUhKioK//zzj/CgXfXhZx5DQ0OsWLECI0eOxIsXLwAAcXFx8PHxQc2aNdGsWTOYm5vjxYsXCAsL00gMmjBhAtzc3Er4rKkw/fr1Q0BAgHBD/+DBA/j5+Wnd9/Tp06hbt65o26hRo3D48GEhoe/UqVMaD9kAoE6dOjhz5oyeo9e/5s2bY8WKFfjkk0+EKmTPnj3DqlWr8u3j5eWFxo0bC2MLACeedGBgYIB169Zh6NChQnWGhw8fYuDAgWjevDnq1auH5ORkhIWF4fXr16K+S5YsQePGjUskrsWLFyM8PFyozBYTE4NRo0ahSZMmcHBwQHp6Ou7cuaNxrT9jxgx07ty5RGKiikdu4+Pw4cOi9ps3b+Dt7a3zcXx8fDBt2jR9hUUVkNzGBpGcyHV8eHp64t69e9i/fz+A3OT5Tz75BCtXroSLiwssLCwQExOD0NBQjXtHXl+RvshtfJiYmGDNmjXw8PAQKkanp6fjyy+/xNdffw0XFxfY2dnh7du3iI6Oxv379zWqZTds2BALFy7Ua1xUcUmtALdy5UqsXLlSY3v79u1FhQj0YcaMGbhz5w4uX74MIPfvx9SpU9GgQQM0adIEWVlZ+PvvvxEbGyvqN2zYMAwbNkyvsVDFJaexcejQIVE7JycHc+fO1fk4gwcPFr2sSFRUchofRHJQqsldRkZGGDBgALZv3w4AGpMVUpZkBIChQ4dix44d+OeffwDkVv+YMmUK6tSpg2bNmsHMzAzx8fEICwtDRkYGgNy3fr/44gvMmDFDfyekJ25ubmjXrh2uXbsGIPch9meffYY1a9agRYsWsLCwEG788jJR86o/TJ48uUifOXz4cJw7dw7h4eHYsWMHfvnlF7i6usLOzg4pKSkICwvTKBvdunVrjfLSxTV//nzExcXh7NmzAHLPfePGjdi4cSMcHR3RoEEDWFpaQqFQICUlBZGRkaKy6UREJA9GRkZYu3YtVq9eje3bt4vekI6IiEBERITOx6xduzZ++eUXeHt7i5ami42N1XioohrHjBkzRJXEqOxYWlpi7dq1mD59OpKTk3Xu7+rqik8//RSrVq16p5dkVOXu7g5LS0ssXLgw359jILdaqp+fH8aNG4eff/5Z9DVzc/OSDrNcqVmzJr777jshCRXITUC9c+cO7ty5o7G/mZkZ5s6diw8//LDEYrKwsMCWLVswc+ZMhIaGCtsfPnyodSlOQ0NDTJw4scjX/kT5keP4IJIDjg2i/Ml1fCxevBimpqbCS6QAkJCQIDxzVGdkZITPPvsMo0ePLtG4qGKR2/ho0aIFtm/fDl9fX9ESkOnp6YWuhtGmTRt8/fXXsLGxKZHYiOTA2NgY69atw5w5c3Du3Dlhe1RUFKKiorT2GTZsGPz9/UspQiIiIqL/V+prFQ0ZMkRI7lLl5OSEli1bSjqGqakpNm7cCE9PT1E5vWfPnoluUvKYm5tj3bp1cHR0LHrgJWzt2rXw8PBAZGSksC0+Pl7rAwgTExN8+eWXxVr71czMDJs3b8a4ceMQFRUFhUJR4A1d69atsXXrVpiZmRX5M7XJ+3+5bt06bNu2TfT2XGRkpOj7kR8rKyu9xkREREVjaGiIOXPmYPDgwQgMDMS5c+eEt0O1cXBwQP/+/Qtc7tfe3h779+/Hrl278P333+PJkyda9zM1NUW3bt0wc+ZMVkuQmQ4dOuDo0aPYv38//vjjDzx69AgpKSl48+aNpCWmx40bh65du2Lfvn24efMmoqKikJaWBoVCUQrRl4zu3bvj6NGjCAkJwcmTJxEZGYmkpCRYW1ujVq1a6NatG4YMGSJUMnv16pWov2q1VZKmSZMm2L9/PwIDA7Fv3z4kJiZq7GNiYoKuXbti5syZpbLsiL29PX766Sds374dwcHBWu9jDAwM0KFDB/j4+IiWOSXSJzmODyI54Nggyp8cx0fe89KePXti69atuH79utb7DVNTU/Ts2RPTpk2Dk5NTicdFFY/cxoerqysOHjyI4OBg7Nq1K9/nKnlatWqFkSNH4sMPP4ShoWGJxkYkB1ZWVti8eTN2796NHTt24NGjR1r3a9myJby9vdG7d+9SjpCIiIgol4FSyqyang0ZMgR3794VbfP19dV5yYX4+HisXLkSR48e1SipDeQmdfXp0wc+Pj6oV68eoqOjReX7Clqu4cqVKxgzZozQXrZsGYYMGaJTfHPnzhXKgQO5SxEVJC0tDWvXrsWePXvw5s0bja+bmJige/fu8PHxgbOzMwCIbv4KKnOZ37m/evUK69evx4EDBzQqdQGAra0txo4dCy8vLxgbF54LuH79emzYsEFoa1tiKT9Pnz7F1q1bcfz48QKrexgYGKBJkybo2rUrBg4ciCZNmkg6PhERlS6FQoFbt27h2bNnSEpKQlZWFiwtLVG3bl04OzujVq1aOh8zMjISf//9NxISEpCZmQlra2vY29ujbdu2rGZE5dann36KAwcOCO0bN27A0tKy7AJ6x2VlZeHmzZuIjo5GQkICLCwsUKtWLbi6upbZW+lKpRJ//fUXoqKiEBcXh0qVKqFmzZpwcXGBvb19mcREFZMcxweRHHBsEOVPruPjxYsXuHPnDmJjY/H69WtYW1ujdu3aaNu2LSpXrlxmcVHFIsfx8fz5c9y5cwfx8fFIS0uDgYEBrKysULt2bbi4uKBatWplEheRXNy7dw8RERGIjY2FkZERatasCWdnZzg4OJR1aERERFTBlUlyl769fPkS169fx7Nnz5CZmQlbW1vUrFnznb1ZT0tLw40bNxAVFYX09HRUq1YNNWvWhKurK6pWrVoin6lQKHDt2jU8f/5cqBzRoEEDtGvXDkZGRiXymflRKpW4f/8+Hj16hJcvXyItLQ2VK1eGlZUVGjRogEaNGsHa2rpUYyIiIiIqK7179xberq5fvz5OnjxZxhERERERERERERERERFRaSkXyV1EREREROXRH3/8gbFjxwptd3d3rF69uuwCIiIiIiIiIiIiIiIiolLFRdOJiIiIiGQoMzMTS5cuFW1zd3cvo2iIiIiIiIiIiIiIiIioLDC5i4iIiIiolKxYsQLHjh1DTk5OgfslJiZi/PjxCA8PF7bVq1cP3bp1K+kQiYiIiIiIiIiIiIiISEaMyzoAIiIiIqKKIjIyEtu3b0fNmjXRt29fuLq6on79+rC0tMTr168RHR2Ny5cvIyQkBOnp6UI/AwMDLF26FEZGRmUYPREREREREREREREREZU2A6VSqSzrIIiIiIiIKoKJEyfi3LlzOvUxNDTE559/jtGjR5dMUERERERERERERERERCRbrNxFRERERFRKrK2tddrfwcEB8+bNg5ubW8kERERERERERERERERERLLGyl1ERERERKXo/v37uHjxIkJDQxEVFYXY2FhhCUZra2vY2trC1dUVXbp0QY8ePbgUIxERERERERERERERUQXG5C4iIiIiIiIiIiIiIiIiIiIiIiIZMizrAIiIiIiIiIiIiIiIiIiIiIiIiEgTk7uIiIiIiIiIiIiIiIiIiIiIiIhkiMldREREREREREREREREREREREREMsTkLiIiIiIiIiIiIiIiIiIiIiIiIhlichcREREREREREREREREREREREZEMMbmLiIiIiIiIiIiIiIiIiIiIiIhIhpjcRUREREREREREREREREREREREJENM7iIiIiIiIiIiIiIiIiIiIiIiIpIhJncRERERERERERERERERERERERHJEJO7iIiIiIiIiIiIiIiIiIiIiIiIZIjJXURERERERERERERERERERERERDLE5C4iIiIiIiIiIiIiIiIiIiIiIiIZYnIXERERERERERERERERERERERGRDDG5i4iIiIiIiIiIiIiIiIiIiIiISIaY3EVERERERERERERERERERERERCRDTO4iIiIiIiIiIiIiIiIiIiIiIiKSISZ3ERERERERERERERERERERERERyRCTu4iIiIiIiIiIiIiIiIiIiIiIiGSIyV1EREREREREREREREREREREREQy9H+QxRJtuttlvAAAAABJRU5ErkJggg==",
      "text/plain": [
       "
"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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II4+sEAJKkmOPPTZ33HFHYf9vf/tbnnjiiQbHX7p0ae65554cd9xxK8yYtM022+SGG24o7P/5z39OZWXlR72VTJs2rc6zri+EtDpdd911GTx4cL3BriQpLS3NkCFDCjNBVVRUZNiwYWu0pmT1v6a1w1D/PUNXsmy2u4kTJyZZNlNWhw4dVtp30qRJmTBhQpKkR48e2XzzzVfoM2DAgPz4xz9eabArWTaj1+9///scfPDBSZYtpfjaa6+t0K+kpCSnnHJKYb/2rFz1ueOOOwrvoUGDBtVbHwAAwKfN63Pm1wl2bdmxbX484DO5f/COuWjnz+TsHTbLRTsv27+s/2eyZce2SZJFVdU55+lxeX3O/KYsHwAAYJ2xcOHCXHbZZXV+lk/4wDLCXQBF5MQTTyxs1zdjT33OO++8VS7TV3sJxWeffbbePrVnltptt93yla98ZaXj9erVK2eddVaDddVearExSx2uLjU1NbnxxhsL+xdccEF69Oix0v5f+MIXCmGaJLn++usbvMahhx6agw46aKXHBw8enA033DBJMn/+/HoDOo01e/bsOvsdO3Zs8Jzbb789p59++ip/Pq6jjjoq7dotW6aise/Vj2pNvKa1Z9caPnx4ampq6hyvvRTmvvvum7322ivJstm8VtW3vlm7PqwhQ4YUtlf2bE888cTCtxX+8pe/5IMPPljpeLXDX7VDYQAAAJ9WNTU1ufi5NwvBroHdO+WmQZ/Lfpt0Sdl/ffGurFmz7N+jS24a9LnstVGnJMsCXpc89+YKv/8BAABAUzCHGcBatHyJvTFjxmTq1KmZN29enVmdli9jmCQvvfRSo8Y8+uijV3l8q622SuvWrbNo0aLMmjUr8+bNy3rrrVenT+3ZiI477rgGr3ncccflf/7nf1bZp0ePHoVlHocNG5ZzzjmnwXFXh9deey1Tp05NsmyWqZUtQ1nbKaeckgceeCBJ/TMz/beGnnlJSUm23377Qh3vvPNOvUv5NUbt90SStG3btsFzHn744dx2222r7NOY5S1ffvnlvPjii3nnnXdSXl6+wmxty2cte+WVV1JdXd3gMqIf1Zp4Tbt165atttoqr7/+embMmJFXX321zqxay89p2bJldt1117z44ov529/+ltmzZ2fMmDHZYYcdVuibNC7ctXDhwowaNSqvvPJKZsyYkXnz5qWqqqpwfPkMZcnK/x7YaKONcuihh+a+++7L4sWLc+edd+ab3/zmCv2ef/75jBkzJsmyGc+++MUvNlgfAADAJ93zM8ozoXxRkmUzdl3av09alq76d9aWpc1y2YA++frwsRn3wYK8Vb4oL8wsT7+uHdZGyQAAALBSwl0Aa8GiRYty+eWXZ9iwYZk5c2ajzmlMvw4dOqxyBqNkWQCnU6dOWbRo2Yea5eXldcJdNTU1efnllwv7AwYMaPC6vXv3TpcuXVZZ45e+9KU8+uijSZIf/vCHeeihh3Lsscdm//33zyabbNLgNT6qF198sbC95ZZbZv3112/wnN13372wPXXq1Lz//vurnA2tMUGt2tetPYvZh/XfQbwFCxZ85LEa67bbbsvll1++wtrWK1NRUZG5c+emU6dOa6SeNfWaDho0KK+//nqSZbNv1Q53Pf7440mSXXfdNa1ataqzjONjjz32kcJds2fPzoUXXpjf//73K4T2VmZVf8ZOPfXU3HfffUmWzc5VX7ir9qxdxx13XFq1atWo6wIAwOr2zLQP8veJMzJ5weKmLoV1wLvzFhW2v9ane4PBruValjbLcX2654Jnln1Z7YejxqdnO79HsfZs3LZVDu3VNf27dWzqUgAAgCIi3AWwhs2ZMyf77LNPo2fiWq4x4Y8OHRr37dGysrLCdkVFRZ1jc+fOzdKlSwv7DYXFlttkk01WGTw55ZRT8s9//rMQPnnkkUfyyCOPJEl69uyZPffcM3vvvXeOOOKIdOnSpVHXbIwZM2YUtnv16tWoc7p165ZWrVpl8eJl/8gwc+bMVYa7GvPcV/XMP4zOnTvX2V/V8nvL3Xrrrbn11lvrtA0fPrxOQKk+NTU1Ofnkk/O73/3uw5aZefPmrbFw15p6TQcNGpRhw4YlWfZ8zjjjjCTJhAkT8u677xb6JMn222+fTp06Zc6cORk+fHi+973vJUnefffdvP3220mWva979+5dbz0TJ07MXnvtVRi3sVb198CBBx6YXr16ZeLEiXnhhRfy0ksv1QmdLVq0KHfeeWdh35KMAAA0lWemfZDvjnw9VZa4Yy3r3LIsgzbu3HDHWvbeuHM6tSzLnCUVKV9ambGz56+h6mBFY2fPz8Pvzcov99g6O29g1jgAAGAZ4S6ANexb3/pWIdjVokWLHH/88TnssMOy9dZbZ6ONNkrr1q1TWlqaZNnyfZtttlmSpLq6usGxly+L93HMn1/3Q8o2bdo06rx27dqt8nhpaWnuvffe3HLLLbnmmmvy73//u3Ds3XffzR133JE77rgj3/jGN3L88cfnZz/72QpBpo+i9v00ZgnD2n2XB4EaCtatjufeWP8dUqr9HFe3m266qU6w66CDDsoxxxyTHXfcMZtssknatGmTFi1aFI5vuummmThxYpLGvV8/qjX1mtaeZevxxx9PTU1NSkpK6szEtTwQ16xZs+y55565//7788QTTxSWoWzsrF1f/epXC8Gu9dZbL6ecckoOPPDA9OnTJxtssEFat25dWNaydhBvVc+1WbNmOfnkk3PhhRcmWTZL169//evC8b/85S+ZO3dukmTnnXfOdtttt9KxAGBd9cNR4/LklDlNXQZ86lVW10Ssi6YwoFuHlDVr3Kxdy5U1a5ZdunXIA+82buZ1WN2qampyxoh/p3mztff5E6yL9tioU366y5ZNXQYAQKN8uN9sAfhQJk+enLvvvjvJsiDGP//5z9x00005/PDD85nPfCbt2rUrBLuSxs3Wtbr9d0hr4cKFjTqvMcsDlpSU5OSTT86rr76acePG5cYbb8wJJ5xQZ3ajioqK3Hzzzenfv3+dGZo+qtr382GWMKzd97+XQmxKZWVldZbKfOaZZ9bYta666qrC9sUXX5wHHnggxx9/fD73uc+lY8eOdYJdydp7v66p17Rbt27ZeuutkySzZs0qLE/62GOPJUlat26dXXbZpdB/eeDqgw8+KCwVubxvsvJw11NPPZWnnnqqcC+jRo3KNddckwMPPDCbbbZZ2rZtWwh2JR/uuZ500kmFv0PuuOOOQpgtqbsko1m7AACAdVG7so/23ea2zUsb7gQAAABriZm7ANagRx99NDX/f9mJgw8+uMFl8ZbPgrQ2dejQIWVlZYWlA99777107dq1wfPee++9D3WdPn36pE+fPvn617+eJBk/fnxuuOGG/PKXv0xVVVXeeuutXHzxxbn22ms//E3UUrv2xi6BN3369DqhmNW5TOTqsPfee+fxxx9PkowYMSLvv//+KpeN/CgmTZqUN954I0nSsWPHnHvuuavsX15enjlz1s4sF2vyNR00aFBee+21JMuCWttvv33hWe+22251Am21w1uPPfZY+vXr16iZu5YvR5okJ5xwQj772c+usvYP8/fAxhtvnMGDB+dvf/tb5syZk7/+9a855phj8tZbbxXuo23btjnmmGMaPSYArEt8Ux/WDssy0lTmV1R+pPMWVFat5kqg8UpLSizLCAAA1CHcBbAGvf/++4XtbbfdtsH+TzzxxJosp14lJSXZbrvt8vzzzydJRo8enb59+67ynHfeeedjz7LVp0+fXH311encuXN+9KMfJUnuv//+FcJdH3YJxNq1v/7665k9e3aDyz2OHDmysL3hhhuu9uDUx3XSSSflxz/+cSoqKlJZWZlrr702l19++Wq9Ru336lZbbZWysrJV9n/yyScLwcU1bU2+poMGDcr111+fZNlyiIcddlgmTZpUOFbbdtttl86dO2f27NkZPnx4jj766LzzzjtJkl69ehWWVP1va/rvgVNPPTV/+9vfkiybreuYY47JLbfcUnh9jj766KKajQ4AgHVP/24d88s9ts7f3pmeyQsWN3wCfAxVNTV5bc6ymZxHT5ubiurqD7U0Y0V1dUZNm1vY37pT25R+yM8m4KPauG2rHLbpBoJdAABAHcJdAGtQ7aXWGlrucOHChfn973+/pkuq16BBgwrhrjvuuCNDhw5dZf/bb799tV378MMPL4S7pk2btsLxVq1aFbaXzy62KltvvXU23HDDTJ06NVVVVbn99tvz7W9/e5Xn1F6+rqHZ1ZpCjx49cuyxx+bWW29Nklx55ZX5whe+kJ133nm1XePDvFeTFAJRa8OafE1rB7ieeOKJPProoys9r1mzZtlrr71y3333ZcSIEXVm5Bo4cOBKr/Fhnu3777+f//3f/11ln/928MEHp0ePHpk0aVIeffTRvPnmm4X3SmJJRgAAisPOG3QQVmCtOXfUuDw6eXZmL6nI8Mmzs3+Pxs/Q/djk2ZmzZNnnD/tuvH4u36XPmioTAAAAGqXxX1kC4EPr3bt3Yfsf//hHqqpWPq3/WWedVW+4aW046aSTCttPPvlk/vznP6+076RJk3LVVVc1OObMmTMbde3lsyQlyQYbbLDC8fXXX7+wPXny5AbHKykpyamnnlrYv+SSS1Z53v3335//+7//K+w3FGxrKldeeWV69OiRJKmsrMzgwYPz9NNPr7bxN9tss8IsaWPHjs2ECRNW2vePf/xj/v73v6+2azdkTb6mG2ywQWGZxDlz5uRXv/pVkmVLGfbv33+F/ssDX+Xl5fn5z3++Qnt9av89cP/996+0X1VVVU499dQsXbp0pX3qU1paWvgzXFNTk+OOO64wW9jWW2+d3Xff/UONBwAA8El3ZO8NC9t/GP9+llRVN+q8xVVVuX38f2ZfPnLzbqu9NgAAAPiwhLsA1qB99tknbdq0SZK8+eabOeGEE/LBBx/U6VNeXp5TTz01w4YNS9u2bZugyuSzn/1svvrVrxb2TzjhhNx1110r9BszZkz222+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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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/vvX/u3btqnHjxunq1av64osvNHfuXMXFxWXbpkKFCho5cqTuv//+bJ8tWrRIU6ZMMfTOua5UqVIaPHiwnnjiCbt+T3OqW1ZTpkzR1KlTs223efNmw7a2Vq1apapVq6pnz56GBJnrpk6dmuN+pWtzntv2gli4cKFGjhxpLX/55Zdq1apVjtvntm5SUpImTpyoxYsXKykpKdt2oaGhGjhwoHr27OnQtVF6errmzJmjTz/9VKdPn872eVBQkDp27KgRI0YoNDTUoe8Cz4mNjTU8AL/hhhvsfrFTtWpVhYeH68yZM5KkY8eO6cqVKzkmkx48eNBQtjeBTJKioqIM59fBgwd52e8lbP/uTZs2tXvbqKgoQ9mTU0B89913yszMtJbbtWun0NBQj9UH5lZU4r6gaO+Lp6IW99cfnl9nxmm14RxZ483Hx0eNGze2e9uoqCjDverBgwed8tLTHe1oUTtn4Rgzxn1B2b5TCw8P91BNYHZFKe4dYdtGO9LeN23aVBaLxXovTHvvfbw97i9fvqyff/7ZsIxR1OGNCnvtnTVXJ7e22Jvae5ckd3Xp0sXwD/Xdd9/p5Zdftnv7zMxMff/999ZyeHi4brvtNsM6Cxcu1KhRo+zaX0ZGhjZs2KANGzboySef1AsvvFCgRKW9e/dq0KBBOb4kLayNGzfqiSeesHv9HTt2aPDgwerUqZPeeuutfEcUyUlcXJyefPJJ/f3337muc/bsWb333ntauXKlPv30U6ePgCZd6xHy5ptvav78+fmue+TIEY0cOVKLFi3StGnTFBIS4vT6AADsl5mZqcmTJ+uTTz6xq0d2bGysZsyYocuXL+f6Ox4fH6/+/ftr7969ee4rJSVFy5cv1+rVqzVs2DA9/fTTDtc/LS1NL7zwgmHOblsXLlzQ2LFjdejQIb3xxhvW5e+++64+/fTTXLdLTU3VzJkztXfvXk2fPt3uhI/rLl26pAEDBuSY9HTdqVOnNGzYMB07dszaGyAtLU0vvfRStjnCbfc9fvx4HTp0SG+99ZYsFotDdSuqDhw4oEGDBmV7IZVVQkKC3nnnHf3111+aOHGiXaN4JSUlqW/fvvrrr79yXSc5OVnffPONNmzYYOjgAHOzvY6uVq2aQ9tXrVrVmtyVmZmpI0eO5NgpJutxLBaLQ8epWrVqrvuCuRUmvsz0d896by8pxx5y9ho7dqz++usvnThxQklJSQoODlZYWJgaN26sVq1a6d5772UURC/nTXG/b98+DRs2TLt27dKZM2eUnp5uHWG2RYsWatu2rcOJ2bT3xZM3xb09Vq5caSg78iLsOtp787tw4YJhepRy5co5NLJ2TrF7++23F7pe7mhHi9o5C/uZNe4L4vjx44bnbn5+fg4P0LBp0ybt3btX+/bt09mzZ+Xj46PQ0FBVqVJFN910k9q1a+dQgiXMyZvi/qOPPtLhw4f1zz//KDExUaVKlVJoaKgaNGigli1b6v7771fZsmXt3l9h2vuSJUsqPDzc2sHzzJkzunjxokvescL5vCnuc7NixQpdunTJWm7UqJHq1atXoH3R3sOT3HHt7U3tvUuSu1q1amUYcvCHH37QiBEj7J7CZtOmTYYEqgceeCDbtll7/kpSiRIlVKdOHZUvX17BwcFKSUnRiRMntH//fkNv+lmzZsnPz08jRoxw6DudP39eAwcOtI6aUapUKTVt2lRhYWG6cOFCjiN2OML2+/j7+6t27dqqWLGigoODdfXqVZ06dUr79u3T5cuXrestWbJEmZmZmjhxokPHS0lJUb9+/azBGhAQoGbNmql8+fK6cOGCdu7cqQsXLljX37Ztm55++ml9+eWXTp3GKSUlRQMGDMg2pF6pUqXUuHFjhYeHKzU1VTExMTp8+LD1882bN+vxxx/X119/7fQpuQAA9klPT9ewYcOy9QCRpMjISNWoUUPBwcG6dOmSjhw5oiNHjmT7vbN18uRJPfroo9kSqcuXL6+GDRuqVKlSiouL044dO5Seni7pWjLTu+++q4sXL2rYsGEOfYe33nrLmtgVEBCgqKgoVahQQcnJydq6dathVMt58+apWbNm6tq1q6ZPn25N7PL19VXjxo1VuXJlpaWlaceOHYqPj7dut27dOs2YMUPPPvus3fXKzMzU8OHDrYldQUFBatasmcLCwpSQkKCtW7cabs4mTpyoqKgotWrVSqNHj7Ymdvn7+ysqKkoRERFKTk7Wtm3bdP78eet2CxYsUHR0tLp16+bQv1tRdObMGb344os6efKkJCksLEyNGjVSmTJlcvw3//nnnzVz5kw988wzee43NTVVTz/9tLZu3WpYXrp0aUVFRSk0NFTnz5/X9u3blZSUpOPHj+uZZ55R7969nf8l4XS2iYD2Dtd8XUREhKEcGxub7YH6uXPnDKPIhYWFOZQsalunvJIXYS62Pekdia/AwECVLVvW2uYnJibq/PnzDj3Idoa//vrLMKx5uXLlCvVAc86cOYby+fPndf78eR0+fFiLFy/WhAkT9NRTT+npp59mpGcv5U1xv3fv3mydEU6ePKmTJ09q27Zt+uyzz9SkSRO9/PLLdo2gRXtffHlT3OfnzJkz+umnnwzL2rVr5/B+aO/Nz7aNcXQ0Cle0We5qR4vSOQvHmDHuC+qrr74yPKNr0aKFwx3ps069fl1ycrJOnDihP/74Q9OnT1ebNm00cuTIAicUwPO8Ke6//fZbQzkhIUEJCQk6cuSIfvrpJ7333nt65JFHNGTIELveczrjmU/W0ftjY2MLlPQO9/OmuM/Nd999ZygXpqMd7T08yfba25Hz0d5z0Zvae5ckd1ksFnXu3FnTp0+XdO3G/rffftMdd9xh1/aLFy82lHMbJjA8PFzdunVTu3bt1LRp0xxH40pMTNS8efM0ffp0a1LUp59+qrvvvjvbMMh5mTp1qpKTkxUcHKwRI0bo3//+t+HmMD09XadOnbJ7fzkpXbq0unTpovbt2ys6Olr+/v7Z1rl8+bK+//57TZw40frCeenSpbr77rt133332X2sr7/+WomJibJYLOrZs6eee+45QwZhamqq5s+fr/fee8/677Zt2zZNnTpVzz//fKG+Z1ZvvfWWIbGrUqVKGjFihO69995sCX379u3TG2+8YR31Yv/+/Xr77bc1ZswYp9UHAGC/Dz/80JDYZbFY1K1bNw0cODDHzPaEhAStWLFCX3/9dY77y8zM1EsvvWRI7AoPD9drr72mDh06GB7anzlzRhMmTDBcM3z88cdq0aKF3S+N9+3bp82bN8tisahPnz4aOHBgtt/CKVOmaObMmdZlH3zwgapVq6YpU6ZIkrp166bhw4erfPnyhu/x5Zdf6p133rE+KPvkk0/Us2dPu6ehWr16tRITE+Xv768hQ4aoV69ehocOly5d0pgxY6wjpWZmZur999/XE088oW+//VYWi0VPPfWUBgwYYPhOV69etY60dt3EiRPVuXPnQk2/3Lt3b+v12qOPPmpNbouKisozAf36RfLEiROVkpKikydP6rHHHrN+3qtXr1yTnOztNGCvN998UwkJCapcubJeeeUVtW/f3hBzly9f1vvvv2940fTxxx/rscceU5kyZXLd70cffWRI7CpZsqSef/559ejRw3AtmZqaqnnz5mnixIk6duyYpk2b5tTvB9ewnYY2LCzMoe1t189pWlvb6UFdcQyYkzPiK2tCb1JSkttfHNo+TOzUqZPT2++sEhIS9P7772vjxo2aNGlSnu0zzKkoxH1WO3fuVO/evfXCCy/kO1o77X3xVZTifsKECUpOTraWGzVqpJtuusnpx6G997zCtlm2MeqMNstd7WhROmfhGDPGfUEcPnxYX331lWGZqzqYbdiwQQ899JDefvtt3X///S45BlyrqMS9dC0Z5bPPPtPGjRs1bdo0Va5cOc/13fHMB+bk7XEfHx+vjRs3Wsv+/v7q1KmTS49Jew9XSE9PN9xfBgUFOTSbnTuv7+05jjO47Knq9VEtrlu0aJFdyV3JycmGHl4NGzZU/fr1s613xx13qHPnzvn2vgkJCVG/fv100003qVevXkpNTVVmZqZmzZqlSZMm2f19kpOTFRQUpNmzZ+eYaefr61uo+XIbNmyoX375Jd8hxUuWLKkePXqoZcuWevjhh60JXp9//rlDyV2JiYmSpBdeeEFPPfVUts8DAgL0+OOPq2bNmhowYIB19LPPP/9c3bt3V/Xq1e0+Vm7WrVtneMHfoEEDzZ49O9cX39c/HzhwoH777TdJ0vz589WzZ08ygQHAzXbs2KEZM2ZYy35+fho3bpweeOCBXLcJDQ1V9+7d1b17d+s0ZFl9//33+v33363lsLAwffXVV4qMjMy2bnh4uCZMmKBy5crp888/ty4fPXq0VqxYYdeL4+u/haNHj9YjjzyS7fOAgAA9//zzOnr0qJYtWybp2o3RwIEDlZGRoQEDBuQ4UpjFYlHv3r118uRJa91SUlL0448/6tFHH823Xtfr5uPjo2nTpqlt27bZPi9VqpTeeecd/f3339q2bZskafv27Ro9erQkacyYMerevXu27fz9/TVixAj9888/1sS806dP67fffsvxOPYKCQmx9vDM+m8fGBiYbejbnGRNjrPdrz3bO0NCQoKqV6+uuXPnqkKFCtk+L1mypF599VWdP3/eOjJaSkqKlixZYkhIyyo+Pt6QSOfv76+PPvpIN998c7Z1AwIC1KtXL9WpU0f9+vUzjBoH88p6cynJ4RFubW9GbfeX0zJHp3i1rVNOx4A5OTu+so4+6A4pKSnW38/rCjpSZJ06dXTHHXeoUaNG1pFBL1++rBMnTmjTpk1atGiRYeTpDRs26Nlnn9Xnn3/u0mQyOJ83xH1AQIBatWqlNm3aqH79+goPD1dgYKASExO1f/9+rV69WmvWrLEm+aelpemdd95RcHCwHnzwwVz3S3tffHlD3Nvjxx9/NHS+sVgsGjlypEP7oL33HrZx5orrYEe5qx0tKucsHGfGuHdUamqqnn/+eaWmplqX3XzzzWrfvr3d+wgKCtItt9yi1q1bq27dugoLC5Ofn5/Onz+vPXv2aPny5dq0aZN1/cuXL2vEiBEKCQnRrbfe6tTvA9fzhrivWrWq7rzzTjVp0kSRkZEKCQlRSkqKTp06pS1btmjRokWGWRb27Nmjfv36ad68eQoODs51v+545gNz8oa4z8vixYuVkZFhLbdt29bhZBWJ9h6e563X96485102bnX16tXVokULa3n16tWGm+/cLF++3PCFcxu1q3z58g79AZs3b2548bZy5UrDBaw9nnvuOZcNoRYaGppvYldWkZGRGjx4sLW8Y8cOHTt2zKFjtmzZMsfErqxuvfVW9ezZ01pOS0vTvHnzHDpObrImBZQoUULTpk3Ld0STgIAATZgwwTAVo+1Q7QAA15sxY4Zh+PYBAwbkmdhlKzw8PNsy2/b8tddeyzGxK6sRI0bohhtusJZPnDihlStX2l2PO++8M8fErqyefPJJQzkxMVGNGjXSkCFD8tzuiSeekMVisZavT7For8ceeyzPhKvrSWS2dbvrrrtyTOzKqk+fPoayo3UrqiZMmJBjYldWgwYNMpRzGpb6um+++cYwPXivXr1yTOzKqk2bNoZrL5hb1unSJcdv/GzXt92fVPibS172e6/Cxpft/XJO8eVKq1atsiZSS9c669hOO5qf2267TQsWLNDSpUv1wgsv6P7777e+8G/QoIHatWunkSNHavXq1dmmGNi0aZOhwxm8g9nj/vnnn9e6dev06aefqk+fPrrllltUv3591axZU02bNlX37t310UcfacGCBdk6xf3nP/9RTExMrvumvS++zB739jhw4IBGjRplWPbYY4/ZPWoX7b33sY2zwr5ocUbcuqsdLQrnLArGjHHvqNGjRxumlS5VqpTdM6MEBQXp9ddf1/r16zV16lQ9/vjjatWqlerWravIyEjdeOONevzxx/Xll19q1qxZKleunHXb9PR0DR8+nI5sXsjMcd+0aVPNnj1bq1at0quvvqrOnTuradOmqlmzpurXr6/bbrtNw4YN08qVK9W3b1/DtgcPHsw39mnviy8zx709bEdRzy3XIje09zALru+zc2mXpq5du+rPP/+UdK1HwI8//pjvC9SsPbycPUxg+/btNWvWLEnXpiXau3ev3VMzBgUFqUePHk6rizO0b99eY8eOtZZ37Njh0OgWzzzzjF3r9evXT3PmzLG+nPzhhx/04osvOlZZG4cOHTK8DH3wwQftrnu5cuV0//33a8GCBZKktWvXFqouAADHnD17VqtWrbKWw8PD1b9//0Lt8++//9bOnTut5Tp16tg1fK+vr68GDx5sSLj5/vvvde+999p1XNskp5w0bdpUQUFBhgu/Xr16Gabsy0lERIRq1aqlw4cPS7o2DaQj8pu6R5Jat26dbZltMlpOoqKiDN/J0boVRdHR0WrevHm+69WqVUvVq1e3zsOe179d1mlLfX19802qv65Pnz6aPXu20tPT7VrfXWJiYlzeqzwyMjLPXpNFXdak2dxkTRq1h6Prw8hMcV/Yv7098eVMhX2YKEkdO3a0a73g4GCNHz9egYGBhtGhv/jiC/Xs2ZOpjhxE3OeuX79+dq3XqFEjzZs3Tw8++KBOnDgh6dpzqKlTp+Y5ZXVWtPfuRdwX3JkzZzRgwIBs0zG+9NJLdu+D9t77mTFu3dWOmvG7wz287W//ySefaOHChYb6jB07VtWqVbNr+7CwsFxHLrfVpk0bzZkzRz169LBOTXThwgV99tlnev755x2vPEzDTHFv7ywEAQEB1tGE3n//fevy77//Xv369VPt2rXt2o+Zvjvcy5v+9jt27LC+j5CuTRHp6IwdtPcwK67vXZzcdd9992ns2LG6cuWKpGsPd/NK7jp58qRhOqaCDBOYmZmpS5cu6dKlS4aREq5/llVMTIzdyV2tW7dWUFCQQ3VxhszMTF2+fFlJSUnZRhrLOqSipDx7gNoKCwtTq1at7Fq3bNmyat26tdatWydJOnXqlE6cOJHvfNR5yTpMoyTdc889Dm0fHR1tTe46deqUjh49avdNCACgcDZv3mz4TbVnmuT8bN261VB2ZF72tm3bKiQkxDo6iO2+chMUFKQbb7wx3/UsFouqVq2qAwcOWJfdcsstdh2jevXq1pup8+fP27WNJNWsWdOupOewsDCVKlXK+iIqKCjIrgQl2+/kSN2Kqttuu83udWvVqmVN7srt3y4pKUmHDh2yllu0aGHoxZSX8uXL68Ybb8xzVDBPGDVqlP766y+XHuOTTz7R7bff7tJjOFPW0WSla9PQOcJ2/ZzuN2yXXb+3spft+p64p/Fmnox72/hy9G9vT3y5yvUpf6/z8/NzaITPgnr11Vf122+/6fjx45KuTaXw448/2v1QEtcQ985Rrlw5jRkzxpDc/dNPP+nNN9/MMcGH9t6ziPuCSUpKUr9+/aztriRVq1ZNM2bMKPQ9Yl5o7z2vsNfBrmiz3NWOevM5i8IxY9zba/HixYakFunaaPiOPH9zVO3atfXCCy/o9ddfty5bsGCBhg8fTlK6F/HmuLfVr18/rV692vrsOCMjQ99++22uCem098WXN8e9bUe7Tp06yd/f36XHpL2Hq3B9n51Lk7uCg4N11113acmSJZKkbdu26e+//851iiXbOWDt6dmbnp6uDRs26KefftLOnTsVExOTLakrN/ZME3ld1imfXO3PP//Ujz/+qG3btunQoUN2B5Aj36dhw4b5jjiSVZMmTazJXZK0e/fuQiV32T4wK126tEPTStr+GBw/fpzkLgBwkx07dhjK0dHRhd7n7t27DWV7k6+layN9NmzY0Jogfu7cOcXFxalSpUp5bletWjX5+dl3KZT1BVxwcLDKly/v8HZJSUl2bSNdSx6yV3BwsDW5q1q1avL19XVp3Yoqe3vpSfb92x08eNBwXduwYUOH6tOwYUPTJXchO3ckd9kew9Gp5XmY6L2cHV+lSpUqdJ3s9cMPPxhGH7ztttvsTnAtjICAAD3++OMaP368ddmGDRt42e9FvDnuc3LrrbeqXr161oT69PR0/f7777rrrruyrUt7X3x5a9ynpqbqmWeeMdzLlS9fXrNmzbL7fqmgaO89z7aNccV1sKPc1Y566zmLwjNj3Nvjl19+0SuvvGLoqNm3b189/fTTLj92t27d9P7771vfXZ09e1b79+93eLp2eI63xn1unnzySUPH4I0bN+a6rjue+cCcvDXuU1NTtXTpUsOygoyiXhC093CFwl7f267vrut7r03ukq6dzNeTu6RrGaPDhg3Lcd2s2aT2DBO4detWvf7664aRNBzhyItMR0cQK4jDhw/r9ddf15YtWwq0vSPfp3r16g7tu0aNGoby2bNnHdre1smTJw3lLl26FGp/jiS2AQAK5/Tp04ayI0kxuTl37pyhbPu7k5/IyEjD6J/nzp3LN7mrdOnSdu8/axJYQbdLS0uzezsz162ocmQqwKy9nXL7t0tISDCU84tHW46uD8+wPeccHQXPtu3L6Rwu7DFs13eknYBnOTu+3Dnl6aJFiwxldz1MlK5NCZBVQZ8XwDO8Oe5zc/PNNxvi8MCBAzkmd9HeF1/eGPfp6ekaPny4YWT+MmXK6PPPP3db50vae8+yjTNXXAc7yl3tqDees3AOM8Z9frZs2aLnnnvO8OzioYce0ogRI1x+bOna85ObbrpJK1eutC7jZb938ca4z4vt9cPBgwdzXdcdz3xgTt4a92vXrjU8k65Xr54aNWrklmPT3sMV/Pz8VLJkSV2+fFmSlJycrCtXrqhEiRJ2bW/vuehN7b3Lk7tuvvlmVaxY0ZrM8/3332vo0KHZRl7asWOHYVrB/IYJ/PXXXzVo0CCHM/SycmS+S1dn1e7evVtPPvlkoZKUHPk+jt402q5/feqrgnJ2MlZycrJT9wcAyJ1t0oozLlRsf1cc/Z2yrYM9v1OOjGDpjO3ccQx31K2ocva/3cWLFw1lR3tjm/EB/4cffuhwrxVHhYeHu3T/zmbbYSIuLs6h7W07POTUASMsLEzBwcHWjhxnz55Vamqq3VMd2daJ0W4d48m4t40H23jJS2pqquHBQkhIiMqWLVuwCjpoz549hhfsoaGhuvPOO91ybEmqUqWKoczUw44j7p3L3pikvfcs4t5+mZmZGjVqlFasWGFdFhQUpE8++UT16tVz6bGzor33LGdfBzujzXJXO+pt5yycx4xxn5c9e/ZowIABhplh7rvvPr3xxhsuPa4t2mvv5m1xn5/SpUsrJCTE+uw4LS1NiYmJCgkJybZuYdr7nNZ3dNANeI63xr1tR7vCDqziKNp7uEK1atUMzxlPnjypmjVr2rWtO67vc1rfle29y5O7fHx81LlzZ82YMUOSdOLECf3++++6+eabDes50rM3ISFBL7zwgiGxq0qVKurcubOaN2+uatWqKTw8XIGBgYabt2PHjql9+/bO+FpOlZqaquHDhxsSnsqVK6fOnTsrOjpaNWrUUPny5VWiRAkFBgYatq1fv767q+sU9k6daS9HEtsAAM7FvOlAdradFBy99ilMBwZXcfX0Ot7IdgpVR6YZt13fYrHkOn19ZGSkdu7cKenade+xY8fsnr7Vtk6OTPsKz8a9bTwcPXrU7m1t180ttlwh64jcktSxY0e7X6o6g+09c9aXWbAPce9ctj1K80ogor33HOLefm+99ZbhOW5AQICmT5+uqKgolx87K9p7zwoNDVVYWJg1UenMmTO6fPlytilNcuOqNssd7ai3nbNwHrPGfU5iYmL01FNPGTqe3X777Xr33Xfd3jHQkWshmI83xb29crqGyCm5y7aujrT3ly9fNsyAFB4enuMxYE7eGPfnzp3TunXrrGVfX1/961//cvlxs6K9hyvUqlXLkNx19OhRu5O77D0Xvam9d3lyl3QtM/R6cpd07WFv1uSu1NRU/fjjj9ZyfsME/ve//zWMGtKxY0eNGzcu3wfGly5dKkDtXW/ZsmU6cuSItdyyZUt99NFH+Y7Y4Mg0jIXd1nb9wgZlaGioobx9+3a7h9ADAHhWmTJlDOWLFy8Wuqep7e9KUlKSQ/u0HSWJm2V4mu154uiop0w57R2qV68uf39/a/Lenj177B4d4Pjx4zpz5oy1XKVKlVyvh+vWrWt9SSVdG/XY3gdD27dvN5Tr1Klj13bwvLp16xrKtn/LvOzYscNQdsYUyvZIS0vTkiVLDMvc3VPUtmeo7b0nzM0b4z4/jsQk7X3x5E1xP3nyZM2ZM8da9vX11QcffJCtE6870N57Xp06dbR582ZJUkZGhnbt2qWbbrrJrm1d1Wa5ox31pnMWzmfGuLd14sQJ9enTxzBKXHR0tKZMmZLnbDmuQnvt/bwh7u2VmZmZbVaK3GLStq6OtPc7d+5URkaGtUx77328Le6XLFli6Fx86623ur0DC+09XKFu3br66aefrOXt27frtttus2tb22vv3M5Fb2rv3ZKiX6tWLUPvreXLlxsSrWzngM1r1C5J+uWXX6z/X7p0aY0dO9auFyhZX56YSdbv4+Pjo/Hjx9s1FU9hvs8///zj0PqxsbGGcrly5Qp8bOnaMNlZMTQjAHgP25uCw4cPF3qftr8Ljv5OZU2Szml/gLtVqlTJUD548KBD2x86dMiZ1YGLBAQEGB7spKamGl4m5WXLli2G8i233JLrurYvTG23zU1KSop27dplLZcqVcrto2qg4Jo1a6agoCBreefOnXaP6mcbI7feeqtT65abX3/91dBbrU6dOmratKlbjn2d7TlYoUIFtx4fheONcZ8fR2KS9r548pa4/+KLLzR9+nRr2WKx6O2339Zdd93lsmPmhfbe89q0aWMo29tmxcXF6fjx49ZyZGSkKleu7JQ6uaMd9ZZzFq5hxrjP6uzZs3ryyScN0xA1atRIM2bM8Fjnetpr72f2uHfE/v37DQkwoaGhub5frlKlimrUqGEtHzt2TPHx8XYdh/be+3lb3NuOou7ujnYS7T1cw/b6/s8//7R726znrcViybVTkje1924bfzVrwlZycrKWL19uLWcdytvPzy/fYQKzJhq1aNHCcDOVl23bttlZW/fK+n1q165tdyNfmO+ze/duQxZhfmwb5LxGVrNHs2bNDGVHMiABAJ5l24bbe2OTF9vfFUd+F9LS0rR7925rOSwsLFtiDbxDUZris2bNmoYR5LZs2WL3NNKZmZkO3aTAs9q1a2cof//993ZtZ7teXtPHt23bVn5+/zfo8vLly+0a2nzlypVKTk62lm+77Ta3To+HwgkICDA8EEhOTtbKlSvz3S4lJcVwv+3v76/bb7/dJXW0ZYaHicuWLTOU7e1ZC3PwxrjPS0JCgjZs2GBYFh0dnev6tPfFkzfE/YIFCzRu3DjDstdee80j7fx1tPeeZ3sd/MMPP9i1ne16tvspDHe0o95wzsJ1zBj31yUmJqpPnz6GDpC1a9fWp59+atdgAq5w+PBh7du3z1r29fXVjTfe6JG6oODMHPeOyjqLlJT/9YNtnRcvXmzXccz43eEYb4r7AwcOGN6PlClTxu0dMGjv4SrNmzc3DOawadMmuxKvtm3bZhhEonHjxoqIiMh1fW9p792W3NWxY0fDDdH1h762c8DeeuutCg8Pz3NfWacItPeiNDMzM9v0EGZRkO8j2f9DkpPz589r06ZNdq/7+++/W8sVKlQodJaxbWak7QMZAIB5tWzZUj4+/3cJsXjxYkOPp4Jo3ry5oezI78Ivv/ximPLONvkM7pX1es/RuLB9eF7YuPIki8Wili1bWssnT57M9nI3N+vXr9fJkyddVTU4WYcOHQyxu2TJknxH2N2/f78hHsLDw/OczqhMmTKG4aYvXLigBQsW5HmMzMxMzZ4927DsgQceyHMbmE+nTp0M5dmzZ+ebKPrtt98afhfbtm2r0qVLu6R+WV24cEGrV6+2ln18fPLtuOVsO3bsyPaw/o477nBrHVB43hT3+ZkyZYohWaBSpUpq0KBBruvT3hdfZo77FStW6LXXXjPUZ/jw4Xrsscecfix70d6bQ/369VWvXj1r+fDhw4YZKnJy5coVzZs3z7DMmW2Wu9pRM5+zcC0zxv31YwwYMMDwYr1q1aqaNWuWx0a3z8zM1Lvvvms4N5o1a6YyZcp4pD4oOLPGvaOOHTumr776yrCsbdu2eW7TsWNHQ3nevHm6cuVKntv88ssviomJsZZvuOEGj09HCcd5U9xnHURHku677z63dvahvYcr+fj46P7777eW09PT9eWXX+a73axZswzl/M5Fb2nv3ZbcFRISYuiNvmnTJp04cSLbHLD5TckoyXDT8/fff9t1/MWLFxv+cc0k6/eJjY21a0StzZs3a/369YU6btah1PMyc+ZMw9/IGT9ETZs2NYzSsnz5ckbvAgAvERYWZuj5cfr0ac2cObNQ+4yMjDT8Lhw4cEArVqzId7uMjAxNmzbNsMzdL5JhlPW65vTp0wXetiDbm0337t0N5fHjx+c7XUdqaqrGjx/vymohHz179lT9+vWt/y1cuDDP9SMiIvTQQw9Zy0lJSXrjjTdyvaZPSUnRa6+9Zvi8X79++T50GTRokKH8wQcfGIZ5tzV79mzD9XWjRo3yHB0M5tShQwfDw8Rt27bl+QDj2LFj+uCDD6xli8WiwYMH53ucdu3aGeLe3o5AWS1dutRw39imTZs8e8TlZ/78+YaOUPk5dOiQBg0aZDi3mjVrlmfiJMzJjHH/999/a+3atfZ9gf9v9uzZ2V4e9e/fP9/taO+LJzPGvSRt3LhRw4cPV3p6unVZ37597Yple9HeezfbuBszZowuXLiQ6/rvv/++oU276667dMMNN+S6/sKFCw0x27Nnz3zr5I521F3nLMzJbHF/9epVPfvss4YRwCtUqKAvvviiUNfjWW3fvt2hEcYzMjI0fvx4rVmzxrB8wIABTqkP3M9scb948WKdPXvWztpL8fHxGjhwoC5dumRdVrlyZXXu3DnP7Zo0aaI777zTWj5+/LgmTpyY6/oXLlzQm2++aVhGe++9zBb3OUlPT882GIw9uRa5ob2Hq23atMkQ9/aMdGX77Hz27NnZZp3LauXKlfrpp5+s5fLly6tHjx55HsNb2nu3JXdJxsYkMzNTixcvNkzbEBoaatcfMOuN0+7du7V58+Y819+xY4fGjBnjeIXdJOv3OXfuXL5TucTGxuqFF16we2qf3GzevFmfffZZnuusX79ec+bMsZb9/PzyDX57Zb3RzsjI0KBBg7R//36H9hEbG1ugFw8AgMLp16+fYQq96dOnZ+s5nZecRrXp1auXofzGG2/o6NGjee7n/fffNww5XKlSJd1999121wPOFxkZaf3/48eP69ixY3ZvW6JECcOUmlu2bDG8SPI2t99+u2F0jv3792vYsGGGETyySk5O1tChQ3XgwAF3VdHrJCYm6tixYzn+l1VaWlqu69kzLYuj+vfvb+iNtnz5cg0fPjxbW3f06FE9/fTThpdHNWrU0COPPJLvMZo0aWLoQZSYmKhHH30029S4KSkp+uijjwxTJ1ksFo0YMaJITX1aXFgsFr3wwguGv90777yjGTNmZIvlP/74Q4899pguXrxoXfbAAw/k+SDRmWynZCzMw0RJ+vjjj9W+fXuNHTtWf/31l9LS0nJc78KFC5oxY4a6d++uU6dOWZcHBARo1KhRhaoDPMOMcX/q1Cn1799fXbp00RdffJFnksDevXs1ePBgvf3224blTZs21YMPPpjvsWjviyczxv3u3bv1zDPPGDontG/fXg8//HCu11n2XKfZor33bh06dDCMxH306FE9/vjj2Z7xXrx4UWPGjDEkQAUGBmro0KFOr5M72lEznrNwH7PF/csvv6xff/3VWi5RooTGjh0ri8XiUFuddWQ5W4cPH9ajjz6qxx57TPPnz891tOrMzEz98ccf6t27d7YRM+6++26mIvViZov7b7/9Vu3bt9crr7yiDRs25NqZ8vLly5o7d666du1qeN5msVg0atQou0Y3Gjp0aLakgrFjx2ZLTt+3b58ef/xxw7VPdHS026fHg/OYLe5zsn79ekMH6cjIyELNbEJ7D0l2XyucP38+x/Wc3Wk/IiJCTzzxhLV89epV9enTJ9vU6GlpaZo/f362c2/IkCEqUaJEvsfxhvbeL/9VnOfWW29V+fLlrX/Q2bNn6/z589bP77//frt+SO+55x7DNIHPPvus3n777Ww9aq4Pfzh58mQlJyerbNmyhuOZxT333GMYHnr06NFKT09Xly5d5Ovra11+9epVLV26VBMmTNDZs2cL9X1CQkKUmJiod999V/Hx8Xr22WcNo2Wkpqbqm2++0bvvvmvofd2nTx/VqFGjQMe01b59e/Xo0UNff/21pGujczz00EPq06ePHnnkEVWoUCHH7c6ePatffvlFP//8s3799Vc99dRTatWqlVPqBACwT5MmTdS/f399/PHHkq5dNA0fPlwbN25U//79VbVq1WzbJCQkaOXKlZo3b56aN2+e7QH8v/71Ly1YsMCatH369Gk9+uijev3113XXXXcZHpqePXtW7733XrYRdd544w35+bn18gY2brrpJut85JmZmXrmmWfUt29f1a9fX0FBQYZ1K1asmO3vFR0dbe1t9M8//+i5557TQw89pGrVqhmuE/38/FSxYkUXf5vC8fHx0dtvv62HHnrI+pJq5cqV6tSpkx599FFFR0erTJkySkhI0B9//KF58+ZZXxbfc889+vnnnz1ZfVOaPXu2pk6dmu968fHxufa2//LLL51+7VihQgVNnjxZTz/9tPVvvWzZMq1evVpRUVEKDw9XXFycduzYYUhYDA4O1kcffWT3UOljxozRwYMHrQ8kT548qccee0z16tVTZGSkkpOTtWvXrmz3CEOGDFGbNm2c9G3hbrfffrsGDx6sKVOmSLrWtk6cOFFffPGFGjdurJIlSyomJkYHDx40bHfDDTfojTfecEsdY2JiDEmLpUuXdkqydUJCgubMmaM5c+YoMDBQdevWVfny5RUcHKwrV67oxIkT2rdvX7ZEYF9fX40fP15NmzYtdB3gGWaN+71792rv3r165513FB4ertq1a6tMmTIKDAxUYmKi9u/fn+PUytWrV9fHH38sf39/u45De188mS3uV69ena1TwqpVq7Rq1SqH95VfZ07ae+9lsVg0efJkPfjgg9akuwMHDqhz585q1KiRqlWrpoSEBO3YscMwWookjR07VnXr1nVJvdzRjprtnIX7mC3ulyxZYihfuXJF/fr1c3g/gwcP1rPPPpvnOlu2bNGWLVv02muvqVKlSoqMjFTp0qXl7++vhIQE7dmzR+fOncu2XVRUlN59912H6wTzMFvcS9cStxYsWKAFCxbI399ftWvXVkREhEqXLq2rV68qPj5eu3fvNrzjvO7FF1+0+yV8gwYN9Oabb+rll1+2LpszZ44WLVqkpk2bqkyZMvrnn38MHZCla52QJ02aVKjvCM8yY9zbcnZHu+to74s3e0cDnzBhgiZMmJBtecuWLQ2DBznDkCFDtGvXLm3YsEHStQ4cgwYNUo0aNVSvXj2lpaVpz549io+PN2zXvXv3bDOs5MYb2nu3vv309fXVAw88oM8//1ySst082dvgPPjgg5o9e7aOHDki6doDgGeeeUZVqlRRw4YNFRgYqNOnT2vHjh26fPmypGu9Ff7zn/9oyJAhzvtCTtK2bVvddNNN+uOPPyRdu/h+5ZVX9MEHH6hx48YqVaqU9cfhekbk9ZeFAwcOLNAxe/ToobVr1+rgwYOaPXu2/ve//6l58+YqX768Lly4oB07dmQbWrJZs2ZOH07u1Vdf1alTp6zDNV65ckXTp0/X9OnTVatWLdWoUUPBwcFKTU3VhQsXFBMTY+idBwDwnOeee05///23NQElMzNT8+fP1/z581W7dm1Vr15dwcHBSkpKUmxsrI4cOWKdOiNrj5frfHx8NH78eD366KOKi4uTdG2UhMGDBysiIkINGzZUUFCQNUHCtkd337591bZtWxd/a+Tnvvvu08SJE603d/v379eIESNyXHfVqlXZEgEfe+wxLVmyxDpC6cqVK7P1wJCkKlWqaPXq1U6uvfM1atRI48eP14svvmh9GXX8+PE8b3L79OmjunXrGpK7GIHD/G6++WZNnDhRo0aNsvbKT0lJyXWU4YiICE2cOFG1a9e2+xilSpXSzJkzNXToUG3bts26/MCBAzmO+Obj46P+/fsX+J4B5jFo0CBdvXpVM2fOtP6Wnjt3ztBDP6vmzZtr8uTJ2ZJqXcX2YeJ9992nwMBApx4jJSVFu3btyne9SpUq6b333lN0dLRTjw/3M3vcnzlzJtdezFl16NBBY8eONYzwmB/a++LL7HHvDrT33iciIkKfffaZ9RmBdO35wK5du3L8WwYGBurll1/Wv/71L5fVyV3tKOds8WXGuHe3uLg46/O7vDzyyCMaOXKk0+8P4H5mjvurV69q37592rdvX57rhYaG6q233nJ4dJWuXbvq0qVLGj9+vHWUsKSkJGuSga3atWvrww8/VPny5R06DszHzHF/8eJFw3NzHx+ffKcaLQjae5iBn5+fJk+erBdeeEFr1661Lo+NjVVsbGyO23Tv3l2jR4926Dhmb+/dOi2jJHXr1i3H5bVr17a7l1VAQICmT5+e7R/p+PHjWrFihZYsWaJNmzZZE7uCgoI0ZcoUNW7cuHCVd6FJkyapVq1ahmWnT5/WmjVrtGTJEv3222/WxC5/f3+99dZbdk1hmZvAwEDNmDHDOgpXamqqNm3apCVLlmjdunU5JnZ98sknTm+Qr/8tBwwYkG3kjpiYGK1Zs0Y//PCDfv75Z/3+++85JnaFhIQ4tU4AAPv4+vpq0qRJevrpp+XjY7ykOHz4sLUNX7NmjWJiYqwPOvNSuXJl/e9//zNMWSxdG4VnzZo1Wrp0abapOnx9fTV8+PBcE4jgXsHBwZo0aZJCQ0MLtH3z5s310ksvGUYv9XadOnXS9OnTFRERked6fn5+evnll/XSSy/pypUrhs94+O8d7rnnHi1evFidOnXKdajn0qVL6+GHH9bixYsL9DKyUqVKmjt3rp5//nlVqVIlx3UsFotat26tL7/80i1DwMP1LBaLhg0bptmzZ6tVq1a5JnxWqVJFzz//vObOnZtvm+MsGRkZ+v777w3LunTpUuj9Dhw4UO3bt1fZsmXzXddisahBgwb6z3/+o2XLlvGiv4gwU9zXrl1b/fv3V1RUlF2jLZYsWVIdOnTQV199pSlTpjiU2HUd7X3xZKa4dxfa+6KhXr16WrRokfr27aty5crluI6/v7/atWunb775Ro8++qjL6+SOdrQ4nrP4P2aMe1dp0aKFnnjiCd1www12jZgfHBysrl27avHixfrPf/7Di/4ixCxx36tXL3Xs2NHuNrVmzZoaMWKEVqxYUeBpsx5//HF98803uvPOO3M9D8LDw9WvXz8tWLBAderUKdBxYD5miXtby5YtM0wH3bp160LPdEF7DzMLCQnRjBkzNGbMmDzb2KZNm2rq1KkaO3as3SOoZ2Xm9t6SeX1YBDfq1q1btuHKnn/+eYeHij19+rQmTJigH3/8MdvoHdK1l2AdOnTQ4MGDVa1aNR07dswwjFxew8xu2rRJvXr1spbfeeedXBPTcvPyyy9r0aJF1nJ+Q5AnJSVp0qRJ+uabb7K90JOu/TDccccdGjx4sBo0aCBJql+/vvXzrl27aty4cTnuO7fvfvHiRU2ZMkXfffddtoQuSSpXrpyeeOIJ9enTx65GfMqUKYapcnIakSM3R48e1SeffKKff/5ZCQkJua5nsVhUr1493XbbbercuXO2BAAAgPsdOnRI06ZN09q1a7NN3ZFVZGSkOnbsqB49euQ6/a50bZrHr7/+Wl988YX++eefHNcJCAjQ7bffrqFDh9o9xHG7du2s0945MjRsz549raPvODJilCPXAvb+pttyx3cqSN3Onj2rRYsWaePGjTp06JAuXLigK1euKOulZ17XCYcOHdLChQv1119/KTY2VklJSdaeErnVeeHChRo5cqS1nNf0e46sa8vRa7zrkpKStHjxYq1YsUIxMTE6d+6cQkNDVbFiRd1+++3q1q2b9d9jxowZmjhxonXb5cuXO21qbLhHUlKS/vzzT8XFxenChQsKCwtT5cqVFR0d7bQHHZmZmdq+fbtiY2N16tQplShRQhEREWrSpIkqVarklGPAnE6cOKFdu3YpPj5eV65cUYUKFVSjRg1FRUUVyZH+4uLi9PfffysuLk4JCQlKSUlRYGCgQkJCFBERoaioqAIlz8C7mCXur169qsOHD+vYsWOKj4/XpUuXlJaWpuDgYIWEhKhWrVpq2LChU6cKp70vvswS9+5Ce180pKWl6a+//tKxY8d05swZlSpVShUrVlTz5s0VFhbmkTq5qx0tbucs/o8Z495VUlJSdPDgQR0/flynT59WcnKy0tPTVbp0aZUpU0Z169ZVvXr1snUERdFjlrg/e/asDh06pLi4OJ0/f15XrlyRn5+fypQpo/DwcDVp0sTpI6qcPXtW27Zt08mTJ3Xp0iWFh4eratWquvHGG516HwDzMUvcuwPtPcxu7969Onz4sOLj4+Xr66uIiAg1aNBAkZGRTjuG2dp7jyR3Odv58+e1ZcsWHT9+XCkpKSpXrpwiIiIUHR2tkiVLerp6Drv+Iig2NlbJyckqW7asIiIi1Lx5c5c9wEhNTdUff/yhEydOWF801qhRQzfddJPbR87IzMzUvn37dOjQIZ0/f15JSUkqWbKkQkJCVKNGDdWpU6fAo4EAAFwrNTVVW7du1fHjx3Xu3Dnri66qVauqQYMGBeo5EhMToz179ujMmTNKSUlRaGioKlWqpOjoaEYzQpH10ksvGaY5+/PPPxUcHOy5CgEAAAAAAAAAAMAjikRyFwAAAFCU3H333dZR66pXr64VK1Z4uEYAAAAAAAAAAADwBMbJAwAAAExk48aNhulImzZt6sHaAAAAAAAAAAAAwJNI7gIAAABMIiUlRW+99ZZhWadOnTxUGwAAAAAAAAAAAHgayV0AAACAC40fP17Lli1TRkZGnuudPXtWTz/9tA4ePGhdVq1aNd1+++2uriIAAAAAAAAAAABMys/TFQAAAACKspiYGH3++eeKiIjQvffeq+bNm6t69eoKDg7WpUuXdOzYMW3YsEGLFy9WcnKydTuLxaK33npLvr6+Hqw9AAAAAAAAAAAAPMmSmZmZ6elKAAAAAEVV//79tXbtWoe28fHx0ahRo/T444+7plIAAAAAAAAAAADwCozcBQAAALhQaGioQ+tHRkZq5MiRatu2rWsqBAAAAAAAAAAAAK/ByF0AAACAi+3bt0+//fabtm3bptjYWMXHx1unYAwNDVW5cuXUvHlz3XrrrbrzzjuZihEAAAAAAAAAAACSSO4CAAAAAAAAAAAAAAAAAFPy8XQFAAAAAAAAAAAAAAAAAADZkdwFAAAAAAAAAAAAAAAAACZEchcAAAAAAAAAAAAAAAAAmBDJXQAAAAAAAAAAAAAAAABgQiR3AQAAAAAAAAAAAAAAAIAJkdwFAAAAAAAAAAAAAAAAACZEchcAAAAAAAAAAAAAAAAAmBDJXQAAAAAAAAAAAAAAAABgQiR3AQAAAAAAAAAAAAAAAIAJkdwFAAAAAAAAAAAAAAAAACZEchcAAAAAAAAAAAAAAAAAmBDJXQAAAAAAAAAAAAAAAABgQiR3AQAAAAAAAAAAAAAAAIAJkdwFAAAAAAAAAAAAAAAAACZEchcAAAAAAAAAAAAAAAAAmBDJXQAAAAAAAAAAAAAAAABgQiR3AQAAAAAAAAAAAAAAAIAJkdwFAAAAAAAAAAAAAAAAACb0/wC+OfUqdUNkFwAAAABJRU5ErkJggg==",
      "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"
   ]
  },
  {
   "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: Mon Dec 15 2025\n",
      "\n",
      "Python implementation: CPython\n",
      "Python version       : 3.13.5\n",
      "IPython version      : 9.3.0\n",
      "\n",
      "pytensor: 2.35.1\n",
      "aeppl   : not installed\n",
      "xarray  : 2025.6.1\n",
      "\n",
      "arviz     : 0.23.0.dev0\n",
      "pymc      : 5.26.1\n",
      "numpy     : 2.2.6\n",
      "matplotlib: 3.10.3\n",
      "pandas    : 2.3.0\n",
      "\n",
      "Watermark: 2.5.0\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": {
  "kernelspec": {
   "display_name": "pymc",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  }
 },
 "nbformat": 4,
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