> ## Documentation Index
> Fetch the complete documentation index at: https://docs.cloudthinker.io/llms.txt
> Use this file to discover all available pages before exploring further.

# 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.

<Frame>
  <img src="https://mintcdn.com/cloudthinker/ezLyBgfIZEMSknyR/images/code-review/leaderboard.png?fit=max&auto=format&n=ezLyBgfIZEMSknyR&q=85&s=fc932105c913d35a6c84b091675e1445" alt="Leaderboard" width="2922" height="1714" data-path="images/code-review/leaderboard.png" />
</Frame>

## 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

```text theme={null}
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:

```text theme={null}
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](https://docs.oobeya.io/gitwiser-repo-analytics/git-analytics-metric-definitions/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                        |

<Tip>
  Review the leaderboard weekly to track quality trends and workload balance across your team.
</Tip>

## Related

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