Skip to main contentThe Contribution Analytics feature provides detailed analysis of individual contributions using advanced metrics and categorization systems. This analytical view helps understand the nature and impact of different types of work within your repository.
Overview
Contribution Analytics goes beyond simple commit counts to analyze the characteristics, complexity, and impact of each contribution. The system categorizes contributions into meaningful segments that help identify work patterns and contributor specializations.
Quadrant Classification System
The Four Contribution Quadrants
Visual Analytics
Scatter Plot Visualization
The analytics display contributions as an interactive scatter plot that visualizes the distribution of contributions over time. The chart shows:
- Unique Contributors: Up to 50 unique contributor avatars on desktop (25 on mobile) are displayed as interactive elements
- Duplicate Contributions: When the same contributor has multiple PRs, subsequent contributions appear as subtle gray squares
- Visual Hierarchy: Contributor avatars always render on top of gray squares for better visibility
- Time Distribution: Desktop displays contributions based on the configured time range (default 30 days), while mobile optimizes for performance with a fixed 14-day window
Contributor Mapping
Each contributor’s work patterns become visible through their distribution across the timeline, revealing activity patterns and collaboration dynamics.
Time Series Analysis
Contributors mapped by impact and effort metrics
Every contribution is automatically classified into one of four categories based on impact and effort analysis:
- 🟢 High Impact, High Effort: Major features, significant refactoring, and architectural changes
- 🟡 High Impact, Low Effort: Critical bug fixes, security patches, and targeted improvements
- 🔵 Low Impact, High Effort: Documentation updates, test additions, and infrastructure work
- ⚪ Low Impact, Low Effort: Minor tweaks, typo fixes, and routine maintenance
Impact Calculation
Impact is determined by analyzing:
- Files Modified: Number and type of files affected
- Lines Changed: Total additions and deletions
- Code Complexity: Structural changes to the codebase
- Review Activity: Discussion volume and reviewer engagement
Effort Assessment
Effort metrics consider:
- Development Time: Time between commits and PR lifecycle
- Revision Cycles: Number of review iterations required
- Change Scope: Breadth of modifications across the codebase
- Testing Requirements: Associated test changes and coverage impact
Insights and Patterns
Contributor Specialization
Analytics reveal contributor expertise areas:
- Feature Developers: Contributors concentrated in high-impact, high-effort quadrant
- Maintainers: Balanced distribution across all quadrants
- Bug Fixers: High activity in high-impact, low-effort area
- Infrastructure Contributors: Focus on low-impact, high-effort improvements
Project Health Indicators
Quadrant distribution reveals project characteristics:
- Feature-Heavy Projects: High concentration in major development quadrants
- Mature Projects: Balanced distribution with emphasis on maintenance
- Rapid Development: High velocity in quick-impact areas
- Technical Debt Focus: Emphasis on high-effort, infrastructure improvements
Filtering and Analysis
Quadrant Filtering
Select specific quadrants to filter the entire repository view, focusing analysis on particular types of contributions and their associated contributors.
Contributor Focus
Filter analytics to specific contributors to understand their individual contribution patterns and areas of expertise within the project.
Time Range Correlation
Analyze how contribution patterns change over the 30-day period, identifying seasonal patterns, project phases, and team evolution.
Strategic Applications
Resource Planning
Use quadrant analysis to understand where team effort is concentrated and identify areas that may need additional attention or different types of contributors.
Skill Development
Identify contributors who might benefit from expanding into different quadrants and create mentorship opportunities for skill diversification.
Project Prioritization
Understand the balance between feature development, maintenance, and infrastructure work to inform future planning and resource allocation.
Contribution Analytics transforms raw GitHub data into strategic insights that help teams understand not just what work is being done, but the nature and impact of that work on project success.
Contributors mapped by impact and effort metrics
Every contribution is automatically classified into one of four categories based on impact and effort analysis:
- 🟢 High Impact, High Effort: Major features, significant refactoring, and architectural changes
- 🟡 High Impact, Low Effort: Critical bug fixes, security patches, and targeted improvements
- 🔵 Low Impact, High Effort: Documentation updates, test additions, and infrastructure work
- ⚪ Low Impact, Low Effort: Minor tweaks, typo fixes, and routine maintenance
Impact Calculation
Impact is determined by analyzing:
- Files Modified: Number and type of files affected
- Lines Changed: Total additions and deletions
- Code Complexity: Structural changes to the codebase
- Review Activity: Discussion volume and reviewer engagement
Effort Assessment
Effort metrics consider:
- Development Time: Time between commits and PR lifecycle
- Revision Cycles: Number of review iterations required
- Change Scope: Breadth of modifications across the codebase
- Testing Requirements: Associated test changes and coverage impact