NBAとCBAを事例とするプレイヤーのスタイルとリーグ間パフォーマンス適応メカニズムの深層学習ベースの研究

This study explores how deep learning and interpretable modeling can reveal the impact of player styles on performance across basketball leagues. By examining stylistic features and their influence on cross-league adaptation, the research aims to provide a quantitative framework to understand performance mechanisms in diverse competitive environments.

Game data from players and teams spanning the 2019-2024 seasons were collected as samples. The study first clustered and modeled players' technical styles using principal component analysis (PCA), t-distributed stochastic neighbor embedding (t-SNE), and Gaussian mixture models. It then employed a branch-type multilayer perceptron (Branch-MLP), combined with the SHAP (SHapley Additive exPlanations) algorithm, to conduct interpretable analyses of mainstream tactical structures.

Findings reveal that the NBA prioritizes offensive efficiency and coordinated team play, whereas the CBA emphasizes ball-possession control and physical confrontation. The Branch-MLP model demonstrated high accuracy in tactical recognition tasks. Quantitative evaluations further showed that interior defense-focused role players maintained more stable performance across leagues, while perimeter ball-handling players exhibited greater variability.

This study advances the quantitative analysis of athletic performance by integrating deep learning with interpretable analytics. Its insights provide actionable references for training, player transfers, and youth talent development, supporting data-driven decisions in professional basketball management.