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Tutorial: Hierarchical modeling with repeated binary trial data

In this tutorial we will demonstrate the application of hierarchical models with data from the 1970 season of Major League Baseball (MLB) found in the paper by Efron and Morris 1975.

Learning Outcomes​

On completion of this tutorial, you should be able:

• to prepare data for running a hierarchical model with Bean Machine;
• to execute a hierarchical model with Bean Machine;
• to explain what different pooling techniques tell us about the data; and
• to run diagnostics to understand what Bean Machine is doing.

Problem​

Data are from Efron and Morris 1975, which are taken from the 1970 Major League Baseball season for both the American and National leagues, Wikipedia (Major League Baseball). These data are an example of repeated binary trials in that a player has multiple opportunities (trials) for being at-bat and they can either land a hit or miss the ball (binary outcome). Many useful scenarios can be modeled as having repeated binary trials. For example; browser click data, watching a video to completion, voting for or against propositions in a session of congress/parliament, and even rat tumor development, see Tarone.

Our task is to use the given baseball data and create a hierarchical model that estimates the chances each player has to land a hit when they are at-bat.

Prerequisites​

We wil be using the following packages within this tutorial.

• arviz and bokeh for interactive visualizations; and
• pandas for data manipulation.
# Install Bean Machine in Colab if using Colab.import sysif "google.colab" in sys.modules and "beanmachine" not in sys.modules:    !pip install beanmachine
import osfrom io import StringIOimport arviz as azimport beanmachine.ppl as bmimport pandas as pdimport torchimport torch.distributions as distfrom beanmachine.ppl.model import RVIdentifierfrom beanmachine.tutorials.utils import baseballfrom bokeh.io import output_notebookfrom bokeh.plotting import gridplot, showfrom IPython.display import SVG, Markdown

The next cell includes convenient configuration settings to improve the notebook presentation as well as setting a manual seed for torch for reproducibilty.

# Plotting settingsaz.rcParams["plot.backend"] = "bokeh"az.rcParams["plot.bokeh.figure.dpi"] = 60az.rcParams["stats.hdi_prob"] = 0.89# Manual seed for torchtorch.manual_seed(1199)# Other settings for the notebook.smoke_test = "SANDCASTLE_NEXUS" in os.environ or "CI" in os.environ

Data​

We explore the data below to help inform our decisions about priors and sampling distributions for our models. The insights will be used to create logical procedures for processing information, see McElreath for further discussion about creating data stories, and their importance for model building.

The raw data contain the following columns.

Column nameDescription
FirstNamePlayer's first name
LastNamePlayer's last name
At-Bats45 for each player
HitsHits in first 45 at-bats
BattingAverageBatting average after first 45 at-bats
RemainingAt-BatsBatting average for the remainder of the season
SeasonAt-BatsNumber of at-bats for entire season
Season HitsNumber of hits for entire season
SeasonAverageBatting average for entrie season

The selected players include Roberto Clemente explicitly because he was presumed to be an outlier. See Efron and Morris, 1977 for further discussion.

We now read the data in as a pandas dataframe. The data are replicated below using a StringIO object rather than reading from a CSV file.

# Data are from Efron and Morris (1975).data_string = """FirstName,LastName,At-Bats,Hits,BattingAverage,RemainingAt-Bats,RemainingAverage,SeasonAt-Bats,Season Hits,SeasonAverageRoberto,Clemente,45,18,0.4,367,0.346,412,145,0.352Frank,Robinson,45,17,0.378,426,0.2981,471,144,0.306Frank,Howard,45,16,0.356,521,0.2764,566,160,0.283Jay,Johnstone,45,15,0.333,275,0.2218,320,76,0.238Ken,Berry,45,14,0.311,418,0.2727,463,128,0.276Jim,Spencer,45,14,0.311,466,0.2704,511,140,0.274Don,Kessinger,45,13,0.289,586,0.2645,631,168,0.266Luis,Alvarado,45,12,0.267,138,0.2101,183,41,0.224Ron,Santo,45,11,0.244,510,0.2686,555,148,0.267Ron,Swaboda,45,11,0.244,200,0.23,245,57,0.233Rico,Petrocelli,45,10,0.222,538,0.2639,583,152,0.261Ellie,Rodriguez,45,10,0.222,186,0.2258,231,52,0.225George,Scott,45,10,0.222,435,0.3034,480,142,0.296Del,Unser,45,10,0.222,277,0.2635,322,83,0.258Billy,Williams,45,10,0.222,591,0.3299,636,205,0.251Bert,Campaneris,45,9,0.2,558,0.2849,603,168,0.279Thurman,Munson,45,8,0.178,408,0.3162,453,137,0.302Max,Alvis,45,7,0.156,70,0.2,115,21,0.183"""with StringIO(data_string) as f:    df = pd.read_csv(f)

The next cell contains standard pandas transformations to make the data clearer for later analysis. We also rename a few columns for readability.

# Rename columns in order to make the data clearer.renames = {    "Hits": "Current hits",    "At-Bats": "Current at-bats",    "Season Hits": "Season hits",    "SeasonAt-Bats": "Season at-bats",}df = df.rename(columns=renames)# Concatenate the first and last names together.df["Name"] = df["FirstName"].str.cat(df["LastName"], sep=" ")# Keep only those columns we will use in the analysis.df = df[    ["Name", "Current hits", "Current at-bats", "Season hits", "Season at-bats"]].copy()# Show the resultant dataframe.Markdown(df.set_index("Name").to_markdown())
NameCurrent hitsCurrent at-batsSeason hitsSeason at-bats
Roberto Clemente1845145412
Frank Robinson1745144471
Frank Howard1645160566
Jay Johnstone154576320
Ken Berry1445128463
Jim Spencer1445140511
Don Kessinger1345168631
Luis Alvarado124541183
Ron Santo1145148555
Ron Swaboda114557245
Rico Petrocelli1045152583
Ellie Rodriguez104552231
George Scott1045142480
Del Unser104583322
Billy Williams1045205636
Bert Campaneris945168603
Thurman Munson845137453
Max Alvis74521115

The above dataframe shows us that each player had a different number of opportunities to be at-bat over the course of the season; the Season at-bats column. We also see a defined slice in time where all the players have had the same number of opportunities to be at-bat, the Current at-bats column, which is all 45. The number of successful hits a player has made within the first 45 trials is given in the Current hits column, as well as the total number of hits for the season in Season hits.

Below we see an ordered scatter plot of the Current hits column.

# Required for visualizing in Colab.output_notebook(hide_banner=True)current_hits = baseball.plot_current_hits(df)show(current_hits)
loading...

The data are telling us that each player is unique (there are not a lot with the same number of hits), but also that they are similar. The players are similar because they are all highly trained in their sport, and have a much better chance of hitting a ball when at-bat then an average person not in the MLB. The data also tell us that we have captured a slice in time where each player has been given the same number of opportunities to be at-bat, and all of them had different amounts of success at landing a hit. We can estimate the chance a player has at hitting a ball when at-bat.

Models​

We will create three separate models using the above data as we explore how to create a hierarchical model using Bean Machine. The models we will be creating are:

Each model will estimate a baseball player's chance of landing a hit when they are at-bat. We can assume that every time a player is at-bat is a new independent Bernoulli trial (see Wikipedia (Bernoulli trial)) so we can model the chance a player has when at-bat as a Binomial distribution, see Wikipedia (Binomial).

$p(y_n\mid\phi)=\text{Binomial}(y_n\mid K_n,\phi)$

where

• $\phi$ is our prior;
• $y_n$ are the number of times a player makes a hit (number of successes);
• $K_n$ is the number of times that player has been at-bat (number of trials).

NOTE We can implement this model in Bean Machine by defining random variable objects with the @bm.random_variable and @bm.functional decorators. These functions behave differently than ordinary Python functions.

Semantics for @bm.random_variable functions:
• They must return PyTorch Distribution objects.
• Though they return distributions, callees actually receive samples from the distribution. The machinery for obtaining samples from distributions is handled internally by Bean Machine.
• Inference runs the model through many iterations. During a particular inference iteration, a distinct random variable will correspond to exactly one sampled value: calls to the same random variable function with the same arguments will receive the same sampled value within one inference iteration. This makes it easy for multiple components of your model to refer to the same logical random variable.
• Consequently, to define distinct random variables that correspond to different sampled values during a particular inference iteration, an effective practice is to add a dummy "indexing" parameter to the function. Distinct random variables can be referred to with different values for this index.
• Please see the documentation for more information about this decorator.

Complete-pooling model​

The image above is a graphical representation of what complete pooling does: it assumes that everyone's chance of landing a hit is sampled from the same population distribution $\phi$. This model assumes that all the player's chances of hitting a ball are the same, which is an extreme statement compared to our data story that said all players were similar. This is something we will fix in later model.

If we assume no prior knowledge about the players then our prior ($\phi$) is a uniform distribution from $[0, 1]$. Bean Machine works well with Uniform priors, but we will choose to use a $\text{Beta(1, 1)}$ distribution as our prior because it is a conjugate prior to a Binomial distribution. Our complete-pooling model can be written as

\begin{aligned} \phi & \sim\text{Beta}(1,1)\\ y & \sim\text{Binomial}(K,\phi). \end{aligned}

Beta priors vs Uniform priors​

Below we can see why swapping a Uniform distribution for a Beta(1, 1) is appropriate as they are the same.

# Required for visualizing in Colab.output_notebook(hide_banner=True)complete_pooling_priors = baseball.plot_complete_pooling_priors()show(complete_pooling_priors)
loading...

We can create our complete-pooling model in Bean Machine using the @bm.random_variable decorator.

N = df.shape[0]current_at_bats = torch.tensor(df["Current at-bats"].astype(float).values)current_hits = torch.tensor(df["Current hits"].astype(float).values)@bm.random_variabledef complete_pooling_phi() -> RVIdentifier:    return dist.Beta(1, 1)@bm.random_variabledef complete_pooling_y() -> RVIdentifier:    return dist.Binomial(current_at_bats, complete_pooling_phi().expand((N,)))

In the code cell above we have defined our population prior and chance of landing a hit when at-bat in the top-level namespace of our notebook. Bean Machine can take advantage of creating models as classes as well, and we illustrate how to do that below for our complete-pooling model. We will not use the class method for the rest of the tutorial, however, it does show you how to accomplish creating the model as a class object in Python.

We make use of the expand method on pytorch distribution objects to perform batch sampling. This has the advantage of being more computationally-efficient than explicit iteration. Bean Machine is also able to take advantage of the implicit broadcasting of pytorch tensors and pytorch samplers like dist.Binomial allowing us to sample or observe all $y$ values at once.

class CompletePoolingModel:    @bm.random_variable    def complete_pooling_phi(self) -> RVIdentifier:        return dist.Beta(1, 1)    @bm.random_variable    def complete_pooling_y(self, i: int, k: int) -> RVIdentifier:        return dist.Binomial(k, self.complete_pooling_phi())complete_pooling_model = CompletePoolingModel()

Complete-pooling inference​

Inference is the process of combining model and data to obtain insights, in the form of probability distributions over values of interest. Bean Machine offers a powerful and general inference framework to enable fitting arbitrary models to data.

Our inference algorithms expect a dictionary where the keys are @bm.random_variable invocations, and tensor data as our observational values. You can see this in the example below.

complete_pooling_observations = {complete_pooling_y(): current_hits}complete_pooling_observations
Out:
{RVIdentifier(wrapper=<function complete_pooling_y at 0x7f24101392d0>, arguments=()): tensor([18., 17., 16., 15., 14., 14., 13., 12., 11., 11., 10., 10., 10., 10.,         10.,  9.,  8.,  7.], dtype=torch.float64)}

We are ready to run inference on our model and observations. Bean Machine supports a rich library of inference algorithms that share a common infer method with the following arguments:

NameUsage
queriesA list of @bm.random_variable targets to fit posterior distributions for.
observationsThe Dict of observations we built up, above.
num_samplesNumber of samples to build up distributions for the values listed in queries.
num_chainsNumber of separate inference runs to use. Multiple chains can verify inference ran correctly.

For this particular problem, we will use the GlobalNoUTurnSampler inference method. Bean Machine's GlobalNoUTurnSampler is the NUTS sampler you have probably used before with stan or pymc3. We have chosen to use the NUTS sampler here because it can be easily compared to other probabilistic tools.

num_samples = 2 if smoke_test else 5000num_adaptive_samples = 0 if smoke_test else 2500num_chains = 4complete_pooling_queries = [complete_pooling_phi()]complete_pooling_samples = bm.GlobalNoUTurnSampler().infer(    queries=complete_pooling_queries,    observations=complete_pooling_observations,    num_samples=num_samples,    num_chains=num_chains,    num_adaptive_samples=num_adaptive_samples,)
Out:
Samples collected:   0%|          | 0/7500 [00:00<?, ?it/s]Samples collected:   0%|          | 0/7500 [00:00<?, ?it/s]Samples collected:   0%|          | 0/7500 [00:00<?, ?it/s]Samples collected:   0%|          | 0/7500 [00:00<?, ?it/s]

Complete-pooling analysis​

The complete_pooling_samples object contains our inference results. We have only one parameter in this model, $\phi$, which was supplied as a single query parameter when we ran inference. After inference has been run, we need to ensure our model has fit the data appropriately. We begin this analysis by printing out summary statistics with two important statistics $\hat{R}$ (r_hat) and effective sample size (ess) values, see Vehtari et al.

complete_pooling_summary_df = az.summary(    complete_pooling_samples.to_xarray(),    round_to=4,)Markdown(complete_pooling_summary_df.to_markdown())
meansdhdi_5.5%hdi_94.5%mcse_meanmcse_sdess_bulkess_tailr_hat
complete_pooling_phi()0.26640.01540.24120.29030.00020.00016700.835567.351.0009

Measuring variance between- and within-chains with $\hat{R}$ (r_hat)​

$\hat{R}$ is a diagnostic tool that measures the between- and within-chain variances. It is a test that indicates a lack of convergence by comparing the variance between multiple chains to the variance within each chain. If the parameters are successfully exploring the full space for each chain, then $\hat{R}\approx 1$, since the between-chain and within-chain variance should be equal. $\hat{R}$ is calculated as

$\hat{R}=\frac{\hat{V}}{W}$

where $W$ is the within-chain variance and $\hat{V}$ is the posterior variance estimate for the pooled rank-traces. The take-away here is that $\hat{R}$ converges towards 1 when each of the Markov chains approaches perfect adaptation to the true posterior distribution. We do not recommend using inference results if $\hat{R}>1.01$. More information about $\hat{R}$ can be found in the Vehtari et al paper.

Effective sample size (ess)​

MCMC samplers do not draw independent samples from the target distribution, which means that our samples are correlated. In an ideal situation all samples would be independent, but we do not have that luxury. We can, however, measure the number of effectively independent samples we draw, which is called the effective sample size. You can read more about how this value is calculated in the Vehtari et al paper, briefly it is a measure that combines information from the $\hat{R}$ value with the autocorrelation estimates within the chains. There are many ways to estimate effective samples sizes, however, we will be using the method defined in the Vehtari et al paper.

The rule of thumb for ess_bulk is for this value to be greater than 100 per chain on average. Since we ran four chains, we need ess_bulk to be greater than 400 for each parameter. The ess_tail is an estimate for effectively independent samples considering the more extreme values of the posterior. This is not the number of samples that landed in the tails of the posterior. It is a measure of the number of effectively independent samples if we sampled the tails of the posterior. The rule of thumb for this value is also to be greater than 100 per chain on average.

We can use arviz to plot the evolution of the effective sample size for each of our parameters. The below plots show over each successive draw from our model, the estimated effective sample size for our parameters. The red horizontal dashed line shows the rule-of-thumb value needed for both the bulk and tail ess estimates. When the model is converging properly, both the bulk and tail lines should be roughly linear.

# Required for visualizing in Colab.output_notebook(hide_banner=True)data = {"φ": complete_pooling_samples.get(complete_pooling_phi()).numpy()}complete_pooling_ess_plot = az.plot_ess(data, kind="evolution", show=False)[0][0]complete_pooling_ess_plot.plot_width = 600complete_pooling_ess_plot.plot_height = 400complete_pooling_ess_plot.y_range.start = 0complete_pooling_ess_plot.outline_line_color = "black"complete_pooling_ess_plot.grid.grid_line_alpha = 0.2complete_pooling_ess_plot.grid.grid_line_color = "grey"complete_pooling_ess_plot.grid.grid_line_width = 0.2show(complete_pooling_ess_plot)
loading...

When the model is converging properly, both the bulk and tail lines from the ess evolution plot should be roughly linear. We can see that both the ess_bulk and ess_tail are above the rule-of-thumb value of 400 (the red dashed line), which indicates that the model is fitting the data.

Below we have two more diagnostic plots for individual random variables: Rank plots and autocorrelation plots.

• Rank plots are a histogram of the samples over time. All samples across all chains are ranked and then we plot the average rank for each chain on regular intervals. If the chains are mixing well this histogram should look roughly uniform. If it looks highly irregular that suggests chains might be getting stuck and not adequately exploring the sample space.
• Autocorrelation plots measure how predictive the last several samples are of the current sample. Autocorrelation may vary between -1.0 (deterministically anticorrelated) and 1.0 (deterministically correlated). We compute autocorrelation approximately, so it may sometimes exceed these bounds. In an ideal world, the current sample is chosen independently of the previous samples: an autocorrelation of zero. This is not possible in practice, due to stochastic noise and the mechanics of how inference works.
# Required for visualizing in Colab.output_notebook(hide_banner=True)complete_pooling_diagnostics_plot = gridplot(    [baseball.plot_complete_pooling_diagnostics(complete_pooling_samples)])show(complete_pooling_diagnostics_plot)
loading...

The first figure is the distribution for each chain we ran inference for. We can see the distributions look well behaved (i.e. there are not a lot of oscillations). Next to the distribution plot is the rank plot we discussed above. We can see from the histograms for each chain that these plots all look fairly uniform. The black dashed line is used as a guide to ensure the bars are uniform. Finally next to the rank plot are the autocorrelation plots for each chain for this query. We can see some correlations, but overall the plots show the chains have mixed.

Below we plot our final diagnostic, which is the posterior for our parameter $\phi$. Here we see an interval of 89% that defines the highest density interval (HDI), which tells us where the greatest amount of density for the posterior lies. Why an 89% HDI instead of 95%? To prevent you from thinking about a 95% confidence interval. See McElreath for further discussion on this subject.

# Required for visualizing in Colab.output_notebook(hide_banner=True)complete_pooling_phi_posterior = az.plot_posterior(    {"φ": complete_pooling_samples.get(complete_pooling_phi()).numpy()},    show=False,)[0][0]complete_pooling_phi_posterior.plot_width = 400complete_pooling_phi_posterior.plot_height = 400complete_pooling_phi_posterior.y_range.start = 0complete_pooling_phi_posterior.outline_line_color = "black"complete_pooling_phi_posterior.grid.grid_line_alpha = 0.2complete_pooling_phi_posterior.grid.grid_line_color = "grey"complete_pooling_phi_posterior.grid.grid_line_width = 0.2show(complete_pooling_phi_posterior)
loading...

What this is telling us from our model and data is that every player will have the same chance of hitting a ball when at-bat. This finding is not correct and is evident when we plot all the players in the same plot showing their chances of success when at-bat.

# Required for visualizing in Colab.output_notebook(hide_banner=True)complete_pooling_model_plot = baseball.plot_complete_pooling_model(    df,    complete_pooling_samples,    complete_pooling_phi(),)show(complete_pooling_model_plot)
loading...

The above plot shows our predictions for each player's chance of hitting a ball when at-bat, with error bars being the 89% HDI and the markers being the mean of the posterior. The orange horizontal line shows the population mean from the data (i.e. $Current\ hits / Current\ at\ bats$), and the orange box shows the standard deviation for the actual data.

We know every player is unique, but we have not captured that information as the plot shows that every player is the same. The model also tells us that poor hitting players are just as likely to land a hit when at-bat than very good hitting players. Recall that at the beginning of this section we stated that this type of model will overestimate player's chances that hit poorly while underestimating player's chances that hit well. We will revisit this in the next section, but take note that this is the case for this model.

In hindsight our complete pooling model may seem trivial in that we successfully obtained the mean and its standard deviation through a probabilistic programming manner. Why would we do this when we can calculate the mean and the standard deviation from the given data and get pretty close to the values we would calculate using a PPL technique? Well, we can do better, but before we do better we are going to make another model that is equally as bad as our complete pooling one. The reason why we do this will be come evident when we get to our third and final model the partial-pooling model.

No-pooling model​

No-pooling is the polar opposite to complete-pooling, where instead of treating each player identically we will treat each player as having their own separate chance of landing a hit when at-bat. Essentially we will be creating a model for each player (every player has their own distribution to sample from, $\theta$) and each model will have no influence on any of the other models. We will see later on that choosing a no-pooling model overestimates top batter's chances while underestimating poor batter's chances. Note that this is the opposite to what the complete-pooling model did and to our data story that says players are similar not exactly unique.

We will use Bean Machine to create this type of model as follows.

\begin{aligned} \theta&\sim\text{Beta}(1,1)\\ y&\sim\text{Binomial}(K,\theta) \end{aligned}
@bm.random_variabledef no_pooling_theta() -> RVIdentifier:    return dist.Beta(1, 1).expand((N,))@bm.random_variabledef no_pooling_y() -> RVIdentifier:    return dist.Binomial(current_at_bats, no_pooling_theta())

Coding the no-pooling and complete-pooling models are quite similar. Note that the difference between the two models is that we use expand on the prior and not in the likelihood.

No-pooling inference​

Just like before we will use the GlobalNoUTurnSampler inference method. We construct the observations dictionary exactly to how we constructed it in the complete-pooling model.

no_pooling_observations = {no_pooling_y(): current_hits}

The queries we are interested in from this model are all the $\theta$'s as they are the distributions for each player's individual chances of landing a hit.

no_pooling_queries = [no_pooling_theta()]no_pooling_samples = bm.GlobalNoUTurnSampler().infer(    queries=no_pooling_queries,    observations=no_pooling_observations,    num_samples=num_samples,    num_chains=num_chains,    num_adaptive_samples=num_adaptive_samples,)
Out:
Samples collected:   0%|          | 0/7500 [00:00<?, ?it/s]Samples collected:   0%|          | 0/7500 [00:00<?, ?it/s]Samples collected:   0%|          | 0/7500 [00:00<?, ?it/s]Samples collected:   0%|          | 0/7500 [00:00<?, ?it/s]

No-pooling analysis​

Again we will start our analysis of our no pooling model with summary statistics and trace plots.

no_pooling_summary_df = az.summary(no_pooling_samples.to_xarray(), round_to=4)Markdown(no_pooling_summary_df.to_markdown())
meansdhdi_5.5%hdi_94.5%mcse_meanmcse_sdess_bulkess_tailr_hat
no_pooling_theta()[0]0.4050.07360.30290.53710.00160.00112288.86842.9081.0061
no_pooling_theta()[1]0.38240.07110.26830.49220.00170.00131810.191004.451.0019
no_pooling_theta()[2]0.36360.06850.2560.4710.00110.00083631.72309.971.0016
no_pooling_theta()[3]0.34010.06820.2340.45050.00140.0012453.191067.641.0002
no_pooling_theta()[4]0.32130.06630.20980.41750.00130.00092762.092601.811.0011
no_pooling_theta()[5]0.32130.06560.21210.42180.00110.00083426.871760.761.002
no_pooling_theta()[6]0.29980.06590.19740.40280.00130.00092672.632501.461.0013
no_pooling_theta()[7]0.27580.06350.18150.38110.00120.00083019.292692.561.0014
no_pooling_theta()[8]0.2550.06210.15180.34810.00120.00082893.213360.921.0015
no_pooling_theta()[9]0.25450.0640.15510.35820.00120.00082871.611404.81.0118
no_pooling_theta()[10]0.2350.06270.14380.33880.00120.00092936.81843.951.0049
no_pooling_theta()[11]0.23260.05950.13630.32270.00120.0012915.721542.881.0013
no_pooling_theta()[12]0.23390.06040.14130.32890.00110.00082763.863586.331.0013
no_pooling_theta()[13]0.23330.06030.13840.32380.00130.00092207.721269.421.002
no_pooling_theta()[14]0.23460.0650.13310.33890.00240.0019798.532698.8031.0118
no_pooling_theta()[15]0.21170.06110.11070.29810.00160.00111355.85712.3441.0129
no_pooling_theta()[16]0.19160.05670.09720.2730.0010.00073040.461727.261.008
no_pooling_theta()[17]0.16960.05460.08320.25290.0010.00083504.731593.751.0084

All the $\hat{R}$ values look close to 1, and our ess for both the bulk and tail regions are large. Below we plot all the diagnostics we did for the complete-pooling model above. Since we created a model for each player, we have a large number of diagnostic plots.

# Required for visualizing in Colab.output_notebook(hide_banner=True)no_pooling_diagnostic_plots = baseball.plot_no_pooling_diagnostics(    no_pooling_samples,    no_pooling_theta(),    df,)no_pooling_diag_plots = gridplot([*no_pooling_diagnostic_plots])show(no_pooling_diag_plots)
loading...

All of our visual diagnostics above look good. Posteriors are smooth, ess plots look linear, density and rank plots for each chain look uniform and autocorrelation plots are decent. Our model successfully created parameters for each player (all the $\theta$s). From the posterior plots (the plots with the HDI statements) we can see that the players have different chances of hitting a when at-bat. Roberto Clemente appears to have a much higher chance of getting a hit than Max Alvis does.

In the below plot we show the predicted chance of landing a hit when at-bat for our first two models. As we stated at the beginning of this section the no-pooling model underestimates weak hitting player's chances of a hit, and overestimates good hitting player's chances of a hit. Recall that the complete-pooling model did the exact opposite, which was to overestimate weak player's chances of hitting and underestimating strong player's chances of hitting when at-bat. This effect is quite apparent when looking at the no-pooling plot to the right vs the complete-pooling plot on the left.

We again plot posterior means for each player and the whiskers show the 89% HDI. The grey line is found by setting

$y=\frac{\text{Current hits}}{\text{Current at-bats}}.$

Since this is also our x-axis, we have a straight line.

# Required for visualizing in Colab.output_notebook(hide_banner=True)no_pooling_model_plot = baseball.plot_no_pooling_model(    no_pooling_samples,    no_pooling_theta(),    df,)complete_no_plot = gridplot([[complete_pooling_model_plot, no_pooling_model_plot]])show(complete_no_plot)
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Player's chances for the no-pooling model show a wide spread from around 0.2 to around 0.4. It turns out that a hitting average for MLB players in the high 300's (0.3 in our model) is quite rare, see Wikipedia (All-time-players) for historical context. Our no-pooling model certainly overestimated strong player's chances of landing a hit when at-bat, and it did so with gusto.

So far we have not done a good job of telling our data story through our models. Our complete-pooling model said every player was the same, while our no-pooling model said all players are uniquely different. However, our data story said that all players are different, but similar. What we have done so far is to model the extreme cases for our data. In the next section we will combine these two models such that we have a more accurate representation of player's chances when at-bat.

Partial-pooling model​

Partial pooling combines complete-pooling and no-pooling models to create a hybrid model that creates separate models for each player and simultaneously models the population. We do not have any data on the MLB population, but we can still create a model of it through our hierarchical model. Essentially what the model does is create separate parameters for each player (the $\theta$s) and assumes that each $\theta$ is sampled from the same distribution $(\phi)$, see above. The partial-pooling model does a better job of estimating all batter's chances using information about the population. It will also do a better job of estimating poor hitter's chances and good hitter's chances.

Our model is then

\begin{aligned} \phi&\sim\text{Beta}(1, 1)\\ \kappa&\sim\text{Pareto}(1, 1.5)\\ \theta&\sim\text{Beta}(\phi * \kappa, (1- \phi) * \kappa)\\ y&\sim\text{Binomial}(K, \theta) \end{aligned}

The new component of the model is the $\kappa$ parameter, which is sampled from a Pareto distribution. $\kappa$ has been introduced to this model because $\theta$, the distribution for each player's chance of getting a hit when at-bat, has priors $\alpha$ and $\beta$, which both need to be estimated. $\kappa$ can be thought of as the population "concentration". For a more thorough discussion about $\kappa$, see Carpenter 2016.

Priors for the partial-pooling model​

Just as we did before, we plot a few curves of the Pareto distribution with varying parameters to get a sense of what our priors look like.

# Required for visualizing in Colab.output_notebook(hide_banner=True)pareto_plot = baseball.plot_pareto_prior()show(pareto_plot)
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We can code our partial pooling model easily in Bean Machine. All we need to do is reintroduce $\phi$ as our estimate for the population's chance for hitting a ball when at-bat, keep our model for each individual's chance for successfully hitting when at-bat $\theta$, and introduce $\kappa$ as our population concentration.

@bm.random_variabledef partial_pooling_phi() -> RVIdentifier:    return dist.Beta(1, 1)@bm.random_variabledef partial_pooling_kappa() -> RVIdentifier:    return dist.Pareto(1, 1.5)@bm.random_variabledef partial_pooling_theta() -> RVIdentifier:    alpha = partial_pooling_phi() * partial_pooling_kappa()    beta = (1 - partial_pooling_phi()) * partial_pooling_kappa()    return dist.Beta(alpha, beta).expand((N,))@bm.random_variabledef partial_pooling_y() -> RVIdentifier:    return dist.Binomial(current_at_bats, partial_pooling_theta())

Partial-pooling inference​

Again we construct our observations into a dictionary, and sample using the GlobalNoUTurnSampler. The major difference between our previous models and this one, is that we now have more than one Bean Machine random variable to include in the queries parameter of the infer method.

partial_pooling_observations = {partial_pooling_y(): current_hits}partial_pooling_queries = [    partial_pooling_kappa(),    partial_pooling_phi(),    partial_pooling_theta(),]partial_pooling_samples = bm.GlobalNoUTurnSampler().infer(    queries=partial_pooling_queries,    observations=partial_pooling_observations,    num_samples=num_samples,    num_chains=num_chains,    num_adaptive_samples=num_adaptive_samples,)
Out:
Samples collected:   0%|          | 0/7500 [00:00<?, ?it/s]Samples collected:   0%|          | 0/7500 [00:00<?, ?it/s]Samples collected:   0%|          | 0/7500 [00:00<?, ?it/s]Samples collected:   0%|          | 0/7500 [00:00<?, ?it/s]

Partial-pooling analysis​

Just as before, we check the $\hat{R}$ and ess summary statistics as well as the rank and autocorrelation plots using arviz.

partial_pooling_summary_df = az.summary(partial_pooling_samples.to_xarray(), round_to=4)Markdown(partial_pooling_summary_df.to_markdown())
meansdhdi_5.5%hdi_94.5%mcse_meanmcse_sdess_bulkess_tailr_hat
partial_pooling_kappa()94.379106.3269.3574182.742.90262.15182828.551818.261.0011
partial_pooling_theta()[0]0.32310.0520.24280.40390.00050.000410488.813218.71.0004
partial_pooling_theta()[1]0.31380.0510.23680.39650.00050.000311139.912959.91
partial_pooling_theta()[2]0.30470.04840.2280.37970.00040.000313442.613862.21.0002
partial_pooling_theta()[3]0.29530.04670.21940.36630.00040.000316300.313858.71
partial_pooling_theta()[4]0.28590.04570.21570.35950.00030.000218709.913630.11.0005
partial_pooling_theta()[5]0.28550.04550.21510.35830.00030.000218825.113729.91.0003
partial_pooling_theta()[6]0.27650.04510.20630.34950.00030.000219152.1129591.0007
partial_pooling_theta()[7]0.26740.04430.19440.33590.00030.000219496.412683.81.0005
partial_pooling_theta()[8]0.25740.04330.18580.32450.00030.000220478.812399.81.0003
partial_pooling_theta()[9]0.25750.04410.18680.32660.00030.000219430.911696.71.0003
partial_pooling_theta()[10]0.24920.04350.18110.31930.00030.000217827.812261.11
partial_pooling_theta()[11]0.24910.04350.17780.31670.00030.000217092.113110.41
partial_pooling_theta()[12]0.24860.04360.1780.31680.00030.000217226.113164.11.0001
partial_pooling_theta()[13]0.24860.04370.180.31920.00030.000217574.413216.61.0001
partial_pooling_theta()[14]0.24890.04410.17670.31740.00030.000218707.413415.21.0004
partial_pooling_theta()[15]0.23940.04350.16660.30510.00040.000313857.612263.81.0002
partial_pooling_theta()[16]0.23030.0440.15970.29960.00040.000312717.7122511.0001
partial_pooling_theta()[17]0.22070.04470.15130.29310.00050.00039818.48112331.0006
partial_pooling_phi()0.26880.02140.23340.30090.00020.000110339.711845.51.0001
# Required for visualizing in Colab.output_notebook(hide_banner=True)partial_pooling_diag_plots = gridplot(    [*baseball.plot_partial_pooling_diagnostics(partial_pooling_samples, df)])show(partial_pooling_diag_plots)
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All summary diagnostics $(\hat{R}\text{ and }ess$) look good. All our ess evolution plots also look linear and breach the 400 rule-of-thumb threshold. Rank plots all look fairly uniform and our autocorrelation plots look good. You will notice that the autocorrelation plot for $\kappa$ has highly correlated draws with initial lags. We could sample for longer chains in order to try and alleviate these correlations.

Below we show how the model affects estimated chances of hitting when at-bat for each player. We compare this new model to the other model plots and see that the partial-pooling model effectively increases weak player's chances of landing a hit, while also decreasing strong player's chances of a hit.

# Required for visualizing in Colab.output_notebook(hide_banner=True)partial_pooling_model_plot = baseball.plot_partial_pooling_model(    partial_pooling_samples,    df,)model_plots = gridplot(    [        [            complete_pooling_model_plot,            no_pooling_model_plot,        ],        [            partial_pooling_model_plot,        ],    ])show(model_plots)
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The partial-pooling model has shifted our predictions. In fact, it has captured our data story, which stated that MLB players are unique (they do not lie on the population mean orange line) and yet they are not all that different (they do not fall on the grey line). What we see is that player's chances of hitting a ball when at-bat have moved closer to the mean line from their positions in the no-pooling model.

# Required for visualizing in Colab.output_notebook(hide_banner=True)shrinkage_plot = baseball.plot_shrinkage(    no_pooling_samples, partial_pooling_samples, df)show(shrinkage_plot)
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Conclusion​

To sum up

• Our complete-pooling model

• overestimated weak player's chances of landing a hit
• underestimated strong player's chances of landing a hit
• gave us posterior mean estimates within the population mean.
• Our no-pooling model

• underestimated weak player's chances of landing a hit
• overestimated strong player's chances of landing a hit.
• Our partial-pooling model

• estimated a player's chance of landing a hit regardless if they hit poorly or well.

With our limited data we were able to create a model that gives us a player's chance of success when at-bat. Bayesian inference has given us a way to make more accurate predictions about a player's chance of success with a small data set and Bean Machine made creating those models very easy.