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Activity Number:
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339
- Novel Applications of Statistics in Sports
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Type:
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Contributed
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Date/Time:
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Tuesday, August 9, 2022 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Statistics in Sports
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Abstract #322908
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Title:
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Distance Based Bayesian Clustering with Applications to Field Goal Attempts of Professional Basketball Players
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Author(s):
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Hou-Cheng Yang*
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Companies:
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U.S. Food and Drug Administration
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Keywords:
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sport statistics;
bayesian method;
mixture of finite mixtures;
MCMC
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Abstract:
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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.
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Authors who are presenting talks have a * after their name.
