Bayesian group learning for shot selection of professional basketball players

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 the 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. Finally, our proposed learning approach is further illustrated in analyzing shot charts of selected players in the NBA's 2017-2018 regular season.

Automated summary

This paper presents a group learning approach using a mixture of finite mixtures model based on LGCP to analyze the underlying heterogeneity structure of shot selection among professional basketball players in the NBA. The proposed method can estimate the number of groups and configurations simultaneously, and an efficient MCMC algorithm is developed for the model. Simulation studies demonstrate the performance of the approach, which is further illustrated by analyzing shot charts of selected players in the NBA's 2017-2018 regular season.