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We publish wins and losses together so readers can inspect a model’s record rather than being shown only selected successes. A visible history can support scrutiny, but it is not proof that the figures have been independently verified, nor a promise of future performance or betting returns.
Why show the losing picks?
A record that includes losing selections gives readers a fuller view of what happened than a collection of winning examples. Winsmore says it publishes a pick for each race and records the outcome, including losing runs such as “0 from 11.” That is the publisher’s account of its practice; the available material does not independently audit the underlying selections or results. Winsmore’s explanation of how it works describes its approach.
Showing losses matters because a model’s performance cannot be judged from its best outcomes alone. Readers can see that losing streaks occur even within a record the publisher describes as successful over the long run. Historical results can help frame questions about a model; they cannot establish that the model will continue to perform or guarantee a return.
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What Winsmore says its record is based on
Winsmore says its model draws on more than 90,000 UK races and over 800,000 individual runs since 2020, with Betfair pricing data going back to 2015. It also says it tracks more than 900 selections each month and recalibrates as results settle. These are figures published by Winsmore; the page excerpt available for them does not state a publication year, and they should not be read as independently verified performance statistics.
Those volume figures describe the data and activity Winsmore says it uses. They do not, by themselves, show the model’s win rate, profit, return on investment (ROI), or performance against a benchmark. The available sources do not establish independently audited figures for those measures or a common basis for comparing them.
How to judge a published track record
A headline win count is only useful when readers can tell what was counted and how outcomes were converted into performance measures. When comparing records, look for these details:
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- Coverage: Are all qualifying selections shown, including losses, or only highlighted picks? How are races with no selection represented?
- Timing: Is each pick timestamped or otherwise fixed before the race result is known?
- Settlement: Which result source is used? How are non-runners, void bets, dead heats and later result corrections treated?
- Prices and returns: Is the record reporting wins, flat-stake profit, ROI or another measure? Which odds or prices are used, and when were they captured? What stake assumption applies?
- Scope and sample: What dates, race types, venues and geographies are included?
- Model versions and corrections: Can readers identify which version produced a selection, and see whether past data or reported figures were corrected?
These questions matter because the same selections can produce different reported returns under different price and settlement rules. FormRace’s published methodology, for example, describes how such choices affect ROI and discloses a historical pricing issue that required older figures to be rerun. Its warning that historical performance does not predict future results is relevant to any model record. FormRace’s methodology and performance information provides an example of the disclosures readers can examine.
Wins and losses are not the same as profit
A win/loss count says how often selections won under the stated rules. It does not tell readers whether a series of bets made money. Profit and ROI also depend on the price available for each selection, the assumed stake, and how all outcomes—including voids or dead heats—are settled. A strike rate without those details should not be treated as a return figure.
For that reason, a useful public record should make its calculation reproducible: identify the selections included, the relevant prices and their capture times, the stake convention, and the settlement rules. If prices or historical data are later corrected, the record should explain what changed and which figures were rerun. Methodology disclosures like FormRace’s illustrate why readers should check the rules behind an ROI figure, not just the headline.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What official racing data can—and cannot—show
Official and industry data can help readers understand racing and support research, but it does not validate a particular prediction model. The British Horseracing Authority says its racing-statistics publication is intended to make key data available to decision-makers and the public. Its explanation of horse performance figures describes ratings calculated for each horse in each race; those ratings provide context about racing performance, not a measure of a model’s predictive success. The BHA’s racing statistics information and explanation of horse performance figures provide that context.
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The Jockey Club describes Equibase as the Thoroughbred industry’s official database and has reported making a year of past-performance data and results charts available for research and evaluation. That illustrates the value of historical data for analysis; it is not evidence that any particular model is accurate. Equibase is the industry database referenced by The Jockey Club.
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