Today we’re letting everyone in on a sneak-preview of our latest tool: the college cross-admit preference tool. We think it’s a simple but powerful way to see which colleges are most favored by admitted college students.

To use it is simple: type in the names of two colleges that you want to compare (perhaps Florida and Florida State?). You’ll then see which fraction of site members prefers which school. Preference is determined by the relative fraction of members admitted to both schools who end up attending one or the other. For example, if 25% of students admitted to both College A and College B ultimately go to College B, we say they prefer College B over College A. When the results are statistically significant at the 95% level, you’ll see the results lit up in bright colors.

For the hardcore college admissions followers out there, this will remind you of this graphic from a 2006 NY Times article. One difference is that our list isn’t limited to 17 schools; as the data continues to become available, we’ll display this information for all 1700 schools that we track.

Requests? Feedback? Suggestions? Let us know.

On each college page you’ll find a “Personalized Analysis” tab. This now shows you about a dozen graphs, breaking the numbers down by accepted/rejected/applying. Your own score range is highlighted. (Note: you have to be logged in to see the graphs.)

I’ve attached an example for those of you who aren’t logged in.

Yesterday I updated the algorithm that spits out the probability (your chances of admission) for each person at each school. These new probabilities should be substantially more predictive than the old ones. (This change will not move <50% predictions above 50%, or vice versa. What has changed is only the scale; some rankings that were previously 56% might now be 75%, wholly dependent on which school we’re talking about.)

Previously, I had incorrectly converted between odds and probability. Now, I have changed the algorithm to correctly convert between the two. (Why this matters is idiosyncratic to how we process the data; suffice it to say that this matters.) In looking over the numbers at a couple of schools, this seems to have substantially increased the reliability of the predictions. It’s still not perfect, but it’s better.

To learn more about this, you can read some posts by ‘badass’ about this previous problem. 

As ‘badass’ noted, a good model should, more or less, work like this:
Say that there are 25 people who each have a 20% chance of getting in. If the model isn’t great, then maybe 15 of those (60%) will get in, or maybe 1 of those (4%) will get in. If the model is great, then 1 in 5 (20%), or about 5 of those people, should get in. The models aren’t perfect, so this won’t be exactly the case, but now we’re much closer to that at many colleges. 

(As an aside, you can see how a ‘perfect’ model will necessarily get predictions ‘wrong’. A perfect model that gives 10 people a 60% chance should be “wrong” on 4 of them: 4 of them should not get in. One that gives 10 people a 90% chance should be “wrong” on 1 of them. For the model-maker, these are the desired results.)

Now when you visit a school’s page, you’ll be able to click through to “Personalized Analysis”, which shows where you fall vs all admitted students (ever) and current applicants (for the next admissions cycle). Currently it is limited to GPA and SAT scores, but we plan to expand this to automatically show all of the categories that apply to you; i.e., if you listed an AP English Language score, we’ll graph that out for you.

Today, I finally put the finishing touches on a form I’ve been working on for a couple of weeks now. It’s the page where you create your college profile. This is an important page, because the computer largely bases its predictions on the information you give it here. Starting today, you’ll be asked to indicate which high school you’re attending (out of a list of about 35,000). If it’s not there, you can easily add it without leaving the page. And for privacy’s sake, you’re invited to hide your high school information before you’re even asked for it.

The benefit in disclosing where you go to high school is that it will allow for better predictions.  Unless you think that it’s an accident that Stuyvesant and Andover flood the Ivies with their graduates every year, you’ll probably agree with me that high school “strength” is a fair indicator of future college acceptance. Well, even if you don’t agree with me, we’ll be testing this hypothesis together over the next few weeks.

Are there any patterns that you think will jump out? How do you think your your high school will compare?