Hungarian Grand Prix 2026

HAD managed their MEDIUM tyres better than the FP2 model predicted — by 74%

• ›FP2 model predicted 0.2696s/lap degradation on MEDIUM
• ›Actual race stint slope: 0.0701s/lap (Δ -0.1995s/lap)
• ›Over 17 laps, that compounds to ≈3.4s of more efficient pace vs the model's expectation

FP2 long-run data is the basis of the tyre degradation model. A 74% 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 HAD'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.

BOT managed their MEDIUM tyres better than the FP2 model predicted — by 91%

• ›FP2 model predicted 0.1199s/lap degradation on MEDIUM
• ›Actual race stint slope: 0.0111s/lap (Δ -0.1088s/lap)
• ›Over 12 laps, that compounds to ≈1.3s of more efficient pace vs the model's expectation

FP2 long-run data is the basis of the tyre degradation model. A 91% 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 BOT'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 39% of race pace ranks correctly; BOR was the exception

• ›7 of 18 drivers finished within 3 positions of their FP2 long-run rank
• ›BOR: FP2 ranked P20, race result P11 (-9 positions)
• ›Best prediction match: LEC — FP2 P4, actual P4

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 39% of the field within 3 positions, the model's practice-session intelligence is working as designed.

BOR'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 P19; they finished P18 — 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 P19, actual P18 (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.

LEC was quickest through the mid sector twisty, the biggest sector gain of any driver in the comparison group — over 1.94s ahead of the field median.

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