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Does luck play a role in the determination of the rank positions in football leagues? A study of Europe’s ‘big five’

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  • Sumit Sarkar

    (XLRI - Xavier School of Management)

  • Sooraj Kamath

    (XLRI - Xavier School of Management)

Abstract

It is common wisdom that luck plays a role in sports, along with skill. However, there is no consensus among researchers on what constitutes luck. One strand of the literature studied randomness in sports, most of which did the analysis at the levels of pitch actions, or at the match level. There is no empirical study to assess the role of luck in the determination of rank positions in football (soccer) leagues. In this paper, we define X-factor as unforeseen and unaccounted factors and quantify it as the difference between actual and predicted values of performance or outcome variables. For league football, we have perceived the difference between actual and expected goal difference as the X-factor effect in performance, and the difference between actual and expected points as the X-factor effect in outcome. Further, we have ideated that a plausible role of luck cannot be ruled out if the X-factor effect on outcome is significant while that on performance is not. Conducting analyses of variance on observations from seven seasons (2014–15 to 2020–21) in the top tier leagues of England, Spain, Germany, Italy, and France, we detected the presence of a significant and systematic X-factor effect. We have studied the role of luck using Tukey’s HSD test. In general, luck does not play any significant role in determining the rank positions in league football.

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  • Sumit Sarkar & Sooraj Kamath, 2023. "Does luck play a role in the determination of the rank positions in football leagues? A study of Europe’s ‘big five’," Annals of Operations Research, Springer, vol. 325(1), pages 245-260, June.
  • Handle: RePEc:spr:annopr:v:325:y:2023:i:1:d:10.1007_s10479-021-04369-6
    DOI: 10.1007/s10479-021-04369-6
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    1. Albert James, 2006. "Pitching Statistics, Talent and Luck, and the Best Strikeout Seasons of All-Time," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 2(1), pages 1-32, January.
    2. Cai, Weihong & Yu, Ding & Wu, Ziyu & Du, Xin & Zhou, Teng, 2019. "A hybrid ensemble learning framework for basketball outcomes prediction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 528(C).
    3. Joaquin Gonzalez-Rodenas & Michalis Mitrotasios & Rafael Aranda & Vasilis Armatas, 2020. "Combined effects of tactical, technical and contextual factors on shooting effectiveness in European professional soccer," International Journal of Performance Analysis in Sport, Taylor & Francis Journals, vol. 20(2), pages 280-293, March.
    4. Connolly, Robert A. & Rendleman, Richard J., 2008. "Skill, Luck, and Streaky Play on the PGA Tour," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 74-88, March.
    5. 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.
    6. Sarah R. Bailey & Jason Loeppky & Tim B. Swartz, 2020. "The Prediction of Batting Averages in Major League Baseball," Stats, MDPI, vol. 3(2), pages 1-10, April.
    7. Steven D. Levitt & Thomas J. Miles, 2014. "The Role of Skill Versus Luck in Poker Evidence From the World Series of Poker," Journal of Sports Economics, , vol. 15(1), pages 31-44, February.
    8. G. K. Skinner & G. H. Freeman, 2009. "Soccer matches as experiments: how often does the 'best' team win?," Journal of Applied Statistics, Taylor & Francis Journals, vol. 36(10), pages 1087-1095.
    9. Romain Gauriot & Lionel Page, 2019. "Fooled by Performance Randomness: Overrewarding Luck," The Review of Economics and Statistics, MIT Press, vol. 101(4), pages 658-666, October.
    10. David John A. & Pasteur R. Drew & Ahmad M. Saif & Janning Michael C., 2011. "NFL Prediction using Committees of Artificial Neural Networks," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 7(2), pages 1-15, May.
    11. Craig Wright & Steve Atkins & Remco Polman & Bryan Jones & Lee Sargeson ., 2011. "Factors Associated with Goals and Goal Scoring Opportunities in Professional Soccer," International Journal of Performance Analysis in Sport, Taylor & Francis Journals, vol. 11(3), pages 438-449, December.
    12. Chih-Hai Yang & Hsuan-Yu Lin & Chiang-Ping Chen, 2014. "Measuring the efficiency of NBA teams: additive efficiency decomposition in two-stage DEA," Annals of Operations Research, Springer, vol. 217(1), pages 565-589, June.
    13. Marc Brechot & Raphael Flepp, 2020. "Dealing With Randomness in Match Outcomes: How to Rethink Performance Evaluation in European Club Football Using Expected Goals," Journal of Sports Economics, , vol. 21(4), pages 335-362, May.
    14. Loeffelholz Bernard & Bednar Earl & Bauer Kenneth W, 2009. "Predicting NBA Games Using Neural Networks," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 5(1), pages 1-17, January.
    15. Hvattum, Lars Magnus & Arntzen, Halvard, 2010. "Using ELO ratings for match result prediction in association football," International Journal of Forecasting, Elsevier, vol. 26(3), pages 460-470, July.
    16. Ioannis Asimakopoulos & John Goddard, 2004. "Forecasting football results and the efficiency of fixed-odds betting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 23(1), pages 51-66.
    17. Hongyou Liu & Will Hopkins & A. Miguel Gómez & S. Javier Molinuevo, 2013. "Inter-operator reliability of live football match statistics from OPTA Sportsdata," International Journal of Performance Analysis in Sport, Taylor & Francis Journals, vol. 13(3), pages 803-821, December.
    18. Ian G. McHale & Łukasz Szczepański, 2014. "A mixed effects model for identifying goal scoring ability of footballers," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 177(2), pages 397-417, February.
    19. Pelechrinis Konstantinos & Winston Wayne, 2021. "A Skellam regression model for quantifying positional value in soccer," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 17(3), pages 187-201, September.
    20. Ben-Naim Eli & Vazquez Federico & Redner Sidney, 2006. "Parity and Predictability of Competitions," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 2(4), pages 1-14, October.
    21. Goddard, John, 2005. "Regression models for forecasting goals and match results in association football," International Journal of Forecasting, Elsevier, vol. 21(2), pages 331-340.
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