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F1

Chinese Grand Prix

Round 2 · 2026
Shanghai
v 0.3.008 Jun 2026, 01:04 UTC
Based onQualifyingPracticeHistoryConstructor formWeatherRegulations
Kimi Antonelli is the model's pick at 22.4% (80% CI 21.6–23.1%) for Chinese Grand Prix.

Top 10 drivers

bars are win / podium / points probabilitiesWeekend analytics →
  1. 1Kimi Antonelli
    ANTKimi Antonelli
    MercedesMercedesHIGH
    WIN
    22.4%
    POD
    48.0%
    PTS
    59.7%
    Why · 7 factors
    • Qualifying pacew 0.46

      Quali-pace score 1.00 (1.0 = pole).

    • Practice long-run pacew 0.15

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

    • Constructor recent formw 0.20

      Team points trend score 0.98 across the last 5 races.

    • Driver skill (Elo)w 0.10

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

    • Track history (time-weighted)w 0.02

      Recency-weighted finish score 0.74, 5-year window, 12-month half-life, reg-discounted ×0.41.

    • Chassis at this circuitw 0.06

      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 ≈ 40.0% (team × circuit).

  2. 2George Russell
    RUSGeorge Russell
    MercedesMercedesHIGH
    WIN
    21.0%
    POD
    46.8%
    PTS
    60.5%
    Why · 7 factors
    • Qualifying pacew 0.46

      Quali-pace score 0.95 (1.0 = pole).

    • Practice long-run pacew 0.15

      Long-run score 0.78 (1.0 = fastest median stint).

    • Constructor recent formw 0.20

      Team points trend score 0.98 across the last 5 races.

    • Driver skill (Elo)w 0.10

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

    • Track history (time-weighted)w 0.02

      Recency-weighted finish score 0.82, 5-year window, 12-month half-life, reg-discounted ×0.41.

    • Chassis at this circuitw 0.06

      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 ≈ 40.0% (team × circuit).

  3. 3Charles Leclerc
    LECCharles Leclerc
    FerrariFerrariHIGH
    WIN
    9.6%
    POD
    30.9%
    PTS
    70.4%
    Why · 7 factors
    • Qualifying pacew 0.46

      Quali-pace score 0.92 (1.0 = pole).

    • Practice long-run pacew 0.15

      Long-run score 0.70 (1.0 = fastest median stint).

    • Constructor recent formw 0.20

      Team points trend score 0.61 across the last 5 races.

    • Driver skill (Elo)w 0.10

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

    • Track history (time-weighted)w 0.02

      Recency-weighted finish score 0.43, 5-year window, 12-month half-life, reg-discounted ×0.41.

    • Chassis at this circuitw 0.06

      Constructor-lineage best-finish score 0.35 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. 4Pierre Gasly
    GASPierre Gasly
    AlpineAlpineHIGH
    WIN
    8.9%
    POD
    29.6%
    PTS
    60.6%
    Why · 6 factors
    • Qualifying pacew 0.46

      Quali-pace score 0.83 (1.0 = pole).

    • Practice long-run pacew 0.15

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

    • Driver skill (Elo)w 0.10

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

    • Track history (time-weighted)w 0.02

      Recency-weighted finish score 0.16, 5-year window, 12-month half-life, reg-discounted ×0.41.

    • Chassis at this circuitw 0.06

      Constructor-lineage best-finish score 0.38 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
    7.3%
    POD
    25.4%
    PTS
    81.8%
    Why · 6 factors
    • Qualifying pacew 0.46

      Quali-pace score 0.78 (1.0 = pole).

    • Practice long-run pacew 0.15

      Long-run score 0.82 (1.0 = fastest median stint).

    • Driver skill (Elo)w 0.10

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

    • Track history (time-weighted)w 0.02

      Recency-weighted finish score 0.47, 5-year window, 12-month half-life, reg-discounted ×0.41.

    • Chassis at this circuitw 0.06

      Constructor-lineage best-finish score 0.89 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. 6Lewis Hamilton
    HAMLewis Hamilton
    FerrariFerrariHIGH
    WIN
    5.6%
    POD
    20.9%
    PTS
    68.4%
    Why · 7 factors
    • Qualifying pacew 0.46

      Quali-pace score 0.93 (1.0 = pole).

    • Practice long-run pacew 0.15

      Long-run score 0.63 (1.0 = fastest median stint).

    • Constructor recent formw 0.20

      Team points trend score 0.61 across the last 5 races.

    • Driver skill (Elo)w 0.10

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

    • Track history (time-weighted)w 0.02

      Recency-weighted finish score 0.41, 5-year window, 12-month half-life, reg-discounted ×0.41.

    • Chassis at this circuitw 0.06

      Constructor-lineage best-finish score 0.35 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. 7Franco Colapinto
    COLFranco Colapinto
    AlpineAlpineHIGH
    WIN
    5.4%
    POD
    19.1%
    PTS
    58.7%
    Why · 5 factors
    • Qualifying pacew 0.46

      Quali-pace score 0.73 (1.0 = pole).

    • Practice long-run pacew 0.15

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

    • Driver skill (Elo)w 0.10

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

    • Chassis at this circuitw 0.06

      Constructor-lineage best-finish score 0.38 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. 8Max Verstappen
    VERMax Verstappen
    Red Bull RacingRed Bull RacingHIGH
    WIN
    3.5%
    POD
    13.8%
    PTS
    72.0%
    Why · 6 factors
    • Qualifying pacew 0.46

      Quali-pace score 0.81 (1.0 = pole).

    • Practice long-run pacew 0.15

      Long-run score 0.00 (1.0 = fastest median stint).

    • Driver skill (Elo)w 0.10

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

    • Track history (time-weighted)w 0.02

      Recency-weighted finish score 0.91, 5-year window, 12-month half-life, reg-discounted ×0.41.

    • Chassis at this circuitw 0.06

      Constructor-lineage best-finish score 0.89 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. 9Liam Lawson
    LAWLiam Lawson
    Racing BullsRacing BullsHIGH
    WIN
    3.1%
    POD
    13.0%
    PTS
    72.3%
    Why · 6 factors
    • Qualifying pacew 0.46

      Quali-pace score 0.65 (1.0 = pole).

    • Practice long-run pacew 0.15

      Long-run score 0.89 (1.0 = fastest median stint).

    • Driver skill (Elo)w 0.10

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

    • Track history (time-weighted)w 0.02

      Recency-weighted finish score 0.42, 5-year window, 12-month half-life, reg-discounted ×0.41.

    • Chassis at this circuitw 0.06

      Constructor-lineage best-finish score 0.38 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. 10Oscar Piastri
    PIAOscar Piastri
    McLarenMcLarenHIGH
    WIN
    3.0%
    POD
    10.4%
    PTS
    54.6%
    Why · 7 factors
    • Qualifying pacew 0.46

      Quali-pace score 0.90 (1.0 = pole).

    • Practice long-run pacew 0.15

      Long-run score 0.59 (1.0 = fastest median stint).

    • Constructor recent formw 0.20

      Team points trend score 0.23 across the last 5 races.

    • Driver skill (Elo)w 0.10

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

    • Track history (time-weighted)w 0.02

      Recency-weighted finish score 0.86, 5-year window, 12-month half-life, reg-discounted ×0.41.

    • Chassis at this circuitw 0.06

      Constructor-lineage best-finish score 0.97 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).

Most-likely podium combinations

approximate · 1st-2nd-3rd
  1. 1
    P1ANT·P2RUS·P3LEC
    1.97%
  2. 2
    P1ANT·P2RUS·P3GAS
    1.89%
  3. 3
    P1RUS·P2ANT·P3LEC
    1.88%
  4. 4
    P1RUS·P2ANT·P3GAS
    1.80%
  5. 5
    P1ANT·P2LEC·P3RUS
    1.72%
  6. 6
    P1ANT·P2GAS·P3RUS
    1.63%
  7. 7
    P1RUS·P2LEC·P3ANT
    1.62%
  8. 8
    P1ANT·P2RUS·P3HAD
    1.62%
  9. 9
    P1RUS·P2ANT·P3HAD
    1.55%
  10. 10
    P1RUS·P2GAS·P3ANT
    1.54%

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.