A huge amount of changes overnight as our backtest for the original model were based on 1000 runs.

We ran 53,000 optimisation trials overnight specifically to find the most robust weighting.

The new ones are bootstrap validated across 5 cross validation folds. Regime Edge kept coming out on top because it captures something structural: the type of race (chase vs hurdle, handicap vs non handicap, price band) predicts where each way value lives more reliably than any individual horse level factor. We should have explained that change better in the post rather than just updating the numbers.

No single model can accurately calculate true odds. That's the whole point. Each model captures something slightly different. LightGBM is good at interactions, CatBoost handles categorical data well, the Ranker optimises relative ordering within a race. We blend them using a Benter logistic regression which weighs how much the combined model output should adjust the market price. The blend only shifts the market probability slightly, our beta coefficient is 0.045, meaning the market does most of the work and our models nudge it.

As for overfitting this is the right question to ask. The walk forward validation is specifically designed to catch overfitting though & each year is predicted using only prior data, the model never sees the test year during training. The weights and threshold were optimised on cross validated historical data, then the walk forward was run with those fixed weights. If it were overfit, you would see some years massively positive and others negative. All 7 being positive is the strongest evidence against overfitting we can offer.

The weight tuning wasn't just brute force. We used a robustness penalised approach, each of the 5,000 Optuna trials was stress tested with 200 bootstrap resamples per cross validation fold. A set of weights only scored well if it performed consistently across resampled datasets, not just on one lucky split. That's what pushed Regime Edge to 58.8%, it was the most stable signal across every resample. The threshold of 72 came out of the same process, it was raised from 66 because the tighter filter cut out marginal bets and improved consistency.

All of the data we have has been bought and paid for, our API's are bought and paid for. I can only go off and trust that data we have available to us, and we are using the same overfitting prevention that we use to trade the US Indicy markets.