Quantitative Study of Tennis Match Momentum and Flow Based on Machine Learning

Momentum is a critical factor influencing the dynamics and outcomes of tennis matches. To enhance the predictive accuracy of these fluctuations, this study utilizes machine learning techniques and big data analytics to improve the prediction of these fluctuations. The study conducts a comprehensive analysis to identify the correlation between a player's momentum and 14 key features in a tennis match. An optimized BP neural network model, based on Levenberg-Marquardt theory, was developed to predict match flow and quantify the stalemate degree. The model is evaluated using a confusion matrix and ROC curve, affirming its predictive validity, where the results revealed an F1 Score and an AUC, both exceeding 0.5. With big data, this approach not only enhances the spectator experience by visualizing match dynamics but also aids in strategy development and training optimization for competitors. This research highlights the practical applications of quantitative modeling in understanding and forecasting the pivotal moments in tennis.