Gain visibility into true engineering performance. The Leaderboard balances Quality (AI Scores) and Impact (Complexity) to highlight developers who ship robust code, not just those who ship the most lines. By normalizing scores against your team’s average, it creates a fair playing field for everyone.
Both metrics contribute equally (50/50 weighting), ensuring that high-volume output doesn’t mask low-quality work, and that complex architectural improvements are recognized appropriately.
What you get
- Team-relative scoring that’s meaningful within your team context
- Balanced assessment preventing gaming through quantity over quality
- Fair comparison where large refactors count proportionally more than trivial changes
- Quality incentives through AI review scores (1-10 scale)
Leaderboard Score = (Normalized Quality Score + Normalized Impact Score) / 2
- Normalized Quality Score = Author’s Average AI Score / Team Average AI Score
- Normalized Impact Score = Author’s Total Impact / Team Average Impact
Understanding the score
| Score | Interpretation |
|---|
| = 1.0 | Exactly at team average on both quality and productivity |
| > 1.0 | Above team average (top performer) |
| < 1.0 | Below team average |
| 2.0 | Twice as productive/high-quality as the average |
Impact calculation
Each Merge Request’s impact score measures the complexity of the change:
MR Impact = (files_changed × 6.0) + (lines_added × 0.14) + (lines_deleted × 0.28)
| Metric | Weight | Rationale |
|---|
| Files Changed | 6.0 | Cross-file changes indicate higher complexity |
| Lines Added | 0.14 | New code requires understanding and integration |
| Lines Deleted | 0.28 | Deletions often require more careful analysis (2× addition weight) |
| Minimum Impact | 1.0 | Floor value to prevent division issues |
Example calculation
Team Data:
| Author | Avg AI Score | Total Impact |
|---|
| Alice | 8.5 | 450 |
| Bob | 7.2 | 280 |
| Carol | 9.0 | 120 |
- Team Avg AI Score = (8.5 + 7.2 + 9.0) / 3 = 8.23
- Team Avg Impact = (450 + 280 + 120) / 3 = 283.33
Alice’s Leaderboard Score:
- Normalized Quality = 8.5 / 8.23 = 1.03
- Normalized Impact = 450 / 283.33 = 1.59
- Leaderboard Score = (1.03 + 1.59) / 2 = 1.31
Alice scores 1.31 — 31% above team average!
Use Cases
The leaderboard is more than just a ranking—it’s a diagnostic tool for engineering health.
1. Identifying Mentors & Leads
Developers with consistently high Leaderboard Scores (High Quality + High Impact) are ideal candidates for:
- Mentoring junior developers
- Leading complex architectural refactors
- Reviewing critical PRs
2. Spotting Burnout & Process Issues
- High Impact, Low Quality: A developer might be rushing to meet deadlines, sacrificing quality. This is a signal to check workload.
- High Quality, Low Impact: Could indicate being stuck on a difficult problem, lack of tasks, or over-optimization.
3. Balancing Team Load
If one developer has significant impact score dominance, the team has a high “Bus Factor”. Use the leaderboard to ensure knowledge and workload are distributed more evenly.
4. Quality Gamification
Encourage the team to improve their “AI Score” by writing cleaner, more maintainable code, turning code review into a positive feedback loop rather than a chore.
Reference
