United States Grand Prix
Race Winner Prediction
Race date October 25, 2026 · generated Oct 13, 2026, 6:53 PM UTC · rain scenario weighted at 0%
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 |
|---|---|---|---|---|---|---|
| 1 | ▸ANT | Mercedes | 24.8% ± 0.7% | 56.2% | 95.3% | 3.96 |
| 2 | ▸VER | Red Bull | 21.3% ± 0.6% | 51.4% | 94.3% | 4.29 |
| 3 | ▸RUS | Mercedes | 11.3% ± 0.4% | 35.1% | 88.3% | 5.65 |
| 4 | ▸LEC | Ferrari | 11.2% ± 0.6% | 34.5% | 88.1% | 5.69 |
| 5 | ▸NOR | McLaren | 10.9% ± 0.3% | 34.4% | 88.1% | 5.69 |
| 6 | ▸HAM | Ferrari | 8.6% ± 0.2% | 29.2% | 84.5% | 6.27 |
| 7 | ▸PIA | McLaren | 4.3% ± 0.3% | 17.4% | 74% | 7.82 |
| 8 | ▸HAD | Red Bull | 2.6% ± 0.2% | 12% | 65.6% | 8.87 |
| 9 | ▸LAW | RB F1 Team | 1.9% ± 0.1% | 9.8% | 60.7% | 9.45 |
| 10 | ▸LIN | RB F1 Team | 1.1% ± 0.1% | 6% | 49.8% | 10.68 |
| 11 | ▸GAS | Alpine F1 Team | 0.6% ± 0.1% | 3.6% | 39.4% | 11.87 |
| 12 | ▸HUL | Audi | 0.6% ± 0.1% | 3.4% | 38.3% | 12.02 |
| 13 | ▸COL | Alpine F1 Team | 0.5% ± 0.1% | 3.3% | 37.7% | 12.07 |
| 14 | ▸BEA | Haas F1 Team | 0.1% | 0.9% | 18.6% | 14.66 |
| 15 | ▸BOR | Audi | 0.1% | 0.8% | 17.8% | 14.83 |
| 16 | ▸OCO | Haas F1 Team | 0.1% | 0.6% | 14.7% | 15.38 |
| 17 | ▸ALO | Aston Martin | 0.1% | 0.9% | 19.2% | 14.61 |
| 18 | ▸SAI | Williams | 0% | 0.3% | 10.1% | 16.3 |
| 19 | ▸ALB | Williams | 0% | 0.1% | 4.7% | 17.88 |
| 20 | ▸PER | Cadillac F1 Team | 0% | 0.1% | 3.9% | 18.25 |
| 21 | ▸STR | Aston Martin | 0% | 0.1% | 3.4% | 18.4 |
| 22 | ▸BOT | Cadillac F1 Team | 0% | 0.1% | 3.5% | 18.35 |
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 — United States Grand Prix: overtaking difficulty Medium, downforce dependency Medium, tire severity High. 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.