Title: The predictive power of ranking systems in association football

Authors: Jan Lasek; Zoltán Szlávik; Sandjai Bhulai

Addresses: Faculty of Sciences, VU University Amsterdam, De Boelelaan 1081a, 1081 HV Amsterdam, The Netherlands; Faculty of Mathematics, Informatics and Mechanics, University of Warsaw, Banacha 2, 02-097 Warsaw, Poland ' Department of Computer Science, Computational Intelligence Group, VU University Amsterdam, De Boelelaan 1081a, 1081 HV Amsterdam, The Netherlands ' Department of Mathematics, Stochastic Operations Research, VU University Amsterdam, De Boelelaan 1081a, 1081 HV Amsterdam, The Netherlands

Abstract: We provide an overview and comparison of predictive capabilities of several methods for ranking association football teams. The main benchmark used is the official FIFA ranking for national teams. The ranking points of teams are turned into predictions that are next evaluated based on their accuracy. This enables us to determine which ranking method is more accurate. The best performing algorithm is a version of the famous Elo rating system that originates from chess player ratings, but several other methods (and method versions) provide better predictive performance than the official ranking method. Being able to predict match outcomes better than the official method might have implications for, e.g., a team's strategy to schedule friendly games.

Keywords: FIFA ranking; predictive capabilities; predictive power; team rankings; player ratings; team strength; football predictions; association football; soccer predictions; football teams; soccer teams; national teams; match outcomes.

DOI: 10.1504/IJAPR.2013.052339

International Journal of Applied Pattern Recognition, 2013 Vol.1 No.1, pp.27 - 46

Available online: 27 Feb 2013 *

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