Why Individual Productivity Metrics Hurt Teams (and What to Do Instead)
The False Promise of Measuring Developers
In many engineering orgs, there’s a temptation to reduce complexity to numbers. Lines of code, PRs merged, velocity points. These metrics feel concrete, comparable, objective. But they’re not.
Trying to measure developer productivity through individual metrics often does more harm than good. It creates the illusion of clarity while introducing new forms of bias and pressure. Worse, it erodes trust, autonomy, and team cohesion — the very foundations of healthy software teams.
So why do so many still chase these numbers? Because they’re easy to collect, easy to sort, and dangerously easy to misinterpret.
The Real Problems with Individual GitHub Metrics
Let’s look at a few examples of commonly used individual metrics and why they fail in real-world contexts:
1. Number of commits
A high commit count might reflect frequent saves, not meaningful progress. A low commit count might mean someone works in larger, more thoughtful units. It says nothing about quality, impact, or complexity.
2. PRs opened or merged
These numbers are easily inflated. Opening many small PRs might look productive, but if each one offloads cognitive load onto reviewers, it's a net drain on team bandwidth.
3. Review activity
Reviewing many PRs quickly might mean someone’s being helpful — or just clicking “Approve” without much thought. Meanwhile, deep technical reviews that prevent bugs may look slow or sparse on the surface.
In all these cases, individual metrics lack the context necessary to interpret them correctly. They tell you what happened, but not why, how, or whether it helped the team.
Context Over Judgment: What GitHub Activity Should Be Used For
At Gitlights, we believe GitHub data can be incredibly valuable — not to judge individuals, but to understand systems.
Our dashboards are designed to help technical teams:
- See patterns of collaboration over time
- Understand how long reviews take, and why
- Identify where code review load is unevenly distributed
- Spot areas of heavy rework, which may signal architectural tension
- Surface how the team is balancing between building new things vs. fixing vs. refactoring
None of this is about evaluating people. It's about seeing the shape of the work — together.
A Better Use of GitHub Analytics
Here are three ways teams use Gitlights to foster better collaboration without slipping into surveillance:
1. Visualizing review dynamics
The pull request dashboard shows who is reviewing whose code, how long it takes, and how often PRs get stuck. This helps teams balance review responsibilities and talk openly about process improvements — without naming and shaming.
2. Surfacing collaboration patterns
Our collaboration graph reveals clusters of high interaction, review bottlenecks, or isolated contributors. It’s not about flagging underperformers, but about asking: Are we reviewing fairly? Is anyone overloaded?
3. Understanding technical investment
The investment balance dashboard helps teams see what they’re actually working on — not what they planned in a sprint, but what they shipped. This helps align technical goals with reality, guiding better discussions about priorities and tradeoffs.
Focus on the Team, Not the Scorecard
Software engineering is deeply collaborative. Optimizing individuals in isolation rarely improves team outcomes. In fact, it often creates perverse incentives: rushing work, avoiding reviews, competing for visibility.
The best teams focus on flow, feedback, and shared responsibility. They create environments where developers feel safe to take time, ask for help, and solve hard problems without gaming metrics.
That’s why Gitlights exists: to give you a clearer picture of the work, without putting anyone under a microscope.
Want to explore your team’s development flow in a healthier way?