We have rolled out our interactive flash scatterplots (also known as scattergrams), available on every college page under the ‘My Analysis’ tab.

These graphs display the accepted and rejected applicants scattered across a 2D canvas according to the variables that you choose. For example, you might look at Unweighted GPA & SAT, or Instate & Average AP Score. To get started with this new tool, see Cornell’s scatterplots.

For any given SAT score, valedictorians appear more likely to get into Cornell than non-valedictorians.

Because there are many, many overlaps, you can set a level of jitter, so each point floats near its true value. For example, if you look at Unweighted GPA and Valedictorian Status, everyone will clump on top of one another. (You either are a valedictorian, or you aren’t, so there are only 2 slots that you might possibly fit into – hence lots of clumping.) If you set a 20% jitter to Valedictorian Status, things will spread out nicely, so you can see what is really going on.

With your feedback and criticism (please post it here or in the forums), we’ll work on improving the tool. Enjoy!

Presenting: our new college rankings.

The college admissions landscape is littered with college rankings. In 1983, US News first ranked American universities. Since then, rankings have been a fixture of the college world: they are produced by various businesses (US News, Princeton Review, Forbes, Atlantic Monthly), and heeded by students and colleges alike. To gain advantage, some universities have been alleged to manipulate their own rankings. And, while some of the factors used in the rankings are justifiable (alumni giving rate), some seem to be arbitrary (peer assessment surveys asking other colleges about your college’s ‘faculty dedication to teaching’). Each year, the methodology changes slightly, producing a slightly different list. In the end, the factors that are used to come up with the rankings seem arbitrary; the occasional change in the weighting of each factor, capricious. There is a need for a new approach.

Criteria for a ‘good’ college ranking system

1. The system should be difficult to game; any ‘gaming’ of the system should actually benefit students. In contrast, consider the allegations that some schools tried to manipulate the US News rankings by encouraging more students to apply in order to decrease their acceptance rate.
2. The factors measured should be relevant to students. In contrast, what Cornell’s dean thinks about the faculty dedication at the University of Texas may be irrelevant.
3. The overall procedure for generating rankings should be stable from year to year. In other words, any change in the rankings between 2008 and 2009 should be explained by a substantive change in the underlying factors, not by an arbitrary change in how those factors are weighted.

The MyChances College Rankings

We have implemented the MyChances College Rankings based on revealed student preference. In this system, the college admissions process is treated like a chess tournament. The colleges play matches (which occur when 2 colleges admit the same student). In each match, there is a winner (the college that the student ends up attending) and a loser. The winner gains points; the loser forfeits them. When a high-ranked school beats a low ranked school, the high-ranked school gains few points, and the low-ranked school loses few points. If a low-ranked school beats a high-ranked opponent, it gains more points than if it beat an equally-matched opponent. After playing many games, the colleges that students prefer rise naturally to the top of the rankings.

Does the method of revealed student preference meet the 3 criteria outlined above? I believe it does.

Consider point #1 (gaming the system). Imagine that MIT wanted to beat out Harvard by trying hard to avoid admitting any students that they thought would be admitted to Harvard. They would end up succeeding in a model based on acceptance rate and yield (since their yield would likely increase), but their actual student body would be less qualified. In the revealed preference model, however, they would be less successful. They would not compete head-to-head with Harvard, so would ‘win’ more. But they would be winning against weaker ‘opponents’, earning fewer points for each victory.

For point #2 (relevance), the idea of revealed preference is that it aggregates the sum total of what matters to students – whatever those factors might be. It is likely that students behave rationally (by attending the school that they find most desirable). So long as other students share similar values, then revealed preference rankings will work well in explaining, and even guiding, their decisions.

For point #3 (stability), the tournament style system is simple and straightforward. It is responsive to changes in student preference over time. It does not rely on aggregations of various statistical factors, or college faculty survey results; nor does it depend upon arbitrary weighting of those factors.

The details of the procedure that we use to generate the rankings, and our use of chess-style Elo points, will be explained in a later post. For an academic treatment of a similar college ranking system, I recommend the working paper, “A Revealed Preference Ranking of U.S. Colleges and Universities,” 2005, by Christopher Avery, Mark Glickman, Caroline Hoxby, and Andrew Metrick (free link).

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.

A few members were asking about the ability to note that they applied early and got deferred. Now, when you go to modify your app, you can mark a checkbox indicating deferred status. Good luck in the regular decision round!

Today we’ve rolled out the first iteration of two newsfeeds: one for the main page, and another for your “My Tools” page.

The mainpage newsfeed gives a summary of all of the goings-on from the last few minutes. It’ll let you know when someone joins the site, rates someone’s chances,  rates an essay, is accepted at a college, or posts in the forums.

The personal (“My Tools”) newsfeed gives a more thorough summary of what’s been going on with the colleges on your list. When someone updates their status there, makes a new wall post, or predicts someone’s chances of admission at that school, you’ll hear about it.

You can direct any questions, comments, or concerns to the forum post on the same topic.

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.)

I’ve updated the script to allow you to add your IB scores. Ultimately, these may be used to improve predictions for students who have attended IB programs.

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.

Two changes today:

1) Each college page now has a “discussion” tab. Discussion from news sources, blogs, and our own applicants has been moved there. Soon, but not yet, you’ll be able to make comments directly on the school’s page.

2) The “discussion” tab allowed us to free up a lot of space in the “Applicants & Predictions” page for each college. In its place, we now show you the students’ class rank and home state. More to come on this.