As a new Senior Technical Manager, I’m looking for reliable engineering measures to evaluate team velocity, beyond vanity metrics like lines of code or commit frequency. I’m interested in practical, outcome-focused indicators that reflect true productivity, collaboration, and project progress. What engineering measures have you found useful or harmful in assessing your team’s performance?
With over a decade in engineering management, I’ve found that the most reliable engineering measure is cycle time, from work started to production. It’s where you start to see real flow patterns and, more importantly, bottlenecks. Pair that with deployment frequency and lead time for changes, and you’ve got a clear picture of how fluidly your team ships code. I’ve learned to steer clear of vanity metrics like lines of code or ticket counts, they rarely align with actual impact. Conversations around blockers, scope creep, and team dependencies have often told me more than any dashboard ever could
Totally agree with @tim-khorev. In my 7 years working across distributed teams, the engineering measure that’s consistently helped us is predictability over raw speed. We do track story points committed vs. completed, but it’s not a performance grade, it helps us refine sprint planning. We also monitor PR throughput and review time, when those are healthy, I know collaboration is strong. Another layer we’ve added is tracking incident counts and time to resolve, not to penalize, but to ensure quality doesn’t take a back seat to delivery. Metrics only matter when they tell a story worth discussing.
Yeah, been there too. I once led a team where leadership emphasized commit counts and ticket velocity as key engineering measures — let’s just say, it backfired hard. People gamed the system, and morale dipped fast. These days, I lean heavily on DORA metrics, especially lead time, deploy frequency, change failure rate, and MTTR. They offer a good mix of speed and stability. But I never stop at numbers, we run retrospectives and engagement check-ins to balance the data with human insight. Any engineering measure should start a conversation, not end one. In creative work like this, that’s where real improvement happens.