Boxup: Formula 1 Data Analytics Platform

A comprehensive Formula 1 telemetry and analytics platform that provides real-time race insights, performance analysis, and machine learning-powered predictions. Built with modern web technologies and a scalable microservices architecture.

Overview

Boxup aggregates and visualizes Formula 1 telemetry data, driver performance metrics, and race analytics in real-time. The platform enables engineers, analysts, and enthusiasts to explore detailed telemetry traces, brake analysis, corner insights, and predictive models for championship standings and race outcomes.

Key Features

• Real-Time Telemetry Visualization: Interactive speed traces, brake analysis, and corner-by-corner performance metrics
• Race Analysis: Comprehensive session data including qualifying segments, practice sessions, and race telemetry
• Driver Performance Analytics: Comparative analysis between drivers with sector breakdowns and performance matrices
• Weather Integration: Real-time weather conditions tracked throughout race sessions
• Championship Predictions: Machine learning models for predicting championship outcomes and race results
• Responsive Dashboard: Modern web interface with real-time updates and interactive visualizations
• Data Ingestion Pipeline: Automated data collection and processing from official F1 sources

Tech Stack

Frontend

• Framework: Next.js 16 (React 19)
• Styling: Tailwind CSS, PostCSS
• Charts & Visualization: Recharts, Plotly.js, react-d3-speedometer
• Real-Time Updates: Socket.io Client
• Testing: Vitest
• Linting: ESLint
• Deployment: Vercel

Backend

• Language: Python 3
• Framework: Flask
• Database: PostgreSQL with TimescaleDB (time-series optimization)
• Migrations: Alembic
• Message Queue: Apache Kafka
• Caching: Redis
• ML Tracking: MLflow

Infrastructure

• Containerization: Docker & Docker Compose
• Orchestration: Kafka for event streaming
• Database: TimescaleDB for time-series telemetry data
• UI Tools: Kafka UI for message queue monitoring

Project Structure

.
├── frontend/                 # Next.js web application
│   ├── app/                 # Page routes and layouts
│   ├── components/          # Reusable React components
│   ├── lib/                 # Utilities and API clients
│   └── types/               # TypeScript type definitions
├── backend/                 # Python Flask API
│   ├── src/backend/        # Core application logic
│   │   ├── api/            # API endpoints
│   │   ├── config.py       # Configuration management
│   │   └── extensions.py   # Database and service extensions
│   ├── migrations/         # Alembic database migrations
│   └── tests/              # Test suite
├── packages/
│   ├── ingestion/          # F1 data ingestion pipeline
│   ├── ml/                 # Machine learning models and training
│   └── workers/            # Background job processors
└── infra/                  # Infrastructure configuration
    ├── docker-compose.yml  # Local development environment
    └── postgres/           # Database initialization scripts

Getting Started

Prerequisites

• Node.js 18+ (for frontend)
• Python 3.9+ (for backend)
• Docker & Docker Compose (for infrastructure)
• PostgreSQL 15+ (or use Docker)

Installation

1. Clone the repository

   git clone <repository-url>
   cd "Formula 1"

2. Start infrastructure services

   cd infra
   docker-compose up -d

   This starts PostgreSQL, TimescaleDB, Kafka, and Kafka UI.

3. Setup backend

   cd backend
   pip install -r ../requirements.txt
   flask db upgrade  # Run migrations
   python main.py

   The backend API will be available at http://localhost:8000

4. Setup frontend

   cd frontend
   pnpm install
   pnpm dev

   The frontend will be available at http://localhost:3000

Configuration

Environment variables are managed through .env files. Key configurations:

• DATABASE_URL: PostgreSQL connection string
• REDIS_URL: Redis connection URL
• KAFKA_BOOTSTRAP_SERVERS: Kafka broker addresses
• MLFLOW_TRACKING_URI: MLflow server URL
• AUTO_INGEST_ENABLED: Enable automatic F1 data ingestion

Development

Running Tests

Frontend:

cd frontend
pnpm test

Backend:

cd backend
pytest

Code Quality

Frontend linting:

cd frontend
pnpm lint

Database Migrations:

cd backend
alembic upgrade head

API Documentation

The backend provides REST API endpoints for:

• /api/sessions - Race and practice session data
• /api/drivers - Driver information and statistics
• /api/laps - Lap telemetry and timing
• /api/analysis - Performance analysis and comparisons
• /api/health - Service health checks

Deployment

The project is configured for deployment on:

• Frontend: Vercel (optimized for Next.js)
• Backend: Railway or similar container platforms
• Infrastructure: Kubernetes-ready with Docker Compose for local development

Data Pipeline

1. Ingestion: Automated service pulls official F1 data
2. Processing: Data is validated, enriched, and stored in TimescaleDB
3. Streaming: Events are published to Kafka topics for real-time consumers
4. Analysis: ML models process telemetry for predictions
5. Visualization: Frontend retrieves and visualizes data via REST API

Contributing

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

License

This project is licensed under the GNU Affero General Public License v3.0 (AGPL-3.0). See the LICENSE file for details.

Support

For issues, questions, or suggestions, please open an issue on the repository.

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Last Updated: June 2026