Prediction Credibility
Every race prediction is locked before the result is known, then scored against the actual outcome. This page shows the running season score and how our model compares to naive baselines — the qualifying grid order and championship standing. A credible model must consistently beat both.
Full Scoreboard →Model vs Baselines — Spearman ρ per Round
Model accuracy compared to simply using the qualifying grid order or championship standing as a prediction.
Pit Window Confidence Calibration
Stated confidence vs actual within-2-lap hit rate per confidence bucket. A well-calibrated model's bars should be close in height.
Qualifying Prediction Accuracy
% of drivers whose predicted qualifying time was within 0.3 s of their actual best lap. Green ≥ 60%, yellow ≥ 35%.
| Event | Within 0.3s |
|---|---|
| R1 Australian Grand Prix | 25.0% |
| R3 Japanese Grand Prix | 36.4% |
| R4 Miami Grand Prix | 4.5% |
| R5 Canadian Grand Prix | 0.0% |
| R6 Monaco Grand Prix | 45.5% |
| R7 Barcelona Grand Prix | 0.0% |
| R8 Austrian Grand Prix | 0.0% |
| R9 British Grand Prix | 9.1% |
| R10 Belgian Grand Prix | 0.0% |
| R11 Hungarian Grand Prix | 14.3% |
| R12 Dutch Grand Prix | 0.0% |
| R13 Italian Grand Prix | 4.5% |
| R14 Spanish Grand Prix | 4.8% |
Spearman ρ measures rank correlation between predicted and actual finishing order (1.0 = perfect, 0 = random, −1 = inverted). Grid baseline is the ρ achieved by simply using the qualifying grid order as the prediction — the minimum bar any useful model must clear. Championship baseline is the ρ achieved by predicting in championship-standing order. All predictions are locked 90 minutes before session start — no retroactive changes.