This paper explores the accuracy and usefulness of football win-probability models for analysts, clubs, and fans. Since football is a low-scoring and unpredictable game, probability models help us interpret uncertain outcomes and support strategies and decisions. The paper looks into Sam Green’s expected goals model and how it laid the foundation for future forecasting. It also evaluates how FiveThirtyEight’s and Herbinet’s models have expanded on this framework by integrating simulations, team strength ratings, and machine learning. By assessing the strengths and weaknesses of these three models, this paper identifies the most accurate approach for forecast match results.