Each year high school football players sign letters of intent with college football programs. The NCAA governs this matching market with strict rules that are designed to protect amateurism. DuMond et al. (J Sports Econ 9(1):67–87, 2008) develop a model of athlete choice. I consider the matching puzzle from the program’s perspective: What factors increase the likelihood that a school will successfully recruit an athlete? Like DuMond et al., I find that the state of play matters. However, my results suggest that football programs are willing to recruit outside their borders. In addition, the results align with prior findings about cheating in the NCAA. This extends the literature on college sport recruiting and may provide insight into other matching puzzles in academic, medical, and business job markets.
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Perhaps the football programs care more about maximizing revenues then winning. However, Brown and Jewell (2004) suggest that a program’s revenues are a function of its skill level, the quality of its opponents, market demand, and its past success. Higher-skilled teams make more revenue because they win more often. Coaches of winning teams (that go to championship games) earn bonus money based on winning. It seems reasonable to suggest that DI football organizations prefer winning to losing.
These estimates have not been updated since 2006 and likely represent a lower bound of the rents earned by schools today.
Initially, I chose the top 100 because of my interest in the DuMond et al. study. For comparison purposes, it seemed prudent to examine the same cross-section of athletes that they did.
Summary statistics are not listed in Table 1 but are available upon request.
In this study, the significance of the NCAA violation effect is largely driven by USC’s program.
Under Saban’s leadership, Alabama was the BCS Champion in 2009, 2011, and 2012.
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I thank Roger Blair for organizing the symposium on NCAA behavior and Brad Humphreys, Nathan Wozny, Tobin McKearin, and Aaron Albert for their generosity and helpful comments.
Appendix: Variable Names and Descriptions
Appendix: Variable Names and Descriptions
Number from 1 to 12 representing the number of athletes recruited from the top 100 in a given year
Number from 1 to 100 assigned by Rivals.com indicating player ability
Reported height of the athlete from Rivals.com in inches
Reported weight of the athlete from Rivals.com in pounds
A number from 0 to 46 representing the number of conference championships the college has won
A dummy variable equal to 0 if the college program has not been a national champion in the sample period and 1 if they have been a national champion
A dummy variable equal to 0 if the college is not in the Southeastern Conference (SEC) and 1 if they are a member of the conference
A dummy variable equal to 0 if the college-athlete match is not within the same state and 1 if the match is within the same state
A dummy variable equal to 0 if the college is not under probation or rumored to be during the sample period and 1 if they are under probation, sanctions or rumored to be
An interacted dummy term equal to 1 if the player is from Alabama and is a defensive back
An interacted dummy term equal to 1 if the player is from Alabama and is a running back
An interacted dummy term equal to 1 if the player is from California and is a linebacker
An interacted dummy term equal to 1 if the player is from Florida and is an offensive lineman
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Harris, J.S. State of Play: How Do College Football Programs Compete for Student Athletes?. Rev Ind Organ 52, 269–281 (2018). https://doi.org/10.1007/s11151-017-9602-z
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