Lap Velocity: Performance Telemetry for Jira

Transform static Jira tickets into high-performance telemetry. Our app identifies bottlenecks as "Brake Zones" and automatically radios the team to ensure every sprint reaches a podium finish.

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🏎️ Inspiration

In the high-stakes world of Formula 1, engineers at Williams Racing don't just look at where a car is on the track; they analyze real-time telemetry—speed, tire wear, and lap velocity. Most software teams, however, treat Jira issues like "black boxes" where we only see the current status, not the performance data behind it.

I was inspired to bridge this gap by treating every Jira task like an F1 car. By visualizing the "telemetry" of a task—how it accelerates through the pipeline or where it hits "Brake Zones"—teams can optimize their workflow with the same precision as an elite pit wall.

🏁 What it does

Lap Velocity transforms static Jira tickets into a high-performance command center.

  • Real-time Telemetry: A custom dashboard that visualizes every status change in an issue's history.

  • Race Rating: A dynamic 0–100% score that calculates task health by penalizing "stagnation" and "regressions".

  • DRS (Drag Reduction System): Automatically detects high-speed task completion (under 1 hour) and rewards it with an active DRS badge.

  • Automated Pit Wall Radio: A backend trigger that acts as a proactive coach, posting automated comments to the team as tasks move through the pipeline.

🛠️ How I built it

I built this project as a solo developer, handling everything from architecture to UI design using the Atlassian Forge platform.

  • Analytics Engine: Built with Node.js, the backend fetches the issue changelog via the Jira REST API to reconstruct the "Lap History".

  • Performance Math: I implemented custom logic to calculate the Race Rating. If $T$ is the time in a status, a "Brake Zone" is triggered if $T > 24\text{ hours}$.

  • Command Center UI: A React-based Custom UI dashboard designed with a high-contrast Williams Racing aesthetic.

  • Forge Triggers: I utilized avi:jira:updated:issue to create the automated "Engineer" that posts real-time coaching comments.

🚧 Challenges I ran into

  • Solo Architecture: Balancing the development of a complex data sorting algorithm with a polished frontend required careful time management.

  • Data Synthesis: Jira stores status changes as individual events; I had to build an algorithm to calculate the delta between these events to find the true "Time-in-Status".

  • Trigger Loops: Ensuring the automated radio didn't trigger itself into an infinite loop of comments required precise permission scoping in the manifest.yml.

🏆 Accomplishments that I'm proud of

I am incredibly proud of successfully taking a complex idea from concept to a fully functional Forge app as a solo participant. Seeing the "Pit Wall Radio" automatically provide feedback to a developer based on their work velocity was a major "podium moment" for me.

📚 What I learned

During this hackathon, I mastered the Atlassian Forge development lifecycle, specifically regarding Custom UI and Event-Driven Triggers. I learned how to transform raw event logs into meaningful performance metrics and how to manage a full-stack project independently under a tight deadline.

🚀 What's next for Lap Velocity: Performance Telemetry for Jira

The next step is to move from the "Driver" view to the "Constructor" view. I plan to add a Constructor’s Championship leaderboard that compares the "Lap Velocity" of different teams across an entire Atlassian site, allowing Williams Racing to identify their fastest engineering squads.

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