Fancy models are easy to build and hard to trust. Machine learning for sports forecasting works best when you start small: one precise question, a handful of features you can explain in a sentence, and a baseline that says every home team wins 55 percent of the time. This lesson shows why a forecast is a probability, not a pick, and why finding a price worth taking is a separate job. You learn to set an information cutoff, test in time order so nothing from the future leaks in, and score forecasts by hand with the Brier score. A complicated model earns its place only by beating the simple one.
Learning objectives
You will learn what a forecasting model actually needs, how to build a simple baseline before anything fancy, and how to judge a model with a score instead of a feeling. By the end you should be able to explain why a time-ordered test matters and compute a basic accuracy score by hand.
