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Estimation of final standings in football competitions with premature ending: the case of COVID-19

Author

Listed:
  • Paolo Gorgi

    (Vrije Universiteit Amsterdam)

  • Siem Jan Koopman

    (Vrije Universiteit Amsterdam)

  • Rutger Lit

    (Vrije Universiteit Amsterdam)

Abstract

We study an alternative approach to determine the final league table in football competitions with a premature ending. For several countries, a premature ending of the 2019/2020 football season has occurred due to the COVID-19 pandemic. We propose a model-based method as a possible alternative to the use of the incomplete standings to determine the final table. This method measures the performance of the teams in the matches of the season that have been played and predicts the remaining non-played matches through a paired-comparison model. The main advantage of the method compared to the incomplete standings is that it takes account of the bias in the performance measure due to the schedule of the matches in a season. Therefore, the resulting ranking of the teams based on our proposed method can be regarded as more fair in this respect. A forecasting study based on historical data of seven of the main European competitions is used to validate the method. The empirical results suggest that the model-based approach produces more accurate predictions of the true final standings than those based on the incomplete standings.

Suggested Citation

  • Paolo Gorgi & Siem Jan Koopman & Rutger Lit, 2020. "Estimation of final standings in football competitions with premature ending: the case of COVID-19," Tinbergen Institute Discussion Papers 20-070/III, Tinbergen Institute.
    • Handle: RePEc:tin:wpaper:20200070
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    References listed on IDEAS

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    1. Siem Jan Koopman & Rutger Lit, 2015. "A dynamic bivariate Poisson model for analysing and forecasting match results in the English Premier League," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 178(1), pages 167-186, January.
    2. Babatunde Buraimo & Rob Simmons & Marek Maciaszczyk, 2012. "Favoritism And Referee Bias In European Soccer: Evidence From The Spanish League And The Uefa Champions League," Contemporary Economic Policy, Western Economic Association International, vol. 30(3), pages 329-343, July.
    3. Goddard, John, 2005. "Regression models for forecasting goals and match results in association football," International Journal of Forecasting, Elsevier, vol. 21(2), pages 331-340.
    4. Koopman, Siem Jan & Lit, Rutger, 2019. "Forecasting football match results in national league competitions using score-driven time series models," International Journal of Forecasting, Elsevier, vol. 35(2), pages 797-809.
    5. Hvattum, Lars Magnus & Arntzen, Halvard, 2010. "Using ELO ratings for match result prediction in association football," International Journal of Forecasting, Elsevier, vol. 26(3), pages 460-470, July.
    6. Pascual, Lorenzo & Romo, Juan & Ruiz, Esther, 2006. "Bootstrap prediction for returns and volatilities in GARCH models," Computational Statistics & Data Analysis, Elsevier, vol. 50(9), pages 2293-2312, May.
    7. Blasques, Francisco & Koopman, Siem Jan & Łasak, Katarzyna & Lucas, André, 2016. "In-sample confidence bands and out-of-sample forecast bands for time-varying parameters in observation-driven models," International Journal of Forecasting, Elsevier, vol. 32(3), pages 875-887.
    8. M. J. Maher, 1982. "Modelling association football scores," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 36(3), pages 109-118, September.
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    Cited by:

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    2. J. James Reade, 2023. "Large Sporting Events and Public Health and Safety," Economics Discussion Papers em-dp2023-04, Department of Economics, University of Reading.
    3. Roy Cerqueti & Raffaele Mattera & Valerio Ficcadenti, 2025. "Kendall correlations and radar charts to include goals for and goals against in soccer rankings," Computational Statistics, Springer, vol. 40(4), pages 1849-1872, April.

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    More about this item

    Keywords

    Bivariate Poisson; COVID-19; paired-comparison models; sport statistics;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models

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