A comprehensive Formula 1 race strategy simulation and telemetry analysis tool that combines real F1 data with advanced mathematical modeling to optimize pit stop strategies.

• Realistic tire degradation curves with cliff effects
• Temperature and fuel load effects on performance
• Support for SOFT, MEDIUM, HARD, INTERMEDIATE, and WET compounds
• Configurable parameters for different tracks and conditions

• Dynamic weather simulation during races
• Track temperature calculation based on air temp, humidity, and cloud cover
• Rain intensity modeling affecting tire choice and performance
• Weather-based safety car probability adjustments

• Realistic safety car event generation
• Pit stop time reduction under safety car conditions
• Field compression effects on lap times
• Strategic advantage calculations for SC pit stops

• Brute force optimization for 1-stop and 2-stop strategies
• Multiple tire compound combinations
• Detailed stint analysis and timing
• Strategy comparison and visualization

• Modern Streamlit-based dashboard
• Real-time strategy optimization
• Interactive charts and visualizations
• Weather and safety car configuration
• Database integration for saving results

• PostgreSQL backend for storing strategies and results
• Session management and historical data
• Tire parameter storage and retrieval
• Performance analytics and comparisons

• Python 3.11+
• PostgreSQL (for database features)
• Docker (optional, for containerized deployment)

1. Clone the repository

git clone https://github.com/yourusername/F1-Telemetry-Strategy-Simulator.git
cd F1-Telemetry-Strategy-Simulator

2. Install dependencies

pip install -r infra/requirements.txt

3. Set up database (optional)

# Using Docker
docker-compose -f infra/docker-compose.yml up -d db
# Or install PostgreSQL locally and create database
create db f1_strategy

4. Run the application

streamlit run app/Home.py

The application will be available at http://localhost:8501

# Build and run with Docker Compose
cd infra
docker-compose up --build
# Access the application at http://localhost:8501

1. Select Race Parameters

   • Choose year, Grand Prix, and session type
   • Configure weather conditions
   • Enable/disable safety car simulation

2. Run Optimization

   • Click "Load Session & Optimize Strategy"
   • View optimal pit stop strategy
   • Analyze tire degradation curves

3. Compare Strategies

   • See multiple strategy options
   • Compare total race times
   • Analyze stint details

• Weather Simulation: Adjust temperature, humidity, and precipitation
• Safety Car Events: Enable realistic SC scenarios
• Database Storage: Save and retrieve strategy results
• Historical Analysis: Compare strategies across different races

Customize tire performance in sim/tyres.py:

tire_params = {
 "SOFT": {
 "base": 75.0, # Base lap time (seconds)
 "k": 0.10, # Degradation rate
 "cliff_at": 18, # Cliff effect start lap
 "cliff_penalty": 0.30, # Cliff penalty (seconds)
 "max_life": 25 # Maximum useful life
 }
}

Adjust weather parameters in the Streamlit sidebar or modify defaults in sim/weather.py.

Configure SC likelihood in sim/sc_models.py:

self.base_sc_probability = 0.15 # 15% chance per race
self.lap_sc_probability = 0.002 # 0.2% chance per lap

tm = TyreModel(tire_params)
lap_time = tm.lap_time(compound, tyre_life, track_temp, fuel_load)

weather = WeatherModel()
forecast = weather.simulate_weather_forecast(race_laps, conditions)

sc = SafetyCarModel()
events = sc.generate_safety_car_events(race_laps, weather_forecast)

total_time, stints = simulate_plan(race_laps, start_compound, pit_plan, tyre_model)

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

• FastF1 for F1 data access
• Streamlit for the web interface
• Plotly for interactive visualizations
• Formula 1 for the amazing sport and data

Built with ❤️ for F1 by Yash Bhalla