The Building of Players’ Momentum Models in Tennis

This passage develops a model that can quantify the momentum of tennis players and predict match fluctuations. First, data preprocessing is performed: missing values are filled with means, non-numeric variables are assigned values, and variables are merged using the difference method to reduce data dimensions. Then, passage uses Principal Component Analysis (PCA) to reduce data dimensions and evaluate player performance through K-means clustering, which is displayed in the matches. To explore the relationship between win rates and momentum, passage uses beta distribution and Markov chain to simulate match results, finding that win rate fluctuations are highly correlated with score differences and match progress, quantifying momentum. Next, based on the LSTM algorithm, passage develops a prediction model that uses momentum scores to predict fluctuation changes, with Root Mean Square Error (RMSE) for the training and test sets being 0.02137 and 0.02396, respectively. Passage uses the CHAID model to analyze influencing factors and provide recommendations controllable by coaches and players. Finally, this passage uses the BO-LSTM algorithm to predict momentum fluctuations in women's tennis matches, introducing world ranking and age factors, and the model demonstrates good generalization capability.