Tennis Match Prediction Model Based on Deep Learning and Multi-Source Information Fusion

In the intersection of sports science and data analysis, this study delves into the application potential of deep learning techniques in tennis tournament analysis. Specifically, we used data from the Australian Open, French Open, US Open, and Wimbledon tennis tournaments held annually from 2012 to 2023 as the basis for our research. Through rigorous data preprocessing procedures, we aimed to uncover the performance characteristics of athletes during matches and their underlying correlations with match outcomes. By integrating advanced deep learning architectures, such as the multi-head attention mechanism, we captured the intricate and dynamic information within the matches. Additionally, we employed cross-validation strategies to enhance the generalization capability of our models. Notably, our study innovatively incorporated athletes' individual abilities as input features into the prediction models, leading to a marked improvement in the accuracy of predicting match results. This finding not only offers novel perspectives and tools for the field of sports analytics but also provides a scientific basis for coaching teams to devise personalized training plans and adjust competition strategies in real-time, ultimately contributing to the enhancement of athletes' competitive performance and tournament victory rates.