incident #001·class data integrity · ml·status resolved
> the problem
From the outside a Formula 1 race looks like chaos. Can live timing and telemetry actually predict who finishes on the podium — without quietly fooling yourself with data leakage?
> the approach
Pull live timing and telemetry through FastF1, engineer features from it, then train and compare tree-based models — judged by an honest top-3 hit-rate rather than a flattering accuracy score.
> architecture.log
01INFOFastF1 ingestion pulls live timing + telemetry per session
02INFOLeakage-safe feature pipeline: strictly time-ordered splits + walk-forward season backtesting
03INFORandom Forest vs. gradient-boosting models, compared on top-3 hit-rate
04INFOFlask REST API + dashboard, containerized with Docker on a public cloud VM
05INFOAutomated race-data fetching and model retraining
error · root cause
Data leakage. Naive random splits let information from the future bleed into training and inflate the score. The fix was strict time-ordering and walk-forward backtesting across seasons — so every prediction is made only from the past.
info · resolution
An honest metric beats an impressive one. Top-3 hit-rate under walk-forward validation tells the truth; a shuffled split just tells you what you want to hear.