Real-Time Momentum Prediction in Tennis Based on Gradient Boosting Trees with LGBM

The 2023 Wimbledon Gentlemen's final, where Carlos Alcaraz triumphed over Novak Djokovic, captivated global audiences and highlighted the dynamic nature of momentum shifts in elite sports. As sports technology evolves, there is an increasing demand for advanced data-driven methodologies to model and predict athlete performance in real time. This study leverages cutting-edge data mining techniques and machine learning algorithms to develop a comprehensive mathematical framework centered on quantifying and predicting momentum fluctuations during high-stakes tennis matches. By integrating real-time player data, including serve speed, rally length, and unforced errors, the proposed model employs predictive analytics to dynamically assess player states and forecast match outcomes with high accuracy. The framework is designed to ensure computational efficiency and scalability, aligning with modern standards for real-time data processing. Furthermore, the model incorporates deep learning architectures to capture non-linear relationships in player performance metrics, enhancing its predictive capabilities. This research contributes to the growing body of knowledge in sports analytics and provides a foundation for future advancements in real-time decision support systems for coaches, broadcasters, and fans. By bridging the gap between big data and sports science, this study underscores the transformative potential of artificial intelligence in revolutionizing how athletic performance is analyzed and understood.