How to tell if your model actually works
How do you know a betting model is not just fitted to the past?
Any model can be made to fit the past. The only question worth asking is whether it beats the thing it is replacing on data it has never seen.
Hold out, in time order
Split your graded bets chronologically and keep the last 30% back. Fit on the earlier part, judge on the later part. Random splits leak: markets move together within a day, so a random holdout shares information with its training set and flatters everything.
At the 30-bet minimum this engine will fit on, that is 21 bets to fit and 9 to judge — thin, which is the point of the minimum. Below it, nothing is fitted at all.
Beat the incumbent, not zero
A candidate that scores well out of sample but no better than the weights already in use is not an improvement, and adopting it is churn dressed as progress. The bar is the incumbent's out-of-sample score, and a candidate that fails it is refused rather than blended in at a small weight.
Per-book, or not at all
Books are not interchangeable. A weight fitted across all of them describes none of them. Below 20 graded bets at a book, this engine declines to fit that book's weight rather than fitting a bad one — because a weight with no evidence behind it and a weight with evidence behind it look identical downstream.
The signal to fit against
Closing line value, not profit. Profit over a few hundred bets is mostly variance; CLV resolves on every bet and correlates with the thing you are trying to have. It is a supervision signal, not a scoreboard.