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Using Forecasting to Detect Corruption in International Football

  • J. James Reade


    (University of Birmingham)

  • Sachiko Akie

    (Akita International University)

Corruption is hidden action aimed at influencing the outcome of an event away from its competitive outcome. It is likely common in all walks of life yet its hidden nature makes it diffcult to detect, while its distortionary influence on resource allocation ensures the importance of trying to detect it both practically and economically. This paper further develops methods to detect corrupt activity using data from 63 bookmakers covering over 9,000 international football matches since 2004, in particular assessing a claim made in early 2013 by Europol that the outcomes of almost 300 international matches since 2009 were fixed. We explore the divergence between two kinds of forecasts of match outcomes: those by bookmakers, and those constructed by econometric models. We argue that in the absence of corrupt activity to fix outcomes these two forecasts should be indistinguishable as they are based on the same information sets, and hence any divergence between the two may be indicative of corrupt activity to fix matches. In the absence of corroborating evidence we cannot declare any evidence procured in our manner as conclusive regarding the existence or otherwise of corruption, but nonetheless we argue that is it indicative. We conclude that there is mild evidence regarding potentially corrupt outcomes, and we also point towards yet more advanced strategies for its detection.

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Paper provided by The George Washington University, Department of Economics, Research Program on Forecasting in its series Working Papers with number 2013-005.

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Length: 26 pages
Date of creation: May 2013
Date of revision:
Handle: RePEc:gwc:wpaper:2013-005
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  1. Justin Wolfers & Eric Zitzewitz, 2006. "Interpreting Prediction Market Prices as Probabilities," NBER Working Papers 12200, National Bureau of Economic Research, Inc.
  2. Charles F. Manski, 2004. "Interpreting the Predictions of Prediction Markets," NBER Working Papers 10359, National Bureau of Economic Research, Inc.
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  4. Olmo, Jose & Pilbeam, Keith & Pouliot, William, 2011. "Detecting the presence of insider trading via structural break tests," Journal of Banking & Finance, Elsevier, vol. 35(11), pages 2820-2828, November.
  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. Stefani Ray & Pollard Richard, 2007. "Football Rating Systems for Top-Level Competition: A Critical Survey," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 3(3), pages 1-22, July.
  7. Gary S. Becker, 1974. "Crime and Punishment: An Economic Approach," NBER Chapters, in: Essays in the Economics of Crime and Punishment, pages 1-54 National Bureau of Economic Research, Inc.
  8. Preston, Ian & Szymanski, Stefan, 2000. "Racial Discrimination in English Football," Scottish Journal of Political Economy, Scottish Economic Society, vol. 47(4), pages 342-63, September.
  9. 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.
  10. Karen Croxson & J. James Reade, 2011. "Exchange vs Dealers: A High-Frequency Analysis of In-Play Betting Prices," Discussion Papers 11-19, Department of Economics, University of Birmingham.
  11. 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.
  12. Justin Wolfers, 2006. "Point Shaving: Corruption in NCAA Basketball," American Economic Review, American Economic Association, vol. 96(2), pages 279-283, May.
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