F1 Strategy Optimizer

cleaned high-frequency telemetry via the FastF1 API, engineering track-physics features (fuel load, rubber degradation) for statistical modeling

The F1 Race Strategy Optimizer is an interactive data science application that leverages machine learning to simulate tire degradation and evaluate Formula 1 pit stop strategies. Built with Python, Streamlit, and XGBoost, the app utilizes real telemetry data extracted via FastF1 from the Bahrain Grand Prix to accurately predict per-lap performance based on tire compound, tire age, and lap progression. Users can interactively configure and compare 1-stop versus 2-stop stint strategies in real time, adjusting parameters like pit lane time penalties to observe how different tactical choices impact total race duration. By rendering dynamic stint degradation curves and strategy breakdowns through Plotly visualizations, this project transforms complex motor-racing data into an intuitive tool for exploring predictive modeling in motorsport.

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