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Playing on artificial turf may be an advantage for Norwegian soccer teams

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  • Hvattum Lars Magnus

    (Molde University College, P.O. Box 2110 N-6402 Molde, Norway)

Abstract

Soccer is as popular as ever, and the sport attracts significant attention from spectators, sponsors, media, and academics. One aspect of the sport that has received relatively little attention, is the effect of the playing surface on the sporting performance of a team. In particular, this paper is concerned with measuring the performance of teams that switch from playing their games on natural grass to playing their games on artificial turf. It is shown that teams, on average, achieve improved results after switching, and that this, at least in part, can be explained by an increased home field advantage.

Suggested Citation

  • Hvattum Lars Magnus, 2015. "Playing on artificial turf may be an advantage for Norwegian soccer teams," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 11(3), pages 183-192, September.
  • Handle: RePEc:bpj:jqsprt:v:11:y:2015:i:3:p:183-192:n:2
    DOI: 10.1515/jqas-2014-0046
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    References listed on IDEAS

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    1. V. Barnett & S. Hilditch, 1993. "The Effect of an Artificial Pitch Surface on Home Team Performance in Football (Soccer)," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 156(1), pages 39-50, January.
    2. Dobson,Stephen & Goddard,John, 2011. "The Economics of Football," Cambridge Books, Cambridge University Press, number 9780521517140, September.
    3. Lars Magnus Hvattum, 2013. "Analyzing Information Efficiency In The Betting Market For Association Football League Winners," Journal of Prediction Markets, University of Buckingham Press, vol. 7(2), pages 55-70.
    4. Constantinou Anthony Costa & Fenton Norman Elliott, 2013. "Determining the level of ability of football teams by dynamic ratings based on the relative discrepancies in scores between adversaries," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 9(1), pages 37-50, March.
    5. 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.
    6. Hvattum, Lars Magnus & Arntzen, Halvard, 2010. "Using ELO ratings for match result prediction in association football," International Journal of Forecasting, Elsevier, vol. 26(3), pages 460-470, July.
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    Cited by:

    1. van Ours, Jan C., 2017. "Artificial Pitches and Unfair Home Advantage in Professional Football," CEPR Discussion Papers 12341, C.E.P.R. Discussion Papers.
    2. Jan C. Ours, 2019. "A Note on Artificial Pitches and Home Advantage in Dutch Professional Football," De Economist, Springer, vol. 167(1), pages 89-103, March.

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