F1 Driver Telemetry Analysis

A comprehensive Jupyter notebook for analyzing Formula 1 driver telemetry data using the FastF1 library. This project provides real-time F1 data access, visualization, and analysis capabilities.

🏎️ Features

• Real F1 Data Access: Connect to official Formula 1 telemetry data using FastF1
• Driver Analysis: Analyze individual driver performance and telemetry
• Multiple Visualizations: Static (matplotlib) and interactive (plotly) charts
• Driver Comparison: Compare performance between different drivers
• Data Export: Export telemetry data to CSV for further analysis
• Robust Error Handling: Gracefully handles missing telemetry data
• Performance Optimized: Includes caching for faster data loading

📊 Available Data

Telemetry Data

• Speed (km/h)
• Throttle (%)
• Brake (%)
• Gear (when available)
• RPM (when available)
• Steering Angle
• Distance (track position)

Timing Data

• Lap Times
• Sector Times
• Pit Stop Times
• Position Data

Additional Data

• Weather Conditions
• Tire Information
• Track Layout

🚀 Quick Start

Prerequisites

• Python 3.8+
• Jupyter Notebook

Installation

1. Clone the repository:

   git clone https://github.com/lspellman-opsguru/f1-data.git
   cd f1-data

2. Install dependencies:

   pip install -r requirements.txt

3. Launch Jupyter Notebook:

   jupyter notebook f1_telemetry_analysis.ipynb

4. Run the cells to start analyzing F1 data!

📈 Usage

Basic Analysis

1. Select a Race: Choose from available F1 races (2023-2024)
2. Pick a Driver: Select a specific driver to analyze
3. Get Telemetry: Extract detailed telemetry data for analysis
4. Visualize: Create charts and graphs of driver performance

Example Code

import fastf1
import os

# Enable caching
os.makedirs('cache', exist_ok=True)
fastf1.Cache.enable_cache('cache')

# Load a race
race = fastf1.get_session(2024, 'Bahrain', 'R')
race.load()

# Get driver data
driver_laps = race.laps.pick_driver('VER')
fastest_lap = driver_laps.pick_fastest()
telemetry = fastest_lap.get_telemetry()

# Analyze speed profile
import matplotlib.pyplot as plt
plt.plot(telemetry['Distance'], telemetry['Speed'])
plt.title('Speed Profile')
plt.show()

📋 Notebook Sections

1. Setup & Connection - Import libraries and connect to F1 data
2. Race Selection - Choose race and driver
3. Telemetry Extraction - Get detailed telemetry data
4. Visualizations - Multiple chart types and analysis
5. Advanced Analysis - Statistical summaries and correlations
6. Driver Comparison - Compare multiple drivers
7. Data Export - Save data for further analysis

🛠️ Dependencies

• fastf1>=3.1.0 - F1 data access
• pandas>=1.5.0 - Data manipulation
• numpy>=1.21.0 - Numerical computing
• matplotlib>=3.5.0 - Static plotting
• seaborn>=0.11.0 - Statistical visualization
• plotly>=5.0.0 - Interactive plotting
• jupyter>=1.0.0 - Notebook environment

📊 Visualization Types

Static Charts (Matplotlib)

• Speed profiles over distance
• Throttle and brake input analysis
• Lap time comparisons
• Gear usage patterns

Interactive Charts (Plotly)

• Multi-panel telemetry displays
• Zoomable and hover-enabled charts
• Dynamic data exploration

Analysis Types

• Speed Analysis: Track speed variations and top speeds
• Driving Style: Throttle/brake patterns and gear usage
• Performance Comparison: Driver vs driver analysis
• Statistical Summary: Key performance metrics

🔧 Configuration

Cache Settings

The notebook automatically creates a cache/ directory for storing downloaded F1 data. This improves performance by avoiding re-downloading the same data.

Data Availability

Different F1 sessions may have varying levels of telemetry data. The notebook handles missing data gracefully and will show available information.

📁 Project Structure

f1-data/
├── f1_telemetry_analysis.ipynb  # Main analysis notebook
├── requirements.txt             # Python dependencies
├── cache/                       # FastF1 data cache (auto-created)
└── README.md                    # This file

🎯 Use Cases

• Performance Analysis: Understand driver performance patterns
• Race Strategy: Analyze tire usage and pit stop strategies
• Technical Analysis: Study car setup and driving techniques
• Educational: Learn about F1 data analysis and visualization
• Research: Use data for machine learning or statistical analysis

🤝 Contributing

Contributions are welcome! Feel free to:

• Add new visualization types
• Improve data analysis methods
• Add support for additional F1 data sources
• Enhance error handling
• Add documentation

📝 License

This project is open source and available under the MIT License.

🔗 Links

• FastF1 Documentation: https://docs.fastf1.dev/
• Formula 1 Official: https://www.formula1.com/
• GitHub Repository: https://github.com/lspellman-opsguru/f1-data

📞 Contact

• GitHub: @lspellman-opsguru
• Website: https://opsguru.io
• Twitter: @lance_spellman

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Happy Racing! 🏁