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What Drives The Value Of Multimedia Rights

By Jonathan A. Jensen, PHD, University of North Carolina - Chapel Hill

The Center for Research in Intercollegiate Athletics (CRIA) at the University of North Carolina at Chapel Hill estimates that multimedia rightsholders allocate more than $500 million annually in guaranteed rights fees to institutions in the Football Bowl Subdivision (CRIA, 2018). 


In a recently published paper, UNC-Chapel Hill Sport Administration graduate students Jacob Spreyer and Javonte Lipsey and faculty members Nels Popp, Robert Malekoff, and I analyzed this market, with a goal of better understanding the value drivers for these rights and to develop a valid and reliable statistical model that could be utilized to generate additional insights, with the goal of assisting both institutions and rightsholders in data-driven decision-making. 


The paper is part of a special issue in the Journal of Global Sport Management that is oriented towards an international audience and entitled “Beyond the US: The Globalization of Intercollegiate Athletics.” Those with a university library subscription can access the full paper here. For those who do not, a limited number of free downloads are available from the publisher via this link.  


We were able to source a total of 91 current multimedia rights (MMR) agreements between public institutions and rightsholders, 67 of which clearly stated a guaranteed rights fee paid to the institution on an annual basis, and therefore were available for analysis. 


A total of $277.18 million was allocated to these 67 institutions during the 2017-18 academic year, an average of $4.13 million per university. The rightsholders represented in the dataset included IMG College, Learfield, OUTFRONT, Nelligan, CBS, and Fox Sports. The five largest annual rights fees in the dataset were allocated to UCLA, Ohio State, Kentucky, Connecticut, and Nebraska. Not unsurprisingly, Power Five institutions earned an average of $6.08 million on an annual basis, compared to just $1.59 million for Group of Five institutions. Rights fees across Power Five conferences were similar, ranging from an average of $6.77 million for members of the Big Ten to $5.41 million for Big 12 institutions. 


To develop our statistical model, we began by gathering a large number of potential predictor variables that all have a foundation in the literature, having been proven in prior research to be predictive of various revenue-related outcomes in intercollegiate athletics (such as broadcast rights fees, naming rights, apparel agreements, and television ratings). 


These variables were grouped in various categories, such as institution-related (e.g., membership in a Power Five conference, enrollment, and number of student-athletes), market-related (number of TV households, population, and median income in each institution’s home market), and groups of performance-related variables for both football and men’s basketball. Performance-related variables included demand-related data, such as attendance and stadium capacity, as well as variables reflecting the historical and recent performance of these programs. 


A systematic, hierarchical model building approach was utilized, which resulted in each group of variables being inserted into the model in stages. Once all four groups of variables were inserted into the model, more than 80% of the variance in multimedia rights fees was explained, suggesting the variables chosen were particularly effective at predicting the outcome variable. Once all of the institution and market-related variables were controlled for, only variables that represented the performance of each institution’s men’s basketball program (represented by each program’s total number of appearances in the NCAA men’s basketball tournament) and demand for each school’s football program (represented by the size of its football stadium), were statistically significant predictors of MMR fees. These results suggest that demand for individual institutions is driven by the success of these two programs, independent of other factors. 


Given the size of the dataset, a predictive model featuring a small number of the most influential variables was constructed. It controlled for membership in a Power Five conference, and included one institution-related variable (number of student-athletes, which served as a proxy for the size of each institution’s athletics program), as well as one variable each related to men’s basketball (tournament appearances) and football (stadium capacity). The final model explained nearly 75% of the variance in rights fees with just four variables.  


Now that the predictive model was constructed, data from a different year could be plugged into the model in order to generate predictive values on an institution and rightsholder basis. Interestingly, 9 of the top 10 institutions with the largest disparity between what the institutions were actually receiving and the predicted value generated by the model (i.e., underpaid institutions) were partners with Learfield. Across these nine institutions, the data suggest that Learfield saved nearly $20 million in negotiations. The schools that the model suggested are receiving more than they should based on these factors were partners with a variety of different rightsholders, including IMG, Fox (Auburn), JMI (Kentucky), and Learfield (Oklahoma State). 


While not privy to actual revenue earned by rightsholders via the agreements, these results suggest that Learfield was able to strategically partner with institutions they felt delivered a positive return on investment for the firm. Across the 30 Learfield agreements in the dataset, the model suggests Learfield realized a savings of 18.3% in rights fees across these institutions. In contrast, the model suggests that IMG College paid a price premium of 10.6% across its 29 agreements. 


The model was also utilized to generate predicted values for agreements not included in our database, including both public and private institutions. The model suggests that among public institutions not included in the database the rights owned by the University of Texas, Penn State, Alabama, Oklahoma, and Georgia are most valuable. Among private institutions, the model suggests that the rights for the University of Notre Dame, Southern California, Syracuse, Duke, and Stanford are most valuable.


Given that the model included a variable reflecting the number of student-athletes in each athletics program, the model also generated a coefficient that reflects each student-athlete’s value in MMR fees on an annual basis, while controlling for affiliation in a Power Five conference and performance in men’s basketball and football. The coefficient suggests that each student-athlete is valued at $3,295 in multimedia right fees on an annual basis. Together with data from a similar study predicting revenue from agreements with apparel partners (Jensen et al., 2016), which placed the value of each student-athlete at $1,747, these two models suggest that the value of each student-athlete in revenue from apparel and MMR agreements is $5,042 on a per institution basis, in each academic year. 


This correlation between the size of the athletic department (as measured by numbers of student-athletes) and MMR revenue suggests that universities that offer a more broad-based athletics program with a large number of sports will realize greater revenues from such agreements, as each additional student-athlete provided an opportunity to compete also generates additional exposure for the institution’s sponsors. Given that each university in the dataset offers the same number of scholarships for football and men’s and women’s basketball, this finding also suggests that there is incremental value for universities in rights fees from the sponsorship of additional sports (e.g., field hockey, gymnastics, lacrosse, softball, volleyball, wrestling, etc.), particularly given the emergence of conference networks that provide new opportunities for broadcast exposure not previously available for these sports. 


There are several acknowledged limitations to this research that should be noted. First, as a cross-sectional analysis, only one year of data was analyzed (specifically, data from the 2017-18 academic year). Thus, the amount of time since the renewal of an agreement or the amount of time before the next renewal could certainly impact the valuations for particular institutions. In just one example, UNC-Chapel Hill negotiated a new agreement with Learfield that was not released to the public until December of 2018, after this paper was accepted for publication. Therefore, the figures used in this study are reflective of their prior agreement that had begun in July of 2008 and was previously scheduled to continue through the 2020-21 academic year. In the future, it is recommended that a multilevel analysis that can assess change over time is employed, rather than a cross-sectional approach. 


In addition, there are certainly additional conditions and factors that may be predictive of rights fees that we are unable to measure in a quantitative model. Given that our database only includes public institutions, one can also make the case that these insights may not be generalizable to private institutions. Many agreements also include signing bonuses and thresholds after which the institution may realize additional revenue. This analysis only takes into account the guaranteed rights fees the institution will receive on an annual basis, given that it is unknown whether these incremental revenue thresholds were achieved. 


Despite these limitations, this model and the insights it generates are useful first steps towards an analytically-based approach to the study of the relationships between multimedia rightsholders and institutions of higher learning, and provides ample evidence of the efficacy of such approaches.