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Activity Number:
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141
- Bayesian Meets Basketball
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Type:
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Invited
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Date/Time:
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Tuesday, August 10, 2021 : 10:00 AM to 11:50 AM
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Sponsor:
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Section on Statistics in Sports
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Abstract #314454
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Title:
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Analysis of Professional Basketball Field Goal Attempts via a Bayesian Matrix Clustering Approach
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Author(s):
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Guanyu Hu* and Weining Shen and Fan Ying
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Companies:
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University of Missouri - Columbia and University of California Irvine and Microsoft
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Keywords:
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Basketball Shot Charts;
Bayesian Nonparametrics;
Matrix-variate distributions;
Mixture of Finite Mixtures;
Model-Based Clustering
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Abstract:
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In this paper, we develop a model based clustering approach for matrix response data to analyze the underlying heterogeneity structure of shot selection among professional basketball players in the NBA. Particularly, we propose a mixture of finite mixtures (MFM) model for heterogeneity learning. Our proposed method estimates the number of clusters and cluster configurations simultaneously. The theoretical properties of our proposed method are established. An efficient Markov Chain Monte Carlo (MCMC) algorithm is designed for our proposed model. Extensive simulation studies are carried out to examine empirical performance of the proposed methods. We further apply the proposed methodology to analyze shot charts of selected players in the NBA’s 2017–2018 regular season.
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Authors who are presenting talks have a * after their name.
