Offensive Lineup Analysis in Basketball with Clustering Players Based on Shooting Style and Offensive Role
This work addresses the need for better lineup optimization in basketball by providing quantitative analysis of playing style compatibility, though it is incremental as it builds on prior lineup studies by focusing on offensive roles and shooting styles.
The study tackled the problem of quantifying how player compatibility affects scoring efficiency in basketball offense by clustering players based on shooting style and offensive role, and found that specific combinations of players, such as certain two-player pairings, led to improved efficiency with concrete predictive insights from machine learning models.
In a basketball game, scoring efficiency holds significant importance due to the numerous offensive possessions per game. Enhancing scoring efficiency necessitates effective collaboration among players with diverse playing styles. In previous studies, basketball lineups have been analyzed, but their playing style compatibility has not been quantitatively examined. The purpose of this study is to analyze more specifically the impact of playing style compatibility on scoring efficiency, focusing only on offense. This study employs two methods to capture the playing styles of players on offense: shooting style clustering using tracking data, and offensive role clustering based on annotated playtypes and advanced statistics. For the former, interpretable hand-crafted shot features and Wasserstein distances between shooting style distributions were utilized. For the latter, soft clustering was applied to playtype data for the first time. Subsequently, based on the lineup information derived from these two clusterings, machine learning models Bayesian models that predict statistics representing scoring efficiency were trained and interpreted. These approaches provide insights into which combinations of five players tend to be effective and which combinations of two players tend to produce good effects.