Research on Prediction of Tennis Competition Fluctuations Based on CRITIC Weight Method and CNN-LSTM Model

This paper mainly studies the correlation between the "momentum" index and match outcome in tennis matches, and establishes a model to predict competition fluctuations. First, the index weights associated with "momentum" were determined by the CRITIC weight method, and the Spearman correlation coefficient between "momentum" and "performance coefficient" was 0.479, which indicated that there was a significant correlation between these two. Then, this paper analyzed the correlation between other indicators and the "performance coefficient", and found that the successful first serve, unforced error, running distance, hitting number and other indicators had an important influence on the fluctuation of the game. In order to predict competition fluctuations, CNN and LSTM were combined to form a prediction model, with good results in the 2023 Wimbledon competition data (RMSE <1). In addition, the prediction model was used to prove that competition fluctuations and player wins are not random, and using at least one game data to explore the factors associated with the change of competition situation, laying the foundation for developing predictive models. The index weight allocation was conducted by CRITIC method, which explored the relationship between "momentum" and the result of competition using Spearman correlation analysis, established the CNN-LSTM deep learning model for prediction, and achieved high prediction accuracy.