This project performs analysis of Formula 1 telemetry data using the FastF1 ecosystem to study driver performance across laps. Telemetry data provides detailed information about car behavior such as speed, throttle input, braking force, gear selection, and lap timing.
The objective of this project is to compare drivers using real race data and visualize performance differences through clear and structured plots. The analysis highlights how drivers differ in braking points, acceleration patterns, and overall lap efficiency.
This project demonstrates the practical use of data analytics techniques in motorsports and shows how telemetry can be used to extract performance insights.
- Retrieval of official Formula 1 session telemetry data
- Comparison of driver performance metrics
- Visualization of speed profiles across laps
- Analysis of throttle and brake application patterns
- Identification of performance differences between drivers
- Data-driven insights into racing behaviour
- Python
- FastF1
- Pandas
- NumPy
- Matplotlib
F1-telemetry-analysis/
│
├── src/ # core analysis scripts
├── data/ # cached telemetry data
├── images/ # generated visualizations
├── docs/ # supporting documentation
│
├── main.py
├── requirements.txt
└── README.md
Telemetry analysis is widely used in professional motorsports for:
- performance optimization
- race strategy planning
- driver comparison
- engineering insights
- data-driven decision making
Potential extensions of this project include:
- multi-race comparison
- interactive dashboards
- sector-wise performance metrics
- predictive lap time modelling
- advanced telemetry feature engineering