Q: What does the “DECISION” field mean?
ADMIT: You’re an auto-admit and will likely be admitted, unless the school is practicing yield protection, or you clearly didn’t take the application seriously. This corresponds roughly to a greater than 75% chance of being admitted.
STRONG CONSIDER: You have a good to great chance of being admitted. This corresponds roughly to a 63% to 75% chance of being admitted.
CONSIDER: You have a decent chance of being admitted; you may be waitlisted, or possibly rejected. This corresponds roughly to a 38% to 62% chance of being admitted.
WEAK CONSIDER: You have a poor to moderate chance of being admitted; there is the distinct possibility you will be waitlisted or rejected. This corresponds roughly to a 25% to 37% chance of being admitted.
DENY: You’re an auto-reject and will likely be rejected without other compelling non-LSAT/GPA characteristics. This corresponds roughly to a less than 25% chance of being admitted.
Q: How does all of this work? It just looks like a bunch of random numbers to me.
A: See the “About” section for further details.
Q: Do you store users’ LSAT and GPA information?
A: No, none of the LSAT and GPA information inputted into the model by the user is stored.
Q: Why is there an asterisk after some schools’ names?
A: A number of law schools don’t publish admission index formulas, either because they don’t want it to be public information or because they truly don’t have such formulas. The asterisk indicates such a school. You may notice that the absence of an index formula is more common among the Tier 1 law schools than other tiers.
Q: How did you create formulas for schools that don’t publish formulas?
A: A combination of regression analysis using 25%/75% LSAT and GPA data along with submitted user data and modifications to the resulting formula using submitted user data and law school applicant data available online. They’re not perfect at predicting, but then again, neither are the published schools’ formulas.
Q: What does % AT/BELOW mean?
A: “% AT/BELOW is based on an applicant’s numbers. Users should keep in mind that the estimated percentage chance, despite its apparent precision, is merely a very rough approximation, and the textual predictions will provide a more accurate idea of an applicant’s chances of admission. The “% At/Below field is calculated by determing the equation for the line that describes the datapoints for 25th and 75th percentiles (y) and admission index scores (x), and then substituting the user’s adjusted index score into the formula.
Q: What does the LSAT/GPA RATIO mean?
A: LSAT/GPA RATIO represents the LSAT to GPA ratio for a given school’s formula. A school with a 3.5 LSAT/GPA RATIO weights the applicant’s LSAT 3.5 times more than the applicant’s GPA in their index formula.
Q: What does the Adj. field mean?
A: The Adj. field will return “true” if your prediction is being adjusted behind-the-scenes to account for being URM, splitter-ness, poor GPA, or binding early decision.
Q: I’m a splitter (high LSAT relative to low GPA), and I think this gives me an overly-pessimistic chance of admission. What do you think?
A: Splitters are harder to predict since there is a point where being below a school’s 25% GPA is going to basically be the same to them when it comes to calculating the admission cycle’s 25% GPA (and its effect on rankings). That said, it’s worth noting that all schools weight the LSAT more than GPA (usually between 3.5x to 4.8x more than GPA), and there is a point where a poor GPA does hurt your chances, perhaps irreversibly at some schools. With the release of Version 2.6 on October 04, 2009, a revamped splitter algorithm was implemented that should make most splitter predictions only slightly more pessmistic (roughly 2% to 5% more pessimistic) than actual admission results would suggest.
Q: I’m a reverse-splitter (high GPA relative to low LSAT), and I think this gives me an overly-pessimistic/optimistic chance of admission. What do you think?
A: See answer to the splitter question (above); under the current prediction model, reverse-splitters do not receive a point boost, unlike traditional splitters (high LSAT relative to low GPA). Keep in mind that high GPA applicants are easier to come by for law schools than high LSAT applicants.
Q: Why do you have a separate checkbox for underrepresented minorities (URMs)? How does it work?
A: URMs tend to have an improved chance of admission when compared to non-URM candidates with the same LSAT and GPA. URM races are generally considered to be African-American, Mexican-American, and Native American. There are arguments both for and against this practice, but the fact remains that it does exist. For the purposes of this prediction model, URM candidates receive a raw point boost to their index formula score; this boost is based off of a percentage of the roughly median applicant’s index score at a given school. This model gives the same percentage boost at all schools.
Q: I noticed that I have a much better chance of being admitted to a school if I’m a URM. I’m not a URM, but should I put a URM race down on my applications?
A: No, you shouldn’t. Aside from being highly unethical and defeating the goal of URM-based admission, you may encounter severe difficulty when attempting to be admitted to the bar when a character-and-fitness investigation determines that you falsified your race on your application. If a law school finds out you’re lying during the application process, they will likely notify all of the other schools you applied to, and you’ll be summarily rejected at all of them.
Q: How does the GPA calculator work?
A: See the “About” section for details.
Q: Is it okay not to send in transcripts from colleges when it would hurt my cumulative GPA? I only took classes there during high school, I got sick and withdrew, it’s too much trouble, etc.
A: No, it’s not okay; it’s unethical and unfair to other applicants. LSAC/LSDAS publishes specific rules on what transcripts to send them. Generally speaking, you need to send in transcripts for all collegiate-level work attempted before earning your Bachelor’s. Exceptions apply to study-abroad programs, and AP classes are generally excluded. Failure to comply may result in a hold being placed on your LSDAS file, or it could come back to haunt you during a character-and-fitness investigation.
Q: Why are there different 25%/75% LSAT and GPA numbers for part-time programs when compared to full-time programs?
A: Part-time programs usually have different (weaker) 25%/75% LSAT and GPA numbers compared to their full-time program counterparts. You may be well be a stronger candidate for the part-time program than the full-time program at a given school.
Q: Where do you find the data for all of this?
A: A number of information sources were used in compiling the data included in this model. These sources are: Internet Legal Research Group, Law School Admission Council, Law School Numbers, and US News & World Report. A number of Top-Law-Schools.com forum members have also kindly contributed information and feedback that has led to the ongoing improvement of this model. TLS forum member OperaSoprano has also provided valuable support and feedback; without OperaSoprano’s suggestion, I might have never created the part-time program prediction portion of the model. TLS forum member CyLaw has kindly offered a number of suggestions and support for Law School Predictor.
Q: Why do you not use LSDAS major GPA, instead of LSDAS cumulative GPA, for Colorado and Seattle? Their published admission index formulas use major GPA.
A: True. Nevertheless, having reviewed applicant data for the schools in question and determined that there is usually not much of a difference in terms of the accuracy of the predictions when cumulative GPA is used instead, I decided to revise the model. Eliminating the input field for LSDAS major GPA helps speed up the overall user experience with minimal effect on accuracy of the predictions.
Q: Why didn’t these predictions work for me? I was admitted/denied at [law schools], but the spreadsheet said [the opposite]…
A: If law school admission was entirely numbers-based, there would be no need to have the niceties of a personal statement, etc. While schools do place heavy consideration on the numbers (a cynic might argue that the PS is only there to justify the application fee), they also want to ensure a diverse and interesting class and reward prominent and wealthy donors for their support. Besides which, this model does not claim to be 100% accurate, and it becomes worse at predicting when the applicant is an extreme splitter or reverse-splitter. Enhancements are underway and ongoing to improve predictions for splitters.
Q: How accurate is this model? How does it compare to other law school prediction models available on the Internet?
A: For user data compiled through April 2009, when the spreadsheet said: Admit (admitted): 93.3% accuracy; Pos. Consider (admitted): 83.3% accuracy; Neg. Consider (waitlisted or rejected): 64.4% accuracy; Deny (rejected): 81.8% accuracy; Overall: 81.9% accuracy. The chi-square p-value was .0126, significant at the .02 level. Please note that the two consider categories have now become three categories: Strong Consider, Consider, and Weak Consider. For more recent accuracy data, see the Accuracy page.
Q: Why aren’t non-US law schools and/or non-ABA law schools included?
A: An attempt has been made to include all American ABA law schools, including those that are provisionally accredited. There is no plan at this time to add non-ABA schools, although Canadian law schools may be included in future versions. Applicants in the US who are considering non-ABA schools should carefully consider the risks and employment prospects involved.
Q: I think I have a special situation. Could you please do some custom number-crunching for me?
A: Yes, as long as you’re fairly specific about what you want me to do, and you’re not trying to get me to redo the entire prediction model visit the Contact page. While responses and response times to emails are not guaranteed and email volume has increased, you will likely receive a response in 14 business days.
Q: I emailed you 15/30/60 whole minutes ago with a request and you haven’t responded to my email. When will you get around to sending me a reply?
A: While responses and response times to emails are not guaranteed and email volume has increased, you will likely receive a response in 14 business days. If 14 business days have gone by and you have yet to receive a response, please try contacting me again. Please understand that I do this prediction model largely as a hobby in the hope that it might offer law school applicants a picture of what their chances might be at admission. I’m also a law school student at the University of Virginia, which is a time-consuming endeavor (and also a money-consuming endeavor, although that’s not really relevant), and I try to have some semblance of a life outside of Law School Predictor.
Q: I think one of your figures is off for a particular law school. Can you fix it?
A: Yes, please submit your correction via the Contact page.
Q: Am I allowed to repost this spreadsheet online?
A: No, you’re not. Law School Predictor is a project of Real World Machine LLC. The current prediction model is under exclusive license to Top-Law-Schools.com and is officially hosted at LawSchoolPredictor.com. Real World Machine LLC retains the copyright to this work and any derivative works. It took (and takes) a lot of time and drudgery to create/update this model.
Q: I found this on a website other than Top-Law-Schools.com or LawSchoolPredictor.com. Does this other website (i.e. not Top-Law-Schools.com or LawSchoolPredictor.com) host an officially-licensed versions?
A: No, they don’t. Law School Predictor is a project of Real World Machine LLC. The current prediction model is under exclusive license to Top-Law-Schools.com and is officially hosted at LawSchoolPredictor.com. Real World Machine LLC retains the copyright to this work and any derivative works. It took (and takes) a lot of time and drudgery to create/update this model. Please visit the Contact page if you have a found website infringing on our content or violating our Terms & Conditions of Use.
Q: I’d like to submit feedback and/or suggestions for this model. Who should I contact?
A: Please visit the Contact page with your feedback and suggestions. While responses and response times to emails are not guaranteed and email volume has increased, you will likely receive a response in 14 business days.