Intelligent Analysis and Predictive Modeling of Tennis Match Data

In today’s sports environment, prediction has a significant effect on athletes to achieve good performance and improve training efficiency. This study explores the construction of a prediction model for tennis match data based on an intelligent analyzing system in combination with tennis match results. The probabilistic algorithm for predicting tennis tournament performance has been designed and implemented, and the Glicko ranking system has been optimized using the improved Apriori algorithm. After cleaning, integrating, and dimensional standardizing the data from previous tournaments, the player’s match ranking is predicted based on the Glicko ranking system. Players’ eigenvalues, technical averages, and tactical decisions are used to verify the accuracy of the model prediction. All 10 selected features have a certain degree of influence on the match results, as evidenced by the results, suggesting that the model has a certain reference value. According to the ranking prediction based on athletes’ technical averages, the prediction correct rate of both the Australian and French Open reaches no less than 80%, which proves that the model in this paper is able to effectively predict the performance of tennis tournaments.