Momentum Inflection Point Identification and Change Prediction Based on Logistic Regression Models

This paper presents a comprehensive analysis of momentum shifts and their prediction in competitive events leveraging the power of logistic regression models. By integrating crucial indicators that govern the dynamics of competitions, we aim to identify pivotal turning points, or momentum inflection points, during the course of the contests. Our approach meticulously considers multiple dimensions such as surface conditions, personnel changes, and stage-specific factors, ensuring that the model captures the intricate interplay of variables influencing momentum. Through the application of grid search for hyperparameter optimization and rigorous evaluation via 5-fold cross-validation, we demonstrate the model's robustness and achieve satisfactory accuracy levels on the training dataset. Furthermore, we extend our validation to real-world competition scenarios, showcasing the model's generalizability and potential to inform strategic decisions in real time.