Sport statistics
Soccer
Low-scoring 1X2 synthetic matches with draw probability.
Dataset synthetic-v1 · generated 2026-07-26T03:35:42.735Z
Events
2,000
Average margin
4.81%
Model version
1.1.0
Calibration significance
Expected vs observed by participant
Each synthetic participant’s margin-adjusted price-implied rate against its observed win frequency over 2,000 simulated events, with a 95% Wilson interval, a proportion z-test, and a whole-field chi-square goodness-of-fit. Because the pricing and performance models are independent, well-calibrated sides land inside the Wilson band.
| Band | Events | Expected | Observed | 95% Wilson | Difference | Significance |
|---|---|---|---|---|---|---|
| Home | 2,000 | 43.34% | 44.55% | 42.38% – 46.74% | +1.21% | within noise |
| Away | 2,000 | 29.80% | 29.05% | 27.10% – 31.08% | -0.75% | within noise |
| Draw | 2,000 | 26.86% | 26.40% | 24.51% – 28.38% | -0.46% | within noise |
Edge detection
Edge detection
Mathematical discrepancies
6 synthetic discrepancy signals detected (0 strong, 0 notable). Illustrative only — look-ahead signals are not realizable live edges.
Home: model vs marketlook-ahead
Gap between the simulation's true probability and the priced implied probability. Look-ahead: the true value is unknown in real markets.
info-2.68%
Draw: model vs marketlook-ahead
Gap between the simulation's true probability and the priced implied probability. Look-ahead: the true value is unknown in real markets.
info-2.19%
Home: priced vs fair
The priced probability sits above the de-vigged fair line — the built-in margin you pay on this selection.
info+2.11%
Away: priced vs fair
The priced probability sits above the de-vigged fair line — the built-in margin you pay on this selection.
info+1.45%
Draw: priced vs fair
The priced probability sits above the de-vigged fair line — the built-in margin you pay on this selection.
info+1.31%
Away: +EVlook-ahead
Positive expected value per unit under the assumed true probability. Educational only — not a live edge.
info+0.01%
Synthetic, look-ahead discrepancies — illustrative only. They use the simulation’s known true probabilities and are not realizable live edges.
Real-world use case
- Reload a sport's statistics page with a different batch seed and check whether a flagged band stays significant — one-off flags are usually noise.
- Compare a favourite band's tight Wilson interval against a longshot band's wide one to see why long prices need far more events before a deviation means anything.