Pitwall Analytics

A minimal motorsport data analytics dashboard built with Dash, FastF1 and Pandas.

Overview

This project is a starter MVP to explore race sessions, lap times, positions and telemetry.

Structure

• app.py - main Dash app and routing
• pages/ - Dash multipage app pages (home.py, analytics.py)
• data_loader.py - wrappers for FastF1 session loading and caching
• charts.py - Plotly chart helper functions
• assets/style.css - minimal styling
• requirements.txt - dependencies

Setup

1. Create and activate a Python virtualenv (optional but recommended)

python -m venv venv
venv\Scripts\activate

2. Install requirements

pip install -r requirements.txt

3. Run the app

python app.py

4. Open http://127.0.0.1:8050 in your browser.

Notes

• The app enables a FastF1 local cache at .ff1cache to speed up subsequent loads.
• The Analytics page uses FastF1 to load race sessions; the first load may take extra time.

Professional project structure (recommended)

To make this project more production-ready consider restructuring into a package layout and adding tests and CI:

Recommended layout:

• pitwall-analytics/
  • pyproject.toml
  • .gitignore
  • README.md
  • src/
    • pitwall_analytics/
      • init.py
      • app.py (or run.py)
      • data_loader.py
      • charts.py
      • pages/
        • init.py
        • home.py
        • analytics.py
      • assets/
  • tests/
  • requirements.txt

Why this helps:

• src/ + package name reduces accidental imports from project root.
• pyproject.toml enables modern packaging and dependency metadata.
• .gitignore keeps environment and cache files out of git.
• Adding tests/ makes it easy to add unit tests and CI pipelines.

If you want, I can refactor the current code into the src/pitwall_analytics package and wire up a small test and Makefile/tasks next.