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Playoff Uncertainty, Match Uncertainty and Attendance at Australian National Rugby League Matches

Author

Listed:
  • NICHOLAS KING
  • P. DORIAN OWEN
  • RICK AUDAS

Abstract

This paper develops a new simulation-based measure of playoff uncertainty and investigates its contribution to modelling match attendance compared to other variants of playoff uncertainty in the existing literature. A model of match attendance that incorporates match uncertainty, playoff uncertainty, past home-team performance and other relevant control variables is fitted to Australian National Rugby League data for seasons 2004-2008 using fixed effects estimation. The results suggest that playoff uncertainty and home-team success are more important determinants of match attendance than match uncertainty. Alternative measures of playoff uncertainty based on points behind the leader, although more ad hoc, also appear able to capture the effects of playoff uncertainty.
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Suggested Citation

  • 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.
    • Handle: RePEc:bla:ecorec:v:88:y:2012:i:281:p:262-277
    DOI: j.1475-4932.2011.00778.x
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    File URL: http://hdl.handle.net/10.1111/j.1475-4932.2011.00778.x
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    Citations

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    Cited by:

    1. Jeroen Schokkaert & Johan Swinnen, 2016. "Uncertainty of Outcome Is Higher in the Champions League Than in the European Cup," Journal of Sports Economics, , vol. 17(2), pages 115-147, February.
    2. Abhinav Sacheti & David Paton & Ian Gregory-Smith, 2016. "An Economic Analysis of Attendance Demand for One Day International Cricket," The Economic Record, The Economic Society of Australia, vol. 92(296), pages 121-136, March.
    3. Vincent (Vincent Peter) Hogan & Patrick Massey & Shane Massey, 2014. "Analysing Match Attendance in the European Rugby Cup," Working Papers 201412, School of Economics, University College Dublin.
    4. Brad Humphreys & Li Zhou, 2014. "The Louis-Schmelling Paradox and the League Standing Effect Reconsidered," Working Papers 2014-05, University of Alberta, Department of Economics.
    5. Patrick Massey & Shane Massey & Vincent (Vincent Peter) Hogan, 2012. "Analysing Determinants of Match Attendance in the European Rugby Cup," Working Papers 201228, School of Economics, University College Dublin.
    6. Daniel Read & Aaron C.T. Smith & James Skinner, 2021. "A Comparative Analysis of Competitive Balance Between a Closed and an Open League in Rugby League," Journal of Sports Economics, , vol. 22(8), pages 871-892, December.
    7. Dorian Owen, 2014. "Measurement of competitive balance and uncertainty of outcome," Chapters, in: John Goddard & Peter Sloane (ed.), Handbook on the Economics of Professional Football, chapter 3, pages 41-59, Edward Elgar Publishing.
    8. P. Dorian Owen & Nicholas King, 2015. "Competitive Balance Measures In Sports Leagues: The Effects Of Variation In Season Length," Economic Inquiry, Western Economic Association International, vol. 53(1), pages 731-744, January.
    9. repec:qut:auncer:2013_04 is not listed on IDEAS
    10. Brad R. Humphreys & Li Zhou, 2015. "The Louis–Schmelling Paradox and the League Standing Effect Reconsidered," Journal of Sports Economics, , vol. 16(8), pages 835-852, December.
    11. Alexander John Bond & Francesco Addesa, 2020. "Competitive Intensity, Fans’ Expectations, and Match-Day Tickets Sold in the Italian Football Serie A, 2012-2015," Journal of Sports Economics, , vol. 21(1), pages 20-43, January.
    12. Erik Å trumbelj, 2016. "A Comment on the Bias of Probabilities Derived From Betting Odds and Their Use in Measuring Outcome Uncertainty," Journal of Sports Economics, , vol. 17(1), pages 12-26, January.
    13. Dorian Owen & Caitlin Owen, 2017. "Simulation Evidence on Herfindahl-Hirschman Indices as Measures of Competitive Balance," Working Papers 1715, University of Otago, Department of Economics, revised Dec 2017.

    More about this item

    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • L83 - Industrial Organization - - Industry Studies: Services - - - Sports; Gambling; Restaurants; Recreation; Tourism

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