Offensive Lineup Analysis in Basketball with Clustering Players Based on
  Shooting Style and Offensive Role
Kazuhiro Yamada Keisuke Fujii
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.