This guide uses testable rule assumptions and does not infer any platform’s implementation from its name. It is educational material, not betting advice.
Key definition
An AI prediction claim should identify input features, target, training procedure and output meaning. Adding historical frequencies is not a trained model.
How to verify it
A real model needs versioned data, reproducible code, date coverage, exclusion rules and a test set that did not influence training.
Limits and conclusion
If the output is only the theoretical probability of a standard deck, call it probability calculation, not next-round prediction.
Practical checklist
This site connects to no external model, exposes no API key and does not claim a live prediction service.
Practical checklist
- An AI prediction claim should identify input features, target, training procedure and output meaning. Adding historical frequencies is not a trained model.
- If the output is only the theoretical probability of a standard deck, call it probability calculation, not next-round prediction.
- Fix the rules, preserve a complete record, separate training from testing, and only then ask whether a model adds information beyond a simple baseline.