It’s been awhile! I know, I haven’t posted anything in over a month. I’ll talk about that later this week. For now, let’s get back into it.

This week I’m breaking down the economics of men’s college basketball (I’ll get to women’s basketball next week). Yes, it’s March. We’ve dug ourselves out of the snow so now it’s time to make some brackets. But I’ll be looking at the economics, where schools rank, and who's making the most of the money they spend. Here we go.

Who is Spending What?

One of the things I’ve been up to is logging in all the new financial reports. If you don’t know, schools file an annual revenue/expense report with the NCAA in January every year. I’ve been collecting these reports via public records request the last few weeks, scraping the data, and putting together the data sets necessary for this type of analysis.

If you want to play with that data yourself, you can! It’s free. Give it a look. This has the per-school, per-team, per-item revenue/expenses. If you want to know how much Clemson’s golf team spends on food, UConn’s men’s hockey team spends on travel, or anything in between, give it a read.

Before I start using that data to analyze the state of college basketball, a few caveats:

With those caveats, let’s start looking at college basketball at a high level. We can’t see (yet) how schools are allocating their revenue sharing budgets across football, men’s basketball, and every other sport. But this analysis will give us a starting point to compare where teams sit.

No, I’m not looking to ignore the mid-majors. Though many consider the changes in NIL, revenue sharing, and the transfer portal (and moves by the NCAA to expand the tournament) to be the death of mid-majors/cindarellas, I will not ignore them here. The problem is, from a financial perspective, they’re all sitting at the very bottom of the resource table. That’s okay! That’s what makes them fun to watch.

What I thought I’d do is show how their resources compare to the big boys of the FBS. Let’s see how they compare.

There’s more to college basketball than spending money. At some point, the money has to lead to wins. And while the obvious metric to do here is to look at how much schools paid for their wins (something I did with football) I’m going to go a bit deeper here.

I’ve scraped the athletic directories of every Division I athletic department. I then clean that data to get counts of staff sizes for each sport. Consider it a different way of measuring resources (if you have more money, you’ll likely spend lots of it on staff).

Then I looked at how much each team won this year. This makes it an interesting triangulation - I have prior year finances, current year staff composition (a current financial metric), and current on-court success. There are some interesting patterns to look at here.

This section is for articles, podcasts, interviews, and any other college sports related content I found interesting this week. If you have something you’d like to share, shoot me an email and it may be featured.

🎧 uNILateral Decision - a new podcast from the people over at Boise State. I have really enjoyed the first few episodes - they offer both legal and department level analysis on the big moments in NCAA/college sports history. Worth your time.

📚 Sacred Cow BBQ - Kyle Saunders blog found its way to my inbox and I’m glad it did. He provides a sophisticated political economics analysis of the state of college athletics and where we could be going. Not light readings but worth taking the time to digest.

Thanks for reading this week’s issue.

I’ve been away awhile with some major projects. I’ll send a message about those later this week. But I do hope to be back on my weekly cadence as I was before. Thanks for reading!

Until next time,

Greg Chick, PhD
Data Analyst

I’ve already covered some of the nuances of public records requests here, but I wrote about it in the newsletter before. As for the staff size data, that comes from a freeze of the directories I made on January 1st this year. I’ll be making freezes quarterly in order to do some longitudinal analysis. I’ve got some of that data for free to download, as well.