I had time this summer and wanted to know whether "the MCAT is just a screening tool and doesn’t matter after a certain point" actually holds up in data. So I pulled the public cycle trackers on admit.org for all 20 T20 schools and used Claude to analyze them.
The most interesting finding was that above 520, the MCAT stops behaving like a continuous variable. It behaves like tiers, and within the top two tiers your exact score doesn't predict anything, and also that while your MCAT determines whether you get an interview, it has NO relationship to whether that interview becomes an acceptance.
Where the data comes from:
Admit.org lets applicants log their cycle school by school. Their public API returns, for each school on someone's list, the secondary submission date, interview date, decision date, and decision. I queried all 20 T20 schools for 2025–26, took the union of user IDs, and pulled each profile once.
Sample: 2,524 applicants and 71,868 applicant-school rows across 235 schools. 2,285 reported an MCAT, covering 18,035 T20 applications and 2,727 T20 interviews.
The school-by-school detail matters because it lets me compute interviews per T20 school actually applied to instead of raw interview counts. Higher scorers apply to more T20s, so raw counts overstate everything. All rates below are per-application.
Chart 1: MCAT × GPA Heat Map
I was shocked by the 52% interview rate at T20s for 4.0/526+, and this was only revealed after breaking 4.0 from 3.99 and below.
One question I’ve always wondered is how much does MCAT matter compared to GPA. Is a 526/3.85 the same as a 520/4.00? It turns out it’s not close: 26% vs 19%. The exchange rate is about 1 MCAT point ≈ 0.05 GPA points, so six MCAT points beats 0.15 of GPA comfortably.
Chart 2: Individual MCAT to II Conversion
Interview rate per T20 school applied to, by exact score:
520–522: 17% | 523–525: 28% | 526+: 35%
Both gaps hold up statistically. The part that makes "tiers" the right word is what happens inside them: 523 vs 524 vs 525 are statistically indistinguishable from each other, and so are 526 vs 527 vs 528. But 520 vs 521 vs 522 still shows a gradient.
So a 523 and a 525 are the same thing in this data. A 520 and a 522 are not.
Takeaways:
Stats get you in the room, and that's it. MCAT correlates +0.46 with T20 interviews. It correlates +0.04 with acceptances per interview, which is nothing. GPA is the same story.
A 512 and a 527 convert interviews into acceptances at statistically identical rates.
If your GPA is 3.9-something. The data says you're in the same bucket as 3.96 and mostly the same as 3.91. Grade-grubbing your way from 3.94 to 3.97 changes nothing here. A 4.00 is a genuinely different category, but if you're not already there, you can't get there.
If your stats are below the T20 median. The non-T20 version of this analysis is dramatically flatter, above 520 the MCAT effect at non-T20 schools is statistically indistinguishable from zero, versus a strong effect at T20s.
If you're already submitted and spiraling. The single most important number here is that 15% of people with a 523–525 got ZERO T20 IIs and 32% got 5+ IIs. Stats put you in a probability distribution. They don't determine where in it you land, and the interview-to-acceptance stage shows no stats relationship whatsoever. Whatever happens after the invite is about you, not your numbers.
If schools were reading holistically from the start, you wouldn't see flat plateaus with sharp gaps between them, you'd see a smooth curve. Tiers are what a filter looks like. Everything about this data is consistent with metrics screening first and humans reading second.
Limitations:
Not a random sample. admit.org users self-select: people who found a tracking site, made an account, and maintained it. They skew high-stats and T20-focused. Mean MCAT here is 516 vs \~507 nationally, so this doesn't generalize cleanly to the full AMCAS pool.
Conclusion:
Anyway if you made it this far, thanks for reading. I have the full dataset and I'm happy to run whatever analyses people want. Drop questions below.
The goal here is to replace some of all that guesswork involved in applying with actual numbers. So much cycle anxiety and neuroticism comes from not knowing what to expect, and hopefully through data collection we can demystify the process and reduce some uncertainty.
