Engineering Moneyball: How We Benchmarked Google vs Meta

A customer told me he feels like Billy Beane. He can see his team’s good. He can’t get a number to say it. Beane’s 2002 A’s ran $40M against the Yankees’ $126M, stopped measuring what everyone else did, and finished 103-59, one loss behind New York.

Ask an engineering org how it measures delivery: velocity, story points, commits, and a leader who used to ship and trusts his gut. That’s the eye test. I ran on it for 15 years as a CTO. Velocity runs on self-estimates, and AI broke the estimate. Ship in an afternoon what took three days, and the number was fiction before the sprint.

Roadmap delivery is harder to measure than commits, which is why people count commits.

The method. Each merged commit is scored for the depth of work it represents: how it classifies, how much of the codebase it reaches, where it sits in the architecture, whether it fixes someone else’s defect. The sum is one unit, Engineering Throughput Value, scored as a senior engineer would. Work splits into growth, maintenance, and fixes; each commit ties to a Jira or Linear item, so “on the roadmap” is a link, not an opinion, and spend is divided by throughput.

One customer team: 80% faster year over year, $25k a month in tooling, about $200 per ETV, roadmap delivery 36% faster, and 40% of the work mapped to the roadmap. Our published healthy line is 75%.

Then the failure modes. Where classification stands in for structural analysis. What a rewritten history, squash policy, or monorepo layout does to the score without touching the work. Everything a commit can’t see, starting with non-coding overhead. And the case that never makes a marketing page: high token burn, flat roadmap delivery.

Team-level throughput, never a ranking of individuals. Code stays on your infrastructure. No demo.

Commercial interest declared, so the design and limits are public at research.navigara.com. 500.navigara.com runs the same model across Microsoft, Meta, OpenAI, Google, Vercel, and Cloudflare.