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F1

Miami Grand Prix

Round 4 · 2026
Miami Gardens
v 0.3.008 Jun 2026, 01:04 UTC
Based onQualifyingPracticeHistoryConstructor formWeatherRegulations
Max Verstappen is the model's pick at 35.2% (80% CI 34.3–36.1%) for Miami Grand Prix.

Top 10 drivers

bars are win / podium / points probabilitiesWeekend analytics →
  1. 1Max Verstappen
    VERMax Verstappen
    Red Bull RacingRed Bull RacingHIGH
    WIN
    35.2%
    POD
    71.9%
    PTS
    84.3%
    Why · 6 factors
    • Qualifying pacew 0.34

      Quali-pace score 0.97 (1.0 = pole).

    • Practice long-run pacew 0.19

      Long-run score 0.98 (1.0 = fastest median stint).

    • Driver skill (Elo)w 0.13

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

    • Track history (time-weighted)w 0.01

      Recency-weighted finish score 0.87, 5-year window, 12-month half-life, reg-discounted ×0.24.

    • Chassis at this circuitw 0.08

      Constructor-lineage best-finish score 0.88 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
    MercedesMercedesHIGH
    WIN
    17.9%
    POD
    47.3%
    PTS
    64.8%
    Why · 7 factors
    • Qualifying pacew 0.34

      Quali-pace score 0.93 (1.0 = pole).

    • Practice long-run pacew 0.19

      Long-run score 1.00 (1.0 = fastest median stint).

    • Constructor recent formw 0.25

      Team points trend score 0.92 across the last 5 races.

    • Driver skill (Elo)w 0.13

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

    • Track history (time-weighted)w 0.01

      Recency-weighted finish score 0.80, 5-year window, 12-month half-life, reg-discounted ×0.24.

    • Chassis at this circuitw 0.08

      Constructor-lineage best-finish score 0.85 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 ≈ 35.0% (team × circuit).

  3. 3Kimi Antonelli
    ANTKimi Antonelli
    MercedesMercedesHIGH
    WIN
    13.3%
    POD
    41.4%
    PTS
    65.5%
    Why · 7 factors
    • Qualifying pacew 0.34

      Quali-pace score 1.00 (1.0 = pole).

    • Practice long-run pacew 0.19

      Long-run score 1.00 (1.0 = fastest median stint).

    • Constructor recent formw 0.25

      Team points trend score 0.92 across the last 5 races.

    • Driver skill (Elo)w 0.13

      Teammate-only Elo skill score 0.26 (1.0 = top of grid).

    • Track history (time-weighted)w 0.01

      Recency-weighted finish score 0.74, 5-year window, 12-month half-life, reg-discounted ×0.24.

    • Chassis at this circuitw 0.08

      Constructor-lineage best-finish score 0.85 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 ≈ 35.0% (team × circuit).

  4. 4Isack Hadjar
    HADIsack Hadjar
    Red Bull RacingRed Bull RacingHIGH
    WIN
    7.1%
    POD
    30.1%
    PTS
    84.1%
    Why · 6 factors
    • Qualifying pacew 0.34

      Quali-pace score 0.83 (1.0 = pole).

    • Practice long-run pacew 0.19

      Long-run score 0.95 (1.0 = fastest median stint).

    • Driver skill (Elo)w 0.13

      Teammate-only Elo skill score 0.46 (1.0 = top of grid). Familiarity discount ×0.85.

    • Track history (time-weighted)w 0.01

      Recency-weighted finish score 0.47, 5-year window, 12-month half-life, reg-discounted ×0.24.

    • Chassis at this circuitw 0.08

      Constructor-lineage best-finish score 0.88 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).

  5. 5Franco Colapinto
    COLFranco Colapinto
    AlpineAlpineHIGH
    WIN
    6.7%
    POD
    26.4%
    PTS
    60.5%
    Why · 5 factors
    • Qualifying pacew 0.34

      Quali-pace score 0.84 (1.0 = pole).

    • Practice long-run pacew 0.19

      Long-run score 0.95 (1.0 = fastest median stint).

    • Driver skill (Elo)w 0.13

      Teammate-only Elo skill score 0.47 (1.0 = top of grid).

    • Chassis at this circuitw 0.08

      Constructor-lineage best-finish score 0.43 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. 6Lewis Hamilton
    HAMLewis Hamilton
    FerrariFerrariHIGH
    WIN
    4.7%
    POD
    19.6%
    PTS
    69.8%
    Why · 7 factors
    • Qualifying pacew 0.34

      Quali-pace score 0.91 (1.0 = pole).

    • Practice long-run pacew 0.19

      Long-run score 0.98 (1.0 = fastest median stint).

    • Constructor recent formw 0.25

      Team points trend score 0.57 across the last 5 races.

    • Driver skill (Elo)w 0.13

      Teammate-only Elo skill score 0.23 (1.0 = top of grid).

    • Track history (time-weighted)w 0.01

      Recency-weighted finish score 0.66, 5-year window, 12-month half-life, reg-discounted ×0.24.

    • Chassis at this circuitw 0.08

      Constructor-lineage best-finish score 0.75 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).

  7. 7Charles Leclerc
    LECCharles Leclerc
    FerrariFerrariHIGH
    WIN
    4.1%
    POD
    15.2%
    PTS
    68.8%
    Why · 7 factors
    • Qualifying pacew 0.34

      Quali-pace score 0.94 (1.0 = pole).

    • Practice long-run pacew 0.19

      Long-run score 0.51 (1.0 = fastest median stint).

    • Constructor recent formw 0.25

      Team points trend score 0.57 across the last 5 races.

    • Driver skill (Elo)w 0.13

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

    • Track history (time-weighted)w 0.01

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

    • Chassis at this circuitw 0.08

      Constructor-lineage best-finish score 0.75 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).

  8. 8Oscar Piastri
    PIAOscar Piastri
    McLarenMcLarenHIGH
    WIN
    2.3%
    POD
    9.7%
    PTS
    54.4%
    Why · 7 factors
    • Qualifying pacew 0.34

      Quali-pace score 0.91 (1.0 = pole).

    • Practice long-run pacew 0.19

      Long-run score 0.87 (1.0 = fastest median stint).

    • Constructor recent formw 0.25

      Team points trend score 0.33 across the last 5 races.

    • Driver skill (Elo)w 0.13

      Teammate-only Elo skill score 0.44 (1.0 = top of grid).

    • Track history (time-weighted)w 0.01

      Recency-weighted finish score 0.68, 5-year window, 12-month half-life, reg-discounted ×0.24.

    • Chassis at this circuitw 0.08

      Constructor-lineage best-finish score 0.93 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).

  9. 9Pierre Gasly
    GASPierre Gasly
    AlpineAlpineHIGH
    WIN
    2.2%
    POD
    10.4%
    PTS
    57.0%
    Why · 6 factors
    • Qualifying pacew 0.34

      Quali-pace score 0.83 (1.0 = pole).

    • Practice long-run pacew 0.19

      Long-run score 0.52 (1.0 = fastest median stint).

    • Driver skill (Elo)w 0.13

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

    • Track history (time-weighted)w 0.01

      Recency-weighted finish score 0.39, 5-year window, 12-month half-life, reg-discounted ×0.24.

    • Chassis at this circuitw 0.08

      Constructor-lineage best-finish score 0.43 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 TeamHIGH
    WIN
    1.2%
    POD
    5.3%
    PTS
    60.5%
    Why · 7 factors
    • Qualifying pacew 0.34

      Quali-pace score 0.74 (1.0 = pole).

    • Practice long-run pacew 0.19

      Long-run score 0.94 (1.0 = fastest median stint).

    • Constructor recent formw 0.25

      Team points trend score 0.12 across the last 5 races.

    • Driver skill (Elo)w 0.13

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

    • Track history (time-weighted)w 0.01

      Recency-weighted finish score 0.05, 5-year window, 12-month half-life, reg-discounted ×0.24.

    • Chassis at this circuitw 0.08

      Constructor-lineage best-finish score 0.44 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·P3ANT
    3.56%
  2. 2
    P1VER·P2ANT·P3RUS
    3.39%
  3. 3
    P1VER·P2RUS·P3HAD
    2.59%
  4. 4
    P1RUS·P2VER·P3ANT
    2.39%
  5. 5
    P1VER·P2RUS·P3COL
    2.27%
  6. 6
    P1VER·P2HAD·P3RUS
    2.26%
  7. 7
    P1VER·P2ANT·P3HAD
    2.16%
  8. 8
    P1VER·P2HAD·P3ANT
    1.98%
  9. 9
    P1VER·P2COL·P3RUS
    1.93%
  10. 10
    P1RUS·P2ANT·P3VER
    1.90%

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.