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A Dynamic Bivariate Poisson Model for Analysing and Forecasting Match Results in the English Premier League

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Author Info

  • Siem Jan Koopman

    (VU University Amsterdam)

  • Rutger Lit

    (VU University Amsterdam)

Abstract

Attack and defense strengths of football teams vary over time due to changes in the teams of players or their managers. We develop a statistical model for the analysis and forecasting of football match results which are assumed to come from a bivariate Poisson distribution with intensity coefficients that change stochastically over time. This development presents a novelty in the statistical time series analysis of match results from football or other team sports. Our treatment is based on state space and importance sampling methods which are computationally efficient. The out-of-sample performance of our methodology is verified in a betting strategy that is applied to the match outcomes from the 2010/11 and 2011/12 seasons of the English Premier League. We show that our statistical modeling framework can produce a significant positive return over the bookmaker's odds.

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Bibliographic Info

Paper provided by Tinbergen Institute in its series Tinbergen Institute Discussion Papers with number 12-099/III.

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Date of creation: 27 Sep 2012
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Handle: RePEc:dgr:uvatin:20120099

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Web page: http://www.tinbergen.nl

Related research

Keywords: Betting; Importance sampling; Kalman filter smoother; Non-Gaussian multivariate time series models; Sport statistics;

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  1. Francis X. Diebold & Robert S. Mariano, 1994. "Comparing Predictive Accuracy," NBER Technical Working Papers 0169, National Bureau of Economic Research, Inc.
  2. Siem Jan Koopman & Andr� Lucas & Robert J. Daniels, 2005. "A Non-Gaussian Panel Time Series Model for Estimating and Decomposing Default Risk," DNB Working Papers, Netherlands Central Bank, Research Department 055, Netherlands Central Bank, Research Department.
  3. Raffaella Giacomini & Halbert White, 2006. "Tests of Conditional Predictive Ability," Econometrica, Econometric Society, Econometric Society, vol. 74(6), pages 1545-1578, November.
  4. Geweke, John, 1989. "Bayesian Inference in Econometric Models Using Monte Carlo Integration," Econometrica, Econometric Society, Econometric Society, vol. 57(6), pages 1317-39, November.
  5. Koopman, Siem Jan & Shephard, Neil & Creal, Drew, 2009. "Testing the assumptions behind importance sampling," Journal of Econometrics, Elsevier, Elsevier, vol. 149(1), pages 2-11, April.
  6. Goddard, John, 2005. "Regression models for forecasting goals and match results in association football," International Journal of Forecasting, Elsevier, Elsevier, vol. 21(2), pages 331-340.
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