Abstract
In sports, a team or athlete may feel they have momentum, or “power/strength” during a game, but this phenomenon is difficult to measure. Furthermore, it is not clear how various events during a match create or change momentum. This paper presents an approach to quantifying ‘momentum’ by introducing a composite model integrating a Logistic Regression Model and a LASSO-based Sparse ARMAX Model to predict momentum shifts and guide strategic decisions during games. The combined model addresses both static and dynamic aspects of momentum, culminating in a comprehensive prediction of momentum trends. The model’s robustness is evidenced by its high accuracy rates across different tennis matches and gender scenarios (95.6%, 94.9%, and 95.5%). This paper’s findings confirm momentum’s measurable impact on sports outcomes, offering a scientific basis for tactical decision-making.
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