STAMP: A shot-type-aware areal multilevel Poisson model for league-wide comparison of basketball shot charts

TL;DR

The paper introduces STAMP, a hierarchical Bayesian Poisson model that analyzes basketball shot locations by shot type, enabling fair team comparisons and revealing spatial tendencies with improved predictive accuracy.

Contribution

It presents a unified, shot-type-aware multilevel Poisson model for basketball shot chart analysis, incorporating hierarchical effects and efficient Bayesian inference.

Findings

Model outperforms simpler baselines in predictive accuracy.

Provides interpretable spatial and bias summaries.

Analyzes over 300,000 shots from Japanese professional league.

Abstract

Shooting location is a core indicator of offensive style in invasion sports. Existing basketball shot-chart analyses often use spatial information for descriptive visualization, location-based efficiency modeling, or clustering players into shooting archetypes, yet few studies provide a unified framework for fair comparison of shot-type-specific tendencies. We propose the shot-type-aware areal multilevel Poisson (STAMP) model, which jointly models team-level field-goal attempts across predefined court regions, seasons, and shot types using a Poisson likelihood with a possession-based exposure offset. The hierarchical random-effects structure combines team, area, team-area, and team-side random effects with shot-type-specific random slopes for key shot categories. We fit the model using approximate Bayesian inference via the Integrated Nested Laplace Approximation (INLA), enabling efficient analysis of more than $3\times 10^{5}$ shots from two seasons of B.LEAGUE (the men's professional basketball league in Japan). The STAMP model achieves better out-of-sample predictive performance than simpler baselines, yielding interpretable relative-rate maps and left-right bias summaries. Case studies illustrate how the model reveals team-specific spatial tendencies for comparative analysis, and we discuss its limitations and potential extensions.