Season Avg ρ0.787
Rounds Scored18
BY DRIVER — 21 ROUNDS SCORED
| Driver | Count↕ | Within 2▼ | Avg Δ↕ | Bias |
|---|---|---|---|---|
| PER | 15 | 26.7% | -4.07 | OVER |
| STR | 9 | 33.3% | -1.44 | OVER |
| TSU | 3 | 33.3% | +0.33 | NEUTRAL |
| BOT | 12 | 41.7% | -0.25 | NEUTRAL |
| COL | 19 | 42.1% | -1.26 | OVER |
| SAI | 18 | 44.4% | -4.28 | OVER |
| ALO | 13 | 46.2% | -2.54 | OVER |
| ALB | 15 | 46.7% | -1.73 | OVER |
| BEA | 15 | 46.7% | -2.33 | OVER |
| HAD | 15 | 46.7% | -2.07 | OVER |
| GAS | 19 | 47.4% | -1.95 | OVER |
| LAW | 20 | 50.0% | -1.15 | OVER |
| NOR | 16 | 50.0% | -0.12 | NEUTRAL |
| PIA | 15 | 53.3% | +1.87 | UNDER |
| LIN | 20 | 55.0% | -2.00 | OVER |
| OCO | 20 | 55.0% | -1.10 | OVER |
| HUL | 16 | 56.2% | -1.44 | OVER |
| BOR | 19 | 57.9% | -1.32 | OVER |
| VER | 16 | 62.5% | -3.75 | OVER |
| HAM | 20 | 65.0% | -1.25 | OVER |
| LEC | 18 | 66.7% | +0.83 | UNDER |
| RUS | 19 | 73.7% | +2.21 | UNDER |
| ANT | 20 | 85.0% | +0.15 | NEUTRAL |
over = model over-predicted position (finished better than model expected) · under = model under-predicted · Avg Δ: positive means model underestimated finish position
ML ρ: Spearman rank correlation between model-predicted and actual race finishing order · Champ ρ: how a model predicting current championship standings order would score (Phase 92 baseline) · Circuit ρ: how a model using historical circuit results would score (circuit prior baseline) · Grid ρ: correlation between qualifying grid position and actual race finish (naive baseline) · Δ = ML ρ − Grid ρ · positive means model outperforms grid order · ML ρ cell: green = beats championship baseline, red = below · methodology
Pipeline Reliabilityaggregated artifact-reliability data across all 2026 events
- live/timing/driver_countcritical100.0% fail rate · last: Spanish Grand Prix
0 drivers
- live/pace/entry_countcritical100.0% fail rate · last: Spanish Grand Prix
0 entries
- live/currentcritical100.0% fail rate · last: Spanish Grand Prix
timed out