The issue of player compensation in revenue generating college sports has taken center stage in policy debates surrounding college athletics. Some have argued that increased compensation for college athletes will align the interest of the student athlete with institutional goals and could prevent scandals which damage the reputation of universities. Others argue that compensating players would lead to unnecessary professionalization of amateur athletics, further blurring the distinctions between students who play sports for extracurricular benefit as opposed to those doing so as an occupation (Nocera 2016, Benedict and Keteyian 2013). A recent USA Today (Estes 2019) article examined the increase in recruiting budgets and spending from college football programs. In the last 5 years college football programs have increased their spending upwards of 300%. Athletic directors understand the importance of increasing budgets to compete with the best competition.
The existing debate has been about whether athletes in revenue generating sports should be paid, but not how much they should be paid. The debate over compensation has largely neglected the important issue of player valuations—the benchmark that would guide player compensation schemes. Presumably, player valuations should be a guiding principle in any compensation scheme. Proponents of compensation have avoided the issue of how productivity differences between players should factor into any compensation formula. The compensation scheme may need to be more sophisticated and, as in the labor market for professional sports, be tied to player performance or expected performance.
Institutionally, the revenue structure in many athletic conferences is designed to equalize revenues between member schools, which is similar to revenue sharing in professional sports. Revenue sharing is ever changing within conferences. Compensation for athletes may differ substantially between conferences as opposed to within conferences as a result. If this is true, it could be the case that all players within any conference have the same value since so much revenue is redistributed. If player value is found to be heterogeneous despite conference institutional features such as revenue sharing, value could be tied to a variety of additional metrics as they are in most professional sports.
Determining player values in professional sports is inherently difficult. Depending on the sport studied, detailed evidence of player performance is usually lacking. For example, defensive players in football should be compensated based upon what does not occur, which can be difficult to measure accurately. Extending such analysis to college sports is even more difficult as position specific valuations have no precedent and the majority of professional sports use salary caps, signing bonuses, and other labor union and league negotiated particulars which depart from traditional labor theories of wages. There are no existing compensations schemes which could be applied to amateur sports in a straightforward fashion. Similarly, new entrants into professional sports are compensated based on draft position and/or other criteria related to their expected future performance, which does not exist at the college level.
Theoretically, player value should not be uniform. It would follow from a simple labor model that players should be paid their marginal revenue product of labor. This would naturally vary by player and result in differences in compensation. In sports, this is usually estimated with player specific metrics, although its applicability varies by sports. In professional settings the value of the contract can be estimated related to the revenue or profit of a player based upon their performance. In the absence of such information in college sports, we concentrate here on ex ante ratings of players and their relationship to revenue
With these ideas in mind, this paper seeks to estimate the value of college football players using their ex ante star rating determined before a player commits to a specific school. This allows us to infer the values of both offensive and defensive players based upon their expected productivity as cardinally contained in their ratings as high school athletes. Furthermore, ex ante ratings are not biased by the presence or absence of player-specific statistics which could bias productivity estimates of players by position. We are also able to exploit conference- and school-specific effects to estimate valuations using within-conference and within-school variation in recruit quality, team performance, and revenues, allowing more precise estimates of value which account for a variety of institutional revenue features.
Our results show that there is significant heterogeneity in player valuations by recruit rating. Controlling for school heterogeneity (school fixed effects), we find that schools who recruit 5 or 4 star rated recruits can increase total revenue by over $500,000. Schools like USC, Ohio State and Alabama, who on average bring in several highly rated recruits per recruiting class, will bring in millions of dollars more in revenue per incoming class. Overall, we find a high degree of variability in profit by ex ante recruit rating, consistent with the concept that players of higher quality should be better compensated than players of lesser quality. Institutionally, the results show that revenue sharing among conferences does not lead to a weak relationship between player ratings and revenues.
We use recruit data from Rivals.com for ex ante recruit quality. This data records the rating of each specific recruit for each year over the sample period (2002-2012). The recruit ranking data is an ex-ante consensus evaluation as recorded by Rivals.com where five-star is the best possible rating. It is important to note that ratings are cardinal ratings—a five star recruit in any year is a five star recruit in every year. Players are not ordinal ranked by recruiting season.
Additional data on game outcomes and specific bowls was compiled from ESPN, USA Today College Football Encyclopedia, and ESPN College Football Encyclopedia. Bergman and Logan (2014) match the recruiting data to each team’s corresponding performance for every year.
We then compiled data from the Office of Postsecondary Education (OPE) Equity in Athletic Disclosure website. This source lists school reported total revenue, for football for each school from 2002-2013. Beginning with the formation of the College Football Playoff and the creation of conference television networks, revenue for conferences changes discontinuously and we therefore restrict attention to years in which the revenue was predicated on conference-specific agreements with television and bowl games. Total revenue consists of all intercollegiate athletic activities pertaining to that sport. This includes appearance guarantees and options, contributions from alumni, royalties, sponsorships, sport camps, tickets, student activity fees, and government support.
The recruit quality summary statistics are given in Table 1. The average number of five star and four star recruits are far less then the average number of lower rated recruits per class. The difference in average recruit quality varies between conferences.
The SEC on average brings in the highest amount of five stars per recruiting class and has the highest average recruit quality. During the time frame we studied, an SEC team won the national championship 8 out of the 11 years.
The financial summary statistics are given in Table 2. The average annual total revenue for an FBS football program is more than $20 million. The highest grossing conferences are the Big Ten and SEC with each conference team on average bringing over $35 million in revenue. While the average school sees a profit of over $8 million, those in the SEC and Big Ten have close to $20 million in football profit annually.
We approximate player values using an inferential approach described below. The procedure is an intuitive two-step approach which is standard in the literature on player valuation. First, we estimate the relationship between recruit quality and team performance—wins and bowl appearances. We estimate this relationship in three ways: (1) we use simple OLS regression to look across teams, years, and schools; (2) we estimate the relationship using fixed effects for conferences since schools play others within the same conference and, to a first approximation, compete most intensively with each other for the same recruits; (3) we estimate the relationship with school fixed effects to estimate the relationship controlling for between school heterogeneity in recruit quality. Controlling for fixed effects allows us to better control for variations within schools and estimate the marginal revenue effect of a school improving their recruit talent relative to their average.
In the second step, we estimate the effect of performance wins and bowl appearances on total revenue. From the results of the first regression we obtain estimates of the effect of recruit quality on performance. These are then used to infer values through their relationship with the financial variables in the second regression.
5.1 Effect of Recruit Quality on the Team Performance
We first examine the relationship between recruit quality and on the field performance. The analysis utilizes on the field performance such as wins, bowl appearances, BCS appearances, and premier bowl appearance. The results with respect to wins and conference standing (a key determinant of appearance in the bowl season) are listed in Table 4. The effect of higher rated recruits on the field performance is significantly greater than the effect measured for lower rated recruits. The results show that five star recruits increase wins by .437 when using an OLS regression and .306 for team fixed effect regression. As a comparison, a four star recruit increases wins by .159 when using OLS and .0623 with team fixed effects. In both instances, the effect of a five star recruit is more than twice as large as the effect of a four star recruit.
For postseason success, we are mindful of the fact that teams are compensated for appearances and do not receive additional payments for winning (although winning may lead to other revenue for the athletics department). We therefore analyze the relationship between the probability of postseason success and recruit quality in Table 5. There, we see that the school fixed effects have a larger impact than their probit equivalent (Columns 2, 5, 8, and 11). We also see that higher rated recruits have larger impact on Bowl Appearances and Premier bowl appearances when we control for conferences compared to the probit regressions. For example, a five star recruit increases the probability of appearing in a BCS bowl by more than 4% with school fixed effects, where the overall marginal effect is less than 2%. Importantly, five star recruits have no statistically significant effect on the likelihood of appearing in a bowl game overall. From these results, we can conclude that higher rated recruits have a significant impact on performance and the likelihood of appearances in the most lucrative postseason bowls.
5.2 Revenues and Team Performance
To analyze the effect of team performance on financial outcomes, we begin with the OLS and fixed effects regressions of total revenue on team performance. We regress total revenue on wins, bowl appearance, and BCS bowl appearance in Table 6. (In appendix results we also included a specification which included premier bowls- Capital One Bowl, Tangerine Bowl, Cotton Bowl, Gator Bowl or Outback Bowl. These bowls have lucrative payouts and traditionally select teams near the top of their respective conferences.) The OLS regressions show us that each win increases revenue by more than $800k. The result is slightly larger when conference fixed effects are included (Column 2). BCS bowl appearances are the most lucrative and increase revenues by more than $15 million across all schools, but by more than $8 million with conference fixed effects.
The difference between OLS and fixed effects are not uniform, however. Bowl appearances have a positive and significant relationship with total revenue as bowl appearances can increase total revenue for a team by over $5.5 million and over $1.1 million for conference fixed effects and $1.6 million for school fixed effects. At the same time, BCS appearances increase revenue by only $2.1 million with school fixed effects, and the result is not statistically significant.
5.3 Inferred Monetary Values
Taking the results with revenue, we can infer the value of recruits for revenue by ex ante rating. We do so in Table 8. We show the estimates for revenue by rating using all three specifications. In the OLS results, we see that five star recruits are worth more than $650,000 when wins, bowl appearances, BCS bowl appearances, and premier bowl appearances are factored into the valuation. The largest share of the total is due to the increased revenue with respect to wins for five star athletes. The results within conferences are similar, where the revenue increase is slightly less than $600,000. Even looking within schools, we see that five star recruits increase revenue by nearly $200,000, while four star recruits increase revenue by nearly $90,000. The heterogeneity by recruit rating is wide. For example, four star athletes increase revenue much less than five star athletes, and two star athletes are related to negative revenue.
The results support the notion that higher rated recruits bring higher amounts of revenue for colleges At the same time, however, the results show that the estimates for player value are quite sensitive to whether conference or school effects are included in the estimation. This is consistent with the notion that the institutional features of college football, where revenue is shared between conference members, plays a role. It is also consistent with the notion that factoring the traditional performance of schools alters the value of any individual player to a program.
Even though the results are smaller for school and conference fixed effects, the economic impact that higher rated recruits have on colleges is still quite significant. OLS regressions still yield higher total revenue, profit, operating expenses, and total expenditures. The conference fixed effects for total revenue, profit, total expenditure and operating expenditure suggest that not only do the schools reap economic benefits from bringing in higher rated recruits but every team reaps benefits when other teams in the conference bring in higher rated recruits. This makes sense due to the fact that most of the lucrative post season payouts have to be shared equally between teams in a conference. We show that not only do programs who recruit higher rated recruits have more on the field success but they are also more profitable. The importance to college football programs of bringing in higher rated recruits is key to the long term success of the football team, the athletic program and to the university. Our work suggests that schools and athletes need to examine the amounts college football athletes are being compensated.
Find the full paper from Bergman and Logan here.