Italian Grand Prix 2026

ALO managed their SOFT tyres better than the FP2 model predicted — by 97%

• ›FP2 model predicted 0.5535s/lap degradation on SOFT
• ›Actual race stint slope: 0.0176s/lap (Δ -0.5359s/lap)
• ›Over 16 laps, that compounds to ≈8.6s of more efficient pace vs the model's expectation

FP2 long-run data is the basis of the tyre degradation model. A 97% 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.

PER managed their SOFT tyres worse than the FP2 model predicted — by 200%

• ›FP2 model predicted 0.0768s/lap degradation on SOFT
• ›Actual race stint slope: 0.2566s/lap (Δ +0.1798s/lap)
• ›Over 21 laps, that compounds to ≈3.8s of less efficient pace vs the model's expectation

FP2 long-run data is the basis of the tyre degradation model. A 200% 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 PER's pace deficit 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 37% of race pace ranks correctly; PIA was the exception

• ›7 of 19 drivers finished within 3 positions of their FP2 long-run rank
• ›PIA: FP2 ranked P18, race result P5 (-13 positions)
• ›Best prediction match: ANT — FP2 P1, actual P1

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 37% of the field within 3 positions, the model's practice-session intelligence is working as designed.

PIA'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 SAI P16; they finished P13 — this is the model's known bias, not a surprise

• ›Season average delta: better than predicted by 3.0 positions across 22 rounds
• ›This race: predicted P16, actual P13 (Δ -3.0)
• ›Bias direction matched: the model consistently over-rates SAI's race-day performance at this level

SAI's rolling form shows a persistent better-than-predicted trend. The FP long-run data the model uses captures raw pace, but it doesn't fully capture how SAI 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 SAI's race performance converges with their qualifying pace, their predicted positions will be treated as an upper bound rather than a central estimate.

The model predicted PER P21; they finished P18 — this is the model's known bias, not a surprise

• ›Season average delta: better than predicted by 3.1 positions across 22 rounds
• ›This race: predicted P21, actual P18 (Δ -3.0)
• ›Bias direction matched: the model consistently over-rates PER's race-day performance at this level

PER's rolling form shows a persistent better-than-predicted trend. The FP long-run data the model uses captures raw pace, but it doesn't fully capture how PER 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 PER's race performance converges with their qualifying pace, their predicted positions will be treated as an upper bound rather than a central estimate.

The model predicted RUS P1; they finished P2 — 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 P1, actual P2 (Δ +1.0)
• ›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.

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

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