F1 Analytics Dashboard

Overview

A broadcast-style Formula 1 analysis dashboard — lap-by-lap position traces, tyre strategy Gantt charts, and race analytics. Select any season, Grand Prix, and session to load real telemetry data instantly.

The F1 Analytics Dashboard is a comprehensive data visualization platform that brings Formula 1 race analytics to life. Built with a Python backend using FastAPI and FastF1 for telemetry data processing, and a modern Next.js frontend for interactive visualizations.

Key Features

Technology Stack

Challenges

• Processing large volumes of telemetry data efficiently

• Creating real-time visualizations that match broadcast quality

• Handling multiple seasons and race formats

• Optimizing API response times for smooth user experience

Solutions

• Implemented caching strategy with FastF1's built-in cache system

• Used Recharts for performant, responsive data visualizations

• Built a flexible API that handles all F1 session types

• Optimized data structures for quick filtering and rendering

Project Architecture

f1-dashboard/
├── backend/
│   ├── models/
│   │   └── schemas.py
│   ├── routes/
│   │   ├── health.py
│   │   ├── positions.py
│   │   ├── results.py
│   │   ├── seasons.py
│   │   ├── sessions.py
│   │   └── tyres.py
│   ├── services/
│   │   ├── fastf1_service.py
│   │   ├── lap_loader.py
│   │   └── session_service.py
│   ├── utils/
│   │   ├── colors.py
│   │   ├── driver_lookup.py
│   │   └── helpers.py
│   ├── f1_cache/
│   ├── main.py
│   └── requirements.txt
└── frontend/
    ├── app/
    │   ├── api/
    │   ├── sign-in/
    │   ├── sign-up/
    │   ├── layout.tsx
    │   └── page.tsx
    ├── components/
    ├── lib/
    ├── state/
    │   └── raceStore.ts
    ├── types/
    │   └── f1.ts
    ├── utils/
    │   └── keepAlive.ts
    ├── public/
    └── package.json

Key Learnings

• Deep understanding of F1 telemetry data structures

• Advanced React patterns for complex state management

• API design for data-intensive applications

• Performance optimization techniques for large datasets

Future Enhancements

Live timing & telemetry via WebSocket, animated race map, Redis pub/sub layer, and deep analytics with telemetry overlays.