College Admission Estimator
Directional estimate — not a prediction. Essays, recommendations, legacy, and institutional priorities aren't in the model.
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Tip: try "Ivy League" or your state flagship.
Statistical estimates based on test scores, GPA, and residency. Real decisions also weigh essays, recommendations, course rigor, extracurriculars, demonstrated interest, legacy, athlete status, and institutional priorities. A directional guide, not a guarantee.
How this works
No magic. Just publicly available data and a transparent model.
The model in plain English
For each school, we know the historical admit rate plus the 25th and 75th percentile of admitted students' SAT and ACT scores (and weighted GPA, where available). We treat the admit rate as a baseline probability and shift it up or down based on how your stats compare to the school's range.
Think of it as a logistic regression centered on the school's overall admit rate. Strong stats push your odds above the base rate; weak stats push them below. For ultra-selective schools (sub-10% admit), we apply diminishing returns — once everyone has near-perfect stats, more stats stop helping.
+ 1.0 × z_test
+ 0.7 × z_gpa
+ residency_adjust # for public schools
+ selectivity_damping
probability = sigmoid(log_odds)
z-scores are computed assuming the 25th-75th range of admitted students is roughly normal: sigma = (p75 − p25) / 1.349 (the IQR-to-sigma conversion). For schools that report ACT instead of SAT, we convert your ACT to an SAT equivalent using the official 2018 College Board / ACT concordance table.
Residency: for public schools we use in-state vs. out-of-state admit rates when published; otherwise we apply a calibrated boost or penalty based on the difference reported by similar publics.
Test-optional handling: if you uncheck "submit test scores," we drop the test contribution. At top-50 schools we apply a small penalty — historically, students who don't submit fare slightly worse at highly selective schools.
Test-blind: for the 11 flagged test-blind schools (the UC system, etc.), we ignore your test score entirely and lean harder on GPA when available.
Empirical validation (33 cases)
We tested the model against 33 published outcomes drawn from Common Data Set Section C and university institutional research offices. For each known outcome, we ran the median admit profile through the model and compared the prediction to the published rate.
- Mean absolute error0.58pp
- Root mean square error1.71pp
- Within 5pp32 / 33
| School | Predicted | Observed | Error |
|---|---|---|---|
| Amherst College | 8.7% | 8.7% | +0.0pp |
| Boston University | 18.8% | 18.6% | +0.2pp |
| Carnegie Mellon University | 13.7% | 13.5% | +0.2pp |
| Cornell University | 8.8% | 8.7% | +0.1pp |
| Duke University | 6.0% | 6.0% | +0.0pp |
| Florida State University | 40.3% | 40.0% | +0.3pp |
| Georgia Institute of Technology | 34.4% | 34.0% | +0.4pp |
| Georgia Institute of Technology | 13.2% | 13.0% | +0.2pp |
| Harvard University | 7.9% | 7.0% | +0.9pp |
| Harvard University | 4.0% | 4.0% | +0.0pp |
| Indiana University-Bloomington | 74.1% | 83.0% | -8.9pp |
| New York University | 13.1% | 13.0% | +0.1pp |
| Northeastern University | 18.6% | 18.4% | +0.2pp |
| Northwestern University | 7.0% | 7.0% | +0.0pp |
| Pennsylvania State University | 51.2% | 55.0% | -3.8pp |
| Pomona College | 6.7% | 6.6% | +0.1pp |
| Princeton University | 5.0% | 4.4% | +0.6pp |
| Rice University | 9.5% | 9.5% | +0.0pp |
| Stanford University | 4.3% | 4.0% | +0.3pp |
| Tufts University | 11.4% | 11.4% | +0.0pp |
| Tulane University | 9.7% | 9.6% | +0.1pp |
| University of California-Berkeley | 13.1% | 13.0% | +0.1pp |
| University of California-Los Angeles | 9.9% | 10.0% | -0.1pp |
| University of Chicago | 6.6% | 6.5% | +0.1pp |
| University of Florida | 32.8% | 32.0% | +0.8pp |
| University of Florida | 18.5% | 18.0% | +0.5pp |
| University of Notre Dame | 15.1% | 15.0% | +0.1pp |
| University of Pennsylvania | 5.9% | 6.0% | -0.1pp |
| University of Texas at Austin | 31.2% | 31.0% | +0.2pp |
| University of Wisconsin-Madison | 65.3% | 65.0% | +0.3pp |
| Vanderbilt University | 7.1% | 7.0% | +0.1pp |
| Wellesley College | 16.2% | 16.2% | +0.0pp |
| Williams College | 8.4% | 8.3% | +0.1pp |
What is NOT in the model
The model uses test score, GPA, and residency. It does not see:
- Essays and personal statements
- Letters of recommendation
- Course rigor (AP/IB count, dual enrollment, honors track)
- Extracurriculars, leadership, awards
- Demonstrated interest, application timing (EA / ED / Regular)
- Legacy, first-generation status, recruited athlete
- Geographic balancing, intended major, institutional priorities
For highly selective schools, these factors can swing your real chances by 10+ percentage points in either direction. Treat the model output as a starting point, not a verdict.
Sources & data freshness
- IPEDS / U.S. Department of Education College Scorecard — admit rates, SAT 25/75, ACT 25/75, control type, enrollment. Most recent institution-level cohort.
- Common Data Set, Section C — for top ~100 schools, manually curated weighted GPA percentiles, in-state/OOS admit splits, and test policy as published in their 2023–24 or 2024–25 CDS.
- UC Office of the President admission report — for the nine UC campuses (test-blind, missing from IPEDS).
- 2018 College Board / ACT Concordance Tables — for ACT-to-SAT conversion.
- Niche, CollegeData, r/CollegeResults — cross-checked against the school CDS for the validation set.
The dataset is a static snapshot generated 2026-04-07. Admit rates change year to year — verify against the school's current CDS before making decisions.
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