We cover which law schools are “splitter-friendly” in this blog post. The analysis of the data in that post also gives us some insight into which school might also be “reverse-splitter friendly.” For the uninitiated, check out What Are Splitters, Reverse Splitters, and Super Splitters. It breaks down the differences to help you determine which one you are if any.
There are a couple things worth mentioning before we dive into that analysis, though. First, it may be true that splitter-friendliness is more of a “thing” than reverse-splitter friendliness and this certainly makes some sense. The non-cynical reason for believing law schools care about an applicant’s numbers has to do with what those numbers demonstrate regarding the applicant’s potential to be a successful law student and lawyer. The cynical reason, of course, is that schools game their USNWR rankings.
For my money, I think both reasons probably play a role. To a degree, schools care about numbers as a proxy for student quality. So, it makes some sense that they’d be more willing to accept low GPAs if the LSAT score is high than they would the reverse. This is simply because the LSAT provides a more even playing field. It allows for an apples-to-apples comparison among applicants. GPAs, on the other hand, can come from a variety of different majors and schools. Evaluating one against another is a much trickier endeavor.
Another point: it’s tempting to think that a list of reverse-splitter friendly schools would simply be the negative image of a list of splitter-friendly schools, but this is not necessarily the case. It certainly could be the case that a school is friendly to both splitters and reverse-splitters, and is willing to dip lower on the GPA scale to snag high-LSAT candidates as well as dip low on the LSAT-scale to grab applicants with high GPAs. All I’m really trying to say here is that the concepts are not necessarily mutually exclusive.
How to Rank Schools
What I have done here is try to create an index number that incorporates information to answer our questions. The table below shows the index and the data used to compute it. I have to stress that because there is so little data available, and especially little data available on URM applicants, the following applies to non-URM applicants only. It is based entirely on non-URM applicant data. Unfortunately, I had to exclude URM data because it can really skew the overall picture. I also excluded any schools I did not feel that I had sufficient data-points for analysis.
- LSAT Bump. A number from my own regression analysis, and indicates the % increase in the likelihood of admission for each additional LSAT point an applicant has.
- GPA Bump. The GPA equivalent of the LSAT bump (the % increase for each .10 GPA).
- LSAT/GPA Differential. To measure the relative importance of the two, we divide the LSAT Bump by the GPA Bump. The higher the number, the more relative weight the LSAT has.
- Non-Splitter GPA.The average GPA of admitted non-splitter applicants.
- Splitter GPA. The average GPA of admitted splitter applicants.
- GPA Differential. Simply put, this is the difference between the previous two categories, and gives an indication of how much lower on GPA a school will go compared with its average in order to chase high LSAT scores.
- Splitter Success. % of splitter applicants in the data who were accepted.
- Non-Splitter Success. % of the non-splitter applicants in the data who were accepted.
- Splitter vs. Non Success. This is Splitter Success divided by Non-Splitter Success, and gives us a measure of how splitters fare vs. their non-splitter counterparts. If a school admits splitters at a higher percentage than non-splitter, the number will be greater than 1 (and if the opposite is true, it will be less than one). The higher the number, the greater indication that the school is splitter-friendly.
- Index. This is the number I devised to take into account the salient data from the other categories. It is simply (GPA Differential + LSAT/GPA Differential) * Splitter vs Non Success. The higher the number, the more splitter-friendly a school is.
The mean index number for the schools included is 2.31, so I set that as a benchmark, and then broke the schools down into five categories:
- Very Reverse-Splitter Friendly: These schools have an index number that is more than two standard deviations above the mean. (Blue)
- Reverse-Splitter Friendly: These schools have an index number that is between one and two standard deviations above mean. (Green)
- Neutral-Friendly: These schools have an index number that is between the mean and one standard deviation above.
- Neutral-Unfriendly: These schools have an index number that is between the mean and one standard deviation below.
- Reverse-Splitter Unfriendly: These schools have an index number that is more than one standard deviation below the mean.
Indexing in Order of USNWR Ranking
As you can see, only one school — the University of Minnesota — is considered Very Reverse-Splitter Friendly under my analysis, with an additional 14 schools considered to be Reverse-Splitter Friendly. At the other extreme, Reverse-Splitter Unfriendliness is much more common than Very Reverse-Splitter Friendliness, with 12 schools falling into this category, and another 27 schools are in the Neutral-Unfriendly category. Remember, this is categorizing schools by their relationships to each other. Surprisingly, there’s more reverse-splitter friendliness than splitter-friendliness in the Top 14. Still, just as we can compare all the schools among themselves, we can isolate the Top 14 and do the same thing. So, how do they rank relative to each other?
For the Top 14, I kept the categories and color coding the same, but based everything off the mean index score of just the T14, which was 2.42 (ot all that different from the overall average, actually!). Here are the results:
Not much change at all, as you can see. The only real difference is that when we limit schools to the Top 14, Northwestern turns yellow to red. Perhaps the takeaway here is that splitters seem to be relatively more acceptance as you go down the USNWR ranks. For reverse-splitters, there’s not much difference between the top schools and lower-ranked schools. Although, of course, what “high LSAT” and “high GPA” means is obviously going to be different for Harvard than it is for Pepperdine.
As always, please keep in mind that this analysis is based on numbers taken from publicly available applicant-reported data (which I have cleaned to make as accurate as possible), and does not come from the law schools themselves. In other words, I wouldn’t look at any of the numbers in this table as perfect representations of reality, but it does make for interesting comparison and reflection, and hopefully is of some use to those pondering applying to law schools.