F1 Insight - Strategy & Prediction Platform

A high-fidelity Formula 1 strategy simulation and community prediction engine. This platform provides advanced race strategy modeling, historical data analysis, and simulated telemetry replay functionality.

Technology Stack

Frontend

• Framework: React 18 (Vite)
• Styling: Tailwind CSS
• Visualization: Recharts, D3.js (via simple SVG maps)
• State Management: React Context & Hooks

Backend

• Server: Python (Flask)
• Database: PostgreSQL (Production) / SQLite (Development)
• ORM: SQLAlchemy
• Data Source: FastF1 Library (Official F1 Live Timing Archives)

Infrastructure

• Containerization: Docker & Docker Compose
• Cloud: AWS RDS (PostgreSQL)

Key Features

1. Strategy Simulation

Physics-based modeling to simulate race outcomes based on tyre degradation, fuel load, and pit stop strategies.

• Tyre Model: 4th-order polynomial degradation curves based on Pirelli data.
• Race Variables: Stochastic traffic modeling and safety car probability injection.
• Scenarios: Compare 1-stop vs 2-stop strategies under various conditions.

2. Telemetry Analysis & Replay

• Data Visualization: Lap-by-lap comparison of Speed, Throttle, and Brake telemetry.
• Race Replay: Simulated "live" playback of historical races with synchronized track map and leaderboards.
• 2026 Regulations: Support for projected 2026 car performance metrics.

3. Community Predictions

• Voting System: Aggregated community predictions for race winners and safety car probabilities.
• Analysis: Compare user predictions against statistical model outputs.

Setup Instructions

Prerequisites

• Python 3.9+
• Node.js 16+
• PostgreSQL (optional, defaults to SQLite if unconfigured)

Local Development

1. Backend Setup

   cd backend
   python -m venv venv
   # Activate venv (Windows: venv\Scripts\activate, Mac/Linux: source venv/bin/activate)
   pip install -r requirements.txt
   python app.py

2. Frontend Setup

   cd frontend
   npm install
   npm run dev

3. Database Seeding The application requires baseline data to function.

   cd backend
   python seed_history.py

Docker Deployment

To run the full stack using Docker:

docker-compose up --build

Project Structure

• backend/: Flask API, data processing scripts, and simulation logic.
• frontend/: React application, UI components, and visualization modules.
• data_pipeline/: Scripts for fetching and normalizing FastF1 data.

Configuration

Environment variables can be set in a .env file or directly in the environment.

• DATABASE_URL: Connection string for PostgreSQL (e.g., postgresql://user:pass@host:5432/db).
• FLASK_ENV: Set to development or production.

License

Proprietary software. All rights reserved.