Research on Momentum Change Prediction and Its Application Based on Decision Tree Algorithm

In our conversations, we delved into the creation and utilization of machine learning algorithms to scrutinize fluctuations in scores and changes in the game's tempo during tennis games, and how such insights might enhance an athlete's game strategy. We engineered a decision tree-driven classifier to delineate score alterations and pinpoint pivotal moments where momentum swings occurred. This model took into account an array of variables, such as sequential scoring instances, the effectiveness of break points, and the variance in momentum. The model achieved a high level of predictive accuracy, reaching 96%, and provided a visual representation of the game's progression and shifts in momentum. The examination revealed several critical elements that appear to be most closely linked to the shifts in game momentum, notably the conversion rates of break points and sequences of consecutive points scored. We assessed the influence of momentum in games and juxtaposed it against a chance occurrence hypothesis. The findings suggested that changes in momentum are not arbitrary but are correlated with particular elements intrinsic to the game.