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Seeding the UEFA Champions League participants: evaluation of the reforms

Author

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  • Dagaev Dmitry

    (National Research University Higher School of Economics, Myasnitskaya Street 20, Moscow 101000, Russian Federation)

  • Rudyak Vladimir Yu.

    (Lomonosov Moscow State University, Moscow, Russian Federation)

Abstract

We evaluate the sporting effects of the seeding system reforms in the Champions League, the major football club tournament organized by the Union of European Football Associations (UEFA). Before the 2015–2016 season, the teams were seeded in the group stage by their ratings. Starting from the 2015–2016 season, national champions of the Top-7 associations are seeded in the first pot, whereas other teams are seeded by their rating as before. Taking effect from the season 2018–2019, the team’s rating no longer includes 20% of the rating of the association that the team represents. Using the prediction model, we simulate the whole UEFA season and obtain numerical estimates for competitiveness changes in the UEFA tournaments caused by these seeding reforms. We report only marginal changes in tournament metrics that characterize ability of the tournament to select the best teams and competitive balance. Probability of changes in the UEFA national association ranking does not exceed several percent for any association.

Suggested Citation

  • Dagaev Dmitry & Rudyak Vladimir Yu., 2019. "Seeding the UEFA Champions League participants: evaluation of the reforms," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 15(2), pages 129-140, June.
    • Handle: RePEc:bpj:jqsprt:v:15:y:2019:i:2:p:129-140:n:2
    DOI: 10.1515/jqas-2017-0130
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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. 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.
    5. 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.
    6. Jeff Horen & Raymond Riezman, 1985. "Comparing Draws for Single Elimination Tournaments," Operations Research, INFORMS, vol. 33(2), pages 249-262, April.
    7. 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.
    8. Jaume García & Plácido Rodríguez, 2002. "The Determinants of Football Match Attendance Revisited," Journal of Sports Economics, , vol. 3(1), pages 18-38, February.
    9. Ryvkin, Dmitry, 2011. "The optimal sorting of players in contests between groups," Games and Economic Behavior, Elsevier, vol. 73(2), pages 564-572.
    10. 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(1), pages 17-27, March.
    11. 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.
    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. M. J. Maher, 1982. "Modelling association football scores," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 36(3), pages 109-118, September.
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    Cited by:

    1. László Csató, 2024. "Club coefficients in the UEFA Champions League: Time for shift to an Elo-based formula," International Journal of Performance Analysis in Sport, Taylor & Francis Journals, vol. 24(2), pages 119-134, March.
    2. L'aszl'o Csat'o & D'ora Gr'eta Petr'oczy, 2020. "Bibliometric indices as a measure of performance and competitive balance in the knockout stage of the UEFA Champions League," Papers 2005.13416, arXiv.org, revised Sep 2023.
    3. Lapré Michael A. & Palazzolo Elizabeth M., 2022. "Quantifying the impact of imbalanced groups in FIFA Women’s World Cup tournaments 1991–2019," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 18(3), pages 187-199, September.
    4. Lapré Michael A. & Palazzolo Elizabeth M., 2023. "The evolution of seeding systems and the impact of imbalanced groups in FIFA Men’s World Cup tournaments 1954–2022," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 19(4), pages 317-332, December.
    5. 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.
    6. Chen Cohen & Ishay Rabi & Aner Sela, 2023. "Optimal seedings in interdependent contests," Annals of Operations Research, Springer, vol. 328(2), pages 1263-1285, September.
    7. L'aszl'o Csat'o, 2023. "Club coefficients in the UEFA Champions League: Time for shift to an Elo-based formula," Papers 2304.09078, arXiv.org, revised Oct 2023.
    8. Lunander Anders & Karlsson Niklas, 2023. "Choosing opponents in skiing sprint elimination tournaments," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 19(3), pages 205-221, September.
    9. Oliver Engist & Erik Merkus & Felix Schafmeister, 2021. "The Effect of Seeding on Tournament Outcomes: Evidence From a Regression-Discontinuity Design," Journal of Sports Economics, , vol. 22(1), pages 115-136, January.
    10. László Csató, 2020. "The UEFA Champions League seeding is not strategy-proof since the 2015/16 season," Annals of Operations Research, Springer, vol. 292(1), pages 161-169, September.
    11. László Csató, 2020. "Optimal Tournament Design: Lessons From the Men’s Handball Champions League," Journal of Sports Economics, , vol. 21(8), pages 848-868, December.
    12. Csató, László & Petróczy, Dóra Gréta, 2022. "Hogyan számszerűsíthető az ösztönzéskompatibilitás? Esettanulmány a sport világából [Quantifying incentive compatibility: a case study from the world of sports]," Közgazdasági Szemle (Economic Review - monthly of the Hungarian Academy of Sciences), Közgazdasági Szemle Alapítvány (Economic Review Foundation), vol. 0(7), pages 841-852.
    13. Csató, László, 2023. "How to avoid uncompetitive games? The importance of tie-breaking rules," European Journal of Operational Research, Elsevier, vol. 307(3), pages 1260-1269.
    14. Girish Ramchandani & Daniel Plumley & Adam Davis & Rob Wilson, 2023. "A Review of Competitive Balance in European Football Leagues before and after Financial Fair Play Regulations," Sustainability, MDPI, vol. 15(5), pages 1-15, February.
    15. Csató, László, 2022. "Quantifying incentive (in)compatibility: A case study from sports," European Journal of Operational Research, Elsevier, vol. 302(2), pages 717-726.
    16. L'aszl'o Csat'o & D'ora Gr'eta Petr'oczy, 2024. "The myth of declining competitive balance in the UEFA Champions League group stage," Papers 2406.19222, arXiv.org, revised Sep 2024.
    17. Csató, László & Bodnár, Gergely, 2023. "Mérhetnénk jobban a csapatok erejét a Bajnokok Ligájában? Fontos megjegyzés az Európai Labdarúgó-szövetség számára [How to better measure team strength in the Champions League. An important message," Közgazdasági Szemle (Economic Review - monthly of the Hungarian Academy of Sciences), Közgazdasági Szemle Alapítvány (Economic Review Foundation), vol. 0(7), pages 813-827.
    18. Csató, László & Petróczy, Dóra Gréta, 2020. "Miért igazságtalan a 2020-as labdarúgó-Európa-bajnokság kvalifikációja? [Why is qualification for the 2020 European association football championship unfair?]," Közgazdasági Szemle (Economic Review - monthly of the Hungarian Academy of Sciences), Közgazdasági Szemle Alapítvány (Economic Review Foundation), vol. 0(7), pages 734-747.

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    More about this item

    Keywords

    competitive balance; Monte-Carlo simulations; seeding; tournament; UEFA Champions League;
    All these keywords.

    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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