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Forecasting football results and the efficiency of fixed-odds betting

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Author Info

  • 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. Maschke, Mario & Schmidt, Ulrich, 2010. "Das Wettmonopol in Deutschland: Status Quo und Reformansätze," Kiel Policy Brief 18, Kiel Institute for the World Economy (IfW).
  3. Palomino, F.A. & Renneboog, L.D.R. & Zhang, C., 2008. "Information Salience, Investor Sentiment, and Stock Returns: The Case of British Soccer Betting," Discussion Paper 2008-99, Tilburg University, Center for Economic Research.
  4. Jakobsson, Robin & Karlsson, Niklas, 2007. "Testing Market Efficiency in a Fixed Odds Betting Market," Working Papers 2007:12, Örebro University, School of Business.
  5. Franck, Egon & Verbeek, Erwin & Nüesch, Stephan, 2010. "Prediction accuracy of different market structures -- bookmakers versus a betting exchange," International Journal of Forecasting, Elsevier, vol. 26(3), pages 448-459, July.
  6. 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 2005-016, Tilburg University, Tilburg Law and Economic Center.
  7. 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.
  8. 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.
  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. Nikolaus Beck & Mark Meyer, 2012. "Modeling team performance," Empirical Economics, Springer, vol. 43(1), pages 335-356, August.
  11. 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.

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