[2N4-GS-10x] AI application

1:45 PM - 2:00 PM JST(4:45 AM - 5:00 AM UTC)

[2N4-GS-10x-02] Comparing Team Tendencies in Shot Charts Using an Areal Poisson Hierarchical Model

〇Kazuhiro Yamada¹, Keisuke Fujii^1,2 (1. Nagoya University, 2. RIKEN)

[[online]]

Keywords:

Sports,Statistical modeling,Spatial analysis,INLA,Basketball

Shooting location is a core indicator of offensive style in invasion sports. Existing basketball shot-chart analyses often use spatial information for descriptive visualization or clustering players into shooting archetypes, yet few studies provide a unified framework for fair comparison of shot-type-specific tendencies. We propose the areal Poisson hierarchical 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 × 10^5 shots from two seasons of B.LEAGUE (the men’s professional basketball league in Japan). The proposed model achieves better out-of-sample predictive performance than simpler baselines, yielding interpretable relative frequency percentile maps and court left-right bias summaries.