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Do Soccer Associations Really Spend on a Good Thing? Empirical Evidence on Heterogeneity in the Consumer Response to Match Uncertainty of Outcome

Author

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  • Men-Andri Benz
  • Leif Brandes
  • Egon Franck

    () (Institute for Strategy and Business Economics, University of Zurich
    Institute for Strategy and Business Economics, University of Zurich
    Institute for Strategy and Business Economics, University of Zurich)

Abstract

The purpose of this study is to analyze whether previous results describing the effect of uncertainty of outcome on match attendance in team sports have been driven by heterogeneity in fan demand. We apply censored quantile regression methods and place particular emphasis on the relationship between match uncertainty and attendance demand, as previous results are highly ambiguous. This is the more surprising as each season association and league officials continue to spend millions on enhancing this uncertainty. We also control for season ticket holders, who are unlikely to be influenced by match specificities. Based on data from German soccer, our results indicate that fan demand shows heterogeneity across quantiles and that increasing match uncertainty of outcome exclusively benefits teams who already face strong attendance demand.

Suggested Citation

  • Men-Andri Benz & Leif Brandes & Egon Franck, 2006. "Do Soccer Associations Really Spend on a Good Thing? Empirical Evidence on Heterogeneity in the Consumer Response to Match Uncertainty of Outcome," Working Papers 0009, University of Zurich, Center for Research in Sports Administration (CRSA), revised 2008.
    • Handle: RePEc:rsd:wpaper:0009
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    References listed on IDEAS

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    Cited by:

    1. Dominik Schreyer & Sascha L. Schmidt & Benno Torgler, 2013. "Any Given Sunday: How Season Ticket Holders' Time of Stadium Entrance Is Influenced by Outcome Uncertainty," CREMA Working Paper Series 2013-21, Center for Research in Economics, Management and the Arts (CREMA).
    2. Humphreys, Brad R. & Paul, Rodney J. & Weinbach, Andrew P., 2013. "Consumption benefits and gambling: Evidence from the NCAA basketball betting market," Journal of Economic Psychology, Elsevier, vol. 39(C), pages 376-386.
    3. Budzinski, Oliver & Pawlowski, Tim, 2014. "The behavioural economics of competitive balance: Implications for league policy and championship management," Ilmenau Economics Discussion Papers 89, Ilmenau University of Technology, Institute of Economics.
    4. Wen-Jhan Jane, 2014. "The Relationship Between Outcome Uncertainties and Match Attendance: New Evidence in the National Basketball Association," Review of Industrial Organization, Springer;The Industrial Organization Society, vol. 45(2), pages 177-200, September.
    5. Dennis Coates & Brad R. Humphreys & Li Zhou, 2014. "Reference-Dependent Preferences, Loss Aversion, And Live Game Attendance," Economic Inquiry, Western Economic Association International, vol. 52(3), pages 959-973, July.
    6. Petr A. Parshakov & Kseniya O. Baydina, 2017. "Brands or Uncertainty? An Empirical Test of the Uncertainty of Outcome Hypothesis in Russian Football," HSE Working papers WP BRP 163/EC/2017, National Research University Higher School of Economics.
    7. Budzinski, Oliver & Feddersen, Arne, 2015. "Grundlagen der Sportnachfrage: Theorie und Empirie der Einflussfaktoren auf die Zuschauernachfrage," Ilmenau Economics Discussion Papers 94, Ilmenau University of Technology, Institute of Economics.
    8. Lahvicka, Jiri, 2013. "Does Match Uncertainty Increase Attendance? A Non-Regression Approach," MPRA Paper 48571, University Library of Munich, Germany.
    9. Leif Brandes & Egon Franck & Philipp Theiler, 2013. "The group size and loyalty of football fans: a two-stage estimation procedure to compare customer potentials across teams," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 176(2), pages 347-369, February.
    10. Besters, Lucas, 2018. "Economics of professional football," Other publications TiSEM d9e6b9b7-a17b-4665-9cca-1, Tilburg University, School of Economics and Management.
    11. Schreyer, Dominik & Schmidt, Sascha L. & Torgler, Benno, 2016. "Against all odds? Exploring the role of game outcome uncertainty in season ticket holders’ stadium attendance demand," Journal of Economic Psychology, Elsevier, vol. 56(C), pages 192-217.
    12. Dorian Owen, 2014. "Measurement of competitive balance and uncertainty of outcome," Chapters,in: Handbook on the Economics of Professional Football, chapter 3, pages 41-59 Edward Elgar Publishing.
    13. Nicholas King & P. Dorian Owen & Rick Audas, 2012. "Playoff Uncertainty, Match Uncertainty and Attendance at Australian National Rugby League Matches," The Economic Record, The Economic Society of Australia, vol. 88(281), pages 262-277, June.
    14. Coates, Dennis & Humphreys, Brad & Zhou, Li, 2012. "Outcome Uncertainty, Reference-Dependent Preferences and Live Game Attendance," Working Papers 2012-7, University of Alberta, Department of Economics.
    15. Lahvicka, Jiri, 2012. "Using Monte Carlo simulation to calculate match importance: the case of English Premier League," MPRA Paper 40998, University Library of Munich, Germany.
    16. Pawlowski, Tim & Budzinski, Oliver, 2014. "Competitive balance and attention level effects: Theoretical considerations and preliminary evidence," Ilmenau Economics Discussion Papers 84, Ilmenau University of Technology, Institute of Economics.
    17. Lahvicka, Jiri, 2010. "Attendance of ice hockey matches in the Czech Extraliga," MPRA Paper 27653, University Library of Munich, Germany.

    More about this item

    Keywords

    heterogeneous fan demand; censored quantile regression;

    JEL classification:

    • D12 - Microeconomics - - Household Behavior - - - Consumer Economics: Empirical Analysis
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C24 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Truncated and Censored Models; Switching Regression Models; Threshold Regression Models
    • L83 - Industrial Organization - - Industry Studies: Services - - - Sports; Gambling; Restaurants; Recreation; Tourism

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