elo-rating-systems

To implement a unified Elo rating system across multiple sports, you create sport-specific classes that inherit from a base abstract class, enforcing a consistent interface for predict, update, and expected_score calculations. This ensures standardized rating logic while allowing per-sport parameters like K-factor and home advantage.

The expected score formula in an Elo rating calculation is used to determine the anticipated probability of a team winning before the match. It is a required method in the unified interface, providing the baseline needed to calculate rating updates after actual results are recorded.

To add Elo ratings for a new sport, you extend the base abstract class and implement all required abstract methods. You invoke the parent class initializer with sport-specific parameters like K-factor and home advantage to establish the default rating behavior for the new sport.

Yes, you can refactor existing sport rating classes to inherit from the base Elo class while maintaining legacy methods. By using backward-compatible adapters, existing implementations transition to the unified interface without breaking previously deployed code dependencies.

Yes, this Elo rating approach supports per-sport parameters by passing K-factor and home advantage values during class initialization. This allows accurate modeling for diverse leagues like NBA, NHL, MLB, NFL, EPL, Ligue1, NCAAB, WNCAAB, and Tennis using a shared update logic.

You should not use a shared update logic for multi-sport Elo ratings if a specific sport requires fundamentally distinct mathematical models rather than just varying parameters. The unified base class assumes all sports can operate under the standard expected score and update formula with adjusted K-factors.