Race Winner Prediction

5.474 km lap length20 cornersMedium overtaking difficulty

Race date September 13, 2026 · generated Sep 13, 2026, 5:57 PM UTC · rain scenario weighted at 10%

Predicted vs. Actual

Predicted winner

NOR (34.6%)

Actual winner

ANT (missed)

Podium hits

2 / 3

Brier score (win)

0.0392

The model gave the actual winner (ANT) a 20.2% chance beforehand. Predicted live, before the race. See the track record page for the model's accuracy across every graded race.

Our model: 0.0392 · Grid-based baseline (always pick the polesitter): 0.1000 (model beats the baseline — lower is better)

Where the podium call missed

HAM (predicted podium) didn't finish — Retired.
VER finished P2 — the model projected around P6.

Predicted vs. actual finishing position, every driver

Green, right — finished better than the model expected (underrated). Red, left — finished worse (overrated). Sorted by size of the miss.

Win Probability — Top 10

Error bars show ± 1 standard deviation of win probability across batches of the Monte Carlo simulation — how much this estimate would wobble on a re-run, not a formal confidence interval.

Full Prediction Table

Click a row to see what drove that driver's score.

Pos	Driver	Team	Win %	Podium %	Points %	Exp. Pos	Actual
1	▸NOR	McLaren	34.6% ± 0.6%	67.5%	96.8%	3.26	3
2	▸ANT	Mercedes	20.2% ± 0.6%	52.3%	93.6%	4.31	1
3	▸HAM	Ferrari	9.2% ± 0.4%	31.5%	84.6%	6.14	22
4	▸VER	Red Bull	8.1% ± 0.3%	29.4%	83.6%	6.34	2
5	▸RUS	Mercedes	7.5% ± 0.4%	26.8%	80.4%	6.76	5
6	▸LEC	Ferrari	6% ± 0.4%	23.2%	77.8%	7.17	4
7	▸PIA	McLaren	3.7% ± 0.4%	15.4%	67.4%	8.52	8
8	▸LAW	Red Bull	2.8% ± 0.2%	12.3%	61.4%	9.23	6
9	▸COL	Alpine F1 Team	1.7% ± 0.1%	8.2%	51%	10.44	7
10	▸LIN	RB F1 Team	1.7% ± 0.1%	8.1%	50.5%	10.53	9
11	▸HUL	Audi	1% ± 0.1%	5.2%	39.4%	11.85	10
12	▸GAS	Alpine F1 Team	1% ± 0.1%	5.3%	42.1%	11.51	12
13	▸BOR	Audi	0.9% ± 0.1%	5.2%	40%	11.77	13
14	▸OCO	Haas F1 Team	0.4% ± 0.1%	2.6%	27.3%	13.37	11
15	▸ALB	Williams	0.3% ± 0.1%	1.8%	23.3%	13.85	15
16	▸SAI	Williams	0.3% ± 0.1%	1.8%	24.2%	13.73	19
17	▸TSU	RB F1 Team	0.2% ± 0.1%	1.4%	19.9%	14.39	14
18	▸ALO	Aston Martin	0.1% ± 0.1%	1%	16.6%	14.83	17
19	▸PER	Cadillac F1 Team	0.1%	0.5%	10.7%	15.88	20
20	▸BOT	Cadillac F1 Team	0.1%	0.4%	9.4%	16.12	18

Methodology

Each driver's simulated race pace is sampled from a distribution weighted by qualifying pace percentile, top-speed ranking, season championship standings, historical finishing position at this circuit, tire degradation, and pit-stop delta. The simulation runs 100,000 times, with 10% of runs treated as a wet-race scenario (estimated from this circuit's own rainfall history).

This circuit's weighting — Spanish Grand Prix: overtaking difficulty Medium, downforce dependency Medium, tire severity Medium. Higher overtaking difficulty shifts weight toward grid position; lower downforce dependency shifts weight toward straight-line top speed; higher tire severity shifts weight toward tire-degradation management. See circuit_profiles.py for every circuit's numbers and how they translate into weights.