I like the granularity of your categories very much. And you use them very well. I think on CC, rather than people suggesting every T20 school is a reach for everyone (even if they are a 4.0 and have a 1600 and an interesting story), we should be able to evaluate schools for applicants somewhat objectively in 3 categories.
Category 1 - Average accepted applicant has stronger profile than yours
Category 2 - You have similar profile to average accepted applicant
Category 3 - You have stronger profile than average accepted applicant
Although this would be based on school, grades, course difficulty and scores, there would be a bump or ding for the other stuff. I think conversations about the $$, because there are many, are hard to incorporate within a classification.
In the case of the OP info shared, Michigan would be a category 1. Ohio State would have been on the low end of category 2. Villanova since not ED would have been on the low end of category 1 and for me so would Wisconsin. Maryland would be on the high end of category 2.
Whoa! I think this is a big leap from the original post discussing a 3.6x student and a list consisting mostly of large publics. I do not know if any conclusions can be drawn from this exercise that would apply to a 4.0/1600/âinteresting storyâ student and a list containing T20s.
What is the purpose of a chance me / match me, and what is the purpose of categorizing a school as a reach? It is really just to help a person come up with a balanced list. No matter who the student is, there is no such thing as a balanced list that consists only of T20 schools.
So based on our HSâs SCOIR data, there is a set of schools for which I am not sure Category 3 is meaningful, meaning there is still a mix of acceptances, waitlistings, and rejections all the way to the top of the scattergram. And for some of those, even at the top as many or more people are being waitlisted (outcome unknown) or rejected as accepted.
I would say barring a known hook, those still count as no better than Lower Probability for everyone from our HS, and we would call them Reaches.
That said, weâll probably also call something a Reach if it has a generally below 25% acceptance rate (adjusted for residency and school/major as relevant), no matter the numbers/SCOIR scattergram. However, if the scattergram: (A) as a lot of data points; and (B) indicates more than half the people in your range get admitted (without waitlisting first), we might call that something like a Softer Reach. But some might call it a Harder Target if they were feeling good about fit.
And then absent SCOIR data like that, or equivalent, for an individualâs high school, I am not sure even with a 4.0 UW/1600 that I would be comfortable saying a sub-25% acceptance rate option was anything but some form of Reach. The problem is not the 1600, but the 4.0 UW, because we know from many AOs that these days they simply do not treat every 4.0 UW the same, and indeed not even every âmax rigorâ 4.0 UW the same, because the available courses and grading standards vary so much between different secondary schools.
So without actual data indicating that half or more of such people from that high school are actually getting admitted to a sub-25% college/school/major, it seems pretty risky to me to assume the odds are actually really good.
To me that is what it means to say every college/school/major like that is a âreach for everyoneâ. But with enough detail and HS-specific historical data, maybe a softer reach. And of course actual hooks are a whole other thing.
Indeed, and I note this is all a lot easier if you are not particularly a rankings-focused/peer-competition/external-validation sort of kid and your goal is just to apply so something like 8-10 (affordable) colleges, all of which you would be excited to attend. You can use conservative definitions of Likelies, Targets, and Reaches and still come up with such a list, and you need not think it is a failure of plan if you end up attending a Likely or Target, or only have one choice of Reach, or so on.
But if your goal is try to figure out which combination of 1 âsafetyâ and 19 other applications will lead to the highest possible ranking outcome, or most âimpressiveâ collection of offers, or so on, then you might feel the need not to âwasteâ applications on schools that end up not highly-ranked/prestigious enough for such purposes. And then you might think it a problem if a conservative definition of Reach leads you to include such a college when it turns out you could have used that slot to take another shot at a T10 or Ivy or whatever you are gunning for.
But that of course is a mindset most of the people here, including me, strongly try to discourage.
The issue is we have a near zero visibility to profiles of accepted students at most schools. Matriculated data can also be quite scant as many schools donât show GPA on their CDS (and if they do, itâs sometimes a GPA calculated in a different way than whatâs used for admissions.) Test score data at test optional schools often only includes only matriculated students who applied with a test.
Many HSs (but not most AFAIK) use Scoir or Naviance etc. and the scattergrams can help categorize schoolsâŠbut only if the school loads data accurately and completely (and by round of admission.)
Budget comes first, unless the family is full pay and happy to pay full COA at all schools. For example, if a family wonât qualify for need based aid but have a budget less than full COAâŠthey are merit hunting (and/or looking at low COA schools). Obviously the college lists/categorizations for students in each of those examples would generally be different.
Here are the definitions used at my kidsâ HS (large public). As part of the advising process, students are encouraged to try to find a few schools that fit into each category.
Likely: 60%+ acceptance rate, your stats are in top 50%
Match: 40-60% acceptance rate, your stats are in top 50%
Reach: 10-40% acceptance rate, your stats in top 25% (I personally think of this as a ârealistic reach.â Some might consider schools in this category with 25%+ acceptance rate a match, but at our HS, a student would put anything with sub 40% acceptance rate in the reach category of their list.)
You will notice that some colleges wonât fit in these categories (namely those with sub 10% acceptance rate, or those where your stats are below average). Those would also be considered reaches, but as a list building exercise, students are encouraged to try to find some reaches that fit the definition above.
Edited to add: Our HS does not use Naviance / SCOIR and we do not have class rank, so students do not have a simple way to compare themselves to other students at our HS. Our HS sends kids to a very wide range of colleges, including all of the most selective ones⊠but the same framework above is used for the kids targeting (and getting in) to HYPSM etc, as well as the kids targeting 80-90% acceptance rate schools.
The issue is this is a moving target from year to year, and sometimes what happened in previous years just wonât apply to the current application cycle. Applicants could double, be much more or less qualified than in previous years. Just a moving target.
We always need to include the year the data was taken.
Where would one be getting accepted student data from though? Most selective schools donât share that, although some doâŠ(but the student always has to calculate their GPA in the same way as any given school that provides accepted student GPA ranges.)
RightâŠbut even enrolled data can help. And most schools DO give the number of applicants, for example.
None of what anyone predicts here is more than an educated guess, in my opinion. Iâm saying this because even students with perfect stats get denied admission.
Yes, I definitely question how useful the advice may be, when itâs focused on âchancingâ someone with extremely high stats who is looking at colleges with sub 20% or sub 10% acceptance rate and trying to figure out how likely they are to get in. Iâm not sure how useful we can be in that situation.
On the other hand I think the advice here can be really useful when any student needs help finding matches and likelies that they can afford and would be happy to attend.
The other thing that SCOIR/Naviance lacks is background information about applicants - most importantly, whether or not they are hooked. Sometimes you might be able to figure it out when there are outliers or if there arenât many acceptances and you happen to know that the kid who got in last year was a recruited athlete/legacy/donor kid etc. Even if you are a â3â based on stats (this describes S24) it doesnât mean certain schools arenât high reaches (even if your âchanceâ is 20% instead of 5% you are still almost certain to be rejected).
Sometimes, this estimate is way off, or does not take into account factors like applied-to major. For example, San Jose State University has about an 80% frosh admission rate, and a student with a 3.9 (CSU recalculated) GPA would be well above the top 50%. While that student may be almost certain to be admitted for majors like philosophy or physics, that student would be almost certain to be rejected for computer science, based on the schoolâs published prior year admission thresholds.
Well, a Top 20 only list would have excluded Michigan (#21) from the application list. That could have been a list offering zero admissions and requiring a gap year. Iâve read the threads by high stats students being shut out except by a not well thought out safety and itâs heartbreaking. Heartbreaking not because they were shut out, but because they missed out on lots of other opportunities that other colleges could have provided.
Yes, I absolutely agree that our HSâs framework doesnât include admission rate in the studentâs desired major, and it also needs more emphasis on affordability. Itâs far from perfect
I gave it here as an example because I think itâs interesting how simple it is, and how itâs easy for students to categorize schools for themselves, by looking at the schoolâs acceptance rate and their own stats.
(Since our school is a large HS without much advising time per student, the framework has to be easy for students to use themselves.)
Even on CC, I donât think the majority of posters are in the 4.0/1600/âinteresting storyâ category. But even those students need some likely and affordable options on their lists. CC is often useful in helping to come up with such options that the student and family can be happy with.
I also think CC can be helpful in giving a student perspective about the strengths and weaknesses of their stats and ECs etc, relative to other students. This might not affect âchancingâ but can be helpful for the student in putting together and fine tuning the actual application (essays, EC descriptions).
