Searching Past Applicants

The core filters are LSAT range, GPA range, and application cycle. LSAT and GPA use paired inputs with a slider; narrower ranges surface applicants who most closely resemble your profile, while wider ranges give you more data to work with at the cost of precision. Cycles are selected as checkboxes, with recent cycles checked by default and an option to include earlier cycles going back to 2003.

A collapsible Demographics section adds filters for URM status, non-traditional status, years out of school (KJD through 10+), and soft factor tier (T1 through T4). A separate Application Timing section lets you restrict results to applications submitted between specific months of the cycle, which is useful if you want to see outcomes for early or late submitters specifically. Most applicants should leave these collapsed and work with stats and cycle alone.

Results come back as a sortable grid with one row per law school. Each row shows the total applications matching your filters along with the share that were accepted, waitlisted, and rejected. Schools are ranked by LSD's ranking by default, and a quick-filter box lets you narrow the list by name. If a search matches more than 100,000 applications, the grid caps the results and suggests tightening your criteria.

Clicking a school opens a detail modal with the outcome bar for that school, accepted LSAT and GPA ranges, and a table of individual applicants whose results rolled up into the row. Each applicant name links to their profile, where you can see their full cycle, softs, and any additional context they've shared. That drill-down is the point of the feature — the aggregate numbers tell you what happened, but the individual profiles tell you why.

Outcome bars on the chance-me panel and the school detail modal break results into finer categories than a simple accept/waitlist/reject split. Accepted applicants are divided between those currently attending, those still deciding, and those who withdrew after being accepted. Waitlisted applicants appear in a secondary mini-bar that shows how the waitlist ultimately resolved — pulled off to an acceptance, rejected from the list, or withdrawn.

Headline acceptance rates hide waitlist movement. A school that accepts 10% outright but pulls another 15% from the waitlist behaves very differently from one with a flat 10% acceptance rate and no waitlist activity.

Applicant search and the admissions predictor answer different questions. The predictor returns a single probability for a specific school, produced by a model trained on the full dataset and adjusted for factors the raw numbers can't capture on their own. Applicant search, by contrast, shows you the raw outcomes behind the model — real applications, real decisions, linked to real profiles.

Use the predictor when you want a calibrated estimate for a named school. Use applicant search when you're building or stress-testing a school list, sanity checking a predictor number against the underlying data, or trying to understand what a range of outcomes actually looked like for people with stats like yours.