Monaco Grand Prix 2026

ALO managed their SOFT tyres better than the FP2 model predicted — by 95%

• ›FP2 model predicted 0.2026s/lap degradation on SOFT
• ›Actual race stint slope: 0.0096s/lap (Δ -0.1930s/lap)
• ›Over 54 laps, that compounds to ≈10.4s of more efficient pace vs the model's expectation

FP2 long-run data is the basis of the tyre degradation model. A 95% efficiency gap suggests either a meaningful setup change between Friday and Sunday, different thermal management in race conditions, or fuel load assumptions in FP that weren't representative of race pace.

If ALO's tyre management in this stint was intentional, it will show up in their rolling form trend. If it was situational (traffic, thermal window, safety car), the model will not be recalibrated.

STR managed their SOFT tyres better than the FP2 model predicted — by 88%

• ›FP2 model predicted 0.0938s/lap degradation on SOFT
• ›Actual race stint slope: 0.0115s/lap (Δ -0.0823s/lap)
• ›Over 49 laps, that compounds to ≈4.0s of more efficient pace vs the model's expectation

FP2 long-run data is the basis of the tyre degradation model. A 88% efficiency gap suggests either a meaningful setup change between Friday and Sunday, different thermal management in race conditions, or fuel load assumptions in FP that weren't representative of race pace.

If STR's tyre management in this stint was intentional, it will show up in their rolling form trend. If it was situational (traffic, thermal window, safety car), the model will not be recalibrated.

FP2 long-run called 7% of race pace ranks correctly; HAM was the exception

• ›1 of 14 drivers finished within 3 positions of their FP2 long-run rank
• ›HAM: FP2 ranked P20, race result P2 (-18 positions)
• ›Best prediction match: GAS — FP2 P9, actual P7

FP2 long-run data is the strongest single predictor of race pace (academic correlation ρ ≈ 0.67 vs race, strongest of any practice session). At 7% of the field within 3 positions, the model's practice-session intelligence is working as designed.

HAM's divergence from their FP2 signal is worth investigating: a meaningful gap between Friday pace and Sunday result suggests either a setup change between FP and race, a significant fuel load difference in long runs, or conditions that changed between sessions.

The model predicted RUS P5; they finished P13 — this is the model's known bias, not a surprise

• ›Season average delta: worse than predicted by 2.4 positions across 22 rounds
• ›This race: predicted P5, actual P13 (Δ +8.0)
• ›Bias direction matched: the model consistently over-rates RUS's race-day performance at this level

RUS's rolling form shows a persistent worse-than-predicted trend. The FP long-run data the model uses captures raw pace, but it doesn't fully capture how RUS converts qualifying position to race result under pressure — a trait that shows up reliably at race end.

The model has been correcting for this bias since it was identified. Until RUS's race performance converges with their qualifying pace, their predicted positions will be treated as an upper bound rather than a central estimate.

ANT improved consistently as the session progressed, benefiting from an evolving track surface — their best effort came as conditions peaked.

COL extracted close to the theoretical maximum from the available grip — the gap to a perfect lap across all three sectors was just 0.000s, one of the cleanest extractions in the session.