Sport statistics
Horse racing
Synthetic multi-runner fields with independent performance and pricing models for calibration research.
Dataset synthetic-v1 · generated 2026-07-26T03:36:42.407Z
Events
2,000
Average margin
18.15%
Model version
1.0.0
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 |
|---|---|---|---|---|---|---|
| Runner 1 | 2,000 | 22.96% | 24.00% | 22.18% – 25.92% | +1.04% | within noise |
| Runner 2 | 2,000 | 22.96% | 24.10% | 22.28% – 26.02% | +1.14% | within noise |
| Runner 3 | 2,000 | 13.92% | 14.90% | 13.41% – 16.53% | +0.98% | within noise |
| Runner 4 | 2,000 | 13.17% | 11.50% | 10.18% – 12.97% | -1.67% | p 0.027 |
| Runner 5 | 2,000 | 8.80% | 7.20% | 6.15% – 8.42% | -1.60% | p 0.011 |
| Runner 6 | 2,000 | 6.82% | 6.30% | 5.32% – 7.45% | -0.52% | within noise |
| Runner 7 | 2,000 | 4.62% | 4.90% | 4.04% – 5.94% | +0.28% | within noise |
| Runner 8 | 2,000 | 3.44% | 3.50% | 2.78% – 4.40% | +0.06% | within noise |
| Runner 9 | 2,000 | 3.31% | 3.60% | 2.87% – 4.51% | +0.29% | within noise |
Edge detection
Edge detection
Mathematical discrepancies
14 synthetic discrepancy signals detected (0 strong, 2 notable). Illustrative only — look-ahead signals are not realizable live edges.
Runner 1: priced vs fair
The priced probability sits above the de-vigged fair line — the built-in margin you pay on this selection.
notable+4.07%
Runner 2: priced vs fair
The priced probability sits above the de-vigged fair line — the built-in margin you pay on this selection.
notable+4.07%
Runner 4: calibration drift
Observed win rate differs from the price-implied rate by a statistically significant margin in this sample.
info-4.00%
Runner 1: 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-3.39%
Runner 4: 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-3.38%
Runner 5: calibration drift
Observed win rate differs from the price-implied rate by a statistically significant margin in this sample.
info-3.16%
Runner 2: 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-3.15%
Runner 1: calibration drift
Observed win rate differs from the price-implied rate by a statistically significant margin in this sample.
info-3.03%
Runner 3: priced vs fair
The priced probability sits above the de-vigged fair line — the built-in margin you pay on this selection.
info+2.47%
Runner 4: priced vs fair
The priced probability sits above the de-vigged fair line — the built-in margin you pay on this selection.
info+2.33%
Runner 5: 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-2.22%
Runner 3: 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-2.11%
Runner 5: priced vs fair
The priced probability sits above the de-vigged fair line — the built-in margin you pay on this selection.
info+1.56%
Runner 6: priced vs fair
The priced probability sits above the de-vigged fair line — the built-in margin you pay on this selection.
info+1.21%
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.