Leaderboard

Understand the Developer Leaderboard scoring formula that balances code quality with productivity.

The leaderboard scores each developer by balancing Quality (AI review scores) and Impact (code complexity), so it rewards engineers who ship robust code, not just those who ship the most lines. Find it under Code Review → Leaderboard once your team has a few reviewed PRs.

Why the leaderboard

• Team-relative scoring — both metrics are normalized against your own team’s average, so scores stay meaningful in your context
• Balanced assessment — quality and impact contribute equally (50/50), so high-volume output can’t mask low-quality work
• Fair comparison — large refactors count proportionally more than trivial changes
• Quality incentives — the 1–10 Quality Score turns code review into a positive feedback loop
• Diagnostic, not just a ranking — it surfaces workload imbalances and quality trends that PR counts alone don’t show

Quality score

Each PR receives a Quality Score on a 1–10 scale. The score starts at 10 and is reduced by the number and severity of findings — critical issues carry a higher penalty than low-severity ones. The Team Quality Score shown on the dashboard is the average across all developers.

Core formula

Contribution Score = (Normalized Quality Score + Normalized Impact Score) / 2

Where:

• Normalized Quality Score = Author’s Average Quality Score / Team Quality Score
• Normalized Impact Score = Author’s Total Impact / Team Average Impact

Contribution Score	Label	Interpretation
≥ 1.5	Excellent	Significantly above team average
1.0 – 1.5	Good	Above team average
0.8 – 1.0	Average	At or near team average
< 0.8	Needs Improvement	Below team average

A score of exactly 1.0 means the developer sits exactly at the team 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

The impact formula is derived from the Oobeya GitWiser Coding Impact Score methodology.

Example calculation

Team data:

Author	Avg Quality Score	Total Impact
Alice	8.5	450
Bob	7.2	280
Carol	9.0	120

Team averages:

• Team Quality Score = (8.5 + 7.2 + 9.0) / 3 = 8.23
• Team Avg Impact = (450 + 280 + 120) / 3 = 283.33

Alice’s Contribution Score:

• Normalized Quality = 8.5 / 8.23 = 1.03
• Normalized Impact = 450 / 283.33 = 1.59
• Contribution Score = (1.03 + 1.59) / 2 = 1.31

Alice scores 1.31 — 31% above the team average.

Reading the leaderboard

Use the score combinations as a diagnostic for engineering health, not just a ranking:

Pattern	Signal	Action
High Quality + High Impact	Top performer	Ideal mentor, lead reviewer, or owner of complex refactors
High Impact + Low Quality	Possible burnout — shipping fast, cutting corners	Check workload and deadlines
High Quality + Low Impact	May be stuck on a hard problem or under-utilized	Check task allocation
Uneven Impact distribution	High “Bus Factor” — knowledge concentrated in one person	Redistribute knowledge and workload

Review the leaderboard weekly to track quality trends and workload balance across your team.

Related

Skill matrix

See per-developer proficiency across Security, Performance, Correctness, and Patterns domains

Code review analytics

Track code review trends, developer performance, and team health over time