JDS Journal of Data Science 1683-86021680-743X1680-743X School of Statistics, Renmin University of China JDS1062 10.6339/22-JDS1062 Data Science in Action A Joint Analysis for Field Goal Attempts and Percentages of Professional Basketball Players: Bayesian Nonparametric Resource Wong-ToiEliot1 YangHou-Cheng2 ShenWeining1 HuGuanyuguanyu.hu@missouri.edu3∗ Department of Statistics, University of California Irvine, USA Department of Statistics, Florida State University, USA Department of Statistics, University of Missouri, USA Corresponding author. Email: guanyu.hu@missouri.edu. 20239820222116886 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.

9120221772022 2023 The Author(s). Published by the School of Statistics and the Center for Applied Statistics, Renmin University of China.2023 Open access article under the CC BY license.

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.

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