BEST HOLDOUT2025–26 chronological holdout
BoutMetrics ML
62.4%accuracy
- TEST SAMPLE
- 591
- BRIER SCORE
- 0.223
Best leak-free public-stats winner pipeline tested on this holdout.
BOUTMETRICS MODEL LAB
Useful signal.
No magic tricks.
Our custom ML pipeline extracts useful signal from public fight data. The market still knows more.
BEST WINNER MODEL62.4%
591-fight
temporal holdout
WHAT WORKED
BoutMetrics ML beat the stacking ensemble on accuracy, Brier score, log loss, and AUC.
WHAT DIDN’T
Closing odds still beat our public-data model decisively; value bets and method props lost money.
HOW WE USE IT
For strike, duration, and matchup context—not as a claim that public data beats the market.
WINNER MODELS
Out-of-sample comparison
Accuracy is only one metric. Brier score measures probability quality; lower is better.
AUDITED MODELSame 2025–26 holdout
Stacking ensemble
62.1%accuracy
- TEST SAMPLE
- 591
- BRIER SCORE
- 0.2286
Faithfully retrained on the identical split for an apples-to-apples bar.
AUDITED MODELSeparate 2025 holdout
Logistic baseline
62.2%accuracy
- TEST SAMPLE
- 407
- BRIER SCORE
- 0.2379
Strong simple baseline, but measured on a different test window.
THE REALITY CHECK
The market knew more
BoutMetrics ML was the best public-data pipeline in the lab. On 332 fights with matched closing odds, the consensus market was still much sharper.
MARKET ACCURACY68.4%332 matched fights
MODEL ACCURACY60.5%Same real-odds subset
PRODUCT RULE
No “value bet” badges from public-stat winner probabilities. The evidence does not support them.
REPEATABLE SIGNALS
What mattered most
Recent formLast 3–5 fights
Age differenceYouth showed a consistent edge
Career win rateUseful, but market-aware
ExperienceSecondary context
Reach & heightModest on their own
Relative importance is summarized across the audited linear-model reports; it is not a universal causal ranking.
BOUTMETRICS ML
Current projection layer
Held-out fighter-rows since July 2025. Lower MAE is better.
Significant strikes
26.6Composed total: predicted rate × predicted duration
MAE · 40.3 naive · n=726Fight duration
295sA modest improvement over historical averages
MAE · 309s naive · n=726Over / under lean
66.9%Research result without owned totals odds
accuracy · 65.0% naive · n=726Takedowns
1.14Did not beat the baseline; display as experimental
MAE · 1.09 naive · n=726VALIDATION STANDARD
Past trains.
Future tests.
- 01Chronological splitModels train only on fights that happened before the test period.
- 02Probability qualityAccuracy sits beside Brier score and sample size.
- 03Market realityReal-odds backtests are disclosed—even when they lose.
- 04Uncertainty firstLow agreement becomes a warning, not false confidence.
FROM RESEARCH TO MATCHUP
Use the model where it belongs.
The public lab shows how the system performs. A free account unlocks the fight-specific story, model agreement, and projections.
Create free account →