[1D4-GS-10] AI application: Behavioral model

[1D4-GS-10-04] Analysis of the impact of player combinations on scoring efficiency in basketball

a machine learning approach using clustering of shooting styles based on Wasserstein distance incorporating dynamic features and clustering of offensive roles

〇Kazuhiro Yamada¹, Keisuke Fujii^1,2 (1. Nagoya University, 2. RIKEN)

Keywords:Machine Learning, Sports, Clustering

In a basketball game, the players compete against each other in a five-on-five match. In particular, it is important for players with different playstyles to cooperate and score efficiently during possessions, which take place many times in one game. In a previous study, the compatibility of players was examined using clustering results based on each player's statistics, called stats, but the findings obtained were considered to be limited due to the method of selecting features that included both offense and defense. This study focuses only on offense and aims to examine more specifically the impact of player combinations on scoring efficiency. In this study, two different methods are used to capture the playstyles of players on offense: one is a newly proposed method that clusters the tendency of shots based on the Wasserstein distance, the distance between distributions, which considers the set of shots of each player as a probability distribution using shooting features created from tracking data. The other is a method for clustering players' roles in the offense, which is a modification of the existing method. By creating and interpreting a machine learning model that predicts stats representing scoring efficiency from information on lineups based on these two clusterings, new insights into the compatibility of players were obtained.