Our Data & Methodology

Data Sources

AdmitBase calculates match scores using official institutional data, not self-reported applicant outcomes. Every number on the platform traces back to a verifiable, public disclosure.

The Match Score Algorithm

AdmitBase produces a single match score from 0 to 100 for each school, representing how competitive your profile is relative to that school’s admitted class. The calculation has three steps:

1. Percentile estimation. Your test score and GPA are each placed on a percentile curve derived from the school’s 25th, 50th, and 75th percentile data. If your number falls between two benchmarks, linear interpolation estimates your position. Values above the 75th or below the 25th percentile are extrapolated with a cap at the 1st and 99th percentiles.
2. Weighted combination. The test-score percentile and GPA percentile are combined using program-specific weights:
   • Law: 60% LSAT / 40% GPA — reflecting the outsized role the LSAT plays in law admissions decisions.
   • Medical: 50% MCAT / 50% GPA — medical schools weight both metrics more equally.
   • Dental: 50% DAT / 50% GPA — consistent with the holistic review used by dental admissions committees.
3. Category assignment. The weighted score maps to one of four categories:
   • Safety (75–100): At or above the school’s 75th percentile. Strong admit likelihood; scholarship potential is high.
   • Target (50–74): Near the median. Genuinely competitive.
   • Reach (25–49): Below median. Admission possible but not probable without exceptional soft factors.
   • Far Reach (0–24): Significantly below the school’s floor.

Why Official Data Matters

Many popular admissions predictors rely on self-reported outcomes from online communities. While useful for anecdotal perspective, this data has a well-documented problem: selection bias.

Applicants who participate in online admissions forums are disproportionately high-achieving. Studies of self-reported datasets show acceptance rates that are approximately 10 percentage points higher than official institutional figures for the same schools. This means self-reported predictors systematically overestimate your chances.

AdmitBase avoids this by using only official data that schools are required to disclose. The 25th/50th/75th percentile benchmarks reflect the actual enrolled class, not a self-selected subset of forum users.

The Score Calculator (Inverse Match)

The Score Calculator runs the match algorithm in reverse. Given your GPA and a target match category at a specific school, it determines the exact test score you would need. This is useful for study planning: if you have a 3.6 GPA and want to be a Target at Georgetown, the calculator tells you the LSAT score required to reach that threshold. Available on Basic and higher plans.

ROI Calculator Methodology

The ROI Calculator models the financial return of attending a specific school. It uses:

• Tuition data from ABA 509 disclosures, AAMC, and ADEA, adjusted annually.
• Median starting salaries by school tier and program type — law salaries reflect the bimodal distribution (BigLaw ~$225,000 vs. public interest ~$60,000–$75,000); medical and dental salaries use specialty-weighted averages.
• Loan modelling at the current Federal Grad PLUS rate (7.05% for 2025–2026), with standard 10-year repayment and income-driven repayment options (PAYE, SAVE/REPAYE, IBR).
• Living expenses estimated by cost-of-living region (high/medium/low), derived from school location.
• Break-even calculation — the number of years until cumulative post-graduation earnings exceed total educational cost (tuition + living + opportunity cost), assuming 3% annual salary growth.

What Match Scores Do Not Capture

Match scores are based on quantitative admissions data only. They do not account for:

• Personal statement quality
• Letters of recommendation strength
• Work experience or extracurricular depth
• URM status or diversity considerations
• Interview performance (medical and dental)
• State residency preferences (especially for public medical and dental schools)
• Yield protection or institutional priorities

These factors matter — in some cases, significantly. A match score tells you where your numbers place you in the statistical distribution. It does not predict admission. Use it as one input alongside your own research and, if appropriate, professional guidance.

Data Update Cadence

• Law school data is updated annually when new ABA 509 disclosures are published, typically in the spring. The current dataset spans the 2023–2025 admissions cycles.
• Medical school data is updated annually following AAMC data releases, typically in the fall.
• Dental school data is updated annually following ADEA data releases.
• Community data (decision waves, outcome reports) is updated in real time as users report their results.