Race Incident Intelligence System

Centralized intelligence system to log, analyze, and surface high-impact race incidents using structured data and workflows for F1 teams like Williams.

Comment

Inspiration

Formula 1 teams like Williams operate in extremely high-pressure, data-intensive environments. During a race weekend, incidents, penalties, weather changes, and on-track events are constantly discussed, but often captured in unstructured ways such as notes, chats, or ad-hoc logs.

We were inspired by how software teams handle production incidents: with structured metadata, clear workflows, and searchable history. Race Incident Intelligence System applies the same mindset to motorsport, treating race incidents as first-class operational data rather than scattered observations.

What it does

Race Incident Intelligence System provides a structured way to log, track, and analyze race incidents.

Each Race Incident records:

  • Track
  • Session (Practice, Qualifying, Race, etc.)
  • Weather conditions
  • Impact level

This allows teams to quickly surface high-impact incidents, identify patterns across tracks or conditions, and perform data-driven post-race analysis instead of relying on anecdotal recall.

How we built it

The project is built on Jira Software, extended using Atlassian Forge.

Key design choices:

  • A custom work type (Race Incident) instead of overloading generic issues
  • Structured select-list fields for consistency and analytics
  • A Company-managed Jira project for full control over schemas and screens
  • Secure backend access using Forge’s asApp() execution model to query incident data programmatically via JQL

This setup makes the system both operationally useful and automation-ready.

Challenges we ran into

  • Correctly configuring Jira’s work types, schemes, and screens so custom fields appeared reliably
  • Understanding the difference between user context vs app context in Forge (asUser() vs asApp())
  • Navigating Forge app installation, identity, and permissions when accessing Jira data securely
  • Designing a model realistic enough for real F1 teams while keeping it lightweight for a hackathon

Accomplishments that we're proud of

  • Designing a domain-specific incident model that maps cleanly to real motorsport workflows
  • Successfully extending Jira with a custom work type and structured intelligence fields
  • Building a Forge app that securely accesses incident data as an application, not just a user
  • Turning a general-purpose issue tracker into a focused race incident intelligence system

What we learned

  • Structured data enables insights that free-text logs cannot
  • Enterprise tools like Jira are extremely powerful when modeled correctly
  • Security and permissions in real-world systems are layered and must be reasoned about carefully
  • Forge encourages a least-privilege, automation-first approach that fits operational intelligence use cases well

What's next for Race Incident Intelligence System

Future improvements could include:

  • Dashboards showing incident trends by track, weather, or impact
  • Automated alerts for critical incidents during race weekends
  • Integration with telemetry, steward decisions, or external race data feeds
  • Historical analysis across multiple races or seasons for teams like Williams
Share this project:

Updates