Abstract
In tennis, "momentum" usually refers to the momentum or trend formed by a series of events (such as consecutive scoring) in a game, which can have a significant impact on the outcome of the game. After gaining motivation, players or teams often exhibit higher morale and confidence, and are more likely to win consecutive points or games. Specifically, momentum may be reflected in indicators such as continuous scores and break points. In a game, the server is usually more likely to win points, so evaluating momentum requires considering the influence of various factors. The paper believe that athletes are primarily influenced by the psychological impact of momentum, but psychological factors are also an abstract concept. Therefore, the paper have turned our attention to the various behaviors of athletes on the field. Therefore, the paper used five indicators to quantify the momentum score of athletes. To address the coach's concerns, the paper use random testing and correlation analysis to verify the statistical significance of attempting to score and win games, rather than random events. This article uses learning algorithms such as decision tree models to predict the value of target variables at turning points based on feature data. Analyze the contribution of each feature to advantage transformation and ultimately identify the relevant factors that affect advantage transformation.
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