Race Winner Prediction

4.259 km lap length14 cornersHigh overtaking difficulty

Race date August 23, 2026 · generated Sep 8, 2026, 2:54 PM UTC · rain scenario weighted at 0%

Predicted vs. Actual

Predicted winner

ANT (24.8%)

Actual winner

NOR (missed)

Podium hits

2 / 3

Brier score (win)

0.0416

The model gave the actual winner (NOR) a 11.9% chance beforehand. This was a blind backtest — generated using only data available before the race, never this race's own results. See the track record page for the model's accuracy across every graded race.

Our model: 0.0416 · Grid-based baseline (always pick the polesitter): 0.0000 (baseline beats the model this time — lower is better)

Where the podium call missed

HAM (predicted podium) finished P4 instead.
NOR finished P1 — the model projected around P5.

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	▸ANT	Mercedes	24.8% ± 0.6%	55.9%	95.5%	3.96	2
2	▸HAM	Ferrari	19.4% ± 0.7%	48.8%	93.8%	4.47	4
3	▸RUS	Mercedes	14.1% ± 0.5%	40.4%	90.8%	5.15	3
4	▸NOR	McLaren	11.9% ± 0.5%	36.4%	89.3%	5.49	1
5	▸LEC	Ferrari	10.9% ± 0.4%	34.3%	88.5%	5.67	5
6	▸VER	Red Bull	7.2% ± 0.5%	26%	83.2%	6.57	22
7	▸PIA	McLaren	3.9% ± 0.3%	16.5%	73.7%	7.9	6
8	▸HAD	Red Bull	2.9% ± 0.2%	13.3%	68.7%	8.51	—
9	▸LAW	RB F1 Team	1.8% ± 0.2%	8.9%	59.3%	9.58	7
10	▸GAS	Alpine F1 Team	1.2% ± 0.2%	6.4%	52.4%	10.37	10
11	▸LIN	RB F1 Team	0.7% ± 0.1%	4.4%	44.4%	11.25	12
12	▸COL	Alpine F1 Team	0.5% ± 0.1%	3.1%	37.2%	12.07	14
13	▸BEA	Haas F1 Team	0.3% ± 0.1%	2.2%	31.6%	12.77	21
14	▸BOR	Audi	0.2% ± 0.1%	1.3%	24%	13.77	13
15	▸SAI	Williams	0.1%	0.9%	19.8%	14.41	16
16	▸ALB	Williams	0.1%	0.6%	14.7%	15.27	17
17	▸HUL	Audi	0%	0.2%	8%	16.73	8
18	▸STR	Aston Martin	0%	0%	2.7%	18.6	20
19	▸ALO	Aston Martin	0%	0.1%	6%	17.27	9
20	▸OCO	Haas F1 Team	0%	0.3%	11%	16.01	19
21	▸PER	Cadillac F1 Team	0%	0%	2.8%	18.53	15
22	▸BOT	Cadillac F1 Team	0%	0%	2.5%	18.67	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 0% of runs treated as a wet-race scenario (estimated from this circuit's own rainfall history).

This circuit's weighting — Dutch Grand Prix: overtaking difficulty High, downforce dependency High, 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.