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F1

Australian Grand Prix

Round 1 · 2026
Melbourne
v 0.3.008 Jun 2026, 01:02 UTC
Based onQualifyingPracticeHistoryConstructor formWeatherRegulations
Max Verstappen is the model's pick at 36.2% (80% CI 35.3–37.1%) for Australian Grand Prix.

Top 10 drivers

bars are win / podium / points probabilitiesWeekend analytics →
  1. 1Max Verstappen
    VERMax Verstappen
    Red Bull RacingRed Bull RacingHIGH
    WIN
    36.2%
    POD
    71.5%
    PTS
    84.3%
    Why · 5 factors
    • Practice long-run pacew 0.13

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

    • Driver skill (Elo)w 0.09

      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 0.67, 5-year window, 12-month half-life, reg-discounted ×0.15.

    • Chassis at this circuitw 0.05

      Constructor-lineage best-finish score 0.91 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
    18.0%
    POD
    45.1%
    PTS
    60.5%
    Why · 7 factors
    • Qualifying pacew 0.55

      Quali-pace score 1.00 (1.0 = pole).

    • Practice long-run pacew 0.13

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

    • Constructor recent formw 0.17

      Team points trend score 0.47 across the last 5 races.

    • Driver skill (Elo)w 0.09

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

    • Track history (time-weighted)w 0.01

      Recency-weighted finish score 0.58, 5-year window, 12-month half-life, reg-discounted ×0.15.

    • Chassis at this circuitw 0.05

      Constructor-lineage best-finish score 0.72 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).

  3. 3Oscar Piastri
    PIAOscar Piastri
    McLarenMcLarenHIGH
    WIN
    6.9%
    POD
    25.8%
    PTS
    67.6%
    Why · 7 factors
    • Qualifying pacew 0.55

      Quali-pace score 0.82 (1.0 = pole).

    • Practice long-run pacew 0.13

      Long-run score 0.80 (1.0 = fastest median stint).

    • Constructor recent formw 0.17

      Team points trend score 0.61 across the last 5 races.

    • Driver skill (Elo)w 0.09

      Teammate-only Elo skill score 0.44 (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 ×0.15.

    • Chassis at this circuitw 0.05

      Constructor-lineage best-finish score 0.95 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. 4Kimi Antonelli
    ANTKimi Antonelli
    MercedesMercedesHIGH
    WIN
    5.8%
    POD
    19.7%
    PTS
    57.6%
    Why · 7 factors
    • Qualifying pacew 0.55

      Quali-pace score 0.94 (1.0 = pole).

    • Practice long-run pacew 0.13

      Long-run score 0.48 (1.0 = fastest median stint).

    • Constructor recent formw 0.17

      Team points trend score 0.47 across the last 5 races.

    • Driver skill (Elo)w 0.09

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

    • Track history (time-weighted)w 0.01

      Recency-weighted finish score 0.84, 5-year window, 12-month half-life, reg-discounted ×0.15.

    • Chassis at this circuitw 0.05

      Constructor-lineage best-finish score 0.72 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. 5Isack Hadjar
    HADIsack Hadjar
    Red Bull RacingRed Bull RacingHIGH
    WIN
    5.7%
    POD
    23.2%
    PTS
    80.3%
    Why · 6 factors
    • Qualifying pacew 0.55

      Quali-pace score 0.83 (1.0 = pole).

    • Practice long-run pacew 0.13

      Long-run score 0.38 (1.0 = fastest median stint).

    • Driver skill (Elo)w 0.09

      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 0.00, 5-year window, 12-month half-life, reg-discounted ×0.15.

    • Chassis at this circuitw 0.05

      Constructor-lineage best-finish score 0.91 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).

  6. 6Liam Lawson
    LAWLiam Lawson
    Racing BullsRacing BullsHIGH
    WIN
    4.2%
    POD
    18.7%
    PTS
    77.0%
    Why · 6 factors
    • Qualifying pacew 0.55

      Quali-pace score 0.69 (1.0 = pole).

    • Practice long-run pacew 0.13

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

    • Driver skill (Elo)w 0.09

      Teammate-only Elo skill score 0.33 (1.0 = top of grid). Familiarity discount ×0.85.

    • Track history (time-weighted)w 0.01

      Recency-weighted finish score 0.26, 5-year window, 12-month half-life, reg-discounted ×0.15.

    • Chassis at this circuitw 0.05

      Constructor-lineage best-finish score 0.49 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. 7Lando Norris
    NORLando Norris
    McLarenMcLarenHIGH
    WIN
    4.0%
    POD
    17.2%
    PTS
    64.0%
    Why · 7 factors
    • Qualifying pacew 0.55

      Quali-pace score 0.80 (1.0 = pole).

    • Practice long-run pacew 0.13

      Long-run score 0.32 (1.0 = fastest median stint).

    • Constructor recent formw 0.17

      Team points trend score 0.61 across the last 5 races.

    • Driver skill (Elo)w 0.09

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

    • Track history (time-weighted)w 0.01

      Recency-weighted finish score 0.91, 5-year window, 12-month half-life, reg-discounted ×0.15.

    • Chassis at this circuitw 0.05

      Constructor-lineage best-finish score 0.95 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. 8Charles Leclerc
    LECCharles Leclerc
    FerrariFerrariHIGH
    WIN
    3.7%
    POD
    12.9%
    PTS
    62.1%
    Why · 7 factors
    • Qualifying pacew 0.55

      Quali-pace score 0.83 (1.0 = pole).

    • Practice long-run pacew 0.13

      Long-run score 0.37 (1.0 = fastest median stint).

    • Constructor recent formw 0.17

      Team points trend score 0.30 across the last 5 races.

    • Driver skill (Elo)w 0.09

      Teammate-only Elo skill score 0.65 (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 ×0.15.

    • Chassis at this circuitw 0.05

      Constructor-lineage best-finish score 0.71 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).

  9. 9Arvid Lindblad
    LINArvid Lindblad
    Racing BullsRacing BullsHIGH
    WIN
    3.3%
    POD
    14.1%
    PTS
    72.0%
    Why · 5 factors
    • Qualifying pacew 0.55

      Quali-pace score 0.69 (1.0 = pole).

    • Practice long-run pacew 0.13

      Long-run score 0.53 (1.0 = fastest median stint).

    • Driver skill (Elo)w 0.09

      Teammate-only Elo skill score 0.50 (1.0 = top of grid). Familiarity discount ×0.85.

    • Chassis at this circuitw 0.05

      Constructor-lineage best-finish score 0.49 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).

  10. 10Lewis Hamilton
    HAMLewis Hamilton
    FerrariFerrariHIGH
    WIN
    2.8%
    POD
    11.4%
    PTS
    58.9%
    Why · 7 factors
    • Qualifying pacew 0.55

      Quali-pace score 0.80 (1.0 = pole).

    • Practice long-run pacew 0.13

      Long-run score 0.64 (1.0 = fastest median stint).

    • Constructor recent formw 0.17

      Team points trend score 0.30 across the last 5 races.

    • Driver skill (Elo)w 0.09

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

    • Track history (time-weighted)w 0.01

      Recency-weighted finish score 0.51, 5-year window, 12-month half-life, reg-discounted ×0.15.

    • Chassis at this circuitw 0.05

      Constructor-lineage best-finish score 0.71 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).

Most-likely podium combinations

approximate · 1st-2nd-3rd
  1. 1
    P1VER·P2RUS·P3PIA
    3.64%
  2. 2
    P1VER·P2RUS·P3HAD
    3.27%
  3. 3
    P1VER·P2PIA·P3RUS
    2.98%
  4. 4
    P1VER·P2RUS·P3ANT
    2.78%
  5. 5
    P1VER·P2RUS·P3LAW
    2.64%
  6. 6
    P1VER·P2HAD·P3RUS
    2.62%
  7. 7
    P1RUS·P2VER·P3PIA
    2.39%
  8. 8
    P1VER·P2ANT·P3RUS
    2.15%
  9. 9
    P1RUS·P2VER·P3HAD
    2.15%
  10. 10
    P1VER·P2LAW·P3RUS
    2.02%

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.