Mexico City Grand Prix 2026

SAI managed their SOFT tyres better than the FP2 model predicted — by 93%

• ›FP2 model predicted 0.0954s/lap degradation on SOFT
• ›Actual race stint slope: 0.0071s/lap (Δ -0.0883s/lap)
• ›Over 21 laps, that compounds to ≈1.9s of more efficient pace vs the model's expectation

FP2 long-run data is the basis of the tyre degradation model. A 93% 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 SAI'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.

VER managed their SOFT tyres worse than the FP2 model predicted — by 144%

• ›FP2 model predicted 0.0541s/lap degradation on SOFT
• ›Actual race stint slope: 0.1323s/lap (Δ +0.0782s/lap)
• ›Over 23 laps, that compounds to ≈1.8s of less efficient pace vs the model's expectation

FP2 long-run data is the basis of the tyre degradation model. A 144% 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 VER'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 19% of race pace ranks correctly; HAM was the exception

• ›4 of 21 drivers finished within 3 positions of their FP2 long-run rank
• ›HAM: FP2 ranked P20, race result P2 (-18 positions)
• ›Best prediction match: NOR — FP2 P5, actual P5

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 19% 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 SAI P16; they finished P15 — 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 P15 (Δ -1.0)
• ›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 P20; they finished P17 — 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 P20, actual P17 (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.

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

VER was quickest through the stadium frenaje section, the biggest sector gain of any driver in the comparison group — over 1.47s ahead of the field median.