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Using Monte Carlo simulation to calculate match importance: the case of English Premier League

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  • Lahvicka, Jiri

Abstract

This paper presents a new method of calculating match importance (a common variable in sports attendance demand studies) using Monte Carlo simulation. Using betting odds and actual results of 12 seasons of English Premier League, it is shown that the presented method is based on realistic predictions of match results and season outcomes. The Monte Carlo method provides results closest to Jennett’s approach; however, it does not require ex-post information and can be used for any type of season outcome.

Suggested Citation

  • Lahvicka, Jiri, 2012. "Using Monte Carlo simulation to calculate match importance: the case of English Premier League," MPRA Paper 40998, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:40998
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    File URL: https://mpra.ub.uni-muenchen.de/40998/1/MPRA_paper_40998.pdf
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    References listed on IDEAS

    as
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    3. Men‐Andri Benz & Leif Brandes & Egon Franck, 2009. "Do Soccer Associations Really Spend On A Good Thing? Empirical Evidence On Heterogeneity In The Consumer Response To Match Uncertainty Of Outcome," Contemporary Economic Policy, Western Economic Association International, vol. 27(2), pages 216-235, April.
    4. 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.
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    6. Michael Cain & David Law & David Peel, 2000. "The Favourite‐Longshot Bias and Market Efficiency in UK Football betting," Scottish Journal of Political Economy, Scottish Economic Society, vol. 47(1), pages 25-36, February.
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    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    sports attendance; match importance; seasonal uncertainty; Monte Carlo;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • D12 - Microeconomics - - Household Behavior - - - Consumer Economics: Empirical Analysis
    • L83 - Industrial Organization - - Industry Studies: Services - - - Sports; Gambling; Restaurants; Recreation; Tourism

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