Prediction Of Tennis Players' Court Behaviors Based on The Xgboost Model and The Shap Model

Abstract

In tennis competitions, coaches and athletes are in urgent need of quantifiable indicators to comprehensively assess the game situation and make timely tactical adjustments. To meet this crucial demand, this article constructed a "momentum" evaluation and prediction model. Initially, this article conducted an in - depth analysis to screen out variables that impact a player's on - court performance, systematically dividing them into two major categories: game - wide variables and local variables. This article placed particular emphasis on local variables, as they capture the essence of a player's outstanding plays, such as powerful aces or incredible retrievals, which can significantly shift the momentum of the game. This influence is effectively represented through a sophisticated weighting mechanism.Based on the robust XGBoost model, this article’s prediction model is not only highly efficient in processing and analyzing data but also highly practical for real - time application during matches. By quantifying the momentum of both players, it provides valuable support for immediate strategy adjustments. Moreover, the model can generate detailed reports and suggestions based on the momentum data, offering targeted guidance for athletes' daily training and helping coaches formulate more scientific training plans, thus enhancing the overall competitiveness of the players.