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The original was posted on /r/cfb by /u/College_Sports_Fan on 2026-02-18 15:45:06+00:00.

It’s the offseason so I put together something I’ve been curious about for a while - a high-level list of blue bloods across all NCAA sports. Some context:

“Blue blood” selection - Based purely off natty count. I tried to select between 5-8 in most cases but made exceptions where a limited number of programs tower over everyone else and including more programs felt silly (e.g., UConn and Tennessee in WBB). I don’t know each sport well enough to go beyond this admittedly blunt analysis but natty count is a very strong proxy for blue blood status so it feels like this should be correct most of the time. In any event, feel free to let me know where I’m off if you are familiar with one of these smaller sports.

Data and errors - Most of the data was pulled from Wiki pages for each sport or university websites. I am sure there are errors here and there as a lot of the data especially on less popular sports is messy. I’m also sure I fat-fingered some entries so just let me know what mistakes you catch. I excluded fencing data because that sport is a mess of different men’s/women’s/coed competition across eras. The college football data is not the point of this exercise so I stuck with the generally accepted blue blood list of 8 used in this sub and the poll era natty count from wiki.

Findings:

1 - Stanford, UCLA, USC and Texas are in the top tier with double-digit “blue blood” counts across all sports.

2 - Dominance in less competitive sports - College football sometimes feels like it is dominated by a handful of brands but nothing compares to the natty concentration in sports like wrestling, beach volleyball etc.

3 - Blue blood % count - Universities field different numbers of teams. So, I also calculated the % of the time blue blood status is achieved. UCLA, USC and Texas are on top with Stanford and Oregon close behind.

4 - Unexpected underperformers - I was surprised to see Alabama and Notre Dame (2 each) and Ohio State (3) as low as they are. Again, my analysis is very rough but I would have thought they were closer to the top.

DATA LINK - docs.google.com/spreadsheets/d/…/edit?usp=sharing