Are There "Reverse-Splitter Friendly" Law Schools?

    Law School Admissions


    Recently. we published a post on the topic of which law schools are “splitter-friendly,” which seemed to generate a lot of interest. For the uninitiated, a “splitter-friendly” school is one that could be said to take a relatively more forgiving look at an applicant’s grade point average (GPA) as long as that applicant has a high LSAT score. One of the obvious follow-up questions for a post like that is, “Well, what about the reverse situation? Are there schools that are more forgiving of lower LSAT scores for applicants who have stellar GPAs?”

    Thankfully, analysis of the data can give us some insight into what schools might be said to be “reverse-splitter friendly.” (If you could still use some more clarification on the splitter/reverse-splitter terminology, check out Dave Killoran's post that breaks it all down: What are Splitters, Reverse Splitters, and Super Splitters?)

    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…and for my money, I think both reasons probably play a role). And to the degree that schools care about numbers as a proxy for student quality, 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, and allows for an apples-to-apples comparison among applicants. GPAs, on the other hand, can come from a variety of different majors and a variety of different schools, and evaluating one against another is a much trickier endeavor. Not to put too fine a point on it, but in my life I’ve met plenty of people with high GPAs who didn’t strike me as particularly gifted, whereas the people I’ve met who have scored a 170+ on the LSAT are almost uniformly impressive.

    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.

    So, onto the analysis (which uses essentially the same basic methodology from my splitter post). Both that index and the data used to compute it are found in the table below. 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, and is based entirely on non-URM applicant data. I unfortunately had to exclude URM data because it can really skew the overall picture. I also excluded any schools for which I did not feel that I had sufficient data-points for analysis. Just to break down the categories for you:

    LSAT Bump: This is 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: This is the GPA equivalent of the LSAT bump (the % increase for each .10 GPA).

    GPA/LSAT Differential: This is the GPA Bump divided by the LSAT Bump, to give us a measure of the relative importance of the two. The higher the number, the more relative weight the GPA has.

    Non-Splitter LSAT: This is the average LSAT of admitted non-reverse-splitter applicants.

    Splitter LSAT: This is the average LSAT of admitted reverse-splitter applicants.

    LSAT Differential: This is simply the difference between the previous two categories divided by 5 (so as to not give it too much weight as compared to the other variables), and gives an indication of how much lower on LSAT a school will go compared with its average in order to chase high GPAs scores.

    Reverse-Splitter Success: This is the % of reverse-splitter applicants in the data who were accepted.

    Non-Reverse-Splitter Success: This is the % of the non-reverse-splitter applicants in the data who were accepted.

    Reverse Splitter vs. Non Success: This is Reverse-Splitter Success divided by Non-Reverse-Splitter Success, and gives us a measure of how reverse-splitters fare vs. their non-reverse-splitter counterparts. If a school admits reverse-splitters at a higher percentage than non-reverse-splitters, the number will be greater than 1 (and if the opposite is true, it will be less than one). Higher numbers mean a greater likelihood that the school is reverse-splitter friendly.

    Index: This is the number I devised to take into account the salient data from the other categories. It is simply (LSAT Differential + GPA/LSAT Differential) * Reverse-Splitter vs Non Success. The higher the number, the more reverse-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. (Dark Green)

    Reverse-Splitter Friendly: These schools have an index number that is between one and two standard deviations above mean. (Light 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. 

    The results from this analysis are presented below in Table 1, with the schools listed in order of their current 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 analysis categorizes schools only on a relative basis (e.g., Georgia State is relatively more reverse-splitter friendly than the University of Arizona). Perhaps surprisingly, there’s more reverse-splitter friendliness than splitter-friendliness in the Top 14, with Chicago, Penn, and UVA demonstrating a comparatively charitable view of reverse-splitters. Still, just as we can compare all the schools among themselves, we can isolate the Top 14 and do the same thing (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 (not 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 schools are limited only to the Top 14, Northwestern turns from yellow to red. Perhaps the takeaway here is that, while for splitters there seems to be relatively more friendliness 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 is meant by “high LSAT” and “high GPA” 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.

    If you have any questions or comments, please post them below. Thanks!

    Image Application-Glasses-Pen by Flazingo Photos.