Inner pace: A dynamic exploration and analysis of basketball game pace

Abstract

This study aims to investigate the dynamics of basketball game pace and its influence on game outcomes through a novel intra-game segmentation approach. By employing K-means clustering on possession duration, we categorized possessions from 1,141 NBA games in the 2019–2020 season into high-frequency (HFS), low-frequency (LFS), and normal-frequency segments (NFS). A sliding window method was utilized to identify these segments, revealing distinct temporal patterns within games. To analyze the predictive value of these segments, we applied machine learning models, including Random Forest and Light Gradient Boosting Machine (LightGBM), complemented by SHapley Additive exPlanations (SHAP) for interpretability. Our findings demonstrate that HFS segments increase toward the end of each quarter, driven by rapid transitions and tactical urgency, whereas LFS segments dominate the middle phases, reflecting strategic tempo control. NFS accounts for the majority of game time but decreases as the game progresses. The LightGBM analysis highlighted the importance ranking of key performance indicators (KPIs) across different segments and revealed differences in the importance of these indicators within each segment. Compared to traditional methods, our approach provides a finer-grained analysis of game pace dynamics and offers actionable insights for optimizing coaching strategies. This study not only advances the understanding of basketball game rhythm but also establishes a robust framework for integrating machine learning and statistical models in sports analysis.