Searching Past Applicants

Filters, and why you'd use each one

Filters narrow the dataset to applicants who look like you. Start loose, tighten for closer comparisons, loosen again if the sample gets too small.

Stats filters are where most people start. LSAT within a point and GPA within 0.1 gives you a tight comparison group — applicants an admissions committee would read almost identically to you. Widen the ranges for a larger sample. The same panel filters by scholarship amount, useful when you want to see who pulled big awards at a given school rather than who merely got in.

Cycle filter restricts results to specific application years. Recent cycles are checked by default because admissions shifts year to year — the 2020 pandemic cycle, the 2022 GPA inflation, the current cycle's application surge all change what "people with your numbers" means. Three or four recent cycles give a healthy sample; go back further for historical context.

Demographics filters (collapsed by default) cover URM status, non-traditional background, years out of undergrad from KJD through 10+, and a soft tier self-rating from T1 to T4. Use these when some part of your profile is unusual: a KJD applicant and someone seven years out with military experience see different outcomes at the same numbers. If nothing about your background stands out, leave this section alone.

Application timing filter (also collapsed) restricts results to applicants who submitted in specific months. Most people can skip it. If you're deciding whether to rush an application out in January versus waiting a cycle, filtering to late submitters shows how that timing played out at your stats.

What the results tell you

The grid shows one row per school, sorted by LSD ranking by default and sortable by any column. Each row shows, for the applicants matching your filters, how many applied, what share were accepted, waitlisted, and rejected. A quick-filter box narrows by school name. If your filters pull more than 100,000 matching applicants the grid caps — tighten the ranges until you have a focused comparison group.

Clicking a school row opens a detail modal with accepted LSAT and GPA ranges and the individual applicants behind the numbers. Each name links to a full profile: their cycle, softs, scholarship offers, and the school they enrolled at. Percentages show roughly what happened; profiles show what kind of person it happened to.

The waitlist column deserves a second look. Headline acceptance rates hide waitlist movement, and the detail modal breaks waitlists down by how they resolved — pulled from the waitlist, eventually rejected, withdrew. A school that admits 10% outright and pulls another 15% off the waitlist behaves differently than one that admits 10% flat.

A concrete way to use it

Filter to LSAT within one point and GPA within 0.1 of yours, restrict to the past three cycles, and leave demographics alone. Open the five or six most recent applicant profiles. Read each end to end: where they applied, where they got in, what scholarships they were offered, which school they chose. You'll usually spot two or three schools you hadn't considered where your numbers are in range, and sometimes a reach or two that people at your level are pulling off.

Use this alongside the predictor rather than instead of it. The predictor is calibrated across the full dataset and returns a probability for a named school; applicant search shows the raw outcomes and helps you build a list or sanity-check a surprising prediction.