Prediction accuracy: 100 races, scored

Leave-one-race-out validation, 2022-2026. The model never sees the race it is graded on. Live predictions are frozen before lights out and scored against the official result.

The numbers, in one line each

RaceModel's pre-race model averages 3.275 positions of error per driver across 100 Formula 1 races (2022-2026), validated leave-one-race-out.

RaceModel called 203 of 300 podium places correctly across 100 races: a 68% podium hit rate.

Every RaceModel prediction is frozen before the race starts and scored against the official result; no prediction is edited after the fact.

Per season

Every race, graded

Method: exactly what these numbers include

For each race the model is trained on every other race in the dataset and predicts the held-out race from pre-race data only: qualifying, practice pace, driver form, circuit history, reliability. The same pipeline powers the live predictions, which are frozen pre-race and graded on the model changelog.

• MAE covers the full classified field, one error value per driver per race, not just the front-runners.
• DNFs are included. A car predicted P3 that retires and classifies P18 counts as a 15-position miss. Retirements are the largest single source of position error, so excluding them (as some sites do) makes any error number look roughly one position better.
• Predictions use pre-race data only. Nothing from the race itself leaks in, and live predictions are frozen before lights out; nothing is edited after the fact.
• Every race counts. No cherry-picked subset: all 100 races across the 2022-2026 seasons, wet and dry, sprint weekends included.

Comparing accuracy across sites: numbers are only comparable when they share the same basis (DNFs in or out, full field or top 10, sample size, prediction timing). See how F1 prediction accuracy is measured for a side-by-side of published methods.