Data-driven insights into basketball performance: Unveiling the impact of advanced analytics on player and team efficiency
Abstract
This study investigates the impact of data science on basketball performance, comparing key performance indicators (KPIs) across NCAA Division I collegiate basketball and NBA games. Using a dataset of 180 games over three seasons, the study examines metrics such as Player Efficiency Rating (PER), True Shooting Percentage (TS%), and Defensive Rating (DRtg). Machine learning algorithms, including logistic regression, decision trees, and support vector machines, were employed to predict game outcomes and evaluate the relationships between KPIs and team success. The results reveal that in collegiate basketball, elevated shooting accuracy (TS%) and defensive metrics (DRtg) are strong predictors of success, while in the NBA, PER plays a more significant role. The findings highlight the importance of integrating data-driven insights into coaching strategies and performance enhancement, with practical recommendations for teams at both competitive levels. This study fills a gap in the literature by offering a comparative analysis of basketball KPI usage in different competitive environments.
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