How We Measure Startup Engineering Acceleration
VC Deal Flow Signal uses publicly available GitHub data to identify startups showing unusual engineering momentum. This page explains exactly how we source, process, and rank that data, so investors can evaluate the signal quality before acting on it.
What data sources does VC Deal Flow Signal use?
VC Deal Flow Signal uses the public GitHub REST API v3 as its primary data source: the search/repositories endpoint to discover active startup organizations across 15 sector topic clusters, and the stats/commit_activity and contributors endpoints for per-organization data. Bot commits are excluded before aggregation, and no private repositories or scraping is involved.
GitHub API v3 is our primary data source. We query the search/repositories endpoint to discover active startup organizations across 15 sector-specific topic clusters (e.g., machine-learning, fintech, cybersecurity). We then pull per-organization data from the stats/commit_activity and contributors endpoints.
Filtering: We exclude large tech companies (Google, Microsoft, Meta, etc.), major open-source foundations, and organizations with patterns inconsistent with venture-backed startups. The goal is to surface companies in the pre-seed through Series B range.
Geography is derived from the GitHub organization profile location field, mapped to broad regions (US, UK, EU, APAC, Canada, LATAM, MENA).
How does VC Deal Flow Signal measure engineering acceleration?
Engineering acceleration is computed weekly from public GitHub data. The pipeline pulls 14-day commit velocity, contributor count, and repository creation events for roughly 350+ startup organizations across 15 sectors, then expresses each metric as a percentage change versus the prior 14-day window. A breakout must persist into a second 14-day window before it becomes actionable.
Commit Velocity (14-day)
The total number of commits to an organization's most active public repository over a rolling 14-day window. We use GitHub's weekly commit_activity data (52 weeks of history) and sum two consecutive weeks to produce a 14-day figure.
Commit Velocity Change
The percentage change in commit velocity compared to the preceding 14-day window. A startup with 40 commits this period and 20 commits last period shows +100% velocity change. This is the primary ranking signal, it measures acceleration, not absolute volume.
Contributor Count & Growth
The number of unique contributors to the organization's most active repository. Growth is estimated by comparing recent 6-week commit volume to the prior 6-week period. A rising contributor count often signals team expansion, a leading indicator of funding or product-market fit.
New Repositories
The count of public repositories created by the organization in the last 30 days. A burst of new repos often signals infrastructure buildout, new product lines, or framework migrations.
Composite predictor: velocity × contributor diversity (the 3.4× finding)
The single most predictive composite in the SSRN panel of 219 confirmed rounds is 14-day commit-velocity acceleration combined with low top-contributor concentration (Gini coefficient under 0.30 over the same 14-day window).
Orgs that meet both conditions are 3.4× more likely to announce a Series A within 60 days than orgs with high acceleration alone. In other words: velocity matters, but the shape of the velocity matters more. A team where one developer is doing 80% of the commits can spike just as hard as a team where eight developers are sharing the load, but only one of those teams looks like a fundraise candidate to a Series A partner.
Source: SSRN preprint abstract=6606558, panel n=219, regression stratified by stage. Lift survives a 90-day extension of the panel (next refresh: Q3 2026).
What are the five signal types?
Each tracked startup is classified into one of five signal types: engineering hiring burst, when contributor growth exceeds 50%; infrastructure buildout, when three or more new repositories appear in 30 days; deploy frequency spike, when commit velocity rises 150% or more; framework migration, for general acceleration that fits none of the above; and deceleration, when commit velocity falls below the prior 14-day window.
Each startup is assigned one of five signal types based on which metric is driving the signal:
- Engineering hiring burst contributor growth rate exceeds 50%. The team is scaling rapidly.
- Infrastructure buildout 3 or more new repositories in 30 days. The company is expanding its technical surface area.
- Deploy frequency spike commit velocity has increased 150% or more. The team is shipping at an unusually high rate.
- Framework migration general acceleration that doesn't fit the above categories, often indicating a technology stack transition.
- Deceleration commit velocity falls below the prior 14-day window. The team may have shipped a milestone and is regrouping, or is slowing ahead of a pivot or a pause.
How is startup stage estimated?
Stage is estimated from contributor count as a rough proxy for team size: pre-seed shows 1-7 contributors, seed 8-19, Series A/B 20-49, and growth 50 or more. It is an approximation, because not all contributors are employees and not all employees contribute to public repositories.
We estimate startup stage from contributor count as a rough proxy for team size: Pre-seed (1-7 contributors), Seed (8-19), Series A/B (20-49), Growth (50+). This is an approximation, not all contributors are employees, and not all employees contribute to public repos.
How often is the data updated?
Data is refreshed weekly, every Monday morning. The pipeline queries GitHub for the latest 52 weeks of commit history, recalculates all metrics, regenerates sector rankings, and rebuilds the site. Each sector page shows rankings for the current quarter and up to four previous quarters.
Data is refreshed weekly (Monday mornings). The pipeline queries GitHub for the latest 52 weeks of commit history, recalculates all metrics, regenerates sector rankings, and rebuilds the site. Each sector page shows rankings for the current quarter and up to four previous quarters.
What are the known limitations of GitHub-based signals?
Private repositories are invisible, so the signal only covers public engineering activity. Commit volume measures output, not code quality. And engineering acceleration is a leading indicator of traction, not a guarantee of success: it is a screening filter for due diligence, not investment advice.
Private repos are invisible. Some startups keep all or most code in private repositories. Our signal only covers public engineering activity.
Commit volume is not code quality. High commit velocity can reflect rapid feature development, but also refactoring, documentation, or CI/CD noise. We mitigate this by measuring change from baseline rather than absolute counts.
Not investment advice. Engineering acceleration is a leading indicator of traction, not a guarantee of success. Always conduct your own due diligence before making investment decisions.
Explore the live rankings
Every Monday this methodology produces a fresh ranked panel. Jump straight into the data it generates: by sector, by funding stage, or straight to this week's top movers.
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This week's top movers
Related questions worth reading next
If you want the investor-facing version of this methodology, start with the definition pages and comparison pages that turn the raw framework into buyer-language, then use the buyer's guide to decide whether the stack actually fits how you source.
Use the method in practice
Methodology tells you how the signal is computed. The next step is deciding how to use it in sourcing, what to compare it against, and how to test it on your own taste before you trust it with real pipeline time. If the evidence is strong enough, the buyer-side question becomes workflow fit, not whether the signal exists at all.
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