# Analysis of professional basketball field goal attempts via a Bayesian matrix clustering approach

arXiv: https://arxiv.org/abs/2010.08495 · HTML: https://mlsanity.com/paper/2010.08495/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Oct 2020.

## Built on by (most cited first, 3 of 3)

- [Learning Car-Following Behaviors Using Bayesian Matrix Normal Mixture Regression](https://mlsanity.com/paper/2404.16023/) (2024)
- [Row-clustering of a Point Process-valued Matrix](https://mlsanity.com/paper/2110.01207/) (2021)
- [Offensive Lineup Analysis in Basketball with Clustering Players Based on Shooting Style and Offensive Role](https://mlsanity.com/paper/2403.13821/) (2024)

## Abstract (arXiv)

We propose a Bayesian nonparametric matrix clustering approach to analyze the latent heterogeneity structure in the shot selection data collected from professional basketball players in the National Basketball Association (NBA). The proposed method adopts a mixture of finite mixtures framework and fully utilizes the spatial information via a mixture of matrix normal distribution representation. We propose an efficient Markov chain Monte Carlo algorithm for posterior sampling that allows simultaneous inference on both the number of clusters and the cluster configurations. We also establish large sample convergence properties for the posterior distribution. The excellent empirical performance of the proposed method is demonstrated via simulation studies and an application to shot chart data from selected players in the 2017 18 NBA regular season.