Dynamic modelling and prediction of English Football League matches for betting

By M. Crowder, M. Dixon, Anthony Ledford, Michael E. Robinson. 2002

123 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Marketing, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/3a51dd8f4e5f60fe83e91915caa896d38f56e2b4

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

1. Rutger Lit
2. Ian G. McHale
3. David Sendor
4. Richard Verlander
5. S. J. Koopman
6. Herman O. Stekler
7. Christophe Ley
8. Tom Van de Wiele

In Marketing

1. David Sendor
2. Richard Verlander
3. John Goddard
4. Ioannis G. Asimakopoulos
5. Herman O. Stekler

In Nature and Landscape Conservation

1. Ian G. McHale
2. S. J. Koopman
3. Rutger Lit
4. David Sendor
5. Richard Verlander
6. Herman O. Stekler
7. Georgi N. Boshnakov
8. Tarak Kharrat

Citations in the corpus

• A bivariate Weibull count model for forecasting association football scores (2017, background): “First, one can build a model in which the team strengths are assumed to vary stochastically, such as in Crowder et al. (2002), Owen (2011) or Koopman and Lit (2015).”
• Ranking soccer teams on basis of their current strength: a comparison of maximum likelihood approaches (2017, methodology): “Such dynamic models, based also on Poisson distributions, were proposed in Crowder et al. (2002), Koopman and Lit (2015) and Angelini and De Angelis (2017). In future work we shall investigate in detail the dynamic approach and also compare the resulting models to the Bivariate Poisson model with 1 ”
• Forecasting football match results in national league competitions using score-driven time series models (2019, background): “Crowder, Dixon, Ledford, and Robinson (2002) formulate the model of Dixon and Coles (1997) as a non-Gaussian state space model with time-varying strengths of attack and defence, then develop approximating meth-ods for parameter estimation and signal extraction, stating that an exact analysis is too…”
• Forecasting football results and the efficiency of fixed‐odds betting (2004, methodology): “Crowder et al. (2002) develop a procedure for updating the team strength parameters that is computationally less demanding.”
• Issues in Sports Forecasting (2010, result): “Crowder et al. (2002) derive an approximation to the Dixon-Coles model and show that the two models yield similar results.”