Quantifying Momentum Shifts and Predicting Tennis Match Outcomes Using Machine Learning and Time Series Analysis

This paper presents a comprehensive analysis of tennis match momentum using Hidden Markov Models and Exponential Moving Average to quantify and visualize momentum shifts. The authors then leverage Xgboost and LightGBM models to prove the significance of momentum as a key factor in predicting match outcomes, and identify the most relevant indicators for determining momentum changes.