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Structured-Rule-Stump on VCBench

Yagiz IhlamurClassical ML
Board as of 2026-10-07

Structured-Rule-Stump scores F0.5 28.1 on VCBench, the venture capital benchmark from the University of Oxford and Vela Research, with 32.8% precision and 18.0% recall on the 4,500-founder private test set. That is rank 10 of 32, 2.6× the F0.5 of tier-1 VCs and 3.3× Y Combinator, at $0 per 1,000 founders scored.

Structured-Rule-Stump on the VCBench leaderboard today
Rank
#10 of 32
F0.5
28.1
Precision
32.8%
Recall
18.0%
Cost / 1k founders
$0

Scored on the 4,500-founder private test set, mean over three folds. That is 2.6× the F0.5 of tier-1 VCs. See the full leaderboard.

Compared with the reference rows

EntryPrecisionRecallF0.5Cost / 1k
Structured-Rule-Stump32.8%18.0%28.1$0
Think-Reason-Learn Ensemble40.6%30.1%37.9$0.87
Tier-1 VCs23.0%5.2%10.7n/a
Y Combinator14.0%6.9%8.6n/a
Random Classifier9.0%9.0%9.0$0

Human rows are normalized to the dataset's 9% success rate, so every entry is compared on the same base rate. Costs use list prices on 2026-09-24.

How it was scored

No LLM runs at scoring time. A trained model reads structured features from the anonymized profile (education, career history, prior companies) and scores the founder directly, so the API cost at scoring is $0. Training cost is excluded, the same rule applied to every entry.

Read more

  • When Career Data Runs Out: Structured Feature Engineering and Signal Limits for Founder Success (Yagiz Ihlamur, Independent). Structured features and a rule stump on VCBench; Structured-Rule-Stump is on the leaderboard.

Frequently asked questions

What does Structured-Rule-Stump score on VCBench?
Structured-Rule-Stump scores F0.5 28.1 on VCBench, with 32.8% precision and 18.0% recall, rank 10 of 32 as of 2026-10-07.
Does Structured-Rule-Stump beat human investors at predicting founder success?
Yes. Its F0.5 is 2.6 times that of tier-1 VCs (10.7) and 3.3 times Y Combinator (8.6), after both are normalized to the dataset's 9% base rate.
How much does Structured-Rule-Stump cost per 1,000 founders?
$0 at scoring time: no LLM runs when Structured-Rule-Stump scores a founder. Training cost is excluded for every entry.

About VCBench

VCBench is the first benchmark for venture capital. It tests how well AI models, AI-native venture capital methods and human investors predict which startup founders will succeed, on 9,000 anonymized founder profiles. It was built by the University of Oxford and Vela Research, the research arm of Vela Partners, an AI-native quant venture capital firm in San Francisco. Many methods on the leaderboard are open source in Think-Reason-Learn.