Bayesian Group Learning for Shot Selection of Professional Basketball Players

TL;DR

This paper introduces a Bayesian group learning method using mixture models and LGCP to analyze heterogeneity in NBA players' shot selection, estimating group structures and configurations efficiently.

Contribution

It develops a novel mixture of finite mixtures model with LGCP for shot selection analysis, including an efficient MCMC algorithm for simultaneous group estimation.

Findings

Successfully identifies heterogeneity in NBA players' shot patterns.

Accurately estimates the number of player groups and their configurations.

Demonstrates effectiveness through simulation and real NBA data analysis.

Abstract

In this paper, we develop a group learning approach to analyze the underlying heterogeneity structure of shot selection among professional basketball players in the NBA. We propose a mixture of finite mixtures (MFM) model to capture the heterogeneity of shot selection among different players based on Log Gaussian Cox process (LGCP). Our proposed method can simultaneously estimate the number of groups and group configurations. An efficient Markov Chain Monte Carlo (MCMC) algorithm is developed for our proposed model. Simulation studies have been conducted to demonstrate its performance. Ultimately, our proposed learning approach is further illustrated in analyzing shot charts of several players in the NBA's 2017-2018 regular season.