How the System Learns
Every parlay we publish is tracked. The opening odds get snapshotted, the legs get graded against real game results, and the closing line gets compared to what we took at open. The system runs a calibration pass nightly: for every (sport × market × odds bucket) combination, it computes whether the AI's claimed probability matched what actually happened. If picks in a bucket consistently hit less than predicted, the bucket gets penalized. If they hit more, boosted.
On top of that, a logistic regression model retrains nightly on every graded leg and learns which features (sport, market, odds, sharp/square money split, pitcher xERA regression, weather, lineup strength) actually predict winners. It blends with the heuristic estimate so the AI keeps getting smarter without forgetting what the rules already capture. We don't cherry-pick winners. We don't hide losses. The numbers above are what the data actually says.