Machine-learning signals for sharper trotting decisions.

How it works

From noisy racing data to a disciplined signal.

Three stages, one goal: probabilities you can actually reason about.

1. Learn from history

   The model trains on past races — form, driver and trainer patterns, start method, post position, distance, and race conditions — with time-aware validation, so it never peeks at the future.

2. Estimate probabilities

   Every runner gets a calibrated win probability — a number meant to mean what it says over many races, not a vague star rating or a tip.

3. Compare with the market

   Model probabilities are set against market expectations to flag where value may exist — and, just as importantly, where there is no edge at all.

Under the hood

Advanced, disciplined, and honest about uncertainty.