Strategies on Building Your MBA Application Portfolio

By Lawrence Linker

As Founder and Application Coordinator for MBA Link, one of the first things I’m often consulted on is how many schools an applicant should apply to and which ones. This is an extremely important step to get right in the application process, because there is almost no other decision you will make along the way that has a greater influence on how likely it is you will end up getting into a school with which you will be happy.

When considering your application strategy, there are a few key considerations to take into account:

  1. How much do you need to go to business school this year?
  2. How wide is the prestige range of schools you are looking at? ie, do you want to get into Harvard, but would you be willing to go to Kenan-Flagler if accepted?
  3. What is your personal level of risk tolerance?

From a qualitative point of view, I think most applicants well understand the implications of applying to many schools, but I reached out to my friend Wayne Atwell of because I wanted people to see hard data on how different application strategies affect their chances of admission. My comments are in black, Wayne’s are in green.

In order to make this simulation as realistic as possible, I’ve asked Wayne to use data from his pool of applicants.

Thanks, Lawrence. I would like to introduce you to our sample MBA applicant, lets call him Robert. Robert is applying round one and has a 710 GMAT and a 3.5 GPA from his undergraduate university where he majored in engineering. He is 27 and has 5 years of work experience as a consultant. He is considering applying to Harvard, Wharton, Sloan, Tuck and McCombs. Here are his predicted chances if he were to apply to just one school.


Thanks, Wayne.

Unsurprisingly, Robert’s chances of getting into each program tracks well with their known selectivity. With statistics so low, it’s easy to be discouraged. While most applicants would like to believe that if they just hit a certain GMAT number, just talk to the right people, or just want it bad enough, they are sure to get in to the program of their dreams. The statistics do not bear this optimism out.

All is not lost, however. Let’s now look at the probability of a total failure (no admission to any school) by multiplying through the probability of failure for each school. Taking the reciprocal of that gives us the probability of success in getting into at least one of the schools for the applicant. Robert has a 75% chance of being admitted to one of his top schools which means his chance of total failure is only 25%. You have to admit it is a much better looking number.

Now, let’s say Robert really is determined to start business school next year and is still uncomfortable with the above number. He needs to increase his overall probability of getting into at least one school he will be happy to go to, but he also don’t want to completely eliminate the chances of going to his dream school, Harvard.  Over to you, Wayne.

To give Robert a better chance of getting into at least one business school next year, we’re going to have him apply to some less selective programs. Here are the results.

Harvard Cornell UNC Ross McCombs All 5 Schools
12% 47% 51% 51% 46% 94%

Thanks, Wayne. Obviously, Robert’s chances for HBS are unchanged. He has decided to swap out Sloan, Wharton and Tuck for Ross, Johnson, and Kenan-Flagler, less selective programs that offer many of the same benefits. Now when we look at the chance of total success, we see a very different figure.

Needless to say, if Robert gains admission to HBS, we expect he will have an easy decision. But it’s nice to know he is covered in case that doesn’t happen.

Now for our last example, let’s take a look at a different kind of applicant. This applicant has the same statistics as Robert, but they have a very different strategy in mind. For this applicant, it is a highly ranked school or nothing. They can live with not going to business school next year, but they are ready to go if they can get into an ultra-prestigious program.

Let’s take a look at this person’s application profile.

We’re going to have this applicant apply to only the most competitive programs. Find the statistics below.

Stanford Harvard Wharton Sloan Columbia All 5 Schools
4% 12% 13% 14% 36% 59%

Taken individually, none of these chances are particularly encouraging, but put together, the success rate is actually not too bad! Considering this applicant is applying to only the most selective schools, this is a rate of success I would think any applicant should be happy with.

Here are a few additional things to keep in mind when thinking about which and how many schools you should apply to:

  1. Applying to more schools will always give you a higher overall success rate statistically, but practically speaking there is a point of diminishing returns. A poorly executed application will always get a ding, no matter how strong your profile. In our experience, for most people, the magic number of schools to apply to is 5.
  2. The above models don’t take fit into account at all. There are similarities between schools that an intelligent applicant can exploit by applying to schools with similar offerings, decreasing their need to come up with widely different rationale’s for why they want to go to each school. Put another way, actually think about what schools you want to go to, and apply to schools that are a genuine fit, rather than taking a purely random approach.
  3. Applicants often have the belief that school selectivity is totally ordinal. That is to say, if you get denied by a less selective school, a more selective school will certainly deny you. That is absolutely not the case! We see this happen all the time. There is simply an element of luck or chance in the application that can NEVER be removed. But it can be mitigated! More on that soon!

The data used to predict acceptance rates for this post came from GMAT Club.

4 thoughts on “Strategies on Building Your MBA Application Portfolio

  1. Super Pognon

    Correct me if I am wrong, but your assumption is that those events are independent. I think it is not true. This is not dice rolling.

    Admission @McCombs does not guarantee you something about an admission @harvard. But, the opposite is true (95% ?).
    Admission @Harvard does not guarantee you something about an admission @Columbia (70-80% ?). Opposite does 20-30% ?

    1. KP

      Yeah I agree.

      If you chance of getting into Harvard is 10% and Chicago is 20%, if these events are independent, your failure chance will be 90%*80% = 72%. However, I think this is closer to conditional problem (Bayesian statistics). If I am not getting into Harvard, I am less likely to also miss Chicago (especially if we agree that model inputs for probability calculator does not explain 100% of the admission results, which I am sure R^2 wasn’t 1).

      Long story short, people with small probabilities of acceptance are likely to overstate their success rate with this assumption.


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