Sports Analytics Guides

Sports analytics is not a collection of clever statistics; it is a way of asking questions so that the answers can be checked. The guides in this category cover the concepts that recur on every sport page of this site — what a probability means, how a price becomes a probability, what expected value is and is not, why a descriptive statistic can look predictive and fail, and how much data it takes before a result means anything.

They are written for readers who want to understand the method rather than be handed a verdict. Each concept is illustrated with a worked example using real market prices, and each is connected to the way Sportslyx actually behaves: the market-derived win probability on every game, the flat-stake ROI in the public record, the one-calendar-month period used across the product, and the grading rules that decide how a draw or a scratched player counts.

The longest guide in the category walks through building a sports model step by step — choosing inputs, converting ratings into probabilities, calibrating against the market, and running the model live so it can be graded. It maps directly onto Studio, where a stat-weighted model can be run on live games and every run is saved and scored.