Testing the uncertainty outcome hypothesis using data from second tier soccer in Ireland

The Effect of League Design on Spectator Attendance: A Regression Discontinuity Design Approach

“…The estimated distance elasticity suggests a 10% increase in travel distance between stadia reduces attendance by 1.2%, on average and ceteris paribus. This inelastic estimate is in comport with the broader soccer demand literature (e.g., see Buraimo, 2014;Reilly, 2015;Jena & Reilly, 2016). Derby games tend to attract less spectator interest than other games in the last five rounds of Scotland's top tier and, according to the estimate reported in Table 4, attendance is 13% lower compared to a non-derby game, on average and ceteris paribus.…”

“…12. A variable generally used in soccer and other sports demand studies designed to capture match outcome uncertainty (or relative quality) is the ex ante home win probability computed from fixed odds gambling data posted by gambling companies prior to a match (e.g., see Peel and Thomas (1992), Buraimo (2014), Reilly (2015) and Jena and Reilly (2016)). However, since the odds for some teams are affected by the "split" (i.e., the "treatment"), these data are not employed here.…”

Football Spectator No-Show Behavior

“…This is because, on average, those Bundesliga and Bundesliga 2 clubs with larger stadiums are more likely to attract significant numbers of season ticket holders who, in turn, are more likely to miss individual home games at least now and then (e.g., Schreyer et al, 2016a; Schreyer, Schmidt, & Torgler, 2018). While GAME DAY already accounts for potential trends in spectator no-show behavior during the season, SEASON-fixed effects (e.g., Jena & Reilly, 2016; Reilly, 2015) are included to observe potential trends across the three seasons under observation, that is, between the seasons 2014-2015, 2015-2016, and 2016-2017. Finally, we add INTERVAL, which is an explanatory variable capturing the absolute number of days that have passed since the last home game.…”

“…However, many authors have followed since, including Czarnitzki and Stadtmann (2002), Pawlowski and Anders (2012), Rottmann andSeitz (2008), andRoy (2004). In addition, analyzing the demand for smaller football markets such as Austria and Switzerland (e.g., Pawlowski & Nalbantis, 2015), Ireland (e.g., Jena & Reilly, 2016), or Portugal (Martins & Cró, 2016) has recently become increasingly attractive for sports economists.…”

Game Outcome Uncertainty in the English Premier League

“…Because small differences between these three probabilities result in a large THEIL, an increase in this measure is associated with an increase in outcome uncertainty. Furthermore, we not only increase the robustness of our results but also allow for a better comparison with previous research efforts by including a number of additional specifications in which we proxy GOU by (1) the absolute difference in winning probability of home and away team (APD; e.g., Buraimo & Simmons, 2015;Cox, 2015;Di Domizio & Caruso, 2015), (2) ROY (e.g., Benz, Brandes, & Franck, 2009;Roy, 2004), (3) the draw probability (DRAW; e.g., Cox, 2015;Di Domizio, 2010), and (4) the WPH (including its squared term; e.g., Cox, 2015;Jena & Reilly, 2016;Peel & Thomas, 1992). In addition, several explanatory factors are considered which may shape transnational EPL demand (cf.…”