How Much Does College Football Make

The outcome of role player compensation in acquirement generating college sports has taken centre stage in policy debates surrounding higher athletics. Some take argued that increased compensation for higher athletes volition align the interest of the student athlete with institutional goals and could forestall 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 United states Today (Estes 2019) article examined the increase in recruiting budgets and spending from higher football programs. In the concluding 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 bounty has largely neglected the important issue of player valuations—the benchmark that would guide histrion bounty schemes. Presumably, role player valuations should exist a guiding principle in whatever bounty scheme. Proponents of compensation have avoided the effect of how productivity differences between players should cistron into any bounty formula . The bounty scheme may need to be more sophisticated and, every bit in the labor market for professional sports, exist tied to player performance or expected performance.

Institutionally, the revenue construction in many able-bodied conferences is designed to equalize revenues between member schools, which is similar to revenue sharing in professional person sports. Revenue sharing is ever changing within conferences. Compensation for athletes may differ substantially between conferences every bit opposed to within conferences as a upshot. If this is true, it could be the instance that all players inside any conference take the aforementioned value since so much acquirement is redistributed. If player value is found to exist heterogeneous despite conference institutional features such equally revenue sharing, value could be tied to a variety of additional metrics equally they are in nigh professional sports.

Determining player values in professional sports is inherently difficult. Depending on the sport studied, detailed bear witness of player operation is normally lacking. For example, defensive players in football should be compensated based upon what does not occur, which tin can exist hard to measure accurately . Extending such assay to college sports is fifty-fifty more difficult as position specific valuations have no precedent and the bulk of professional sports apply salary caps, signing bonuses, and other labor wedlock and league negotiated particulars which depart from traditional labor theories of wages. At that place are no existing compensations schemes which could exist 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 operation, which does non exist at the college level.

Theoretically, player value should non 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 past player and consequence in differences in bounty. In sports, this is normally 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 higher sports, nosotros 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 higher football players using their ex ante star rating determined before a player commits to a specific schoolhouse. This allows the states to infer the values of both offensive and defensive players based upon their expected productivity as cardinally contained in their ratings every bit loftier schoolhouse athletes . Furthermore, ex ante ratings are not biased past the presence or absence of player-specific statistics which could bias productivity estimates of players past position . We are too able to exploit briefing- and school-specific effects to estimate valuations using inside-conference and within-schoolhouse variation in recruit quality, team performance, and revenues, allowing more precise estimates of value which account for a diverseness of institutional revenue features.

Our results show that there is significant heterogeneity in thespian valuations past recruit rating. Decision-making for school heterogeneity (school fixed effects), we find that schools who recruit 5 or 4 star rated recruits tin can increase full acquirement past over $500,000. Schools similar USC, Ohio Country and Alabama , who on boilerplate bring in several highly rated recruits per recruiting course , will bring in millions of dollars more in revenue per incoming course . Overall, we observe a high degree of variability in profit by ex ante recruit rating, consequent with the concept that players of college quality should exist better compensated than players of lesser quality. Institutionally, the results prove that acquirement sharing among conferences does not pb to a weak relationship between role player ratings and revenues.

Data

We use recruit information from Rivals.com for ex ante recruit quality. This data records the rating of each specific recruit for each twelvemonth over the sample flow (2002-2012). The recruit ranking data is an ex-ante consensus evaluation every bit 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 whatever year is a five star recruit in every year. Players are not ordinal ranked past recruiting season.

Additional data on game outcomes and specific bowls was compiled from ESPN, Us Today College Football Encyclopedia, and ESPN College Football Encyclopedia. Bergman and Logan (2014) lucifer the recruiting data to each squad'south corresponding functioning for every year.

Nosotros then compiled data from the Function of Postsecondary Education (OPE) Disinterestedness in Athletic Disclosure website. This source lists school reported total acquirement, for football game for each school from 2002-2013. Offset with the formation of the College Football Playoff and the cosmos of briefing telly networks, revenue for conferences changes discontinuously and we therefore restrict attention to years in which the revenue was predicated on conference-specific agreements with boob tube and bowl games. Full revenue consists of all intercollegiate able-bodied activities pertaining to that sport. This includes appearance guarantees and options, contributions from alumni, royalties, sponsorships, sport camps, tickets, pupil action fees, and government back up.

The recruit quality summary statistics are given in Table i. The average number of v star and four star recruits are far less then the average number of lower rated recruits per form. 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 eight out of the 11 years.

The financial summary statistics are given in Tabular array 2. The average almanac total acquirement for an FBS football game program is more than than $20 meg. The highest grossing conferences are the Big Ten and SEC with each briefing team on average bringing over $35 one thousand thousand in revenue. While the average school sees a profit of over $viii million, those in the SEC and Big X have close to $20 million in football profit annually.

Methodology

We guess player values using an inferential arroyo described below. The procedure is an intuitive two-step arroyo which is standard in the literature on actor valuation. Offset, we gauge the relationship betwixt recruit quality and squad performance—wins and bowl appearances. We estimate this relationship in three ways: (1) we apply simple OLS regression to wait beyond teams, years, and schools; (2) we estimate the relationship using fixed furnishings for conferences since schools play others within the same conference and, to a showtime approximation, compete near intensively with each other for the aforementioned recruits; (3) we approximate the relationship with school fixed effects to estimate the relationship controlling for between school heterogeneity in recruit quality. Controlling for stock-still effects allows united states of america to ameliorate command 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 issue of performance wins and bowl appearances on total revenue. From the results of the first regression we obtain estimates of the event of recruit quality on performance. These are then used to infer values through their relationship with the financial variables in the 2d regression.

Results

5.i Upshot of Recruit Quality on the Team Performance

We first examine the relationship betwixt recruit quality and on the field operation. The assay utilizes on the field operation such every bit wins, bowl appearances, BCS appearances, and premier bowl appearance. The results with respect to wins and conference standing (a key determinant of advent in the bowl flavor) are listed in Table 4. The issue 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 outcome regression. Every bit a comparison, a iv star recruit increases wins by .159 when using OLS and .0623 with team fixed effects. In both instances, the event of a 5 star recruit is more than twice every bit large as the effect of a 4 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 section). We therefore analyze the human relationship between the probability of postseason success and recruit quality in Table 5. There, we see that the school fixed furnishings have a larger impact than their probit equivalent (Columns 2, 5, 8, and 11). Nosotros also see that higher rated recruits have larger touch on on Bowl Appearances and Premier bowl appearances when nosotros control for conferences compared to the probit regressions. For example, a v star recruit increases the probability of appearing in a BCS bowl by more than four% with schoolhouse stock-still furnishings, where the overall marginal consequence is less than 2%. Importantly, five star recruits accept no statistically significant result on the likelihood of actualization in a basin game overall. From these results, nosotros 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 Squad Performance

To clarify the upshot of squad performance on fiscal outcomes, we begin with the OLS and fixed effects regressions of total revenue on team performance. We regress total acquirement on wins, bowl appearance, and BCS bowl appearance in Tabular array six. (In appendix results we as well included a specification which included premier bowls- Majuscule One Basin, Tangerine Bowl, Cotton fiber Bowl, Gator Bowl or Outback Bowl. These bowls take lucrative payouts and traditionally select teams near the acme of their respective conferences.) The OLS regressions show us that each win increases revenue by more $800k. The effect is slightly larger when conference stock-still effects are included (Cavalcade ii). BCS bowl appearances are the nearly lucrative and increment revenues by more than $15 million beyond all schools, just past more $8 million with briefing fixed furnishings.

The difference between OLS and stock-still effects are not uniform, however. Bowl appearances accept a positive and significant human relationship with total revenue equally bowl appearances tin can increase full revenue for a team by over $5.5 meg and over $ane.1 one thousand thousand for briefing fixed effects and $i.half-dozen million for school fixed effects. At the same time, BCS appearances increase revenue by merely $2.one million with school fixed effects, and the result is not statistically significant.

five.3 Inferred Budgetary Values

Taking the results with revenue, we can infer the value of recruits for revenue by ex dues rating. We do so in Table 8. We show the estimates for revenue by rating using all three specifications. In the OLS results, we come across that 5 star recruits are worth more than $650,000 when wins, bowl appearances, BCS bowl appearances, and premier basin appearances are factored into the valuation. The largest share of the total is due to the increased revenue with respect to wins for 5 star athletes. The results within conferences are similar, where the revenue increase is slightly less than $600,000. Even looking inside schools, we run across that v star recruits increase revenue by nigh $200,000, while four star recruits increase revenue by well-nigh $90,000. The heterogeneity by recruit rating is wide. For example, four star athletes increase revenue much less than v star athletes, and ii star athletes are related to negative acquirement.

The results support the notion that college rated recruits bring higher amounts of revenue for colleges At the same time, however, the results show that the estimates for actor value are quite sensitive to whether briefing or school effects are included in the interpretation. This is consistent with the notion that the institutional features of college football, where revenue is shared between conference members, plays a role. Information technology is also consistent with the notion that factoring the traditional functioning of schools alters the value of whatever individual actor to a program.

Conclusion

Even though the results are smaller for school and conference stock-still furnishings, the economic touch that higher rated recruits have on colleges is still quite meaning. OLS regressions still yield higher total revenue, profit, operating expenses, and full expenditures. The briefing fixed effects for full revenue, profit, total expenditure and operating expenditure suggest that not only exercise the schools reap economic benefits from bringing in higher rated recruits only every squad reaps benefits when other teams in the conference bring in college rated recruits. This makes sense due to the fact that most of the lucrative post season payouts accept to exist shared every bit between teams in a conference. Nosotros evidence that non simply do programs who recruit higher rated recruits have more on the field success just they are also more profitable. The importance to higher football game programs of bringing in higher rated recruits is primal to the long term success of the football team, the athletic program and to the academy. Our work suggests that schools and athletes need to examine the amounts higher football game athletes are being compensated.

Observe the full newspaper from Bergman and Logan here.