Capturing Momentum: Tennis Match Analysis Using Machine Learning and Time Series Theory
This work addresses sports analytics for tennis, providing tools for match prediction and player performance analysis, but it is incremental as it applies existing machine learning methods to this domain.
The paper tackled the problem of analyzing momentum in tennis matches by using hidden Markov models to predict player performance and XGBoost to validate momentum's significance, achieving performance evaluation with LightGBM and identifying key factors through SHAP analysis.
This paper represents an analysis on the momentum of tennis match. And due to Generalization performance of it, it can be helpful in constructing a system to predict the result of sports game and analyze the performance of player based on the Technical statistics. We First use hidden markov models to predict the momentum which is defined as the performance of players. Then we use Xgboost to prove the significance of momentum. Finally we use LightGBM to evaluate the performance of our model and use SHAP feature importance ranking and weight analysis to find the key points that affect the performance of players.