Race Winner Prediction

5.793 km lap length11 cornersLow overtaking difficulty

Race date September 6, 2026 · generated Sep 8, 2026, 2:57 PM UTC · rain scenario weighted at 0%

Predicted vs. Actual

Predicted winner

ANT (24.1%)

Actual winner

ANT (correct)

Podium hits

2 / 3

Brier score (win)

0.0302

The model gave the actual winner (ANT) a 24.1% 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: 0.0302 · Grid-based baseline (always pick the polesitter): 0.0909 (model beats the baseline — lower is better)

Where the podium call missed

HAM (predicted podium) finished P6 instead.
VER finished P3 — 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	24.1% ± 0.6%	54.4%	94.2%	4.15	1
2	▸RUS	Mercedes	16.8% ± 0.4%	44.2%	90.8%	4.97	2
3	▸HAM	Ferrari	16.6% ± 0.5%	44.1%	90.8%	4.97	6
4	▸NOR	McLaren	13% ± 0.5%	37.5%	87.8%	5.56	4
5	▸LEC	Ferrari	8.6% ± 0.4%	28.5%	82.4%	6.5	22
6	▸VER	Red Bull	6.3% ± 0.4%	22.8%	77%	7.3	3
7	▸PIA	McLaren	4.7% ± 0.3%	18.2%	72.2%	7.95	5
8	▸LAW	Red Bull	3.1% ± 0.2%	13.6%	65.2%	8.85	14
9	▸GAS	Alpine F1 Team	2.2% ± 0.2%	10.3%	58.7%	9.63	7
10	▸LIN	RB F1 Team	1.5% ± 0.2%	7.7%	52.3%	10.39	8
11	▸COL	Alpine F1 Team	1% ± 0.1%	5.5%	45.2%	11.24	9
12	▸BEA	Haas F1 Team	0.7% ± 0.1%	4.1%	38.9%	11.98	15
13	▸BOR	Audi	0.5% ± 0.1%	3.1%	33.5%	12.66	11
14	▸HUL	Audi	0.3% ± 0.1%	1.7%	24.4%	13.94	12
15	▸SAI	Williams	0.2% ± 0.1%	1.8%	25.4%	13.78	13
16	▸ALO	Aston Martin	0.1%	0.5%	11.8%	16.13	21
17	▸ALB	Williams	0.1% ± 0.1%	1%	17.9%	14.96	17
18	▸TSU	RB F1 Team	0%	0.1%	4.7%	18.09	10
19	▸BOT	Cadillac F1 Team	0%	0.1%	5.1%	17.92	19
20	▸PER	Cadillac F1 Team	0%	0.1%	5.3%	17.88	18
21	▸STR	Aston Martin	0%	0.1%	4.8%	18.03	20
22	▸OCO	Haas F1 Team	0%	0.5%	11.9%	16.11	16

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 — Italian Grand Prix: overtaking difficulty Low, downforce dependency Low, tire severity Low. 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.