Accelerator Success Simulator
Filtered to accelerator organizations with at least one program marked Heatmap Startup List = yes. Baselines are sorted alphabetically by accelerator name. Program rows are aggregated to accelerator level. Outcomes are shown as per-startup values.
Recommendations
Recommendations compare the current scenario to realistic nearby benchmark levels. They include increases and decreases, one-vertical changes and location-context changes; extreme jumps are filtered out.
Phase 1 — Basics
Location, timing and deal terms
Phase 2 — Potential
Capacity and investment budget
Required cash deployment
Budget coverage
Startup-year evidence for baseline
Model fit
Fit chart uses per-startup outcomes. Funding per startup is capped in the model to avoid mega-outlier extrapolation.
Phase 3 — Working variables
Focus verticals
Founder targeting
Phase 4 — Outcome
Predicted outputs
Follow-on funding raised per startup
Jobs created per startup
Per-startup funding multiplier
Per-startup jobs multiplier
Observed baseline per-startup funding / jobs
Paper portfolio value per startup at selected equity
Cascade view
Module multipliers
Founder and investor personae
Traffic lights show likely reactions to the scenario. Avatars are generated in-page and change mood with the score.
Methodology
Filter: only rows with column AL “Heatmap Startup List” = yes are used, then aggregated by Accelerator Name (267 rows into 132 accelerator organizations). Startups/year: calculated from unique startup-accelerator records by accelerated year from startups.csv column V; if missing, year is inferred from Year Founded and Research date, then averaged across observed cohort years. Total investment/year: original value is calculated as startups accelerated per year × investment per startup; the dashboard also lets users adjust this yearly budget as a separate scenario lever. Outcomes: funding and jobs are displayed per startup, using accelerator-level totals divided by the unique startup/cohort denominator. Prediction: positive ridge models on log(1 + per-startup outcome), with per-startup funding capped for scenario realism. Founder experience variables are capped at observed dataset maxima and shrunk as scenario levers. Recommendations test realistic local moves, include both increases and decreases, and avoid jumps that are far outside the selected baseline.
