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

2026-03-25

AI Summary

This study addresses the absence of a unified and comparable analytical framework for modeling spatial shooting tendencies across different shot types in basketball. The authors propose a shot-type-aware, regionally hierarchical Poisson model that jointly captures team shot attempts across court zones, seasons, and shot categories. The model incorporates possession-based exposure offsets and employs hierarchical random effects to account for team-specific spatial interactions. Using a Poisson likelihood with integrated nested Laplace approximation (INLA) for approximate Bayesian inference, the approach is validated on over 300,000 shots from Japan’s B.LEAGUE. It significantly outperforms baseline methods in out-of-sample predictive accuracy and yields interpretable visualizations—including relative shot-rate maps and left–right court preference summaries—enabling fine-grained comparisons across teams, regions, and shot types.

Technology Category

• Reasoning under Uncertainty: Relational Probabilistic Models
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Web Mining and Content Analysis: Models for Web evolution

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.

Problem

• shot chart
• basketball analytics
• spatial comparison
• shot-type-specific tendencies
• league-wide comparison

Innovation

• shot-type-aware
• areal multilevel Poisson model
• possession-based exposure
• hierarchical random effects
• INLA