Race Winner Prediction

4.381 km lap length14 cornersHigh overtaking difficulty

Race date July 26, 2026 · generated Sep 8, 2026, 2:11 PM UTC · rain scenario weighted at 0%

Predicted vs. Actual

Predicted winner

ANT (33.2%)

Actual winner

NOR (missed)

Podium hits

1 / 3

Brier score (win)

0.0476

The model gave the actual winner (NOR) a 7% 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: 0.0476 · 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 P5 instead.
RUS (predicted podium) finished P7 instead.
NOR finished P1 — the model projected around P6.
VER finished P2 — the model projected around P7.

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	33.2%	66.6%	97.7%	3.22	3
2	▸HAM	Ferrari	19%	50.6%	94.8%	4.29	5
3	▸RUS	Mercedes	12.6%	39.1%	91%	5.2	7
4	▸LEC	Ferrari	11.9%	37.5%	90.7%	5.3	4
5	▸NOR	McLaren	7%	27.1%	84.8%	6.36	1
6	▸PIA	McLaren	5.6%	22.5%	81.5%	6.88	20
7	▸VER	Red Bull	4.3%	18.6%	77.4%	7.45	2
8	▸HAD	Red Bull	2.4%	11.9%	67.6%	8.67	6
9	▸LAW	RB F1 Team	1.6%	8.5%	60.5%	9.49	8
10	▸GAS	Alpine F1 Team	0.9%	5.9%	51.8%	10.44	12
11	▸LIN	RB F1 Team	0.7%	4.2%	44.9%	11.18	10
12	▸COL	Alpine F1 Team	0.3%	2.6%	36%	12.21	15
13	▸BEA	Haas F1 Team	0.2%	1.7%	29%	13.09	19
14	▸BOR	Audi	0.2%	1.4%	26.5%	13.43	11
15	▸SAI	Williams	0.1%	0.8%	20%	14.37	18
16	▸ALB	Williams	0.1%	0.5%	14.5%	15.28	17
17	▸HUL	Audi	0%	0%	3.1%	18.37	9
18	▸OCO	Haas F1 Team	0%	0.3%	10.6%	16.07	16
19	▸BOT	Cadillac F1 Team	0%	0%	3.1%	18.38	22
20	▸ALO	Aston Martin	0%	0.2%	8.2%	16.65	14
21	▸STR	Aston Martin	0%	0%	3.1%	18.36	13
22	▸PER	Cadillac F1 Team	0%	0%	3.2%	18.3	21

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 — Hungarian 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.