Sports Analytics
Predicting F1 Pit Stops
A motorsport analytics project focused on the question every F1 race quietly revolves around: when is the right moment to pit? The work frames pit stop timing as a data problem shaped by laps, tire life, gaps, safety cars, and race position.
Period
May 2026 - Present
Stack
PythonData AnalysisFeature EngineeringMachine Learning
Motivation
Pit strategy is a nice intersection of data and judgment. I wanted to practice building features from event-like sports data while keeping the result understandable to people who actually watch races.
Links
Core Features
- Frames pit stop timing as a prediction and decision-support problem.
- Plans features around tire age, lap context, position, race gaps, and strategy windows.
- Leaves room for both model evaluation and race-by-race explanation.
My Contribution
- Set up the project direction and repository for an interpretable racing analytics workflow.
- Outlined a feature-driven approach instead of treating the model as a black box.
What I Learned
- Practicing how to turn a real-world strategy question into measurable features.
- Learning to keep sports analytics grounded in race context, not just model scores.