State of Play: How Do College Football Programs Compete for Student Athletes?

Abstract

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.

This is a preview of subscription content, log in to check access.

Fig. 1

Notes

  1. 1.

    A larger literature exists with respect to the professional football draft. See Berri and Simmons (2011), Hendricks et al. (2003), and Grier and Tollison (1994) for examples.

  2. 2.

    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.

  3. 3.

    These estimates have not been updated since 2006 and likely represent a lower bound of the rents earned by schools today.

  4. 4.

    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.

  5. 5.

    Summary statistics are not listed in Table 1 but are available upon request.

  6. 6.

    In this study, the significance of the NCAA violation effect is largely driven by USC’s program.

  7. 7.

    Under Saban’s leadership, Alabama was the BCS Champion in 2009, 2011, and 2012.

  8. 8.

    See Harris (2016) and Fleisher et al. (1988) for more detailed reviews of the NCAA crime literature.

References

  1. Albrecht, J., & Vroman, S. (2002). A matching model with endogenous skill requirements. International Economic Review, 43, 283–305.

    Article  Google Scholar 

  2. Berri, D. J., Brook, S., & Fenn, A. (2011). Predicting the NBA amateur player draft. Journal of Productivity Analysis, 35(1), 25–35.

    Article  Google Scholar 

  3. Berri, D. J., & Simmons, R. (2011). Catching a draft: On the process of selecting quarterbacks in the National Football League amateur draft. Journal of Productivity Analysis, 35(1), 37–49.

    Article  Google Scholar 

  4. Brown, R. W. (1993). An estimate of the rent generated by a premium college football player. Economic Inquiry, 31, 671–684.

    Article  Google Scholar 

  5. Brown, R. W. (1994). Measuring cartel rents in the college basketball player recruitment market. Applied Economics, 26(1), 27–34.

    Article  Google Scholar 

  6. Brown, R. W., & Jewell, R. T. (2004). Measuring marginal revenue product in college athletics: Updated estimates. In J. Fizel & R. Forts (Eds.), Economics of college sports (pp. 153–162). Westport, CT: Praeger.

    Google Scholar 

  7. Brown, R. W., & Jewell, R. T. (2006). The marginal revenue product of a women’s college basketball player. Industrial Relations, 45(1), 96–101.

    Google Scholar 

  8. Brown, T., Farrell, K. A., & Zorn, T. (2007). Performance measurement and matching: The market for football coaches. Quarterly Journal of Business and Economics, 46(1), 21–35.

    Google Scholar 

  9. Depken, C. A., II, & Wilson, D. (2006). NCAA enforcement and competitive balance in college football. Southern Economic Journal, 72, 826–845.

    Article  Google Scholar 

  10. DuMond, J. M., Lynch, A. K., & Platania, J. (2008). An economic model of the college football recruiting process. Journal of Sports Economics, 9(1), 67–87.

    Article  Google Scholar 

  11. Fleisher, A. A., Goff, B. L., Shughart, W. F., & Tollison, R. D. (1988). Crime or punishment? Enforcement of the NCAA football cartel. Journal of Economic Behavior and Organization, 10, 433–451.

    Article  Google Scholar 

  12. Fleisher, A. A., Goff, B. L., & Tollison, R. D. (1992). The National Collegiate Athletic Association: A study in cartel behavior. Chicago: University of Chicago Press.

    Google Scholar 

  13. Grier, K. B., & Tollison, R. D. (1994). The rookie draft and competitive balance: The case of professional football. Journal of Economic Behavior & Organization, 25(2), 293–298.

    Article  Google Scholar 

  14. Harris, J. S. (2016). The demand for student-athlete labor and the supply of violations in the NCAA. Marquette Sports Law Journal, 26(2), 411–432.

    Google Scholar 

  15. Harris, J. S., & Berri, D. J. (2015). Predicting the WNBA draft: What matters most from college performance? International Journal of Sports Finance, 10, 199–216.

    Google Scholar 

  16. Hendricks, W., DeBrock, L., & Koenker, R. (2003). Uncertainty, hiring, and subsequent performance: The NFL draft. Journal of Labor Economics, 21(4), 857–886.

    Article  Google Scholar 

  17. Humphreys, B. R., & Ruseski, J. E. (2009). Monitoring cartel behavior and stability: Evidence from NCAA football. Southern Economic Journal, 75(3), 720–735.

    Google Scholar 

  18. Kahn, L. (2007). Cartel behavior and amateurism in college sports. Journal of Economic Perspectives, 21(1), 209–226.

    Article  Google Scholar 

  19. Roth, A. E. (1984). The evolution of the labor market for medical interns and resident: A case study in game theory. Journal of Political Economy, 92, 991–1016.

    Article  Google Scholar 

  20. Roth, A.E. & Sotomayor, M. (1992) Two-sided matching. Handbook of Game Theory, 1, 486–541.

    Google Scholar 

Download references

Acknowledgements

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.

Author information

Affiliations

Authors

Corresponding author

Correspondence to Jill S. Harris.

Appendix: Variable Names and Descriptions

Appendix: Variable Names and Descriptions

SHARE:

Number from 1 to 12 representing the number of athletes recruited from the top 100 in a given year

RANK:

Number from 1 to 100 assigned by Rivals.com indicating player ability

HT:

Reported height of the athlete from Rivals.com in inches

WT:

Reported weight of the athlete from Rivals.com in pounds

CHAMP:

A number from 0 to 46 representing the number of conference championships the college has won

Dchamp:

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

DSEC:

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

DSTATE:

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

DV:

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

DALdb:

An interacted dummy term equal to 1 if the player is from Alabama and is a defensive back

DALrb:

An interacted dummy term equal to 1 if the player is from Alabama and is a running back

DCAlb:

An interacted dummy term equal to 1 if the player is from California and is a linebacker

DFLol:

An interacted dummy term equal to 1 if the player is from Florida and is an offensive lineman

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

Keywords

  • Football recruiting
  • NCAA
  • Matching

JEL Classification

  • J42
  • L13
  • Z21