Momentum prediction model of the tennis game based on the random forest algorithm

Momentum in tennis matches can greatly affect the performance of tennis players, and dynamic monitoring of momentum can help players to achieve better results in the competition. This paper constructs a tennis ball momentum prediction model based on the random forest algorithm. The model selects the nine indicators of the process of winning the competition in the current set, whether to score continuously, whether to serve, gain or lose the point by untouchable shot, relative fatigue, error situation, double fault, rally count, speed of serve, uses the random forest algorithm to predict the momentum. To prevent the model overfitting problem, this paper reduces the index in dimension by PCA. Based on the results of model training, this paper puts forward suggestions on how to improve the score: prepare in advance, good attitude, hold the rhythm, reduce error, play to the score, improve technique. The data of 201910260 ATL of quarter 1 from the game data of the 2019-2020 regular season are trained by a momentum prediction model based on the random forest algorithm, which demonstrate the generalization ability of the model. This model can be used in various sports events to help athletes improve their performance.