Enhancing basketball team strategies through predictive analytics of player performance

This study explores the application of predictive analytics in evaluating player performance in the National Basketball Association (NBA), focusing on rebounds per game (REB), an essential component for better performance and results in basketball. The research employs a comparative analysis of machine learning (ML) models by leveraging a detailed NBA dataset. A key novelty lies in integrating advanced hyperparameter tuning and feature selection, enabling these models to capture complex relationships within the dataset. The Gradient Boosting Regressor demonstrated superior predictive performance, achieving an R² score of 0.8749 after tuning, with Linear Regression following closely at 0.8668. This study also highlights the importance of model interpretability and scalability, emphasizing the balance between predictive accuracy and usability for real-world decision-making. By offering actionable insights for optimizing player strategies and team performance, this research contributes to the growing body of knowledge in data-driven sports analytics and paves the way for more advanced applications in professional basketball management.