By Noah Reimers
Editor’s note: This analysis was initially conducted as a final project for the Harvard Economics Sophomore Tutorial (Economics 970: Firm Strategy and Purpose in the Online Economy) and has been abbreviated from its original format for the purposes of the HSAC blog
For my final project for my Economics Tutorial, I conducted an analysis of College Football revenues and expenses was conducted to determine the effect of money spent in recruiting on the quality of recruits a school signs to play at their school. For the purposes of this analysis, “major college football” includes only the schools within the Football Bowl Subdivision (FBS). The median revenue of FBS programs account for more than 35% of the total median revenue generated by athletic departments. Within the FBS, the top 5 conferences (commonly referred to as the Power 5) hold a large portion of the power and revenue within the FBS. For the purposes of this study, Notre Dame will be included in the Power 5 designation.
In 2014-2015 there were twenty eight athletics programs that listed revenues of $100 million or more. Of those twenty eight, twenty seven of them belong to one of the Power 5 conferences. When we look specifically at the top earners of college football, the top 10 earners from 2000 earned less than $300 million, and by 2011 that number grew to $759 million. A large portion of this money comes from TV rights. In 2014 ESPN signed a $610 million/year contract with all of the FBS teams to broadcast the new 4-team playoff. The members of the Power 5 conferences take home 75% of the money from that contract, while the remaining 25% goes to the non-Power 5 conferences (commonly referred to as the Group of Six). While the top earners are growing at a rapid pace, the lower tiers of the FBS cannot say the same. Conference USA recently signed a new TV contract that will pay each school $300,000-$400,000 per year, about a third of their past contract.
Even with shrinking revenue numbers, institutions of higher learning may continue to subsidize their college football programs. In 2003 a study conducted found that public institutions receive about 6% more in state appropriations if they field an FBS football team. Schools with football teams will receive 3%-8% increases in state appropriations the year following a successful season. In a 2012 working paper from the National Bureau of Economics Research, Michael Anderson found correlations between the success of a college football team and reducing acceptance rates, increased donations, applications, academic reputation, in-state enrollment and incoming students’ SAT scores. For many people, a school’s college football team is their first reference point about an institution of higher learning. Major college football teams and their success have a profound effect on the school as a whole and its public perception.
The effects of a successful major college football team are wide ranging. To field a successful team, quality recruiting is key. There is a positive correlation between the quality of recruiting classes and wins. Bud Elliot of SBNation found that the majority of recruits on FBS national champions were either four or five star. There are only about 300 of those 4 or 5 star recruits every year. The race for those recruits is very important, and one that major programs are willing to shell out money for.
This study aimed to establish a link between the money a major college football program spends on recruiting, and the quality of recruits a team acquires. The teams who earn more revenue, can subsequently spend more in recruiting. If the revenue gap continues to widen, and the above relationship is established, non-Power 5 teams could be shut out completely from competing on a consistent level with upper-level revenue teams. The above discussion about state appropriations and other effects would mean that a shutout of non-Power 5 schools would have profound effects on the institution as a whole.
USA Today, The Des Moines Register and ESPN have compiled revenue and expense data for each of the FBS football programs in the country. The aggregation of the data they collected runs from 2008-2013. The Department of Education gathers revenue and expense data on all of the athletic departments (with the exclusion of some private institutions). To extend the recruiting expense numbers, shares of total expenses spent on recruiting were averaged for the past 3 years. Those ratios were then applied to the real value expenses from 2014 and 2015. That technique gave an estimation of what each team’s level of money spent on recruiting was during 2014 and 2015.
The data on quality of recruiting class was gathered from Rivals.com. This data was assigned on a “Year-1” basis. During the year 2015, a program was recruiting players who are in the Class of 2016 (high school class). To attempt to control for a school/teams’ qualities, a fixed effect was crafted for each observation. The first control was the quality of the team and their strength of schedule. To do so, collegefootballreference.com’s SRS and SOS ratings were used. An additional scale used for the team’s quality was their overall winning percentage and their conference winning percentage. The next control used was the relative prestige of the football program. To do so data from collegefootballreference.com was used to acquire AP poll rankings over the past 25 years. A simple scaling system was used with the AP Poll rankings as follows: A team ranked #1 was given 25 points, #2 was given 24 points and so forth. A scale of 5 was given for rankings in the past 5 years, a scale of 4 for rankings in the 5 years after that and so forth. Another thing controlled for was the membership of the school in one of the Power 5 conferences during the time of the recruitment. I used collegefootballreference.com to look at past conference membership data. This variable was implemented as a dummy variable. This did not provide any differentiation between conferences within the Power 5. Another control used in the model was the amount of blue-chip high school recruit talent located within each state. Between 2009 and 2016 the Top 200 or 300 (the most available from each year) recruits from ESPN were analyzed. The number of recruits in each state was then divided by how many Power 5 schools are in that state. The last control used was the academic ranking of each institution.
3 separate regressions were conducted to analyze the data mentioned above. The first was done solely with the teams who were a part of the Power 5 conferences at the time. There has been some movement in/out of the conferences so this regression included only those during each year.
For the data on Power 5 conference teams a step-wise regression was performed and the independent variables that remained statistically significant and remained in the model were: ln(recruiting money spent), conference winning percentage, overall winning percentage, strength of schedule (SOS ), top recruits per school, and the total AP polls points (program prestige).
A separate step-wise regression was done on the schools that were not a member of one of the Power 5 conferences during each year. That model included the same dependent and independent variables with one exception. The independent variable of “Top recruits per Power 5 schools” were not included in their model. The independent variables that remained statistically significant through the stepwise regression were: ln(recruiting money spent), total AP polls points (program prestige), strength of schedule (SOS) and the quality of the team (as measured by SRS).
The last step-wise regression conducted was done on all the observations in the dataset. This model included all the same variables used in the non-Power 5 conference regression. The independent variables that remained statistically significant through the stepwise regression were: total AP poll points (program prestige), strength of schedule (SOS), quality of team (SRS), conference winning percentage, and the ln(total expenses).
The limitations of this model begin with confounding variables. There are many different factors that may not have been controlled for in this model. The first was the inherent skill coaches at different schools have in recruiting. It is impossible to quantify how good a coach is at establishing a connection with a high school student, and making him feel like he belongs at their school. Some coaches are very gifted at this and can make a huge difference for their program. Another possible confounding variable is the depth chart at football programs. Many players decide where they want to play based on how quickly they can have extensive playing time. That variable would be very tough to control for in this model even with a large amount of time because of the lack of insider knowledge about different players’ true skills at college football programs. One confounding variable that could have impacted the model is the quality of each campus and the extracurricular activities around campus. It may be a small or negligible effect but some players may factor in the social scene at schools as a part of their decision.
For variables that were included, one limitation is certainly the measure of top players per Power 5 school in each state. While this will give some bearing to the amount of high school talent within a state it can be skewed a bit. Many states are very large and just because a player is located within that state does not mean that the school within it is the closest geographically. The other issue with this variable is that a majority of players who sign to play at Power 5 schools are not within the top 200 or 300. Each school signs approximately 30 players. In total, approximately 1600 players commit to Power 5 programs each year. The model cannot account for all those players.
The Stata results of the three regressions are as follows:
The first regression can be thought of as the regression for recruiting the top high school talent. The second regression may make sense to point to the regression for recruiting lower level talent and the third for mid-level talent. Where those lines are drawn is a tough decision. Power 5 and non-Power 5 teams battling against each other for a recruit normally occurs with a mid-level talent. When Power 5 teams are battling each other for high level talent the money they spend on recruiting makes a difference. When non-Power 5 teams are doing the same, money spent on recruiting can also make a difference. The important note for non-Power 5 teams is that the money spent on recruiting in the third model was not significant. That means non-Power 5 conference teams can compete for mid-level talent without spending the same amount of money in recruiting. The only damper is that the natural log of total expenses was significant in the third model. That means that total expenses could be an inhibitor for non-Power 5 teams competing for mid-level talent.
For Power 5 conference teams the prevalence of overall and conference winning percentage reinforces that recruiting and winning may create a continuous cycle. When teams win games, their recruits get better. When their recruits get better, they will win more games. This cycle is a great finding for teams that are already at the top of the college football world, and could explain some team’s staying power. This cycle certainly holds for Power 5 teams recruiting top talent. The significance of SOS in the regression on Power 5 teams could point to high profile games on TV. Often when two top Power 5 teams play each other the game will be nationally televised and draw a large audience from all across the country. National TV games bring programs notoriety and can help in recruiting. Another continuing cycle might develop for Power 5 conference teams because of the high number of conference games played each year. At least 2/3 of each teams schedule is played in conference. Many of those matchups are two high level teams competing against each other and some can be rivalry games. For the non-Power 5 schools this link (SOS) also exists but for a slightly different reason. Two non-Power 5 teams playing each other might not attract a large national audience, but a team in that subgroup playing a Power 5 team might. Matchups against strong Power 5 teams could bring that national audience and account for the significance of SOS. SRS’s significance with the regression for non-Power 5 schools may be the better programs separating themselves in the subgroup. Teams in the Power 5 who are located within a state with a lot of high school talent have a special advantage. They can tap into the states’ resources very well and keep many recruits close to home. Both regressions showed some significance of the past prestige (as measured by AP poll points). It may be more important for non-Power 5 teams as a way to differentiate themselves, just like SRS.
The purpose of this study was to determine the effect of money spent on recruiting on the quality of recruits a team signs. When teams are battling for both high and low level talent there is a significant difference that money spent on recruiting can make. There is no significant difference made by recruiting money spent in competition for mid-level talent, but overall expenses may make a difference. As the expense gap grows between teams in the FBS, non-Power 5 teams may need to try to make up for that difference in other ways. One example could be the uptick in social media recruiting. There is almost no limit to the amount a team can post on social media to try to get high school player’s attention. Another outside of the box way that non-Power 5 teams might be able to compete is through the new option of stipends for college football players. Scholarships now can cover cost of attendance (COA) for athletes. Non-Power 5 schools may be able to close the recruiting gaps through higher stipend payments. Players who have families that are struggling could benefit in a big way from stipends. Whatever creative ways non-Power 5 teams find to try to bridge the gap, that need is urgent, and may become more so.