How to measure casino forecast accuracy fairly

Keep three different scores separate

Exact accuracy asks whether the top single outcome matched the result. Bin accuracy asks whether the top broad group matched. Signal hit rate counts only rounds where a qualifying signal existed. These have different denominators and should never be substituted for one another. No-signal rounds cannot quietly become successful alerts.

For a simple illustration, always choosing a common group could produce a high bin hit rate while identifying none of the less common outcomes. The same-round baseline shows whether a more complex model adds anything to that simple alternative. Compare the saved classification policy too; moving an outcome between bins changes the question being scored.

Probabilities need more than a winner count

Two models can choose the same leading outcome but assign very different probabilities. Log loss scores the probability assigned to the result that actually occurred and penalizes confidently wrong forecasts more heavily. Lower is better on a comparable set of outcomes and rounds. It complements accuracy rather than turning a forecast into a guarantee.

Sample size, selection and fresh periods matter

A score based on a handful of rounds can move sharply with one extra hit or miss. Check the number of predictions scored and the dates covered, then compare learning models with more established records. Accuracy appears after one hundred scored results, but that display threshold does not establish statistical certainty or mean that today's leader will keep its rank tomorrow.

With many models, some can lead over a short period by chance. A useful comparison follows the same selected approach through additional results rather than switching to whichever model just won. Note when you made your choice, compare it with a simple reference and review the later period separately. This gives you a clearer picture of consistency than repeatedly looking back at only the strongest recent stretch.

Record first, score later

A useful performance record starts with the forecast that existed before the result. Match its model name, issue time and table to the round being scored. Later corrections can change the result record, so clear dates and sample counts matter when reviewing performance. A historical demonstration is different from a prediction followed live; compare the recorded sequence rather than relying on an isolated winning screenshot or an unexplained jump in accuracy.