Testing the Uncertainty of Outcome Hypothesis in Football Leagues: An Application of Italian Football League

Jang, Hayley; Lee, Young Ho

doi:10.1080/24704067.2021.1951613

Cited by 3 publications

(2 citation statements)

References 23 publications

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“…To mitigate this concern, we construct five sets of balanced panel data for 2001/02-2003/04, 2004/05-2006/07, 2007/08-2009/10, 2010/11-2012/13, and 2013/14-2016/17 (each three or four seasons). 1 There have been a few literatures analysing stadium attendance demand for Serie A (Bond and Addesa, 2020;Jang and Lee, 2021) and to the best of our knowledge, this is the first study that explore attendance spillover effects in Serie A. The dependent variable is the logged attendance at individual matches.…”

Section: The Data and The Attendance Regression Specificationsmentioning

confidence: 99%

“…Thus, CLU i jt is absolute difference between team i and third-ranked team in 2012/2013 season. CLU i jt represents the status of team i in terms of CL qualification prior to the game between home team i and visiting team j in season t. As discussed by Jang and Lee (2021), CLU i jt may be local in the sense that it is relevant only to the top teams that may possibly advance to the CL. We construct a dummy variable: CL contention (CLDUM), which is 1 if a team is ranked higher than eighth (seventh from the 2011/2012 season), and otherwise 0.…”

Section: The Data and The Attendance Regression Specificationsmentioning

confidence: 99%

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Spatial Attendance Spillover in Football Leagues

Jang

Shin

2022

Int. J. Emp. Econ.

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We analyse the spatial attendance spillover applying spatial panel-data models with the Italian Football League data from 2001/2002 to 2016/2017. Our Quasi-Maximum Likelihood empirical results suggest that no significant spatial interaction was evident in earlier seasons (2001–2013), but modest spatial spillover was in play from 2013 to 2016. In addition, cross-quality spillover exists only locally in the same cities. We use numerical simulations to examine the potential impact of such spillover on attendance distribution and then competitive balance; spillover implies an interaction between the two exclusive markets that are the principal sources of competitive imbalance. Our numerical simulations suggest that spatial spillover may create attendance variations across member teams. The final outcome depends on the spillover sign, the network structure, and the market size distribution. Combining the empirical results with numerical simulations, we find that a recent, slightly positive spillover may modestly reduce attendance disparity.

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Section: The Data and The Attendance Regression Specificationsmentioning

confidence: 99%

Section: The Data and The Attendance Regression Specificationsmentioning

confidence: 99%