Season Avg ρ0.581
Rounds Scored12
ROUND-BY-ROUND ACCURACY
| Rd | Event | ML ρ | Champ ρ | Circuit ρ | Grid ρ | Δ |
|---|---|---|---|---|---|---|
| 1 | Australian Grand Prix | 0.478 | — | — | 0.571 | -0.092 |
| 2 | Chinese Grand Prix | 0.796 | — | — | — | — |
| 3 | Japanese Grand Prix | -0.049 | 0.905 | — | 0.922 | -0.971 |
| 4 | Miami Grand Prix | 0.613 | 0.617 | — | 0.640 | -0.027 |
| 5 | Canadian Grand Prix | 0.426 | 0.527 | — | 0.448 | -0.023 |
| 6 | Monaco Grand Prix | 0.468 | 0.817 | — | 0.525 | -0.057 |
| 7 | Barcelona Grand Prix | 0.648 | 0.927 | — | 0.732 | -0.084 |
| 8 | Austrian Grand Prix | 0.834 | 0.382 | — | 0.871 | -0.037 |
| 9 | British Grand Prix | 0.272 | 0.830 | — | 0.583 | -0.312 |
| 10 | Belgian Grand Prix | 0.900 | 0.818 | — | 0.770 | +0.130 |
| 11 | Hungarian Grand Prix | 0.839 | 0.467 | — | 0.778 | +0.061 |
| 12 | Dutch Grand Prix | 0.742 | 0.800 | — | 0.716 | +0.026 |
Grid ρ = qualifying grid position vs actual finish — naive baseline
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