Foundation
01What Apex Pulse predicts
The core output is each driver’s predicted gap to pole and the resulting qualifying ranking. The public artifacts do not currently claim pole or Q3 probabilities.
Methodology and trust
Apex Pulse turns public practice-session evidence into a qualifying forecast, freezes it before qualifying, and evaluates it only after official results are available.
Prediction foundation
Foundation
01The core output is each driver’s predicted gap to pole and the resulting qualifying ranking. The public artifacts do not currently claim pole or Q3 probabilities.
Foundation
02FastF1 provides public historical practice laps, results, timing, weather and limited public telemetry. Local cached event schedules supply the displayed session times.
How Apex Pulse works
The dataset supports predictions after FP1, FP2 and FP3. Each checkpoint can use only the sessions that have already happened.
Interpretable practice pace, sector performance, tyre context, teammate-relative pace, session conditions and strictly time-aware historical form are the foundation.
Candidate tabular models are compared with strong practice and historical baselines using season-aware, walk-forward evaluation. The selected policy is exported by the backend.
The forecast is written before qualifying and preserved immutably. Settlement is a separate post-qualifying comparison; it never rewrites or fills missing predictions.
Evaluation and transparency
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Gap MAE and RMSE measure pace error. Mean absolute position error and top-k agreement show how well the ranking matched the official order.
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The project has no private team data, paid live feed, fuel state, brake pressure, battery state or real-time telemetry. Public signals are partial and may be stale or incomplete.
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Predictions made before an observed qualifying session are reported separately from historical model-development backtests and legacy noncanonical records.