How to Measure Engineering Performance Across Teams 

Published August 13th, 2025 by Tolga Keskinoglu

For engineering leaders, understanding the performance of individual teams is crucial. But what about understanding performance across different teams? This is where things often get complicated. How do you fairly compare a team working on a legacy system with another tackling greenfield development? Or a platform team versus a product feature team? The desire to benchmark, identify pockets of excellence, and ensure consistent delivery across an organization is strong, yet the path to achieving it is fraught with challenges.

The goal isn't to rank teams for the sake of ranking, but to unlock insights that can elevate the entire engineering department. It’s about identifying what "good" looks like in different contexts, sharing best practices, and ensuring that all teams have the support and processes they need to thrive. However, without a consistent and objective way to measure, leaders are often left relying on gut feelings or, worse, metrics that create misleading comparisons and can even demotivate teams.

The Apples to Oranges Dilemma: Why Traditional Metrics Fall Short

The first hurdle in measuring performance across teams is the "apples to oranges" comparison. Common metrics, while sometimes useful within a single team's context, break down when applied broadly. For instance, Lines of Code (LoC) say little about complexity, quality, or impact, as a team refactoring complex legacy code might produce fewer lines than one scaffolding a new service with boilerplate.

This challenge extends to nearly all common metrics because they often measure only one narrow slice of developer activity. Modern software development is a multifaceted discipline encompassing a wide range of tasks from writing brand new code to issue management, system maintenance, and conducting code reviews. The mix of these activities varies significantly from team to team. To create a fair basis for comparison, it is essential to capture detailed data from as many of these different work areas as possible. Relying on a single dimension like commit frequency or story points will inevitably favor teams whose work aligns with that specific metric while misrepresenting the valuable contributions of others. A true understanding requires a detailed, holistic dataset that reflects the diverse context engineering work is performed in.

Attempting to normalize these manually or create complex spreadsheets often leads to a data quagmire – difficult to maintain, interpret, and trust. The result? A lack of true visibility into how different parts of the engineering organization are performing relative to each other, making it hard to spot systemic issues or opportunities for widespread improvement.

Beyond Individual Metrics: The Need for a Holistic View

To effectively measure and compare engineering performance across diverse teams, a more sophisticated approach is needed. Instead of relying on isolated metrics, which provide only a sliver of the picture, a holistic view that considers multiple dimensions of the development process is essential. Furthermore, any system aiming for fair comparison must be able to normalize for the inherent differences in work, context, and team structure.

This is where a composite index, built from the ground up with these challenges in mind, becomes invaluable. Such an index can analyze the rich digital traces left behind in your existing development tools – commits, pull requests, issue management, and more – to create a standardized measure of effectiveness.

The MECOIS Productivity Index: Enabling Fair Cross Team Comparisons

The MECOIS Productivity Index is designed to provide exactly this kind of objective, comparable insight. Here’s how we address the challenge of measuring performance across teams:

1. Comprehensive Data Collection: We start by extracting a wide array of development artifacts from the tools your teams already use (like GitHub, Jira, etc.). This captures data from various stages of the development lifecycle.

2. Multidimensional Analysis: These raw artifacts are parsed into a range of usable metrics. However, instead of looking at them in isolation, we use data driven statistical methods to group correlating metrics into key dimensions that represent influential aspects of development.

3. Normalized Composite Index: The core of the solution is the calculation of a composite index. This isn't just a simple sum or average. It's an algorithmically derived score that is normalized, meaning it allows for direct and fair comparisons of development effectiveness – whether between different teams, different projects, or even the same team over different time periods.

4. This evidence-based index, developed through extensive research in collaboration with the Professorship for Open Source Software at the Friedrich Alexander University in Germany, moves beyond simplistic counts. It provides a nuanced understanding of how productive teams are, taking into account a diverse set of development activities.

Unlocking Organizational Insights and Driving Improvement

With a reliable way to measure and compare performance across teams, engineering leaders can unlock significant organizational advantages. They can discover which teams are consistently excelling and, more importantly, understand the underlying reasons—be it their processes, tools, or team structures—to see if these elements can be replicated. This understanding allows for targeted support for teams that might be struggling due to systemic issues like unclear requirements or technical debt.

Moreover, when comparisons are fair and data driven, they facilitate valuable knowledge sharing between teams, fostering a culture of learning rather than unhealthy competition. Ultimately, this clearer picture of the engineering organization's overall health and capacity empowers leaders to make more informed strategic decisions regarding resource allocation and planning.

For instance, the MECOIS Productivity Index helped identify a team at a major institution whose high performance workflows proved replicable. It also highlighted another team whose performance dipped due to miscommunications, and another which turned around its performance after effective interventions. Imagine having this level of insight consistently across all your teams.

Conclusion: From Siloed Views to a Quantified Corporation

Measuring engineering performance across teams is no longer about finding the "best" or "worst" team. It's about gaining a holistic understanding of your entire engineering system. It’s about identifying patterns, learning from both successes and challenges, and creating an environment where every team can continuously improve and contribute to their full potential.

By moving beyond flawed traditional metrics and embracing objective, normalized indices like the MECOIS Productivity Index, leaders can unlock a new level of insight, driving not just individual team improvements, but fostering a culture of engineering excellence across the entire organization.

Ready to gain unparalleled clarity into how your engineering teams perform and compare? Subscribe to our private beta and be among the first to experience these transformative insights.