IE interfaces (산업공학)

Korean Institute of Industrial Engineers (대한산업공학회)
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Forecasting the Results of Soccer Matches Using Poisson Model

포아송 확률 모형을 이용한 축구 경기 결과 예측

  • Seong, Hyun (Department of Industrial Engineering, Seoul National University) ;
  • Chang, Woo-Jin (Department of Industrial Engineering, Seoul National University)
  • 성현 (서울대학교 산업공학과) ;
  • 장우진 (서울대학교 산업공학과)
  • Received : 20070100
  • Accepted : 20070500
  • Published : 2007.06.30
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Abstract

As the sales of the Sports Toto, the Korean lottery on sports games, have increased significantly in recent five years, interest in predicting the various results of sports matches has also been raised. Dixon and Coles (1997) proposed a bivariate Poisson model to predict the results of English soccer league matches. In this paper, we pay attention to the physical condition of players that may affect soccer match results and revise Dixon and Coles' model to consider probable fatigue due to the players' short rest followed by their frequent matches. We observed the fatigue effect in the match results, and found positive betting returns available when using our prediction model. Furthermore, the validity of probability-based odds in European and Korean betting markets is analyzed.

Keywords

References

  1. Clarke, S. R. and Norman, J. M. (1995), Ground Advantage of Individual Clubs in English Soccer, The Statistician, 44(4), 509-521 https://doi.org/10.2307/2348899
  2. Dixon, M. J. and Coles, S. C. (1997) Modelling association football scores and inefficiencies in the football betting market, Applied Statistics, 46, 265-280
  3. Dixon, M. J. and Robinson, M. E. (1998), A Birth Process Model for Association Football Matches, The Statistician, 47(3), 523-538
  4. Dixon, M. J. and Pope, P. F. (2004), The value of statistical forecasts in the UK association football betting market, International Journal of Forecasting, 20, 697-711 https://doi.org/10.1016/j.ijforecast.2003.12.007
  5. Forrest, D. and Simmons, R. (2000), Forecasting sports: the behaviour and performance of football tipsters, International Journal of Forecasting, 16, 317-331 https://doi.org/10.1016/S0169-2070(00)00050-9
  6. Goddard, J. and Asimakopoulus, I. (2004), Forecasting Football Results and the Efficiency of Fixed-odds Betting, Journal of Forecasting, 23, 51-66 https://doi.org/10.1002/for.877
  7. Greenhough, J., Birch, P. C., Chapman, S. C., and Rowlands, G. (2002), Football goal distributions and extremal statistics, Physica A, 316, 615-624 https://doi.org/10.1016/S0378-4371(02)01030-0
  8. Hastie, T., Tibshirani, R., and Friedman, J. (2001), The Elements of Statistical Learning, Springer-Verlag New York, Inc
  9. Rue, H. and Salvesen, O. (2000), Prediction and retrospective analysis of soccer matches in a league, The Statistician, 49(3), 399-418