Skip to main content
F1

São Paulo Grand Prix

Round 19 · 2026
São Paulo
v 0.3.008 Jun 2026, 13:45 UTC
Based onHistoryRegulations
Max Verstappen is the model's pick at 35.8% (80% CI 34.9–36.6%) for São Paulo Grand Prix.

Top 10 drivers

bars are win / podium / points probabilitiesWeekend analytics →
  1. 1Max Verstappen
    VERMax Verstappen
    Red Bull RacingRed Bull RacingLOW
    WIN
    35.8%
    POD
    69.3%
    PTS
    84.8%
    Why · 4 factors
    • Driver skill (Elo)w 0.58

      Teammate-only Elo skill score 1.00 (1.0 = top of grid). Familiarity discount ×0.85.

    • Track history (time-weighted)w 0.07

      Recency-weighted finish score 0.93, 5-year window, 12-month half-life, reg-discounted ×0.24.

    • Chassis at this circuitw 0.35

      Constructor-lineage best-finish score 0.92 at this circuit across the last 5 seasons. Driver-agnostic — captures chassis-DNA fit independent of who drove.

    • DNF riskw 0.15

      Per-race DNF probability ≈ 15.0% (team × circuit).

  2. 2George Russell
    RUSGeorge Russell
    MercedesMercedesLOW
    WIN
    9.8%
    POD
    33.4%
    PTS
    75.7%
    Why · 4 factors
    • Driver skill (Elo)w 0.58

      Teammate-only Elo skill score 0.64 (1.0 = top of grid).

    • Track history (time-weighted)w 0.07

      Recency-weighted finish score 0.76, 5-year window, 12-month half-life, reg-discounted ×0.24.

    • Chassis at this circuitw 0.35

      Constructor-lineage best-finish score 0.90 at this circuit across the last 5 seasons. Driver-agnostic — captures chassis-DNA fit independent of who drove.

    • DNF riskw 0.20

      Per-race DNF probability ≈ 23.9% (team × circuit).

  3. 3Charles Leclerc
    LECCharles Leclerc
    FerrariFerrariLOW
    WIN
    7.5%
    POD
    24.5%
    PTS
    66.1%
    Why · 4 factors
    • Driver skill (Elo)w 0.58

      Teammate-only Elo skill score 0.65 (1.0 = top of grid).

    • Track history (time-weighted)w 0.07

      Recency-weighted finish score 0.31, 5-year window, 12-month half-life, reg-discounted ×0.24.

    • Chassis at this circuitw 0.35

      Constructor-lineage best-finish score 0.32 at this circuit across the last 5 seasons. Driver-agnostic — captures chassis-DNA fit independent of who drove.

    • DNF riskw 0.20

      Per-race DNF probability ≈ 29.4% (team × circuit).

  4. 4Lando Norris
    NORLando Norris
    McLarenMcLarenLOW
    WIN
    6.7%
    POD
    21.7%
    PTS
    57.1%
    Why · 4 factors
    • Driver skill (Elo)w 0.58

      Teammate-only Elo skill score 0.61 (1.0 = top of grid).

    • Track history (time-weighted)w 0.07

      Recency-weighted finish score 0.85, 5-year window, 12-month half-life, reg-discounted ×0.24.

    • Chassis at this circuitw 0.35

      Constructor-lineage best-finish score 0.92 at this circuit across the last 5 seasons. Driver-agnostic — captures chassis-DNA fit independent of who drove.

    • DNF riskw 0.20

      Per-race DNF probability ≈ 40.0% (team × circuit).

  5. 5Pierre Gasly
    GASPierre Gasly
    AlpineAlpineLOW
    WIN
    5.9%
    POD
    20.4%
    PTS
    55.2%
    Why · 4 factors
    • Driver skill (Elo)w 0.58

      Teammate-only Elo skill score 0.60 (1.0 = top of grid).

    • Track history (time-weighted)w 0.07

      Recency-weighted finish score 0.63, 5-year window, 12-month half-life, reg-discounted ×0.24.

    • Chassis at this circuitw 0.35

      Constructor-lineage best-finish score 0.64 at this circuit across the last 5 seasons. Driver-agnostic — captures chassis-DNA fit independent of who drove.

    • DNF riskw 0.20

      Per-race DNF probability ≈ 40.0% (team × circuit).

  6. 6Fernando Alonso
    ALOFernando Alonso
    Aston MartinAston MartinLOW
    WIN
    4.0%
    POD
    15.8%
    PTS
    65.8%
    Why · 4 factors
    • Driver skill (Elo)w 0.58

      Teammate-only Elo skill score 0.57 (1.0 = top of grid). Familiarity discount ×0.85.

    • Track history (time-weighted)w 0.07

      Recency-weighted finish score 0.43, 5-year window, 12-month half-life, reg-discounted ×0.24.

    • Chassis at this circuitw 0.35

      Constructor-lineage best-finish score 0.36 at this circuit across the last 5 seasons. Driver-agnostic — captures chassis-DNA fit independent of who drove.

    • DNF riskw 0.15

      Per-race DNF probability ≈ 15.0% (team × circuit).

  7. 7Alexander Albon
    ALBAlexander Albon
    WilliamsWilliamsLOW
    WIN
    3.6%
    POD
    12.9%
    PTS
    50.5%
    Why · 4 factors
    • Driver skill (Elo)w 0.58

      Teammate-only Elo skill score 0.55 (1.0 = top of grid).

    • Track history (time-weighted)w 0.07

      Recency-weighted finish score 0.31, 5-year window, 12-month half-life, reg-discounted ×0.24.

    • Chassis at this circuitw 0.35

      Constructor-lineage best-finish score 0.40 at this circuit across the last 5 seasons. Driver-agnostic — captures chassis-DNA fit independent of who drove.

    • DNF riskw 0.20

      Per-race DNF probability ≈ 40.0% (team × circuit).

  8. 8Esteban Ocon
    OCOEsteban Ocon
    Haas F1 TeamHaas F1 TeamLOW
    WIN
    3.1%
    POD
    11.0%
    PTS
    56.0%
    Why · 4 factors
    • Driver skill (Elo)w 0.58

      Teammate-only Elo skill score 0.47 (1.0 = top of grid). Familiarity discount ×0.85.

    • Track history (time-weighted)w 0.07

      Recency-weighted finish score 0.59, 5-year window, 12-month half-life, reg-discounted ×0.24.

    • Chassis at this circuitw 0.35

      Constructor-lineage best-finish score 0.63 at this circuit across the last 5 seasons. Driver-agnostic — captures chassis-DNA fit independent of who drove.

    • DNF riskw 0.15

      Per-race DNF probability ≈ 15.0% (team × circuit).

  9. 9Carlos Sainz
    SAICarlos Sainz
    WilliamsWilliamsLOW
    WIN
    2.9%
    POD
    10.7%
    PTS
    45.1%
    Why · 4 factors
    • Driver skill (Elo)w 0.58

      Teammate-only Elo skill score 0.50 (1.0 = top of grid).

    • Track history (time-weighted)w 0.07

      Recency-weighted finish score 0.42, 5-year window, 12-month half-life, reg-discounted ×0.24.

    • Chassis at this circuitw 0.35

      Constructor-lineage best-finish score 0.40 at this circuit across the last 5 seasons. Driver-agnostic — captures chassis-DNA fit independent of who drove.

    • DNF riskw 0.20

      Per-race DNF probability ≈ 40.0% (team × circuit).

  10. 10Oliver Bearman
    BEAOliver Bearman
    Haas F1 TeamHaas F1 TeamLOW
    WIN
    2.7%
    POD
    12.0%
    PTS
    59.8%
    Why · 4 factors
    • Driver skill (Elo)w 0.58

      Teammate-only Elo skill score 0.49 (1.0 = top of grid). Familiarity discount ×0.85.

    • Track history (time-weighted)w 0.07

      Recency-weighted finish score 0.63, 5-year window, 12-month half-life, reg-discounted ×0.24.

    • Chassis at this circuitw 0.35

      Constructor-lineage best-finish score 0.63 at this circuit across the last 5 seasons. Driver-agnostic — captures chassis-DNA fit independent of who drove.

    • DNF riskw 0.15

      Per-race DNF probability ≈ 15.0% (team × circuit).

Most-likely podium combinations

approximate · 1st-2nd-3rd
  1. 1
    P1VER·P2RUS·P3LEC
    3.07%
  2. 2
    P1VER·P2LEC·P3RUS
    2.77%
  3. 3
    P1VER·P2RUS·P3NOR
    2.72%
  4. 4
    P1VER·P2RUS·P3GAS
    2.56%
  5. 5
    P1VER·P2NOR·P3RUS
    2.38%
  6. 6
    P1VER·P2GAS·P3RUS
    2.21%
  7. 7
    P1VER·P2RUS·P3ALO
    1.98%
  8. 8
    P1VER·P2LEC·P3NOR
    1.80%
  9. 9
    P1VER·P2NOR·P3LEC
    1.75%
  10. 10
    P1VER·P2LEC·P3GAS
    1.69%

Methodology

Probabilities come from a layered ensemble: historical base rates per circuit, current constructor form (rolling 6 races), qualifying outcome, and a weather adjustment. Confidence intervals are 80% credible bands from the win-share posterior. See 0.3.0 for the active weights.