How Much Is a Win Worth? An Application to Intercollegiate Athletics

Abstract

Intercollegiate athletics in the United States have become a multibillion-dollar industry over the past several decades. In this study, we investigate the short-and long-term direct monetary effects of operating a winning athletics program for an academic institution of higher education. We construct a unique panel dataset from multiple sources and utilize the latest dynamic panel data estimation methods to account for heterogeneity while also addressing endogeneity concerns. We find that success in men's football and basketball has a significant impact on a school's respective football and basketball revenues; however, the effect is different based on the type of school. We find that regular season wins in football account for most of the increase in revenue for established schools whereas invitations to prestigious postseason bowl games play a big part for less-established schools. Furthermore, we find that student population and education quality dissipate the effect of athletic success on monetary payoffs. We find that success in basketball carries over more from the past than in football with additional contemporaneous marginal effects for established schools. We do find, however, that past athletic success carries over significantly to the present in both football and basketball, suggesting the significance of the long-term monetary effect of athletic success to many academic institutions in the United States.

Revenue per Quality of College Football Recruit

“…Brooks (2016), for example, examined the two main factors of revenue growth in college football: on the field performance and fan attendance. Chung (2015) examines the relationship between wins and the effects on short-run and long-run total revenue. He estimates that a single win in college football increases total revenue by 3%.…”

How Do Sales Efforts Pay Off? Dynamic Panel Data Analysis in the Nerlove–Arrow Framework

“…The dynamic panel data methods of Arellano and Bond (1991), Arellano and Bover (1995), and Blundell and Bond (1998) provide a practical approach that can tackle issues (i) and (ii) by first differencing and utilizing the lagged levels and lagged differences as instruments. Because of their practicality, dynamic panel data methods have been used extensively in the marketing and economics literature to examine phenomena of dynamic nature, including the N-A framework (Paton, 2002;Neumayer, 2004;Clark et al, 2009;Xiong and Bharadwaj, 2013;Terris-Prestholt and Windmeijer, 2016;Chung, 2017;Ye et al, 2017;Hirunyawipada and Xiong, 2018). However, these methods are only valid under the assumption of no serial correlation in the idiosyncratic errors, which violates (iii).…”

“…The key advantage of these methods is that they allow us to control for potential biases without relying on strictly exogenous instrumental variables, which in many empirical settings are impossible to obtain. Because of this practicality, dynamic panel data methods have been used in numerous contexts in economics and marketing, including advertising (Clark et al, 2009;Song et al, 2015;McAlister et al, 2016), customer-relationship management (Van Triest et al, 2009;Tuli et al, 2010;Rego et al, 2013), product innovation (Narasimhan et al, 2006;Fang et al, 2016), habit formation (Shah et al, 2014), entertainment marketing (Narayan and Kadiyali, 2016;Mathys et al, 2016;Chung, 2017), social media (Archak et al, 2011), marketing-finance interface (Germann et al, 2015;Feng et al, 2015), market entry (Mukherji et al, 2011), crowd funding (Burtch et al, 2013), political economics (Acemoglu et al, 2008), and growth economics (Durlauf et al, 2005).…”

The impact of special events and fan–player bonding on identified fan consumption – a study of professional soccer in the USA

“…Millions of dollars in revenue are generated or lost annually as a result of fan attendance, television contracts, and team merchandise sales (Chung, 2017). MLS average attendance in its inaugural year was 18,063.…”