Evaluating the role of momentum in tennis based on neural network models

Abstract

The research on the evaluation mechanism and prediction model of tennis player performance is of great significance and value for improving the quality of matches and cultivating excellent athletes. Firstly, by establishing a scientific and reasonable evaluation mechanism, the technical, physical, and psychological performance of athletes in competitions can be objectively analyzed, providing a basis for developing personalized training plans. Firstly, this article selects five parameters representing the stage performance status of athletes and extracts parameter data. Use clustering algorithm (K-means) to classify data. Based on the calculation of weights for different parameters, a score evaluation formula was established. It also provides indicators for the range of athlete evaluation scores, so that status data can be updated to obtain evaluation values at different times. It also provides a momentum evaluation score calculation function. Based on the calculation function, the total integrated momentum of 100 matches was simulated, combined with the logistic regression algorithm, and the results were compared. It has been found that the use of momentum accumulation evaluation mechanism can predict the success probability of players. Meanwhile, Monte Carlo simulation methods were used to simulate the number of player games under random probabilities. It was found that in 100 games, the performance of players fluctuated and contradicted the actual situation. Therefore, the momentum evaluation mechanism is of great significance for predicting the progress of athletes in competitions.