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Journal of Data Science


A Joint Analysis for Field Goal Attempts and Percentages of Professional Basketball Players: Bayesian Nonparametric Resource
Volume 21, Issue 1 (2023), pp. 68–86
Pub. online: 9 August 2022      Type: Data Science In Action      Open accessOpen Access
Received
9 January 2022
Accepted
17 July 2022
Published
9 August 2022

Abstract

Understanding shooting patterns among different players is a fundamental problem in basketball game analyses. In this paper, we quantify the shooting pattern via the field goal attempts and percentages over twelve non-overlapping regions around the front court. A joint Bayesian nonparametric mixture model is developed to find latent clusters of players based on their shooting patterns. We apply our proposed model to learn the heterogeneity among selected players from the National Basketball Association (NBA) games over the 2018–2019 regular season and 2019–2020 bubble season. Thirteen clusters are identified for 2018–2019 regular season and seven clusters are identified for 2019–2020 bubble season. We further examine the shooting patterns of players in these clusters and discuss their relation to players’ other available information. The results shed new insights on the effect of NBA COVID bubble and may provide useful guidance for player’s shot selection and team’s in-game and recruiting strategy planning.

Supplementary material

 Supplementary Material
The real data and R code used to reproduce the results in this paper can be found https://github.com/ewongtoi/nba_shot_charts. Additional tables are presented in the Online Supplementary Material.

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Copyright
2023 The Author(s). Published by the School of Statistics and the Center for Applied Statistics, Renmin University of China.
Open access article under the CC BY license.

Keywords
Chinese restaurant process mixture model shot charts data spatial spline sport analytics

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