Prediction of Competition Results Based on TOPSIS and GS-LSTM Methods: Evidence from Tennis Match

The utilization of machine learning techniques for assessing and predicting match outcomes is a novel and significant problem. This study proposes the utilization of the entropy-weighted TOPSIS method to extract match trends, specifically “momentum,” and employs the GS-LSTM algorithm for predicting tennis match results. Through training and testing on a dataset of 29 matches, the research results validate the reliability of this method in capturing match dynamics and predicting outcomes, with prediction accuracies exceeding 90%. This demonstrates the significant impact of momentum on match results. This paper provides valuable insights for coaches and athletes, aiding in the analysis of match dynamics and the development of training plans.