Monte Carlo
Simulate your admissions season
A single admit probability hides the thing families most want to know: what range of outcomes is actually likely? This simulator plays out your college list across thousands of possible seasons, so you can see your odds of at least one admit, a typical number of acceptances, and where the surprises tend to come from.
Sample data, running live in your browser. These are estimates, not guarantees.
Your portfolio
- Simulated admit rate: 87.8%
- Simulated admit rate: 72.2%
- Simulated admit rate: 44.5%
- Simulated admit rate: 22.1%
- Simulated admit rate: 7.0%
Simulation controls
Expected lowest net price among admits: $20,632
Distribution of admits
Across 10,000 simulated seasons
- 0 admits1.2%
- 1 admit15.0%
- 2 admits41.8%
- 3 admits33.6%
- 4 admits7.9%
- 5 admits0.5%
How this worksReading the Monte Carlo lab▾
A Monte Carlo simulation doesn't try to predict a single outcome. Instead it replays your application season thousands of times. In each replay, every school is admitted or rejected by a coin flip weighted to its admit probability, and we tally what happened.
Run enough seasons and the tallies settle into stable probabilities: how often you got at least one admit, how many admits you typically collected, and how often at least one admit fell within your budget. More iterations means a smoother, more reliable estimate at the cost of a little compute.
The bars show the full distribution. Bar “0 admits” is the shutout risk; the taller the bars to the right, the deeper your options. Affordability counts a season as a win only when your cheapest admit lands at or below the budget you set.
These are estimates, not guarantees. Real admissions depend on factors no model fully captures, and the per-school probabilities are themselves approximations. Treat the output as a way to compare portfolio shapes, not as a forecast of your specific result.
Compare two strategies
Adding one more reach, swapping in a safety, or rebalancing your list changes the whole distribution of outcomes. Put two strategies head to head and see the trade-offs before you commit.
What-if: compare two application portfolios
Adjust each school's admit odds and net price, then see how the shape of a portfolio changes your chances. Same simulation settings apply to both sides for a fair comparison.
Portfolio B is +48 pts more likely to land at least one admit, and the gap on landing an affordable admit is 80 pts in favor of Portfolio B.
Takeaway: a balanced mix — a few likely admits alongside your reaches — usually raises the floor on landing somewhere without giving up your upside.
Portfolio A
Reach-heavy (all long shots)
- ReachAdmit8%Net price
- ReachAdmit12%Net price
- ReachAdmit10%Net price
- ReachAdmit15%Net price
- ReachAdmit18%Net price
Portfolio B
Balanced (safety / target / reach)
- SafetyAdmit85%Net price
- SafetyAdmit70%Net price
- TargetAdmit45%Net price
- TargetAdmit30%Net price
- ReachAdmit8%Net price
These are statistical estimates, not guarantees. Real admissions decisions depend on many factors this model doesn't capture — use the comparison to think about balance, not to predict outcomes.