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Comparing league formats with respect to match importance in Belgian football

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  • Dries Goossens
  • Jeroen Beliën
  • Frits Spieksma

Abstract

Recently, most clubs in the highest Belgian football division have become convinced that the format of their league should be changed. Moreover, the TV station that broadcasts the league is pleading for a more attractive competition. However, the clubs have not been able to agree on a new league format, mainly because they have conflicting interests. In this paper, we compare the current league format, and three other formats that have been considered by the Royal Belgian Football Association. We simulate the course of each of these league formats, based on historical match results. We assume that the attractiveness of a format is determined by the importance of its games; our importance measure for a game is based on the number of teams for which this game can be decisive to reach a given goal. Furthermore, we provide an overview of how each league format aligns with the expectations and interests of each type of club. Copyright Springer Science+Business Media, LLC 2012

Suggested Citation

  • Dries Goossens & Jeroen Beliën & Frits Spieksma, 2012. "Comparing league formats with respect to match importance in Belgian football," Annals of Operations Research, Springer, vol. 194(1), pages 223-240, April.
  • Handle: RePEc:spr:annopr:v:194:y:2012:i:1:p:223-240:10.1007/s10479-010-0764-4
    DOI: 10.1007/s10479-010-0764-4
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    References listed on IDEAS

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    1. Ioannis Asimakopoulos & John Goddard, 2004. "Forecasting football results and the efficiency of fixed-odds betting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 23(1), pages 51-66.
    2. Guillermo Durán & Mario Guajardo & Jaime Miranda & Denis Sauré & Sebastián Souyris & Andres Weintraub & Rodrigo Wolf, 2007. "Scheduling the Chilean Soccer League by Integer Programming," Interfaces, INFORMS, vol. 37(6), pages 539-552, December.
    3. Dries Goossens & Frits Spieksma, 2009. "Scheduling the Belgian Soccer League," Interfaces, INFORMS, vol. 39(2), pages 109-118, April.
    4. Koning, Ruud H. & Koolhaas, Michael & Renes, Gusta & Ridder, Geert, 2003. "A simulation model for football championships," European Journal of Operational Research, Elsevier, vol. 148(2), pages 268-276, July.
    5. Dixon, Mark J. & Pope, Peter F., 2004. "The value of statistical forecasts in the UK association football betting market," International Journal of Forecasting, Elsevier, vol. 20(4), pages 697-711.
    6. G Kendall, 2008. "Scheduling English football fixtures over holiday periods," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 59(6), pages 743-755, June.
    7. Jennett, Nicholas I, 1984. "Attendances, Uncertainty of Outcome and Policy in Scottish League Football," Scottish Journal of Political Economy, Scottish Economic Society, vol. 31(2), pages 176-198, June.
    8. Forrest, David & Simmons, Robert, 2000. "Forecasting sport: the behaviour and performance of football tipsters," International Journal of Forecasting, Elsevier, vol. 16(3), pages 317-331.
    9. Audas, Rick & Dobson, Stephen & Goddard, John, 2002. "The impact of managerial change on team performance in professional sports," Journal of Economics and Business, Elsevier, vol. 54(6), pages 633-650.
    10. Goddard, John, 2005. "Regression models for forecasting goals and match results in association football," International Journal of Forecasting, Elsevier, vol. 21(2), pages 331-340.
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