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Seeding the UEFA Champions League Participants: Evaluation of the Reform

Author

Listed:
  • Dmitry Dagaev

    (National Research University Higher School of Economics)

  • Vladimir Yu. Rudyak

    (National Research University Higher School of Economics)

Abstract

We evaluate the sporting effects of the seeding system reform in the major football club tournament -- the Champions League -- organized by the Union of European Football Associations (UEFA). In the UEFA Champions League, before the 2015-16 season, the teams were seeded in the group stage with respect to their ratings. Starting from the 2015-16 season, national champions of the Top-7 countries are seeded in the first pot, whereas other teams are seeded by their rating as before. We propose a probabilistic model for predicting the score of a single match in UEFA tournaments as well as the whole UEFA season. This model uses clubs' ratings as inputs. Applying Monte-Carlo simulations, we show that the expected rating of the UEFA Champions League winner, as well as the sum of the finalists' ratings, slightly decreased after the reform. At the same time, the difference in the finalists' ratings, which is a measure of competitive balance, increased. The UEFA Europa League became stronger and less balanced. We check the robustness of the results by introducing local fluctuations in the clubs ratings. Also, we study which national associations took advantage of the reform. For seeding rules before and after the reform, we estimate the transition matrix (pij), where pij is the probability of i-th strongest national association moving to j-th position after the season. The effect of reform on a single national association measured by the change in probability to increase or decrease the association's UEFA rank is not more than 3%

Suggested Citation

  • Dmitry Dagaev & Vladimir Yu. Rudyak, 2016. "Seeding the UEFA Champions League Participants: Evaluation of the Reform," HSE Working papers WP BRP 129/EC/2016, National Research University Higher School of Economics.
    • Handle: RePEc:hig:wpaper:129/ec/2016
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    1. Dominik Schreyer & Sascha L. Schmidt & Benno Torgler, 2018. "Game Outcome Uncertainty in the English Premier League," Journal of Sports Economics, , vol. 19(5), pages 625-644, June.
    2. Scarf, Philip & Yusof, Muhammad Mat & Bilbao, Mark, 2009. "A numerical study of designs for sporting contests," European Journal of Operational Research, Elsevier, vol. 198(1), pages 190-198, October.
    3. Zopounidis, Constantin & Doumpos, Michael, 2002. "Multicriteria classification and sorting methods: A literature review," European Journal of Operational Research, Elsevier, vol. 138(2), pages 229-246, April.
    4. M. J. Maher, 1982. "Modelling association football scores," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 36(3), pages 109-118, September.
    5. Dries Goossens & Jeroen Beliën & Frits Spieksma, 2012. "Comparing league formats with respect to match importance in Belgian football," Annals of Operations Research, Springer, vol. 194(1), pages 223-240, April.
    6. Buraimo, Babatunde & Simmons, Rob, 2009. "A tale of two audiences: Spectators, television viewers and outcome uncertainty in Spanish football," Journal of Economics and Business, Elsevier, vol. 61(4), pages 326-338, July.
    7. R. A. Hart & J. Hutton & T. Sharot, 1975. "A Statistical Analysis of Association Football Attendances," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 24(3), pages 308-308, November.
    8. Jeff Horen & Raymond Riezman, 1985. "Comparing Draws for Single Elimination Tournaments," Operations Research, INFORMS, vol. 33(2), pages 249-262, April.
    9. Koning, Ruud H. & Koolhaas, Michael & Renes, Gusta & Ridder, Geert, 2003. "A simulation model for football championships," European Journal of Operational Research, Elsevier, vol. 148(2), pages 268-276, July.
    10. Jaume García & Plácido Rodríguez, 2002. "The Determinants of Football Match Attendance Revisited," Journal of Sports Economics, , vol. 3(1), pages 18-38, February.
    11. Ryvkin, Dmitry, 2011. "The optimal sorting of players in contests between groups," Games and Economic Behavior, Elsevier, vol. 73(2), pages 564-572.
    12. 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.
    13. Tim Pawlowski & Georgios Nalbantis & Dennis Coates, 2018. "Perceived Game Uncertainty, Suspense And The Demand For Sport," Economic Inquiry, Western Economic Association International, vol. 56(1), pages 173-192, January.
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    JEL classification:

    • Z20 - Other Special Topics - - Sports Economics - - - General
    • C44 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Operations Research; Statistical Decision Theory

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