Loading…
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. These are modeled estimates from public data and your profile — not guarantees of admission.
Each bar = the share of 20,000 simulated seasons ending in that many admits. The left bar is a shutout (zero).
Outcomes aren’t independent: a strong applicant tends to clear — or miss — a cluster of similar reaches together, so we link similarly selective schools more tightly. Higher correlation widens the range and raises shutout risk for reach-heavy lists (each school’s own odds stay the same). Treating schools as independent understates that risk. The correlation is a modeling assumption (scaled by selectivity), not a measured value — see the methodology.
Chance of at least one admit — if strong applicants cluster more, your odds tighten.
Refine your reach / target / safety mix for the strongest odds and what you value, then re-simulate — works for a list of any size.
Showing a demo with sample schools. Build your list to optimize your own.
Your 5 candidates fit under the 20-app cap. Here’s the reach/target/safety balance and where you have room to add a reach or lock in a safety.
Your 5 schools all fit under the 20-app cap — no trimming needed. Below is your reach/target/safety mix and the best use of your one Early Decision. Add more candidates to optimize a selection.
Tip: tap the ★ on up to 3 schools to mark them dream schools — the optimizer prioritizes your shot at them and the ED advisor spends your one binding bet on a dream.
These are schools not on your list, matched to your profile and your academic odds. Nothing is added automatically — tap ＋ to add any.
Favorite a few schools (♥) or complete the conjoint, and we’ll suggest tailored matches here.
Complete the conjoint exercise to rank “loved” schools by your real preferences; for now we use selectivity + outcomes. Odds come from your profile. It keeps a balanced reach/target/safety mix and avoids piling on similar reaches (their admits move together, so extra ones add little). The Early Decision boost (~2× your odds) is an estimate that varies by school — treat it as directional. This is a starting allocation — simulate the chosen 20 to see the correlated outcome.
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 an honest 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 22 chance of no admits anywhere (5%). A balanced list keeps this low — these are estimates, not promises.
Chance of at least this many admits among your 2 target schools.
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.
Model accept-v0.3 · baseline heuristic, to be retrained on outcome data.
These outputs are estimates from a baseline model — not guarantees of admission, cost, or outcome.