What data does an AI horse racing model use?
The core inputs across every credible model are: OR (Official Rating) and RPR (Racing Post Rating) for form; TS (Top Speed) for clock; trainer 14-day strike rate and run count for recent yard form; days-since-last-run for freshness; race class and going for context; draw for flat-race draw bias; and live odds + price drift for market signal. Advanced models add pace inferred from past comments, sire/distance preferences, and headgear changes. Racing Alpha's v1.1 model also includes Betfair Exchange drift signals.
What's the difference between AI rating and a tipster's hand-picking?
An AI rating model assigns a SCORE to every horse in every race, every day, using exactly the same rules every time. A hand-picker chooses a subset of races and applies judgement. The AI's strength is coverage and consistency — it never skips a race, never has an off day, and applies the same logic at 2pm and 7pm. The hand-picker's strength is selectivity — they can choose to pass on races where the data is thin or contradictory. Most modern services combine the two: AI screens; human filters.
Are AI models the same as 'machine learning' models?
Roughly yes, in the way the public uses the term. 'AI' is a marketing umbrella; under the hood you'll usually find one of three approaches: (a) heuristic weighted-sum (Racing Alpha v1.0 — explainable, every score breaks into its components); (b) gradient-boosted trees like XGBoost or CatBoost (Racing Alpha v1.1 — handles non-linear interactions, harder to interpret); (c) deep learning / neural networks (rare in horse racing because samples are small and noisy). Boosted trees currently produce the best results on UK/IRE racing data.
How does the AI handle small fields or weak races?
Most good models apply a 'segment filter' — they refuse to pick in race types where the model has historically not produced positive ROI. Common exclusions: very small fields (5 or fewer runners — narrow market, little edge), very young horses (2yo races where form is thin), very long-odds picks (>20/1 where variance overwhelms any edge). Racing Alpha's v1.3 filter is published in /methodology and is part of why the v1.0 advised tip is a subset of the field-wide top-rated runners.
Why don't all AI horse racing models agree?
Because they weight inputs differently. A model that heavily weights recent form will look at one horse and rate it high; a model that weights speed figures will pick a different horse. Both can be right on different races. The honest way to evaluate which is better is to run them side by side over a long sample. Racing Alpha publishes three (v1.0 heuristic, v1.1 ML, Furlong manual) on a public head-to-head dashboard so the punter can see which model's read is currently delivering.