Methodology, racing analysis, and honest simulated-performance analysis.
11 June 2026
SHAP values decompose a machine-learning prediction into per-feature contributions. How they work, how to read the 'Why this pick' panel, and the honest limits of model explanations.
Read more →11 June 2026
Circumference, trap bias, going and grade depth vary far more between greyhound tracks than most analysts assume. A data-driven tour of what separates Newcastle from Romford — and why it matters for racing analysis.
Read more →30 May 2026
Each-way selections look like insurance but the maths is unforgiving on tight place markets. Why TrapStats only flags win selections — and what the numbers showed.
Read more →30 May 2026
A practical guide to every column on a UK or Irish greyhound racecard. What trap numbers tell you, how grades stack, what the form figures mean, and where the market focuses first.
Read more →30 May 2026
A transparent look at the ML pipeline behind TrapStats — what features the model uses, how it's calibrated, and what its honest accuracy looks like on live UK and Irish greyhound racing.
Read more →30 May 2026
Inside-trap dogs do win more often at most UK tracks — but the effect is smaller than analysts think and varies dramatically by venue. What the 180-day data says and how to use it.
Read more →30 May 2026
Two different probability calibrators on a narrow-input ML problem produce wildly different live behaviour. Why isotonic collapsed to a phantom-EV machine — and what Beta does differently.
Read more →29 May 2026
How the Kelly Criterion calculates a mathematically optimal allocation for each selection, why we use a fractional Kelly, and a worked example on a real greyhound race.
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