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F1

Austrian Grand Prix

Round 1 · 2020
Spielberg
v 0.3.008 Jun 2026, 14:27 UTC
Based onQualifyingPracticeHistoryConstructor formWeatherRegulations
Lewis Hamilton is the model's pick at 36.6% (80% CI 35.8–37.4%) for Austrian Grand Prix.

Top 10 drivers

bars are win / podium / points probabilitiesWeekend analytics →
  1. 1Lewis Hamilton
    HAMLewis Hamilton
    MercedesMercedesHIGH
    WIN
    36.6%
    POD
    77.0%
    PTS
    90.1%
    Why · 6 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 0.85 across the last 5 races.

    • Driver skill (Elo)w 0.13

      Teammate-only Elo skill score 0.79 (1.0 = top of grid). Familiarity discount ×0.85.

    • Track history (time-weighted)w 0.03

      Recency-weighted finish score 0.60, 5-year window, 12-month half-life, reg-discounted ×0.42.

    • Chassis at this circuitw 0.07

      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.

  2. 2Max Verstappen
    VERMax Verstappen
    Red Bull RacingRed Bull RacingHIGH
    WIN
    25.5%
    POD
    69.1%
    PTS
    88.7%
    Why · 6 factors
    • Qualifying pacew 0.34

      Quali-pace score 0.81 (1.0 = pole).

    • Practice long-run pacew 0.19

      Long-run score 0.96 (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 0.03

      Recency-weighted finish score 1.00, 5-year window, 12-month half-life, reg-discounted ×0.42.

    • Chassis at this circuitw 0.07

      Constructor-lineage best-finish score 1.00 at this circuit across the last 5 seasons. Driver-agnostic — captures chassis-DNA fit independent of who drove.

    • DNF riskw 0.11

      Per-race DNF probability ≈ 11.3% (team × circuit).

  3. 3Valtteri Bottas
    BOTValtteri Bottas
    MercedesMercedesHIGH
    WIN
    12.7%
    POD
    44.7%
    PTS
    90.1%
    Why · 6 factors
    • Qualifying pacew 0.34

      Quali-pace score 1.00 (1.0 = pole).

    • Practice long-run pacew 0.19

      Long-run score 0.77 (1.0 = fastest median stint).

    • Constructor recent formw 0.25

      Team points trend score 0.85 across the last 5 races.

    • Driver skill (Elo)w 0.13

      Teammate-only Elo skill score 0.06 (1.0 = top of grid). Familiarity discount ×0.85.

    • Track history (time-weighted)w 0.03

      Recency-weighted finish score 0.61, 5-year window, 12-month half-life, reg-discounted ×0.42.

    • Chassis at this circuitw 0.07

      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.

  4. 4Alexander Albon
    ALBAlexander Albon
    Red Bull RacingRed Bull RacingHIGH
    WIN
    5.7%
    POD
    23.4%
    PTS
    86.2%
    Why · 6 factors
    • Qualifying pacew 0.34

      Quali-pace score 0.71 (1.0 = pole).

    • Practice long-run pacew 0.19

      Long-run score 0.91 (1.0 = fastest median stint).

    • Driver skill (Elo)w 0.13

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

    • Track history (time-weighted)w 0.03

      Recency-weighted finish score 0.26, 5-year window, 12-month half-life, reg-discounted ×0.42.

    • Chassis at this circuitw 0.07

      Constructor-lineage best-finish score 1.00 at this circuit across the last 5 seasons. Driver-agnostic — captures chassis-DNA fit independent of who drove.

    • DNF riskw 0.11

      Per-race DNF probability ≈ 11.3% (team × circuit).

  5. 5Charles Leclerc
    LECCharles Leclerc
    FerrariFerrariHIGH
    WIN
    3.3%
    POD
    14.7%
    PTS
    69.0%
    Why · 7 factors
    • Qualifying pacew 0.34

      Quali-pace score 0.65 (1.0 = pole).

    • Practice long-run pacew 0.19

      Long-run score 0.92 (1.0 = fastest median stint).

    • Constructor recent formw 0.25

      Team points trend score 0.52 across the last 5 races.

    • Driver skill (Elo)w 0.13

      Teammate-only Elo skill score 0.51 (1.0 = top of grid). Familiarity discount ×0.85.

    • Track history (time-weighted)w 0.03

      Recency-weighted finish score 0.82, 5-year window, 12-month half-life, reg-discounted ×0.42.

    • Chassis at this circuitw 0.07

      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 ≈ 26.2% (team × circuit).

  6. 6Pierre Gasly
    GASPierre Gasly
    AlphaTauriAlphaTauriHIGH
    WIN
    2.6%
    POD
    10.5%
    PTS
    76.1%
    Why · 6 factors
    • Qualifying pacew 0.34

      Quali-pace score 0.52 (1.0 = pole).

    • Practice long-run pacew 0.19

      Long-run score 0.90 (1.0 = fastest median stint).

    • Driver skill (Elo)w 0.13

      Teammate-only Elo skill score 0.37 (1.0 = top of grid). Familiarity discount ×0.85.

    • Track history (time-weighted)w 0.03

      Recency-weighted finish score 0.61, 5-year window, 12-month half-life, reg-discounted ×0.42.

    • Chassis at this circuitw 0.07

      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 0.11

      Per-race DNF probability ≈ 11.3% (team × circuit).

  7. 7Sergio Perez
    PERSergio Perez
    Racing PointRacing PointHIGH
    WIN
    2.6%
    POD
    11.1%
    PTS
    78.5%
    Why · 7 factors
    • Qualifying pacew 0.34

      Quali-pace score 0.67 (1.0 = pole).

    • Practice long-run pacew 0.19

      Long-run score 0.58 (1.0 = fastest median stint).

    • Constructor recent formw 0.25

      Team points trend score 0.47 across the last 5 races.

    • Driver skill (Elo)w 0.13

      Teammate-only Elo skill score 0.89 (1.0 = top of grid). Familiarity discount ×0.85.

    • Track history (time-weighted)w 0.03

      Recency-weighted finish score 0.54, 5-year window, 12-month half-life, reg-discounted ×0.42.

    • Chassis at this circuitw 0.07

      Constructor-lineage best-finish score 0.54 at this circuit across the last 5 seasons. Driver-agnostic — captures chassis-DNA fit independent of who drove.

    • DNF riskw 0.11

      Per-race DNF probability ≈ 11.3% (team × circuit).

  8. 8Sebastian Vettel
    VETSebastian Vettel
    FerrariFerrariHIGH
    WIN
    2.4%
    POD
    11.5%
    PTS
    64.9%
    Why · 7 factors
    • Qualifying pacew 0.34

      Quali-pace score 0.55 (1.0 = pole).

    • Practice long-run pacew 0.19

      Long-run score 0.93 (1.0 = fastest median stint).

    • Constructor recent formw 0.25

      Team points trend score 0.52 across the last 5 races.

    • Driver skill (Elo)w 0.13

      Teammate-only Elo skill score 0.55 (1.0 = top of grid). Familiarity discount ×0.85.

    • Track history (time-weighted)w 0.03

      Recency-weighted finish score 0.86, 5-year window, 12-month half-life, reg-discounted ×0.42.

    • Chassis at this circuitw 0.07

      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 ≈ 26.2% (team × circuit).

  9. 9Carlos Sainz
    SAICarlos Sainz
    McLarenMcLarenHIGH
    WIN
    1.8%
    POD
    8.1%
    PTS
    52.9%
    Why · 7 factors
    • Qualifying pacew 0.34

      Quali-pace score 0.63 (1.0 = pole).

    • Practice long-run pacew 0.19

      Long-run score 0.76 (1.0 = fastest median stint).

    • Constructor recent formw 0.25

      Team points trend score 0.52 across the last 5 races.

    • Driver skill (Elo)w 0.13

      Teammate-only Elo skill score 0.58 (1.0 = top of grid). Familiarity discount ×0.85.

    • Track history (time-weighted)w 0.03

      Recency-weighted finish score 0.56, 5-year window, 12-month half-life, reg-discounted ×0.42.

    • Chassis at this circuitw 0.07

      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 ≈ 38.8% (team × circuit).

  10. 10Lando Norris
    NORLando Norris
    McLarenMcLarenHIGH
    WIN
    1.7%
    POD
    7.7%
    PTS
    52.8%
    Why · 7 factors
    • Qualifying pacew 0.34

      Quali-pace score 0.76 (1.0 = pole).

    • Practice long-run pacew 0.19

      Long-run score 0.61 (1.0 = fastest median stint).

    • Constructor recent formw 0.25

      Team points trend score 0.52 across the last 5 races.

    • Driver skill (Elo)w 0.13

      Teammate-only Elo skill score 0.38 (1.0 = top of grid). Familiarity discount ×0.85.

    • Track history (time-weighted)w 0.03

      Recency-weighted finish score 0.74, 5-year window, 12-month half-life, reg-discounted ×0.42.

    • Chassis at this circuitw 0.07

      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 ≈ 38.8% (team × circuit).

Most-likely podium combinations

approximate · 1st-2nd-3rd
  1. 1
    P1HAM·P2VER·P3BOT
    7.34%
  2. 2
    P1HAM·P2BOT·P3VER
    5.83%
  3. 3
    P1VER·P2HAM·P3BOT
    5.43%
  4. 4
    P1VER·P2BOT·P3HAM
    4.05%
  5. 5
    P1HAM·P2VER·P3ALB
    3.84%
  6. 6
    P1BOT·P2HAM·P3VER
    2.91%
  7. 7
    P1VER·P2HAM·P3ALB
    2.84%
  8. 8
    P1BOT·P2VER·P3HAM
    2.73%
  9. 9
    P1HAM·P2ALB·P3VER
    2.59%
  10. 10
    P1HAM·P2VER·P3LEC
    2.41%

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.