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Offensive Lineup Analysis in Basketball with Clustering Players Based on Shooting Style and Offensive Role

Kazuhiro Yamada, Keisuke Fujii

TL;DR

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, which provide insights into which combinations of five players tend to be effective and which combinations of two players tend to produce good effects.

Abstract

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.

Offensive Lineup Analysis in Basketball with Clustering Players Based on Shooting Style and Offensive Role

TL;DR

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, which provide insights into which combinations of five players tend to be effective and which combinations of two players tend to produce good effects.

Abstract

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.
Paper Structure (33 sections, 13 equations, 12 figures, 21 tables)

This paper contains 33 sections, 13 equations, 12 figures, 21 tables.

Table of Contents

  1. Introduction
  2. Methods
  3. Dataset
  4. For Shooting Style Clustering
  5. For Offensive Role Clustering
  6. For Lineup Analysis
  7. Preprocessing
  8. For Shooting Style Clustering
  9. For Offensive Role Clustering
  10. Clustering
  11. Shooting Style Clustering
  12. Offensive Role Clustering
  13. Variable Creation for Lineup Analysis
  14. Lineup Analysis
  15. Analysis of the combination of 5 players
  16. ...and 18 more sections

Figures (12)

  • Figure 1: Scatterplot on the feature space of shots whose features are reduced to two dimensions by UMAP; left: all shots, right: 3-pointers.
  • Figure 2: Hierarchical clustering dendrogram of shooting style clustering. The vertical axis represents the 1-Wasserstein distance and the horizontal axis represents each player.
  • Figure 3: Mean Silhouette Coefficients in Shooting Style Clustering
  • Figure 4: Mean Silhouette Coefficients in Offensive Role Clustering
  • Figure 5: Distribution of the Maximum Membership Coefficient for Each Player
  • ...and 7 more figures