Data first
Race, driver, team, track, qualifying, points, fantasy, and market histories are cleaned and transformed into model-ready signals.
Review the data processMETHODOLOGY
Built to earn trust
Good models predict outcomes. Great processes prepare for uncertainty. Auto Racing Lab combines actuarial discipline, predictive analytics, racing context, and visible scorekeeping to turn raw race data into useful forecasts.
GO DEEPER
Research paths
Public pages provide clear context; interactive tools remain one click away.
Fair testing
Past information predicts future races. Holdout and out-of-sample results keep the future out of the past.
Review validationTrack context
Broad form is paired with venue history and specialist signals for tracks with comparable demands.
Explore track contextRace scripts
Fantasy scenarios allow coherent combinations of floor, mean, and ceiling outcomes rather than assuming every competitor peaks together.
Explore the LabDATA FIRST
Most of the work happens before a model is trained.
Raw histories are sourced, cleaned, reconciled, organized, and engineered into useful measurements. In predictive modeling, features are the inputs that summarize evidence such as form, track fit, qualifying position, team strength, pace, and market context.
FAIR TESTING
The future stays out of the past.
Training, tuning, holdout evaluation, and visible scorekeeping are kept distinct. Completed races measure performance on information the forecast could have known at the time; no result is allowed to predict itself.