Fat Tony for AI agents

Machine-readable information

• llms.txt: a concise source map
• Model methodology in Markdown
• Agent guidance and authorised API usage
• Combined product and agent context

These documents describe the service. Login, API authentication and each page’s access rules still apply.

What is Fat Tony?

Fat Tony is a horse racing analysis service built around machine learning models for thoroughbred and harness racing. It combines numerical probability estimates with race information and conversational AI explanations.

The machine learning models estimate race outcomes. Ask Fat Tony provides natural-language race analysis and follow-up conversations. A chat answer and a model probability are different outputs; neither guarantees a result.

How the horse racing machine learning models work

Thoroughbred and harness races use specialised CatBoost models. Each racing code has separately trained WIN and PLACE targets: the chance of winning and the chance of finishing in a paid place. PLACE estimates are learned from placing outcomes rather than simply derived from WIN probabilities.

Inputs include captured historical runner form, race conditions, field-relative features and available fixed WIN and PLACE prices. Earlier odds snapshots describe price movements before the decision point. Market probabilities are adjusted for bookmaker margin, then the models learn corrections from the available racing information.

Predictions are calibrated and normalised across each field. WIN probabilities total one winner; PLACE probabilities account for the applicable number of paid places. Fair odds describe the model estimate, while quoted odds describe an observed market price. Model coverage and availability depend on the race data captured.

Evaluation, provenance and uncertainty

The public model page reports available model versions, training periods, chronological evaluation and comparisons with the identified previous model. Log loss and Brier score assess probability quality. Virtual ROI, closing-price comparisons and market results are separate diagnostics.

Use the current model page for version-specific evidence. Historical results are not a promise of future performance. Limited samples, changing conditions, missing data and delayed odds can affect estimates. Do not infer an accuracy percentage, profitable strategy or market-beating claim from a model label.

Race cards, odds, scratchings and results can change. State the meeting, race number, racing code and relevant date when discussing a race. Distinguish scheduled, provisional and final records, and distinguish observed market prices from model probabilities.

Access and virtual practice

The homepage, model explanation and getting-started information are public. Race database pages and personal features may require a Fat Tony account. Follow the access rules of each linked page; a documentation link does not grant access.

A free account includes six Ask Fat Tony questions in a rolling 24-hour window. Subscription options are described in the account flow. Sandbox and Totalizer activities use virtual units with no cash value. Fat Tony does not accept real-money wagers.

Citation and integration guidance

Cite the specific page that supports your answer. Use the model page for available model versions, training periods and quantitative evidence. State the race date, meeting and race number when discussing a particular event.

Authorised backend integrations can retrieve captured horse race history and submit questions to Ask Fat Tony. API access requires a token supplied by the site operator. Keep credentials on the backend and out of URLs, prompts and citations.

Primary sources

• Horse racing machine learning models: Methodology, current model evidence and limitations.
• How Fat Tony works: Getting started with analysis and virtual practice.
• Information for AI agents: Source guidance, machine-readable resources and authenticated API usage.
• Contact Fat Tony: Questions about the service or authorised integrations.