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Forecasting football results and the efficiency of fixed-odds betting

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  • Ioannis Asimakopoulos

    (Edekt-Ote S.A., Athens, Greece)

  • John Goddard

    (University of Wales Swansea, UK)

Abstract

An ordered probit regression model estimated using 10 years' data is used to forecast English league football match results. As well as past match results data, the significance of the match for end-of-season league outcomes, the involvement of the teams in cup competition and the geographical distance between the two teams' home towns all contribute to the forecasting model's performance. The model is used to test the weak-form efficiency of prices in the fixed-odds betting market. A strategy of selecting end-of-season bets with a favourable expected return according to the model appears capable of generating a positive return. Copyright © 2004 John Wiley & Sons, Ltd.

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File URL: http://hdl.handle.net/10.1002/for.877
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Bibliographic Info

Article provided by John Wiley & Sons, Ltd. in its journal Journal of Forecasting.

Volume (Year): 23 (2004)
Issue (Month): 1 ()
Pages: 51-66

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Handle: RePEc:jof:jforec:v:23:y:2004:i:1:p:51-66

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Web page: http://www3.interscience.wiley.com/cgi-bin/jhome/2966

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Cited by:
  1. Agnes Bäker & Mario Mechtel & Karin Vetter, 2012. "Beating thy Neighbor: Derby Effects in German Professional Soccer," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), Justus-Liebig University Giessen, Department of Statistics and Economics, vol. 232(3), pages 224-246, May.
  2. Palomino, Frederic & Renneboog, Luc & Zhang, Chendi, 2009. "Information salience, investor sentiment, and stock returns: The case of British soccer betting," Journal of Corporate Finance, Elsevier, vol. 15(3), pages 368-387, June.
  3. Nikolaos Vlastakis & George Dotsis & Raphael N. Markellos, 2009. "How efficient is the European football betting market? Evidence from arbitrage and trading strategies," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 28(5), pages 426-444.
  4. 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.
  5. Palomino, F.A. & Renneboog, L.D.R. & Zhang, C., 2005. "Stock Price Reactions to Short-Lived Public Information: The Case of Betting Odds," Discussion Paper 2005-62, Tilburg University, Center for Economic Research.
  6. Egon Franck & Erwin Verbeek & Stephan Nüesch, 2008. "Prediction Accuracy of Different Market Structures – Bookmakers versus a Betting Exchange," Working Papers 0025, University of Zurich, Center for Research in Sports Administration (CRSA), revised 2009.
  7. Nilsson, Håkan & Andersson, Patric, 2010. "Making the seemingly impossible appear possible: Effects of conjunction fallacies in evaluations of bets on football games," Journal of Economic Psychology, Elsevier, vol. 31(2), pages 172-180, April.
  8. Jakobsson, Robin & Karlsson, Niklas, 2007. "Testing Market Efficiency in a Fixed Odds Betting Market," Working Papers 2007:12, Örebro University, School of Business.
  9. Lahvicka, Jiri, 2013. "The Fibonacci Strategy Revisited: Can You Really Make Money by Betting on Soccer Draws?," MPRA Paper 47649, University Library of Munich, Germany.
  10. Herman O. Stekler & David Sendor & Richard Verlander, 2009. "Issues in Sports Forecasting," Working Papers 2009-002, The George Washington University, Department of Economics, Research Program on Forecasting.
  11. Maschke, Mario & Schmidt, Ulrich, 2010. "Das Wettmonopol in Deutschland: Status Quo und Reformansätze," Kiel Policy Brief 18, Kiel Institute for the World Economy (IfW).
  12. 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.
  13. Babatunde Buraimo & David Peel & Rob Simmons, 2013. "Systematic Positive Expected Returns in the UK Fixed Odds Betting Market: An Analysis of the Fink Tank Predictions," International Journal of Financial Studies, MDPI, Open Access Journal, vol. 1(4), pages 168-182, December.
  14. Forrest, David & Goddard, John & Simmons, Robert, 2005. "Odds-setters as forecasters: The case of English football," International Journal of Forecasting, Elsevier, vol. 21(3), pages 551-564.
  15. Nikolaus Beck & Mark Meyer, 2012. "Modeling team performance," Empirical Economics, Springer, vol. 43(1), pages 335-356, August.
  16. del Corral, Julio & Prieto-Rodríguez, Juan, 2010. "Are differences in ranks good predictors for Grand Slam tennis matches?," International Journal of Forecasting, Elsevier, vol. 26(3), pages 551-563, July.

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