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Comparison of Home Advantage in European Football Leagues

Author

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  • Patrice Marek

    (NTIS—New Technologies for Information Society, Faculty of Applied Sciences, University of West Bohemia, 30100 Plzeň, Czech Republic
    These authors contributed equally to this work.)

  • František Vávra

    (NTIS—New Technologies for Information Society, Faculty of Applied Sciences, University of West Bohemia, 30100 Plzeň, Czech Republic
    These authors contributed equally to this work.)

Abstract

Home advantage in sports is important for coaches, players, fans, and commentators and has a key role in sports prediction models. This paper builds on results of recent research that—instead of points gained—used goals scored and goals conceded to describe home advantage. This offers more detailed look at this phenomenon. Presented description understands a home advantage in leagues as a random variable that can be described by a trinomial distribution. The paper uses this description to offer new ways of home advantage comparison—based on the Jeffrey divergence and the test for homogeneity—in different leagues. Next, a heuristic procedure—based on distances between probability descriptions of home advantage in leagues—is developed for identification of leagues with similar home advantage. Publicly available data are used for demonstration of presented procedures in 19 European football leagues between the 2007/2008 and 2016/2017 seasons, and for individual teams of one league in one season. Overall, the highest home advantage rate was identified in the highest Greek football league, and the lowest was identified in the fourth level English football league.

Suggested Citation

  • Patrice Marek & František Vávra, 2020. "Comparison of Home Advantage in European Football Leagues," Risks, MDPI, vol. 8(3), pages 1-13, August.
    • Handle: RePEc:gam:jrisks:v:8:y:2020:i:3:p:87-:d:401986
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    References listed on IDEAS

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    1. Buhagiar, Ranier & Cortis, Dominic & Newall, Philip W.S., 2018. "Why do some soccer bettors lose more money than others?," Journal of Behavioral and Experimental Finance, Elsevier, vol. 18(C), pages 85-93.
    2. Marek Patrice & Ťoupal Tomáš & Šedivá Blanka, 2014. "Modeling and prediction of ice hockey match results," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 10(3), pages 1-9, September.
    3. M. J. Maher, 1982. "Modelling association football scores," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 36(3), pages 109-118, September.
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    1. Philip W. S. Newall & Dominic Cortis, 2021. "Are Sports Bettors Biased toward Longshots, Favorites, or Both? A Literature Review," Risks, MDPI, vol. 9(1), pages 1-9, January.

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