The economics of eSportselements that affect performance

Resumen

Computer and video games are becoming more and more popular. The development of the Internet and gaming software involve many people in this industry. Ten years ago, video games competitions were organized mostly between amateurs, but now it has become professional (Tassi 2012). The growing popularity of competitive computer gaming (eSports) has caused an increase in the number of gamers and in the rewards given as prizes. Approximately 115 million hardcore enthusiasts watched eSports in 2015 and another 115 million were occasional viewers. Those numbers are expected to continue to increase, reaching a projected 427 million by 2019 (Rowell, 2016). Despite this fact, there are a few studies which analyze the economics of eSports, and our goal is to fill this void. In particular, we concentrate on the determinants of performance. Following the idea of Blalock (1979), we perform our analysis on different level: micro, meso and macro. These levels are not necessarily mutually exclusive, but such division is widely used in social science and in economics and management, in particular. We address the issue performance determinants in two ways. The first is the economics of eSports perspective. We analyze features of eSports that have been extensively studied with regard to traditional sports. The main question here is to reveal the determinants of success and compare them to the traditional sports. These results would be potentially interesting for gamers, team, team management, tournament organizers and video game publishers. The second approach to look, at the same time, on the unique features of eSports, which are provided by the “digital” nature of this activity. While most firms are not transparent with respect to their human capital and provide aggregate information at best, professional sport is a good platform for human capital analysis as very detailed data on individuals is available (Kahn 2000). Due to the broadcasting of competitions, broad media coverage and existence of a large betting market, a lot of statistical data related to professional sports is publicly available and accessible. This has recently led to a widespread use of sports data in economics and management research at both, the organizational and the individual level. At the organizational level, sports data have been used to explore issues such as knowledge transfer (Yamamura 2009; Barthel and Wellbrock 2010; Berlinschi, Schokkaert, and Swinnen 2013; Frick and Simmons 2014), clubs’ transfer networks (Lee, Hong, and Jung* 2015; X. F. Liu et al. 2016; Rossetti and Caproni 2016), networks of interactions between teammates (Clemente et al. 2014; Clemente, Couceiro, et al. 2015; Clemente, Martins, et al. 2015), and contributions of players’ efforts to team performance (Weimar and Wicker 2017). Moreover, team composition and its influence on team performance is a separate stream of research. Precise sports data allow to analyze team diversity (Wilson and Ying 2003; Franck and Nüesch 2011, 2010; Ingersoll, Malesky, and Saiegh 2014a), intra-team communication (Lausic et al. 2009), and pay inequalities (Depken 2000; Frick, Prinz, and Winkelmann 2003; Depken and Wilson 2004a; Frick 2007; Coates, Frick, and Jewell 2016) and their impact on team performance. At the individual level, research has addressed the roles of individual team members (athletes) as well as team manager (coaches). Available studies include analyses of e.g. expected and typical career length (Buraimo et al. 2015; Miklós-Thal and Ullrich 2015), salary drivers (Ashworth and Heyndels 2007) with a special emphasis on discrimination (Christiano 1986; Lavoie and Grenier 1992), and players’ behavioral biases (Bar-Eli et al. 2007). Moreover, precise and detailed data on team members’ quality and other features allows identification of the particular role and contribution of a coach. Apart from several contributions on the impact of managerial quality on team performance (Kahn 1993; Dawson, Dobson, and Gerrard 2000a, 2000c; Frick and Simmons 2008b; Frick, Barros, and Prinz 2010; Paola and Scoppa 2012), other topics such as managerial compensation drivers (Tomé, Naidenova, and Oskolkova 2014) and psychological perspectives as managerial self-confidence (Zavertiaeva, Naidenova, and Parshakov 2018) have been addressed too.