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Time varying ratings in association football: the all-time greatest team is.

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  • Rose D. Baker
  • Ian G. McHale

Abstract

type="main" xml:id="rssa12060-abs-0001"> We present a new methodology to estimate time varying team strengths of football teams. Our dynamic model allows for deterministic, rather than stochastic, evolution of team strengths. We use the model to identify the best team in England since the English Football Association was formed and match results were recorded in 1888. Our results suggest that Chelsea in 2007 were stronger than any other team has been but that Manchester United have experienced the period of most dominance.

Suggested Citation

  • Rose D. Baker & Ian G. McHale, 2015. "Time varying ratings in association football: the all-time greatest team is.," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 178(2), pages 481-492, February.
    • Handle: RePEc:bla:jorssa:v:178:y:2015:i:2:p:481-492
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    File URL: http://hdl.handle.net/10.1111/rssa.2015.178.issue-2
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    References listed on IDEAS

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    1. Mark E. Glickman, 1999. "Parameter Estimation in Large Dynamic Paired Comparison Experiments," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 48(3), pages 377-394.
    2. Ian G. McHale & Łukasz Szczepański, 2014. "A mixed effects model for identifying goal scoring ability of footballers," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 177(2), pages 397-417, February.
    3. M. J. Maher, 1982. "Modelling association football scores," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 36(3), pages 109-118, September.
    4. Gianluca Baio & Marta Blangiardo, 2010. "Bayesian hierarchical model for the prediction of football results," Journal of Applied Statistics, Taylor & Francis Journals, vol. 37(2), pages 253-264.
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    Citations

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    Cited by:

    1. László Csató, 2024. "Club coefficients in the UEFA Champions League: Time for shift to an Elo-based formula," International Journal of Performance Analysis in Sport, Taylor & Francis Journals, vol. 24(2), pages 119-134, March.
    2. Wheatcroft, Edward, 2020. "A profitable model for predicting the over/under market in football," LSE Research Online Documents on Economics 103712, London School of Economics and Political Science, LSE Library.
    3. Blaž Krese & Erik Štrumbelj, 2021. "A Bayesian approach to time-varying latent strengths in pairwise comparisons," PLOS ONE, Public Library of Science, vol. 16(5), pages 1-17, May.
    4. L'aszl'o Csat'o, 2023. "Club coefficients in the UEFA Champions League: Time for shift to an Elo-based formula," Papers 2304.09078, arXiv.org, revised Oct 2023.
    5. Boshnakov, Georgi & Kharrat, Tarak & McHale, Ian G., 2017. "A bivariate Weibull count model for forecasting association football scores," International Journal of Forecasting, Elsevier, vol. 33(2), pages 458-466.
    6. Fry, John & Serbera, Jean-Philippe & Wilson, Rob, 2021. "Managing performance expectations in association football," Journal of Business Research, Elsevier, vol. 135(C), pages 445-453.
    7. Holmes, Benjamin & McHale, Ian G., 2024. "Forecasting football match results using a player rating based model," International Journal of Forecasting, Elsevier, vol. 40(1), pages 302-312.
    8. Wheatcroft, Edward, 2020. "A profitable model for predicting the over/under market in football," International Journal of Forecasting, Elsevier, vol. 36(3), pages 916-932.
    9. Francisco Corona & Nelson Muriel & Jesús López-Pérez, 2023. "Who is the greatest team in Liga MX? A dynamic analysis/¿Cuál es el equipo más grande de la Liga MX? Un análisis dinámico," Estudios Económicos, El Colegio de México, Centro de Estudios Económicos, vol. 38(2), pages 225–260-2.
    10. Fry, John & Hastings, Tom & Serbera, Jean-Philippe, 2017. "An analytically solvable model for soccer: further implications of the classical Poisson model," MPRA Paper 82458, University Library of Munich, Germany.

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