A Study of Momentum Analysis Based on Random Forest and ARIMA

The purpose of this study is to investigate the importance of momentum in tennis and how it can be effectively utilized to account for fluctuations in the game. First, player performance is quantified based on a logistic regression model. Then, the effect of momentum on the match outcome is discussed. Finally, a Matching Swing Prediction Model (MSPM) was constructed using Random Forest and ARIMA models. The results of the study show that momentum has a significant effect on match outcomes and that match trends and player streaks are not coincidental phenomena. Through the analysis of key metrics and quantification of player performance, we reveal the importance of key and advantageous shots in winning games, confirming the non-random nature of match reversals and streaks. Ultimately, our model combines historical data to provide effective predictions and recommendations, offering practical strategies for players to cope with fluctuations in matches.