The Use of Forensic Statistics to Identify Corruption in Sport

  • Ian G. McHaleEmail author


The world we live in has changed immeasurably during the last quarter of a century. The role computers have played as a catalyst for this change in modern society cannot be overstated. Soon after the advent of the Internet came social media, which was followed by the dawn of the so-called era of big data, and all of this has happened in less than half a century since Bill Gates and his friends were toying with transistors and capacitors in creating what would eventually become Microsoft and Apple. The era of big data has meant that in recent times, the status of statisticians, analysts and “quants” has been elevated to such heights that interrogating data is now a core activity of mega-corporations like Google and Facebook. Financial markets have long used analysts to help gain an edge over competitors and better build portfolios which balance returns and risk, and so it is no surprise that another type of financial market, the global market on sports betting, is also employing statistics to gain an edge.


  1. Boshnakov, G., Kharrat, T., & McHale, I. G. (2017). A Bivariate Weibull Count Model for Forecasting Association Football Scores. International Journal of Forecasting, 33(2), 458–466.CrossRefGoogle Scholar
  2. Buraimo, B., Migali, G., & Simmons, R. (2016). An Analysis of Consumer Response to Corruption: Italy’s Calciopoli Scandal. Oxford Bulletin of Economics and Statistics, 78, 22–41. Scholar
  3. Dixon, M. J., & Coles, S. G. (1997). Modelling Association Football Scores and Inefficiencies in the Football Betting Market. Journal of the Royal Statistical Society. Series C (Applied Statistics), 46(2), 265–280.CrossRefGoogle Scholar
  4. Dixon, M., & Robinson, M. (1998). A Birth Process Model for Association Football Matches. Journal of the Royal Statistical Society: Series D (The Statistician), 47, 523–538.Google Scholar
  5. Einmahl, J. H. J., & Magnus, J. R. (2008). Records in Athletics Through Extreme-Value Theory. Journal of the American Statistical Association, 103(484), 1382–1391.CrossRefGoogle Scholar
  6. Fama, E. (1970). Efficient Capital Markets: A Review of Theory and Empirical Work. The Journal of Finance, 25(2), 383–417.CrossRefGoogle Scholar
  7. Forrest, D. (2012). The Threat to Football from Betting-Related Corruption. International Journal of Sport Finance, 7(2), 99–116.Google Scholar
  8. Forrest, D., & McHale, I. G. (2015). An Evaluation of Sportradar’s Fraud Detection System. Commissioned Report. Available at
  9. Goddard, J. (2005). Regression Models for Forecasting Goals and Match Results in Association Football. International Journal of Forecasting, 21(2), 331–340.CrossRefGoogle Scholar
  10. Goddard, J., & Asimakopoulos, I. (2004). Forecasting Football Results and the Efficiency of Fixed-Odds Betting. Journal of Forecasting, 23, 51–66.CrossRefGoogle Scholar
  11. Jewell, S., & Reade, J. (2014). On Fixing International Cricket Matches. Working Paper. Available at
  12. Maher, M. J. (1982). Modelling Association Football Scores. Statistica Neerlandica, 36(3), 109–118.CrossRefGoogle Scholar
  13. Stephenson, A. G., & Tawn, J. A. (2013). Determining the Best Track Performances of All Time Using a Conceptual Population Model for Athletics Records. Journal of Quanitiative Analysis in Sports, 9, 67–76.CrossRefGoogle Scholar
  14. Titman, A. C., Costain, D. A., Ridall, P. G., & Gregory, K. (2015). Joint Modelling of Goals and Bookings in Association Football. Journal of the Royal Statistical Society: Series A (Statistics in Society), 178, 659–683.CrossRefGoogle Scholar
  15. Williams, L. V. (2005). Information Efficiency in Financial and Betting Markets. Cambridge: Cambridge University Press.Google Scholar

Copyright information

© The Author(s) 2018

Authors and Affiliations

  1. 1.University of Liverpool Management SchoolLiverpoolUK

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