An Analytical Approach to Predicting Dynamics in Sports Matches Based on Machine Learning Models

Sports events have been a significant component of global culture and economics, and analyzing the fluctuations in athletes' performance is one crucial factor in predicting the result of matches. Quantitative research on players’ momentum has long been a popular topic. Building upon prior research, we have developed a mathematical model based on two combined machine learning methods. Specifically, this paper utilized a random forest model to analyze the importance of various metrics of athletes during matches. This text then applied support vector machines for regression prediction of match scores, using data from Wimbledon Championships for model training, and finally yield satisfactory results. The paper further validated this model on a test dataset, demonstrating its robust predictive capabilities. This not only represents a significant application of artificial intelligence in the realm of sports but also holds the potential for substantial economic and cultural benefits for coaching teams and society in the future.