Situation:
Formula 1 generates massive amounts of telemetry and race data, but understanding what actually drives performance — driver skill, team strategy, or track conditions — is a challenge. Stakeholders such as teams, analysts, and fans need clearer insights into how these factors affect lap times and race outcomes.

Task:
The objective was to analyze F1 racing datasets (lap times, pit stops, driver standings, constructor performance, and track attributes) to identify key performance drivers and predict race outcomes.

Action:

• Data Preparation: Cleaned multiple CSV datasets (laps, races, results, pit stops, and drivers) and merged them into a unified schema.
• Exploratory Analysis: Visualized driver vs. constructor contributions, pit stop efficiency, and seasonal performance trends.
• Statistical Modeling:
  • Correlated lap times with pit stop counts, weather, and circuit attributes.
  • Analyzed constructor dominance and driver performance consistency.
  • Compared qualifying performance vs. race results to highlight strategy effects.
• Machine Learning: Experimented with regression models to predict lap times and race finishing positions using combined driver/team/track features.

Result:

• Identified pit stop efficiency and qualifying performance as the two strongest predictors of race success.
• Showed that constructor performance explains a larger variance in race outcomes than individual driver skill, confirming team engineering’s impact.
• Built visual dashboards showing season-long trends in driver vs. team performance, enabling more transparent performance benchmarking.
• Delivered a framework that can be expanded into predictive race simulations for future F1 events.