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Football Rating Systems for Top-Level Competition: A Critical Survey

Author

Listed:
  • Stefani Ray

    (California State University, Long Beach)

  • Pollard Richard

    (California Polytechnic State University, San Luis Obispo)

Abstract

Comprehensive rankings of football teams have become an important, and occasionally controversial, feature of many football codes. The rationale behind these systems needs to be understood. The historical evolution of the current eight codes is briefly traced from just two: association football and rugby football, played under various forms in the mid 19th century. Based on current rules, the eight codes fall into four groups: first, Australian Rules football and Gaelic football; second, American college football, American professional football and Canadian football; third, rugby union and rugby league; and fourth, soccer. Comprehensive rating systems exist for three codes. For American college football, the Bowl Championship Series or BCS system places top US college football teams into a national championship game and other important "bowl games". That system combines two normally incompatible components, an objective adjustive computer component and a subjective human-poll component. The composite has been controversial in four of the nine years of service, when the computer component differed from the human component resulting in major changes that favored the human component each time. For rugby union, the International Rugby Board or IRB system employs a predictor/corrector adjustment in which defeating a weak team provides less gain than defeating a strong team while losing to a weak team elicits a much larger negative adjustment than losing to a strong team, arguably a fair and efficient methods for rating competitors. For soccer, FIFA have improved the previous rating systems with a new and simpler system which takes into account strength of opponents and game importance; however, all losses are treated as equal regardless of the opponents, and home advantage is ignored. An Elo based system, employing many of features of the IRB system, appears to have advantages over the FIFA system.

Suggested Citation

  • Stefani Ray & Pollard Richard, 2007. "Football Rating Systems for Top-Level Competition: A Critical Survey," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 3(3), pages 1-22, July.
  • Handle: RePEc:bpj:jqsprt:v:3:y:2007:i:3:n:3
    DOI: 10.2202/1559-0410.1071
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    Citations

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    Cited by:

    1. José Daniel López-Barrientos & Damián Alejandro Zayat-Niño & Eric Xavier Hernández-Prado & Yolanda Estudillo-Bravo, 2022. "On the Élö–Runyan–Poisson–Pearson Method to Forecast Football Matches," Mathematics, MDPI, vol. 10(23), pages 1-29, December.
    2. Blackburn McKinley L., 2013. "Ranking the performance of tennis players: an application to women’s professional tennis," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 9(4), pages 367-378, December.
    3. Roberto Gásquez & Vicente Royuela, 2016. "The Determinants of International Football Success: A Panel Data Analysis of the Elo Rating," Social Science Quarterly, Southwestern Social Science Association, vol. 97(2), pages 125-141, June.
    4. Stewart Mark J., 2013. "A proposed general rating system for which the Colley Matrix Rating System is a special case," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 9(2), pages 179-185, June.
    5. Stefani Ray, 2011. "The Methodology of Officially Recognized International Sports Rating Systems," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 7(4), pages 1-22, October.
    6. L.F.M. Groot & J. Ferwerda, 2014. "Soccer jersey sponsors and the world cup," Working Papers 14-07, Utrecht School of Economics.
    7. J. James Reade & Sachiko Akie, 2013. "Using Forecasting to Detect Corruption in International Football," Working Papers 2013-005, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.
    8. Leitner, Christoph & Zeileis, Achim & Hornik, Kurt, 2010. "Forecasting sports tournaments by ratings of (prob)abilities: A comparison for the EUROÂ 2008," International Journal of Forecasting, Elsevier, vol. 26(3), pages 471-481, July.
    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.

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