We're Back For More

Welcome to the NILnomics Newsletter!

Whether you’re new or coming back for issue #2, thank you for taking time out of your day to be here. Last week was the newsletter’s launch, which went better than I had hoped. I’m excited to be back with more data, analysis, and visuals for you. This week you’ll see:

• Roster limits - notes on last week’s analysis was done and additional insights

• House Settlement Salary Cap - breaking down where the number comes from

• College hockey - ticket sales data

Pour some coffee. Get comfortable. Let’s get into it.

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Week 1 Thoughts

Many people emailed, commented, and tweeted me after the release of the first issue. That was great to see! I welcome feedback - just ask me how much I had to go through with my dissertation 😀 

What I didn’t expect, naively, was the rage some online people have. I thought sharing my data and code would prevent any claims that I was trying to obfuscate what my analysis was. That clearly isn’t the case.

In response, I’m going to designate a section for each analysis, after the ‘Quick Takeaways’ section, to explaining the analysis from a technical perspective. Hopefully, moving forward, that will clear up any confusion. Probably not. 🤷 

House, Roster Limits, and Money 💰️ 

Everyone in college athletics is focused on roster limits. Rightfully so. It doesn’t help that we’re in a waiting game while Judge Wilken works through the objections raised by student athletes who believe the early or possible adoption of roster limits by institutions have harmed them.

While we wait, I’m going to give an update on the roster limit analysis I did last week. Here’s the updated data:

Quick Takeaways:

• Adding the FCS schools to the analysis (see Analyst’s Desk notes below) only increases the potential impact of the House Settlement. Significantly. Total number of athletes is 4,408 (P4), 2,102 (G5), and 4,055 (FCS). Overall total is now 10,565.

• Keep in mind the number of schools at each level in the data - 71 (P4), 61 (G5), 126 (FCS). This helps explain why there are so many more FCS athletes potentially impacted compared to the G5.

Analyst’s Desk

The way I approached this analysis was to be as straightforward as possible and conservative when necessary.

• I used the NCAA’s latest EADA data (FY 2023). This dataset has the number of participants disaggregated by institution, sport, and sex.

• I applied the proposed roster limits by sport and sex, counted up the losses from the original roster to the new roster limits, and added up to the aggregations I presented last week.

• Clearly just applying roster limits to the rosters from FY 2023 misses many roster cuts. Stories like Cal Poly’s swim team and similar program/roster decisions are what I’d call a second-order type of roster cut. They’re important, but not what I’m trying to count here.

• This week I’ve added the FCS schools that, though not required to implement roster limits (and many won’t) could see roster reductions if roster limits are implemented. Hence the change in the title of the graph from last week to include potential roster cuts.

• Last week I used the ‘Track & Field’ variable from the EADA data. The problem is, many institutions don’t report that. Most, however, do report ‘All Track & Field Combined.’ If you look at the NCAA EADA data, Sacred Heart claims they have 153 men and 209 women for ‘All Track and Field Combined.’ I count 65 men on their track roster page and 36 on their Cross Country roster page which is 101 total. Not to mention nearly all the Cross Country athletes are in Track and Field. All this is to say that I heard many justified criticisms of this and other parts of the EADA data. It’s far from perfect, but it’s what we have.

Quick Takeaways:

• The same trend as before applies - more male athletes are losing roster spots than women.

• The gap between men and women grew from 1,497 without FCS to 2,979.

Quick Takeaways:

• Interestingly, many FCS schools pop up to the top of the list (Sacred Heart, Cornell, Harvard, Princeton).

• Why would so many Ivy Leagues have bloated rosters? Can’t think of a reason why that’d be…

• It’s shocking how Sacred Heart, an FCS school, has the most roster spots to lose. But if you look at their football roster from 2022-23, you can see why.

Quick Takeaways:

• The disparity of the P4’s pool revenue compared to the G6 is made crystal clear here.

• While a $20.5 million cap is a fraction of the top two (Big 10 & SEC) conference’s pool revenue, it’s larger than all pool revenue for all schools in the MAC, Sun Belt, and Conference USA.

Analyst’s Desk

If you haven’t read sports economist Daniel Rascher’s expert testimony in the House docket - what’re you waiting for? He outlines in detail how the salary cap is computed:

1. Add up the 4 primary components of “pool revenue” at each institution. Pool revenue is those revenues generated by the players that schools are required to share. This includes ticket sales, game guarantees, media rights (including CFP and NCAA distributions), and royalties/licensing/advertisements/ licensing.

2. Average across the P5 schools (yes that includes the Pac-12 as they were an autonomy conference during the calculation process).

3. Add up expenses that conferences already pay to players - medical, meals, grant-in-aid, scholarships, and Allston payments.

4. Expenses account for 29% of pool revenue.

5. Rascher’s analysis is a ‘yardstick’ approach that compares college athletics to similar markets (NBA 🏀 , NFL 🏈 , MLB ⚾️ , and NHL 🏒 ) without the illegal guardrails the NCAA has historically employed. In these markets, labor receives 50% of athlete-related revenue.

6. Thus 22% of the remaining revenue is earmarked for paying players for their NIL. The first year has this figure at $20.5 million.

7. The rate increases 4% every year, with a new calculation, from step 1, every 3 years. Also, interestingly, the class (players) may request 2x throughout the 10 year settlement for a new calculation (probably smart to do this if the SEC/Big 10 media rights are renegotiated off cycle).

Quick Takeaways:

• There are 25 institutions that use under the 22% of pool revenues towards player compensation when the salary cap is set at $20.5 million.

• Those that think there shouldn’t be a salary cap will want this graph - clearly there are many schools capable of paying players over the $20.5 cap.

• If you’re Texas, Ohio State, or Michigan - what are you spending all that extra money on, if not players 🤔 

Analyst’s Desk

To compute this data, I took each the $20.5 million salary cap and divided it by each institution’s pool revenue. I filtered the data down to institution’s who would be spending less than the 22% of expected pool revenue on player compensation given actual pool revenues.

Quick Takeaways:

• Only 8 programs clear $1 million in ticket revenue.

• We’ve all heard the story of how Penn State’s hockey team was a club sport as recently as 2012. Now they have rich donors and are DI. It’s been a staggering jump to the top (they have the fourth most ticket revenue).

• With two of the top three ticket selling programs, it’s safe to say college hockey is beloved in Minnesota.

Analyst’s Desk

I took the MFRS data I scanned for FY 2023 and filtered it down to Ice Hockey. If you can get this granular data anywhere else on the internet, I haven’t seen it.

📖 What I’m Reading/Listening To 🔉 

This week flew by so not much to share. Here’s this week’s best listens:

• NIL Clubhouse had a great interview with player’s lawyer Jonathan Stahler.

• Split Zone Duo had a great interview with Extra Point’s Matt Brown.

Final Thoughts

Thanks for reading.

It was great to respond to some feedback from readers and jump into the salary cap. Hopefully by next week we’ll have some news of the House Settlement. If not, there’s always more hockey data to break down.

Until next time,

Greg Chick, PhD

Data Analyst

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