A Comprehensive Study on the Role of Momentum in the Sports Games

This study aims to explore the effects of momentum and swing on tennis match results. The paper believes that momentum may be a key factor affecting player performance and match results. The research methods include data preprocessing, PCA dimensionality reduction, Pearson correlation coefficient analysis, logistic regression model and random forest model. Data preprocessing includes outlier treatment, one-hot encoding and normalization. PCA is used to reduce the dimension of match indicators and extract the main information. Pearson correlation coefficient analysis is used to test the correlation between match results and momentum. Logistic regression model is used to predict the probability of a player winning a point, and random forest model is used to predict match fluctuations. The study uses data from the 2023 US Open to validate the model. Sensitivity analysis is performed by changing model parameters to ensure model accuracy. The conclusion shows that momentum does affect match results, and our model can predict match performance and fluctuations with high accuracy. This is of great significance for coaches and athletes to formulate strategies and improve match performance. This study is of great value in understanding the momentum effect in tennis and how to use this knowledge to improve athlete performance.