TracksideTelemetry

Live Demo

A Streamlit dashboard for analysing MotoGP 18 telemetry data. It provides lap viewing, lap comparison, editable track zones, telemetry-based findings, and CSV exports.

This is a simulator-data analysis project. It does not control a motorcycle ECU and does not provide real-world riding advice.

Features

• Lap leaderboard with best-lap and coverage metrics
• Track trajectory viewer for each available lap
• Starter and custom track-analysis zones
• Zone profile export/import as JSON
• Reference-versus-comparison lap analysis
• Speed, throttle, front-brake, and rear-slip comparison charts
• Ranked zone findings and selected-zone CSV export

Demo data

The deployed version uses a lightweight public demo dataset with 5 usable laps:

data/demo/laps_summary_demo.csv
data/demo/telemetry_curated_demo.parquet

When full local files are available, the dashboard loads them first:

data/processed/laps_summary.csv
data/processed/telemetry_curated.parquet

Requirements

• Python 3.10 or newer
• pip

Run locally

1. Clone the repository:

git clone https://github.com/YOUR-USERNAME/TracksideTelemetry.git
cd TracksideTelemetry

2. Create and activate a virtual environment:

Windows PowerShell

python -m venv .venv
.\.venv\Scripts\Activate.ps1

Windows Command Prompt

python -m venv .venv
.venv\Scripts\activate

3. Install dependencies:

pip install streamlit pandas numpy plotly pyarrow

4. Start the dashboard:

streamlit run app/dashboard.py

5. Open the local address shown in the terminal, usually:

http://localhost:8501

How to use

1. Open the Dashboard tab to view the lap leaderboard and track trajectory.
2. Select a lap from Choose a lap to display.
3. Open Zone manager and click Load starter zones, or create custom zones.
4. Open Lap comparison, select separate reference and comparison laps, then review zone findings.
5. Select a zone to inspect speed, throttle, braking, and rear-slip traces.
6. Download the selected-zone comparison as a CSV if required.

Project structure

TracksideTelemetry/
├── app/
│   └── dashboard.py
├── data/
│   ├── demo/
│   │   ├── laps_summary_demo.csv
│   │   └── telemetry_curated_demo.parquet
│   └── processed/             # Optional full local dataset
│       ├── laps_summary.csv
│       └── telemetry_curated.parquet
└── README.md

Data priority

The app automatically uses:

1. data/processed/ when the full local dataset is available
2. data/demo/ when the full dataset is unavailable

The active source is displayed in the dashboard as either Full local telemetry dataset or Public demo dataset.