Track record
Accuracy
How the forecasts have actually scored against real results — measured across every scored class-round, with nothing left out. Endurance grids are large, so a car landing within a few places of its prediction is a strong result.
Evidence
4 Oct 2026, 06:23 UTCOver 10 paired rounds, the model no difference demonstrated (last race order).
| Baseline | Race | Rounds | Model | Baseline | Gain | Verdict |
|---|---|---|---|---|---|---|
| Last race order | GTD | 10 | 4.85 | 5.10 | +0.25 | no difference demonstrated |
| Last race order | GTDPRO | 10 | 3.50 | 3.29 | -0.21 | no difference demonstrated |
| Last race order | GTP | 9 | 2.85 | 2.79 | -0.06 | no difference demonstrated |
| Last race order | LMP2 | 7 | 2.95 | 2.98 | +0.03 | no difference demonstrated |
| Season form order | GTD | 10 | 4.85 | 4.93 | +0.08 | no difference demonstrated |
| Season form order | GTDPRO | 10 | 3.50 | 3.57 | +0.07 | no difference demonstrated |
| Season form order | GTP | 9 | 2.85 | 2.87 | +0.02 | no difference demonstrated |
| Season form order | LMP2 | 7 | 2.95 | 2.93 | -0.02 | no difference demonstrated |
Paired bootstrap on the round-by-round difference: 0.251, 95% CI [-0.125, 0.636] in positions gained. Positive means the model is closer to the real finishing order than last race order is; the interval covers zero, so no difference has been demonstrated.
forward evaluation — each round was forecast before it ran and scored afterwards; not a backtest. Historical replays and the forward record are computed separately and never merged.
- Some comparisons straddle zero: a difference has not been demonstrated, in either direction.
- Metrics are only comparable within this series. A position error over this field size means nothing next to another series' number.
By class
Mean err.
2.85
Podium
52%
Winner
22%
Mean err.
2.99
Podium
43%
Winner
14%
Mean err.
3.50
Podium
33%
Winner
0%
Mean err.
4.85
Podium
30%
Winner
20%
Round by round
| Round | Cars | Mean error | Podium | Exact | Within 3 | Winner |
|---|---|---|---|---|---|---|
| R1Daytona International Speedway | 11 | 3.64 | 1/3 | 1/11 | 4/11 | ✗ |
| R2Sebring International Raceway | 11 | 1.64 | 3/3 | 0/11 | 11/11 | ✗ |
| R3Long Beach Street Circuit | 11 | 1.82 | 2/3 | 0/11 | 10/11 | ✗ |
| R4Weathertech Raceway Laguna Seca | 11 | 2.91 | 1/3 | 1/11 | 7/11 | ✗ |
| R5Detroit Street Course | 11 | 2.91 | 1/3 | 4/11 | 7/11 | ✓ |
| R6Watkins Glen International | 11 | 2.18 | 2/3 | 4/11 | 8/11 | ✓ |
| R8Road America | 11 | 4.91 | 0/3 | 0/11 | 3/11 | ✗ |
| R10Indianapolis Motor Speedway Rc | 11 | 2.91 | 2/3 | 2/11 | 8/11 | ✗ |
| R11Road Atlanta | 11 | 2.73 | 2/3 | 1/11 | 7/11 | ✗ |
Does the forecast beat guesswork?
Combined win + podium forecast error across 36 scored class-rounds — lower is better — measured against two naive baselines: simply repeating the last race’s order, and ranking cars by their season points.
Last-race form
baseline error 0.251
Season form
baseline error 0.268
“Matches it” means the model is statistically no worse than that baseline over the rounds scored so far. On a young season with few rounds, matching a strong season-form baseline while beating last-race form is an honest, expected result.
Calibration
Probabilities are calibrated on 11 real rounds of results, per class. Calibrated on real IMSA results, per class stratum.
Continuous evaluation
Every round is re-scored against simple baselines as results come in, so the track record above updates honestly over the season — no cherry-picking, nothing dropped.