Name, Image, and Likeness Analytics

Political considerations will likely drive pending NIL regulations and legislation. There are multiple state legislatures involved, and the NCAA seems to have lost its appetite for creating a new set of rules. The clock is also ticking with Florida’s state legislation scheduled to go into effect this summer. It appears that the NCAA is now hoping that congress will settle the issue.

Ideally, NIL discussions would be based on data and the potential impact on various stakeholders (athletes, schools, fans). However, data limitations and a heated political climate make an analytical, logical, and balanced approach unlikely.

My thesis has long been that NIL rights will fundamentally change collegiate sports and reduce competitive balance. Specifically, I contend that the athlete and the school jointly create an athlete’s NIL value. If this is true, it follows that athletes may benefit from choosing schools that already have potent brands.

To illustrate this point, I collected data on the social media followings for the top 25 high school prospects from 2019. The data was collected before the NBA draft to eliminate the effects of draft position and selection by a high brand equity team.

I also collected variables related to the size of the athlete’s college’s fan base and the athlete’s collegiate performance. The variable for fan base size is 2018 home attendance. The variable for collegiate performance is points-per-game (PPG) in the 2019-2020 season.

I then ran a linear regression with IG Followers as the dependent variable and player rank, 2019-20 PPG, and 2018 attendance as explanatory measures.

Yes, you can argue with the methodology.

You can also claim that I should look at more data.

Or use a different model.

Or that I should add more data, such as the school’s won-loss record in 2019.

What about player interactions?

Is PPG endogenous?

What about position heterogeneity?

You get the idea. This is a simple but good faith analysis.

The results were intuitive and consistent with my conjecture about brand building.

The athlete’s recruiting rank yielded a significant negative coefficient. Remember lower numbers indicate higher ranks. The top player is ranked 1, while the tenth best is assigned the number 10.

The athlete’s points per game did not produce a significant coefficient. The coefficient had a positive sign (better performance produces more followers), but the effect was not significant.

The school’s 2018 attendance (the year before the player arrived on campus) yielded a significant positive effect. The size of the fan base positively affects the player’s social media following.

Let’s repeat something. The fan base's size significantly affects social media following, while the player’s scoring average does not.

As I said – this was a quick and casual analysis. It is not conclusive, but the findings are suggestive of an important point. An athlete’s popularity and brand are influenced by the school he chooses. As NIL rights become a reality, there will be a change in incentives. And as incentives change, so will how athletes choose schools.

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