BS5 - NIL Valuation: A Predictive/Correlational Model
SCURS Disciplines
Business
Document Type
General Presentation (Oral)
Invited Presentation Choice
Not Applicable
Abstract
NIL valuation has become somewhat of a phenomenon in the collegiate sports world. College athletes enter the transfer portal to gain more NIL money. This study seeks to find a definitive model to predict NIL valuation. In earlier studies conducted by Brown et al (2025) and Brown and Jennings (2024), several variables including playing time, sex/gender, university represented, and sport were explored to help create a definitive model to predict NIL valuation. With respect to the 2025 study one variable, university represented, was found to be statistically significant and explained 13% of the model. Therefore, in this study, we are including the variable, university represented in this study and will explore more variables which should add value to the model. These variables are sex/gender, sport, the number of times an athlete has entered the transfer portal, whether the athlete is still a commit (still in high school), and university/college recruiting class score. Exploratory factor analysis will be conducted along multiple regression and a robust discussion of whether or not NIL values need to be capped as some professional sports have capped salaries.
Keywords
NIL Prediction Model, Marketing, Predictive Modeling, Sports
Start Date
10-4-2026 3:25 PM
Location
CASB 102
End Date
10-4-2026 3:40 PM
BS5 - NIL Valuation: A Predictive/Correlational Model
CASB 102
NIL valuation has become somewhat of a phenomenon in the collegiate sports world. College athletes enter the transfer portal to gain more NIL money. This study seeks to find a definitive model to predict NIL valuation. In earlier studies conducted by Brown et al (2025) and Brown and Jennings (2024), several variables including playing time, sex/gender, university represented, and sport were explored to help create a definitive model to predict NIL valuation. With respect to the 2025 study one variable, university represented, was found to be statistically significant and explained 13% of the model. Therefore, in this study, we are including the variable, university represented in this study and will explore more variables which should add value to the model. These variables are sex/gender, sport, the number of times an athlete has entered the transfer portal, whether the athlete is still a commit (still in high school), and university/college recruiting class score. Exploratory factor analysis will be conducted along multiple regression and a robust discussion of whether or not NIL values need to be capped as some professional sports have capped salaries.