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YC Bench: a Live Benchmark for Forecasting Startup Outperformance in Y Combinator Batches

Mostapha Benhenda

Abstract

Forecasting startup success is notoriously difficult, partly because meaningful outcomes, such as exits, large funding rounds, and sustained revenue growth, are rare and can take years to materialize. As a result, signals are sparse and evaluation cycles are slow. Y Combinator batches offer a unique mitigation: each batch comprises around 200 startups, funded simultaneously, with evaluation at Demo Day only three months later. We introduce YC Bench, a live benchmark for forecasting early outperformance within YC batches. Using the YC W26 batch as a case study (196 startups), we measure outperformance with a Pre-Demo Day Score, a KPI combining publicly available traction signals and web visibility. This short-term metric enables rapid evaluation of forecasting models. As a baseline, we take Google mentions prior to the YC W26 application deadline, a simple proxy for prior brand recognition, recovering 6 of 11 top performers at YC Demo Day (55% recall). YC Bench provides a live benchmark for studying startup success forecasting, with iteration cycles measured in months rather than years. Code and Data are available on GitHub: https://github.com/benstaf/ycbench

YC Bench: a Live Benchmark for Forecasting Startup Outperformance in Y Combinator Batches

Abstract

Forecasting startup success is notoriously difficult, partly because meaningful outcomes, such as exits, large funding rounds, and sustained revenue growth, are rare and can take years to materialize. As a result, signals are sparse and evaluation cycles are slow. Y Combinator batches offer a unique mitigation: each batch comprises around 200 startups, funded simultaneously, with evaluation at Demo Day only three months later. We introduce YC Bench, a live benchmark for forecasting early outperformance within YC batches. Using the YC W26 batch as a case study (196 startups), we measure outperformance with a Pre-Demo Day Score, a KPI combining publicly available traction signals and web visibility. This short-term metric enables rapid evaluation of forecasting models. As a baseline, we take Google mentions prior to the YC W26 application deadline, a simple proxy for prior brand recognition, recovering 6 of 11 top performers at YC Demo Day (55% recall). YC Bench provides a live benchmark for studying startup success forecasting, with iteration cycles measured in months rather than years. Code and Data are available on GitHub: https://github.com/benstaf/ycbench

Paper Structure

This paper contains 23 sections, 4 equations, 7 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Predicting Startup Success
  4. Web Visibility and Online Signals
  5. Startup Accelerator Batches as Benchmark Environments
  6. Live Forecasting Benchmarks
  7. Bridging Short-Term Signals and Long-Term Outcomes
  8. YC Bench Benchmark Design
  9. Prediction Task
  10. Evaluation Metrics of Predictive Models
  11. The Pre-Demo Day Score
  12. Traction Score
  13. Attention Score
  14. Pre-Demo Day Score
  15. The YC W26 Dataset
  16. ...and 8 more sections

Figures (7)

  • Figure 1: Top 10% of YC W26 startups by Pre-Demo Day Score. Purple = driven primarily by traction, Dark blue = driven primarily by attention (Google mentions).
  • Figure 2: Top 20 domains (top 10%) by Google mentions during the YC W26 batch (January 1 -- March 17, 2026).
  • Figure 3: Traction Score Leaderboard for YC W26 (log scale).
  • Figure 4: Attention is highly concentrated among YC W26 startups. Left: Distribution of Google mentions (log scale). Middle: Power-law check (log-log plot). Right: Lorenz curve showing extreme inequality (Gini = 0.85). The top 10% of startups capture 81.7% of all mentions.
  • Figure 5: Top 20 domains by Google mentions before the YC W26 application deadline (August 17 -- October 31, 2025). This is the signal used by the baseline.
  • ...and 2 more figures