1,783 applicants · 21,146 decisions
Admissions insight,
grounded in reality.
See where you land among 1,783 real CS applicants — percentile by percentile, school by school, with the comparable applicants and their outcomes shown next to every figure.
How it works
Retrieval and statistics first, prose last
Every number comes from the dataset. The language model writes the explanation around figures it is given — it never invents one.
- 01
Your profile is vectorized
GPA, test scores, rigor, rank, awards, and activities become a feature vector. Fields you leave blank are marked missing rather than filled with an average.
- 02
Comparable applicants are retrieved
The 40 statistically nearest CS applicants are pulled from the cohort, with what each of them actually heard back from your target schools.
- 03
Figures are computed, then explained
Percentiles, observed rates by school and round, and a per-school model where one has measured skill. The written read cites the posts it draws on.
Why single probabilities mislead
Identical numbers, opposite results
The clearest argument for reporting ranges of real outcomes instead of one confident percentage.
Numbers don't separate outcomes
Every reported Georgia Tech decision, by GPA and test score.
195 of 514 reported decisions were acceptances. The clusters overlap heavily — applicants with nearly identical numbers landed on both sides, which is why this tool reports ranges of real outcomes rather than a single probability.
Before you trust any of it
The limits, up front
Measured on held-out data during the build, not asserted afterwards.
What this tool can and can't tell you
Measured on held-out data, not asserted.
- Similar ≠ same outcome
- Retrieved comparables predict a held-out applicant's outcome about 3 percentage points better than randomly chosen posters (0.754 vs 0.722). Real but modest: similar numbers do NOT reliably imply similar results. Use neighbor evidence to show the range of what happened at this level, never as proof of what will happen.
- Half the models don't work
- Fewer than half of per-school models beat majority-class guessing when evaluated on a held-out split (28 of the 63 schools with enough data to test). Check `beats_baseline` before giving any weight to an estimate.
- Low estimates overstate the risk
- Calibration is good in the mid range (predicted 0.25 matched observed 0.25, 0.35 matched 0.37) but overconfident at the very bottom: predictions near 0.06 corresponded to a ~0.14 observed rate. Never read a very low estimate as 'no chance' -- about one in seven of those applicants was admitted.
- What isn't measured at all
- Essays, recommendations, interviews, demonstrated interest and institutional priorities are not in the feature set. At holistic schools these plausibly dominate, which is consistent with those models showing no measurable skill.
This sample is self-selected
See where you actually stand
Sign in to keep every report in your private workspace. Every figure links back to the evidence behind it.