Rivalry Effects and Unbalanced Schedule Optimisation in the Australian Football League
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Like many professional sports leagues, the Australian Football League (AFL) operates an unbalanced schedule in which each team plays other teams an unequal number of times (once or twice) each season. This has led the AFL purposefully to schedule certain matches to be repeated each season with the remaining fixtures mostly randomly allocated. We explore the efficacy of this policy by estimating a fixed (rivalry) effects hedonic demand model for within-season AFL matches. Estimated rivalry effects are imputed into a binary integer program minimisation that provides an optimal profile of rematches against which we consider recent historic scheduling behaviour. As expected, rivalry effects are greatest for the large-market Melbourne ‘troika’ teams, which provides partial support for the AFL’s maintained policy. However, there exists scope for increasing aggregate attendance in the unbalanced part of the season by further attention to selection of rematches. We also observe some decline in interest of the second within-season meetings of popular troika teams and a rise in popularity of the intrastate derbies. Finally, we compare our results to alternative scheduling arrangements for the unbalanced part of the season.
KeywordsDemand for sport Scheduling optimisation Rivalry effects
JEL ClassificationC13 L83 Z28
Earlier versions of this paper were presented at: (1) Internal Workshop, School of Economics, La Trobe University (May, 2012); (2) BET Seminar, Department of Economics, Monash University (May, 2012); (3) Western Economic Association International, 87th Annual Conference, San Francisco (July, 2012); and (4) Seminar, School of Economics, Finance and Marketing, RMIT University (October, 2012). Research assistance was provided by Blake Angell and Carmen Mezzadri. We are grateful for the comments of the Editor, Lawrence J. White, and two anonymous referees that have helped to improve this paper. We would also like to thank Jack Daniels for supplying betting data. All errors are our own.
Conflict of interest
The authors declare that they have no conflict of interest.
- Bertsimas, D., & Weismantel, R. (2005). Optimization over integers (Vol. 13). Belmont: Dynamic Ideas.Google Scholar
- Booth, R. (2007). The economic development of the Australian Football League. In W. Andreff & S. Szymanski (Eds.), Handbook on the economics of sport (pp. 552–564). Northampton: Edward Elgar.Google Scholar
- Drukker, D. M. (2003). Testing for serial correlation in linear panel-data models. Stata Journal, 3(2), 168–177.Google Scholar
- Fort, R., & Quirk, J. (1995). Cross-subsidisation, incentives, and outcomes in professional team sports leagues. Journal of Economic Literature, 33(3), 1265–1299.Google Scholar
- MATLAB. (2012). Version 220.127.116.113 (R2012b). The MathWorks Inc.Google Scholar
- Noll, R. G. (1988). Professional basketball. Stanford University Studies in Industrial Economics, no. 144. Working paper.Google Scholar
- Scully, G. W. (1989). The business of major league baseball. Chicago: University of Chicago Press.Google Scholar
- Wooldridge, J. M. (2002). Econometric analysis of cross section and panel data. Cambridge, MA: MIT Press.Google Scholar