College Monte Carlo estimates admission chances by simulating each applicant inside an agent-based Monte Carlo model: simulated students compete for real seat counts at 192 colleges across six admission rounds, 500 times over. Every input is grounded in Common Data Set statistics, and your chance is the share of simulated cycles in which you were admitted.
Most chancing tools fit a regression to past admission data: GPA + SAT + extracurriculars in, a percentage out. That tells you how the average student with your stats has fared, but it ignores the thing that actually determines who gets in: which other students are competing for the same seats.
College Monte Carlo takes the opposite approach. We build a cohort of student agents — 1,000 in the full 192-college model — each with their own academics, hooks, school context, and college list, and let them compete. A college with 1,500 seats and 50,000 applicants admits its top 1,500 by composite score. Because any single simulated cycle is noisy, we repeat the whole cycle 500 times (a Monte Carlo simulation, the same technique used in finance and physics to model uncertain outcomes) and report the fraction of cycles in which you were admitted at each college.
A longer, chart-illustrated walkthrough of the same model lives on How It Works; a hands-on primer is in the guide how to estimate your admission chances.
The factor set comes straight from the Common Data Set — the standardized statistics colleges publish each year. Section C7 of each college's CDS discloses which admission factors it treats as very important, important, considered, or not considered, and the model weighs the factors colleges say matter:
New to the CDS? Start with the guide what is the Common Data Set.
Every applicant–college pair gets a single admission score. The factors above are combined additively in logit space (log-odds), and the sum is passed through a sigmoid to produce a probability between 0 and 1:
The exact functional form, weights, and per-college constants are proprietary — but how we ground them isn't: every weight is anchored to a published source or peer-reviewed study, and every per-college constant is calibrated against that college's most recent Common Data Set.
Calibration means the simulation is forced to agree with reality where reality is measurable. For each college, the model's constants are tuned so that when the full simulated applicant pool competes for its seats, the resulting simulated acceptance rate tracks the acceptance rate that college actually published in its Common Data Set. The whole model is then validated against held-out years before it ships.
We publish the receipts: How It Works shows all 55 base-mode colleges on one chart, simulated acceptance rate (averaged across 200 Monte Carlo runs) against published CDS rate, with correlation statistics computed live from the shipped calibration data. What the model reproduces is relative selectivity — your odds at Stanford relative to Brown are what should drive where you apply, and that ratio is what the calibration shows works.
Two structural details worth knowing: international students compete for a separate slice of seats per college (3–25% depending on selectivity), so their dynamics don't crowd the domestic pool. And the simulation runs against the implied national applicant pool — your cohort isn't just the visible agents, it's a representative sample of who actually shows up at each school. For background on the headline statistic itself, see college acceptance rates explained.
Real admissions is sequential, and the engine runs the same sequence — most chancing tools collapse this into a single rate. ED commits a student to one school; EA leaves options open; deferrals roll forward; melt happens after May 1.
Binding. Roughly 12–15% of seats, 40–60% of admits. Students gain a substantial admit-probability boost in exchange for forgoing comparison shopping.
Non-binding early. Smaller boost than ED but no commitment trade-off. Tier 1 schools that don't offer ED concentrate here.
Second binding round in January. Used by students whose ED1 was rejected or by late deciders.
The bulk of applications. Largest pool, hardest acceptance rate.
Admitted students choose where to enroll based on a yield model (preference + cost + fit). Some students "melt" — accept then withdraw before fall.
If a college misses its yield target after melt, it activates the waitlist to fill remaining seats.
Per-college early-round multipliers are derived from each college's disclosed early versus regular acceptance rates. Two guides unpack the strategy side: early decision vs regular decision and what a college's yield rate means.
Alongside admission odds, the chancing tools show an estimated net cost at each college. Cost never affects the simulated admission decision — it is a separate estimate layered on top. The anchor is IPEDS net-price data by household income bracket: what students from families like yours actually paid at that college after grants and scholarships, for the 2025–2026 award year.
The full cost engine refines that anchor per family: it computes your FAFSA Student Aid Index (2025–26 formula) from income, assets, home equity, and family size, applies the difference from the bracket-typical SAI with a per-college passthrough, and overlays CSS Profile conventions for meets-full-need private colleges. The guide paying for college: understanding net price explains the concepts behind these numbers.
Every quantitative input is sourced from public institutional data — the Common Data Set, IPEDS, the College Scorecard, NSF, O*NET, BLS, and WICHE — not estimated from anecdote, and we update against current cycles each year. The full provenance table, with per-dataset vintages and refresh cadences, lives on its own page:
Every metric in the product mapped to its source, vintage, and refresh cadence — with links to the primary datasets.
A 20% chance means that in roughly one of five simulated cycles, you got in — not that you will be rejected. Estimates are illustrative and depend on the full profile: holistic inputs like essay quality and extracurricular depth are self-assessed, and no model sees your actual essays or recommendations.
What the calibration supports is relative selectivity — how your odds compare across colleges — which is the question that should drive where you apply. Treat any single percentage as a planning input, not a prediction about you personally.
Further reading: