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The acceptance model scores My student against the sample schools, then runs a Monte Carlo simulationA way of running your college list through thousands of pretend admissions seasons to show the range of likely outcomes, rather than a single guess. of 20,000 admissions seasons.
Strong applicants tend to get multiple admits — higher correlation widens the range of outcomes (per-school odds stay the same).
Chance of at least one admit — if strong applicants cluster more, your odds tighten.
| School | Base rate | Your admit probability | Reach, target, safetyA way to balance a college list: reaches are long shots, targets are solid matches, and safeties are very likely admits and affordable fallbacks. |
|---|---|---|---|
| University of Michigan | 18.0% | 18.9% | reach |
| University of Illinois Urbana-Champaign | 45.0% | 46.8% | target |
| Northwestern University | 7.0% | 8.5% | high reach |
| Purdue University | 53.0% | 54.9% | target |
| Michigan State University | 83.0% | 84.3% | safety |
Each school above has a single admit probability — but one number hides what actually matters to a family: the range of ways the season could unfold across your whole list. A Monte Carlo simulationA way of running your college list through thousands of pretend admissions seasons to show the range of likely outcomes, rather than a single guess. answers that by playing the season out thousands of times. In each simulated season, every school is a weighted coin-flip at its admit probability — linked by the applicant-strength correlation above, so a strong season tends to bring several admits at once — and we tally the results across 20,000 seasons.
How to read the numbers:
The admit probabilities are themselves estimates, so read these as a CalibrationA check that makes the percentages mean what they say — so that across many students, things we call 70% likely really do happen about 70% of the time. range, not a forecast. Outcomes are linked by the applicant-strength correlation (strong applicants tend to get several admits at once), which widens the spread without changing any single school's odds. See the methodology.
About a 1 in 15 chance of no admits anywhere (6.8%). A balanced list keeps this low — these are estimates, not promises.
Across 20,000 simulated seasons, the share that ended with exactly this many admits. The leftmost bar (0 admits) is the shutout risk.
How your 5-school list splits across selectivity bands — a balanced list pairs reaches with enough targets and likelies.
How much your chance of at least one admit would drop if you removed each school — the biggest movers are carrying your list.
Scores aren’t final until they’re in. Here’s how your chance of at least one admit holds up if test scores and GPA come in lower than entered — the downside, not just the expected case.
A resilient list barely moves here; a fragile one drops sharply — a cue to add a likelier school. Estimates, not guarantees.
Efficiency Analyzer is part of the Strategist plan. Upgrade to put it to work on your student’s list.
See Strategist →Model accept-v0.2 · baseline heuristic, to be retrained on outcome data.
These outputs are estimates from a baseline model — not guarantees of admission, cost, or outcome.