Model Builder Methodology

A model changes weighting and interaction behavior on top of stored fighter data. It does not rewrite source bouts, odds, or fighter records.

At a high level, a model can change:

Models do not change the underlying fighter profiles, result records, or odds source data already stored by the platform.

They also do not create extra sample size. A model with attractive recent numbers on a small boxing sample should still be treated as a hypothesis, not proof.

Normalization is fixed to fixed_theoretical. The point of that constraint is comparability: models are evaluated on the same scoring frame rather than each model being allowed to redefine the scale.

That makes model-vs-model inspection cleaner, but it does not remove overfitting risk. It only keeps the scoring surface consistent.

Since Created evaluates from the model's initial creation.

Since Last Edited evaluates only the period after the current parameter set was locked in.

For skeptical review, Since Last Edited is usually the cleaner lens because it avoids mixing different parameter regimes into one headline number.

Backtest Lab is a model inspection surface, not the public tracked-results ledger. It is most useful for comparing parameter sets against the same stored fight, result and odds pool.

All model leans treat every usable directional lean as a one-unit strategy bet. Value/advantage modes add a market-difference requirement before a lean becomes a simulated bet.

Time Safe: On (strict) is the cleanest audit mode. Bout coverage is limited to the strict public-result pool: settled fights that FiteQuant has tracked or recovered with enough pre-fight prediction, result and odds evidence to treat them as public ledger candidates. Strict mode applies pre-fight as-of filtering and disables objective-derived inputs that cannot be calculated without strict leakage risk.

Time Safe: On (non-strict) uses the strict pool plus explicitly verified non-strict rows. These are settled canonical bouts with ProBoxingOdds-backed closing odds and post-official-model fighter timestamp evidence, but without the full public tracked-result status. This mode is designed for lab work: it keeps the time-safe boundary, while allowing approved objective-derived exceptions such as opponent-rating fields for targeted models or custom selections. Height/reach delta is treated as an objective matchup dimension because it is based on fighter profile measurements rather than the fight result.

Time Safe: Off is non-time-safe historical simulation. It can use the broader stored canonical fight and odds pool inside the selected window, including inputs that may reflect current or later-persisted fighter data. In this mode, opponent-rating-derived fields and height/reach delta are treated as normal objective inputs when the selected model uses them. It is useful for exploration, not as evidence of tracked pre-fight performance.

For serious model comparison, change one lens at a time: strategy, market bucket, confidence tier, time-safety mode, or objective history window. The useful signal is usually the pattern across runs, not one isolated ROI cell.

The AI confidence control is not a claim that the model "knows" when it is right. It is a weighting control that changes how much subjective inputs matter when the underlying subjective profile was extracted with stronger or weaker confidence.

If you want to stress-test whether the subjective layer is helping, compare runs with objective influence increased and subjective confidence tightened rather than taking the confidence label at face value.

You can verify model behavior inside the product by changing one parameter block at a time, then checking how the same settled sample behaves in Backtest and Results.

The system is designed so a user can inspect which settings changed, what window they are judging, and whether the resulting prediction set remained coherent.