Background: Understanding the performance metrics that underpin team success in the dynamic world of professional basketball is crucial. This study originated from the increasing academic and professional interest in performance analytics, focusing on how teams in elite leagues, such as the Euroleague and their respective national leagues, adapt and perform based on specific key performance indicators (KPIs). Purpose: The primary objective of this research is to bridge the existing bibliographic gap by comparing the effectiveness of various KPIs in predicting match outcomes in both the Euroleague and National Basketball Leagues. This comparison aims to identify how strategic adaptations and performance measures differ according to the unique demands and styles of the respective competitions. Methodology: The study utilized two main datasets: one encompassing all Euroleague 2022-23 matches and the other compiling cumulative statistics from Euroleague teams over three seasons. Machine learning techniques, including Random Forest, Logistic Regression, and Support Vector Machines, were employed along with the Boruta algorithm for feature selection to enhance predictive accuracy, and SHapley Additive exPlanations (SHAP) for the interpretability of the model output. Results: The analysis identified that certain KPIs, such as effective field goal percentage, defensive ratings, and assists-to-turnover ratio, vary significantly in their impact on game outcomes between Euroleague and National League games. These variations imply that teams may need to tailor their strategies depending on the league in which they play. Conclusions: This study significantly advances the field of sports analytics by providing a detailed comparative analysis of basketball performance metrics across two competitive settings. It offers practical insights that can help coaches and analysts optimize team performance and strategic planning. Moreover, sophisticated data analysis techniques have allowed for a deeper understanding of the complex dynamics that influence basketball game outcomes, thereby making a significant contribution to the literature and practice of sports performance analysis.
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