We simulate 1,000 students competing across 192 colleges over six admission rounds — and we run that simulation 500 times. Here is exactly what's under the hood, with calibration data so you can check our work.
The premise: agents, not formulas
Most chancing tools fit a formula to past admission data: GPA + SAT + extracurriculars → percent. 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.
We take the opposite approach. We build 1,000 student agents — 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. Your chance is what fraction of simulated cycles you ended up in those 1,500.
192
Colleges modeled
17,884
High schools
6
Admission rounds
500
Cycles simulated
Where the numbers come from
Every quantitative input in our model is sourced from public institutional data, not estimated from anecdote. We update against current cycles each year.
Common Data Set (2024–2025). Per-college acceptance rates, SAT 25th/75th percentiles, yield, ED/EA breakdown, class size, demographics. Each college submits the CDS itself; it is the gold standard.
NCES Private School Survey + state DOE files. Long-list backbone of 17,884 high schools. We blend in 12 state DOEs that publish school-level SAT/ACT means.
IPEDS net price calculator data. Net cost by income bracket — used in the chancing UI, not the admission decision.
Published research. Hook multipliers come from peer-reviewed work on legacy and athletic preferences (Arcidiacono et al.; Hurwitz). ED yield elasticity from Avery & Hoxby. The full bibliography lives on the Research Wiki.
How a student gets modeled
Each student agent has two axes of identity. The behavioral axis controls what they care about and how they apply: STEM spike, humanities spike, arts spike, athletic spike, well-rounded, average academic. The structural axis controls the resources they bring to the application: high advantage, moderate advantage, neutral, disadvantaged.
On top of that, each student carries an academic profile (GPA, test scores, AP load), holistic quality signals (extracurriculars, essays, recommendations), and the hook flags that real admissions offices weight differently — legacy, recruited athlete, development case, first-generation. We sample student traits from joint distributions calibrated against published institutional data so the simulated population matches what colleges actually see, not an idealized normal curve.
The way a student builds their college list also matters. We don't have students apply only to reaches — we model realistic list construction the way a strong applicant balances reaches, targets, and safeties. That mirrors what students do in practice and is part of why our simulated competitive pools track the real ones.
How a decision gets computed
Every applicant–college pair gets scored on a calibrated admission model. The model considers four families of inputs:
Academic strength — GPA and standardized scores measured against the specific college's published distribution, not a national average.
Holistic signals — extracurricular depth, essay quality, recommendations, and the school context the student comes from.
Hooks — legacy, recruited athlete, development, and first-generation status. Each carries a different weight, drawn from peer-reviewed admissions research and per-college disclosed data.
Round & pool effects — applying ED gives a real boost; applying RD into a deep pool is harder. We model both separately.
The exact functional form, weights, and per-college calibration constants are proprietary — that's the work we've put into making this accurate. What's not proprietary is how we ground it: every weight is anchored to a published institutional source or a peer-reviewed study, every per-college constant is calibrated against that college's most recent Common Data Set, and the whole model is validated against held-out years before it ships.
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.
The six rounds
Real admissions is sequential. ED commits a student to one school; EA leaves options open; deferrals roll forward; melt happens after May 1. Our engine runs the same sequence — most chancing tools collapse this into a single rate.
Round 1
Early Decision (ED)
Binding. Roughly 12–15% of seats, 40–60% of admits. Students gain a substantial admit-probability boost in exchange for forgoing comparison shopping.
Round 2
Early Action / REA
Non-binding early. Smaller boost than ED but no commitment trade-off. Tier 1 schools that don't offer ED concentrate here.
Round 3
Early Decision II
Second binding round in January. Used by students whose ED1 was rejected or by late deciders.
Round 4
Regular Decision (RD)
The bulk of applications. Largest pool, hardest acceptance rate.
Round 5
Student decisions & melt
Admitted students choose where to enroll based on yield model (preference + cost + fit). Some students "melt" — accept then withdraw before fall.
Round 6
Waitlist activation
If a college misses its yield target after melt, it activates the waitlist to fill remaining seats.
Calibration: how do our rates compare to reality?
Below: every one of our 55 base-mode colleges. Each dot is a college. The horizontal axis is the school's published Common Data Set acceptance rate. The vertical axis is what our simulation produced averaged across 200 Monte Carlo runs. The dashed line is perfect calibration (y = x); the green line is the proportional fit through the data.
Predicted vs. published acceptance rate
55 colleges, 200 simulated cycles, base mode. Hover any point for details.
0.82Pearson r — strength of linear fit
0.61Spearman ρ — rank-order match
1.97×Mean simulated/published ratio
Tier 1 — HYPSMTier 2 — Ivy+Tier 3 — Near-IvyTier 4 — SelectiveTier 5 — Top LAC / Public EliteTier 6 — Selective Public
How to read this chart
Two patterns matter. First, points sit close to the green proportional line — that means our model is internally consistent: a college twice as selective in CDS data is roughly twice as selective in our simulation. Second, no point is wildly off-axis — Stanford lands near Yale, not near a state flagship.
What the model is really doing is reproducing relative selectivity. Your chance at Stanford relative to your chance at Brown is what should drive where you apply, and that ratio is what the calibration shows works. The chart below shows the second face of calibration — how tightly the simulation pins down those numbers as we crank up the number of runs.
Calibration: precision converges with N
A second view of calibration, this time about precision rather than accuracy. Monte Carlo standard error decays as σ/√N — every additional run makes the estimate tighter. The chart below tracks per-college standard error from 30 to 5,000 runs and shows where extra compute stops buying you precision. (We ship 500 runs by default — the elbow.)
Honest disclaimers, because the alternative is overclaiming:
Demonstrated interest. Some colleges weight visit/contact patterns. We don't model this; we treat application as a uniform signal.
Specific essay topics. We score essays as a quality scalar. We can't tell you that one essay angle will work better than another.
Recommender effects. Counselor and teacher letters matter and vary in strength. Captured indirectly via school feeder tier, not individually.
Major-specific admission. Some schools (Penn Wharton, Cornell, UMich CS) admit by major. Base mode treats admission as university-wide.
This year's policy shocks. SAT requirement reversals, Supreme Court rulings on race-conscious admissions, and aid-policy changes propagate slowly into CDS data.
What our chance number is — and isn't
Your chance is the share of 500 simulated cycles in which an agent matching your profile got admitted. It's not a guarantee, a prediction, or a substitute for a counselor. It's a calibrated probability based on public institutional data and a transparent agent-based model.
If you want to read the academic basis behind every parameter, the Research Wiki has 234 documents covering applicant-pool growth, ED dynamics, hook effects, post-SFFA demographics, and more.
