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Survey of the major world sports rating systems

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  • Raymond Stefani

Abstract

Using a common framework, this paper presents a survey of the major world sports rating systems (WSRSs) in skiing (sponsored by the International Skiing Federation (FIS)), men's tennis (Association of Tennis Professionals (ATP)), women's tennis (Women's Tennis Association (WTA)), soccer (Federation of International Football Associations (FIFA)) and golf (Royal and Ancient Golf Club of St Andrews). These systems are not otherwise available in the literature. Each of the WSRSs has three phases: first, the observed results are weighted to provide points for each competition; second, these points are combined to provide a seasonal value; third, the seasonal values are combined to provide a rating. The final result or placement (and not the score or time) is the most important factor in determining points for a given competition. In skiing, men's tennis and women's tennis, the rating is calculated from results over one season, while three seasons are used in golf and six seasons are used in soccer. In cross-country skiing and men's tennis, the seasonal value is calculated from the sum of the best values from that season's competitions. In alpine skiing and women's tennis, the sum of all values from that season's competitions is used. In golf and soccer, an averaging process is used. Besides potentially encouraging more entries, a 'best' system and one using all values also generates simple integer ratings rather than decimal ratings as are obtained with an averaging system. The simplest system is that of FIS in skiing, where one table of points is used for all alpine and cross-country disciplines. In contrast, considering that soccer (as a sport) prides itself on the simplicity of the game, it is surprising that FIFA's system is so complex, It is also surprising in soccer that a 'friendly' (often a pick-up exhibition used for player development) counts two-thirds as much as does a World Cup final played before a worldwide TV audience. It is hoped that this survey will serve as a valuable resource for those studying sports rating systems.

Suggested Citation

  • Raymond Stefani, 1997. "Survey of the major world sports rating systems," Journal of Applied Statistics, Taylor & Francis Journals, vol. 24(6), pages 635-646.
    • Handle: RePEc:taf:japsta:v:24:y:1997:i:6:p:635-646
    DOI: 10.1080/02664769723387
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    Cited by:

    1. Stephan Nuesch & Egon Franck, 2009. "The Role of Patriotism in Explaining the TV Audience of National Team Games—Evidence From Four International Tournaments," Journal of Media Economics, Taylor & Francis Journals, vol. 22(1), pages 6-19.
    2. Fagan Francois & Haugh Martin & Cooper Hal, 2019. "The advantage of lefties in one-on-one sports," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 15(1), pages 1-25, March.
    3. 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.
    4. Halicioglu Ferda, 2009. "Research on the Prediction of the likely Winners of the Euro 2008 Football Tournament," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 5(3), pages 1-15, July.
    5. White, Mark H., 2021. "bwsTools: An R package for case 1 best-worst scaling," Journal of choice modelling, Elsevier, vol. 39(C).
    6. Seungkyu Shin & Sebastian E Ahnert & Juyong Park, 2014. "Ranking Competitors Using Degree-Neutralized Random Walks," PLOS ONE, Public Library of Science, vol. 9(12), pages 1-13, December.
    7. Patrick Barrie, 2003. "A new sports ratings system: The tiddlywinks world ratings," Journal of Applied Statistics, Taylor & Francis Journals, vol. 30(4), pages 361-372.
    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. Imperiale-Hagerman Stephen, 2011. "Socioeconomic Predictors of the 2010 FIFA World Cup," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 7(1), pages 1-13, January.
    10. Ferda HALICIOGLU, 2005. "Forecasting the Professional Team Sporting Events: Evidence from Euro 2000 and 2004 Football Tournaments," Industrial Organization 0508001, University Library of Munich, Germany.
    11. P Dawson & B Morley & D Paton & D Thomas, 2009. "To bat or not to bat: An examination of match outcomes in day-night limited overs cricket," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(12), pages 1786-1793, December.
    12. S R Clarke, 2011. "Rating non-elite tennis players using team doubles competition results," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(7), pages 1385-1390, July.
    13. Niven Winchester & Raymond T. Stefani, 2009. "An innovative approach to National Football League standings using optimal bonus points," Working Papers 0905, University of Otago, Department of Economics, revised Jun 2009.
    14. Bargagliotti, Anna E., 2009. "Aggregation and decision making using ranked data," Mathematical Social Sciences, Elsevier, vol. 58(3), pages 354-366, November.
    15. Kovalchik Stephanie Ann, 2016. "Is there a Pythagorean theorem for winning in tennis?," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 12(1), pages 43-49, March.

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