A mixed-effects spatial point process framework for modeling player shooting behavior in basketball

Abstract Understanding offensive player profiles in basketball requires analyzing both the spatial distribution of shot attempts and the corresponding scoring effectiveness across court locations. Motivated by this, we propose a semiparametric spatial point process model that combines flexible spline-based intensity estimation with player-specific mixed effects to characterize shooting intensity as a function of distance and angle from the basket. The model balances flexibility and interpretability while accommodating the hierarchical structure of player-level data through random effects. We apply this framework to shooting data from the NBA 2024/2025 season, focusing on High-Volume Shooters who exhibit diverse spatial patterns. The model captures both population-level shooting tendencies and individual deviations, revealing substantial heterogeneity in offensive behavior across players. Extending the framework to marked point processes allows us to construct spatial scoring probability maps that quantify efficiency beyond shot frequency. Collectively, the proposed methodology offers a unified statistical approach for modeling spatial shooting behavior and provides actionable insights for player evaluation, tactical decision-making, and roster construction.