Dutch Grand Prix 2026

The model predicted SAI P18; they finished P16 — 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 P18, actual P16 (Δ -2.0)
• ›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 P15 — 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 P15 (Δ -5.0)
• ›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 P3 — 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 P3 (Δ +2.0)
• ›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.

LEC was quickest through the high speed sand dunes, the biggest sector gain of any driver in the comparison group — over 1.80s 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.