FounderRank methodology
How scores are calculated
FounderRank normalizes entrepreneur outcomes into six component scores, then combines them into a 0-100 total. Public ranks include the scored canonical and crawled dataset while verification is treated as source context for later review.
Score components
| Component | Weight | Signal | Formula |
|---|---|---|---|
| Traction | 30% | Measures commercial pull through revenue run rate, segment-adjusted customer base, monthly growth, and market count. | 42% revenue run rate, 25% customer proof normalized against the founder's market segment, 18% monthly growth, 15% market count. |
| Efficiency | 18% | Rewards revenue generated per dollar of outside capital, with a bootstrap bonus when funding raised is zero. | Log-normalized revenue divided by funding raised plus a capital floor, then plus a smaller bootstrap bonus when applicable. |
| Durability | 16% | Credits operating history, repeat-company building, active companies, and exits without letting age alone automatically max the component. | 70% log-normalized operating history, 5% companies built, 15% active-company continuity, 10% exits. |
| Reach | 14% | Combines market footprint with current growth to estimate geographic and demand reach. | 78% normalized market count and 22% normalized monthly growth. |
| Jobs | 12% | Measures employment footprint with market-segment log normalization. | Log-normalized employee count against the founder's market segment reference point. |
| Proof | 10% | Combines public proof with source confidence so high-performing founders still need evidence quality. | 72% public proof and 28% source confidence. |
Required inputs
revenueRunRatemonthlyGrowthcustomerCountfundingRaisedemployeeCountmarketsfoundedYearcompaniesBuiltactiveCompaniesexitspublicProofsourceConfidence
Evidence guidance
At least two evidence sources, two independent source types, company evidence, traction evidence, and 65% average source confidence.
Calibration notes
- Public ranks include the scored canonical and crawled dataset while the ranking model is being calibrated.
- Evidence status is shown as context and contributes through the proof component, but it does not block public rank inclusion.
- Demo records are only used as a local fallback when no canonical or crawled dataset is available.
- Reference points are segment-specific so enterprise, marketplace, infrastructure, and product-led businesses are not compared against one raw customer or employee cap.
- Equal total scores share the same displayed rank and use deterministic ordering only for display stability.