Pavel Prata@pavelprata
Which emerging VCs have the strongest early-stage picking alpha?
Standard emerging manager evaluation still leans heavily on qualitative signals – GP background, thesis articulation, founder references. All useful, but by the time TVPI and DPI tell you something meaningful, you're usually already in or already too late.
So I experimented with a quantitative framework to answer a core LP allocator question: which small, early-stage fund managers consistently back seed-stage companies that go on to raise exceptional Series A rounds – before those outcomes are visible to the broader market?
I started with @harmonic_ai Scout (my fav research tool!) and checked every company globally that raised a first pre-seed or seed round between 2022–2026 (Post-ZIRP). The funnel looks like this:
1/ 55,491 companies raised a pre-seed or seed round – the full opportunity set
2/ 4,368 (7.9%) went on to raise a Series A – the base rate, roughly 1 in 13
3/ 764 (1.4%) qualified as Tier 1 Breakouts – above-median Series A for their vintage year, with at least one top-tier institutional VC (from a defined set of 38 firms: @a16z, @sequoia, @lightspeedvp, @IndexVentures, and peers)
For each of those 1,604 companies, I traced back to every investor who backed them at pre-seed or seed — before the outcome was visible. 4,176 unique investors across the breakout set.
Then I computed a simple ratio for each: breakout companies backed at seed divided by total seed investments in the period. I'm calling this the "Tier 1 Concentration Rate".
After filtering out mega-platforms, accelerators, CVCs, and angels and requiring a minimum of 10 seed deals – 20 emerging managers (sub-$250M AUM) surfaced with notably high concentration rates.
A few things stood out:
1/ Several micro-funds under $100M were placing 25–35% of their seed bets into companies that later raised from @Sequoia, @a16z, @lightspeedvp – consistently, not as one-off flukes.
2/ Participant concentration and lead concentration are different signals. Participant = network and access. Lead = independent conviction before consensus forms. For LP diligence, these deserve to be evaluated separately.
3/ The data has real limitations: ~12% of breakout companies had no named seed investor in the database, we can't cleanly separate Fund I from Fund III for a given manager, and small sample sizes mean some high concentration rates likely reflect luck rather than repeatable skill.
But the core idea holds. "Tier 1 Concentration Rate" is an early, measurable signal of picking ability – observable years before fund-level metrics tell you anything.
For LP allocators evaluating Fund I–III managers, that timing gap is the whole problem. This is one attempt to close it.
What’s your take on this experiment?
