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On the importance of the probabilistic model in identifying the most decisive games in a tournament

Author

Listed:
  • Corona Francisco
  • Wiper Michael Peter

    (Universidad Carlos III de Madrid, Statistics, Getafe, Madrid, Spain)

  • Horrillo Juan de Dios Tena

    (University of Liverpool, Management School, Chatham Street, Liverpool L19 7ZH, UK, Tel.: + 44(0)1514752486)

Abstract

Identifying the decisive matches in international football tournaments is of great relevance for a variety of decision makers such as organizers, team coaches and/or media managers. This paper addresses this issue by analyzing the role of the statistical approach used to estimate the outcome of the game on the identification of decisive matches on international tournaments for national football teams. We extend the measure of decisiveness proposed by Geenens (2014) in order to allow us to predict or evaluate the decisive matches before, during and after a particular game on the tournament. Using information from the 2014 FIFA World Cup, our results suggest that Poisson and kernel regressions significantly outperform the forecasts of ordered probit models. Moreover, we find that although the identification of the most decisive matches is independent of the model considered, the identification of other key matches is model dependent. We also apply this methodology to identify the favorite teams and to predict the most decisive matches in 2015 Copa America before the start of the competition. Furthermore, we compare our forecast approach with respect to the original measure during the knockout stage.

Suggested Citation

  • Corona Francisco & Wiper Michael Peter & Horrillo Juan de Dios Tena, 2017. "On the importance of the probabilistic model in identifying the most decisive games in a tournament," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 13(1), pages 11-23, March.
  • Handle: RePEc:bpj:jqsprt:v:13:y:2017:i:1:p:11-23:n:1
    DOI: 10.1515/jqas-2016-0013
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    1. Corona, Francisco & Forrest, David & Tena, J.D. & Wiper, Michael, 2019. "Bayesian forecasting of UEFA Champions League under alternative seeding regimes," International Journal of Forecasting, Elsevier, vol. 35(2), pages 722-732.
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    3. Corona, Francisco & Forrest, David & Tena Horrillo, Juan de Dios & Wiper, Michael Peter, 2017. "Evaluating significant effects from alternative seeding systems : a Bayesian approach, with an application to the UEFA Champions League," DES - Working Papers. Statistics and Econometrics. WS 24521, Universidad Carlos III de Madrid. Departamento de Estadística.

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