Quantifying and Predicting Tennis Match Momentum: An LGBM-Based Analysis and Visualization Model

Abstract

In any competitive games, the most exciting thing for the audience is the change of the scores of both players, and quantifying the trend of the scores of both players can help coaches provide timely guidance to the players during the games and also help the players in the preparation for the games. The author firstly obtains the data of the players' psychological state, physical reserve and athletic skills during the game, and then compares the LGBM, SVC, MLP and LR models, analyzes the accuracy and regression rate of each model, and then selects the optimal LGBM model, and then calculates the weights of each parameter through the selected model, and sums them up to obtain the quantitative value of "momentum". Finally, the fluctuating images of the two sides' "momentum" in the whole game are drawn, through which the scoring trend can be visualized and the turning points of the momentum can be marked. The use of such methods is a kind of innovation for sports events, which can scientifically help coaches to make decisions, and can bring more exciting sports events to the audience to fully reflect the athletes' style.