IDEAS home Printed from https://ideas.repec.org/a/gam/jecnmx/v2y2014i1p1-19d32999.html
   My bibliography  Save this article

Referee Bias and Stoppage Time in Major League Soccer: A Partially Adaptive Approach

Author

Listed:
  • Katherine G. Yewell

    (Department of Economics, Vanderbilt University, Nashville, TN 37212, USA)

  • Steven B. Caudill

    (Department of Economics, Rhodes College, Memphis, TN 38112, USA
    Department of Economics (Regions Bank Professor Emeritus), Auburn University, Auburn, AL 36849, USA)

  • Franklin G. Mixon, Jr.

    (Center for Economic Education, Columbus State University, Columbus, GA 31907, USA)

Abstract

This study extends prior research on referee bias and close bias in professional soccer by examining whether Major League Soccer (MLS) referees’ discretion over stoppage time (i.e., extra play beyond regulation) is influenced by end-of-regulation match scores and/or home field advantage. To do so, we employ a grouped-data regression model and a partially adaptive model. Both account for the imprecise measurement in reported stoppage time. For the 2011 season we find no home field advantage. In fact, stoppage time is the same with a one or two goal deficit at the end of regulation, regardless of which team is ahead. However, the 2011 results do point to an increase in stoppage time of 12 to 20 seconds for nationally televised matches. For the 2012 season, the nationally televised effect disappears due to an increase in stoppage time for those matches not nationally televised. However, a home field advantage is present. Facing a one-goal deficit at the end of regulation, the home team receives about 33 seconds more stoppage time than a visiting team facing the same deficit.

Suggested Citation

  • Katherine G. Yewell & Steven B. Caudill & Franklin G. Mixon, Jr., 2014. "Referee Bias and Stoppage Time in Major League Soccer: A Partially Adaptive Approach," Econometrics, MDPI, vol. 2(1), pages 1-19, February.
    • Handle: RePEc:gam:jecnmx:v:2:y:2014:i:1:p:1-19:d:32999
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2225-1146/2/1/1/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2225-1146/2/1/1/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. McDonald, James B. & Xu, Yexiao J., 1996. "A comparison of semi-parametric and partially adaptive estimators of the censored regression model with possibly skewed and leptokurtic error distributions," Economics Letters, Elsevier, vol. 51(2), pages 153-159, May.
    2. Caudill, Steven B., 1992. "More on grouping coarseness in linear normal regression models," Journal of Econometrics, Elsevier, vol. 52(3), pages 407-417, June.
    3. Bartolucci, F. & Scaccia, L., 2005. "The use of mixtures for dealing with non-normal regression errors," Computational Statistics & Data Analysis, Elsevier, vol. 48(4), pages 821-834, April.
    4. Vincenzo Scoppa, 2008. "Are subjective evaluations biased by social factors or connections? An econometric analysis of soccer referee decisions," Empirical Economics, Springer, vol. 35(1), pages 123-140, August.
    5. McDonald, James B., 1996. "An application and comparison of some flexible parametric and semi-parametric qualitative response models," Economics Letters, Elsevier, vol. 53(2), pages 145-152, November.
    6. Luis Garicano & Ignacio Palacios-Huerta & Canice Prendergast, 2005. "Favoritism Under Social Pressure," The Review of Economics and Statistics, MIT Press, vol. 87(2), pages 208-216, May.
    7. Ximing Wu & Thanasis Stengos, 2005. "Partially adaptive estimation via the maximum entropy densities," Econometrics Journal, Royal Economic Society, vol. 8(3), pages 352-366, December.
    8. McDonald, James B. & Newey, Whitney K., 1988. "Partially Adaptive Estimation of Regression Models via the Generalized T Distribution," Econometric Theory, Cambridge University Press, vol. 4(3), pages 428-457, December.
    9. Beard, T Randolph & Caudill, Steven B & Gropper, Daniel M, 1991. "Finite Mixture Estimation of Multiproduct Cost Functions," The Review of Economics and Statistics, MIT Press, vol. 73(4), pages 654-664, November.
    10. Mark B. Stewart, 1983. "On Least Squares Estimation when the Dependent Variable is Grouped," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 50(4), pages 737-753.
    11. Butler, Richard J, et al, 1990. "Robust and Partially Adaptive Estimation of Regression Models," The Review of Economics and Statistics, MIT Press, vol. 72(2), pages 321-327, May.
    12. Steven Caudill & James Long, 2010. "Do former athletes make better managers? Evidence from a partially adaptive grouped-data regression model," Empirical Economics, Springer, vol. 39(1), pages 275-290, August.
    13. Neil Rickman & Robert Witt, 2008. "Favouritism and Financial Incentives: A Natural Experiment," Economica, London School of Economics and Political Science, vol. 75(298), pages 296-309, May.
    14. Dawson, Peter & Dobson, Stephen, 2010. "The influence of social pressure and nationality on individual decisions: Evidence from the behaviour of referees," Journal of Economic Psychology, Elsevier, vol. 31(2), pages 181-191, April.
    15. Pettersson-Lidbom, Per & Priks, Mikael, 2010. "Behavior under social pressure: Empty Italian stadiums and referee bias," Economics Letters, Elsevier, vol. 108(2), pages 212-214, August.
    16. Long, James E & Caudill, Steven B, 1991. "The Impact of Participation in Intercollegiate Athletics on Income and Graduation," The Review of Economics and Statistics, MIT Press, vol. 73(3), pages 525-531, August.
    17. Steven B. Caudill & Daniel M. Gropper & Valentina Hartarska, 2009. "Which Microfinance Institutions Are Becoming More Cost Effective with Time? Evidence from a Mixture Model," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 41(4), pages 651-672, June.
    18. Babatunde Buraimo & David Forrest & Robert Simmons, 2010. "The 12th man?: refereeing bias in English and German soccer," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 173(2), pages 431-449, April.
    19. Babatunde Buraimo & Rob Simmons & Marek Maciaszczyk, 2012. "Favoritism And Referee Bias In European Soccer: Evidence From The Spanish League And The Uefa Champions League," Contemporary Economic Policy, Western Economic Association International, vol. 30(3), pages 329-343, July.
    20. Steven Caudill, 2012. "A partially adaptive estimator for the censored regression model based on a mixture of normal distributions," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 21(2), pages 121-137, June.
    21. Thomas J. Dohmen, 2008. "The Influence Of Social Forces: Evidence From The Behavior Of Football Referees," Economic Inquiry, Western Economic Association International, vol. 46(3), pages 411-424, July.
    22. Sutter, Matthias & Kocher, Martin G., 2004. "Favoritism of agents - The case of referees' home bias," Journal of Economic Psychology, Elsevier, vol. 25(4), pages 461-469, August.
    23. Phillips, Robert F., 1991. "A constrained maximum-likelihood approach to estimating switching regressions," Journal of Econometrics, Elsevier, vol. 48(1-2), pages 241-262.
    24. Joseph Price & Marc Remer & Daniel F. Stone, 2012. "Subperfect Game: Profitable Biases of NBA Referees," Journal of Economics & Management Strategy, Wiley Blackwell, vol. 21(1), pages 271-300, March.
    25. Phillips, Robert F., 1994. "Partially adaptive estimation via a normal mixture," Journal of Econometrics, Elsevier, vol. 64(1-2), pages 123-144.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Paul Bose & Eberhard Feess & Helge Mueller, 2022. "Favoritism towards High-Status Clubs: Evidence from German Soccer," The Journal of Law, Economics, and Organization, Oxford University Press, vol. 38(2), pages 422-478.
    2. Andrea Albanese & Stijn Baert & Olivier Verstraeten, 2020. "Twelve eyes see more than eight. Referee bias and the introduction of additional assistant referees in soccer," PLOS ONE, Public Library of Science, vol. 15(2), pages 1-15, February.
    3. Thomas Dohmen & Jan Sauermann, 2016. "Referee Bias," Journal of Economic Surveys, Wiley Blackwell, vol. 30(4), pages 679-695, September.
    4. Peter Dawson, 2014. "Refereeing and infringement of the rules," Chapters, in: John Goddard & Peter Sloane (ed.), Handbook on the Economics of Professional Football, chapter 24, pages 401-418, Edward Elgar Publishing.
    5. Steven Caudill & James Long, 2010. "Do former athletes make better managers? Evidence from a partially adaptive grouped-data regression model," Empirical Economics, Springer, vol. 39(1), pages 275-290, August.
    6. Andrés Picazo-Tadeo & Francisco Gónzalez-Gómez & Jorge Guardiola Wanden-Berghe, 2011. "Referee home bias due to social pressure. Evidence from Spanish football," Working Papers 1119, Department of Applied Economics II, Universidad de Valencia.
    7. Steven Caudill, 2012. "A partially adaptive estimator for the censored regression model based on a mixture of normal distributions," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 21(2), pages 121-137, June.
    8. Andrés J. Picazo-Tadeo & Francisco González-Gómez & Jorge Guardiola, 2011. "The importance of time in referee home bias due to social pressure. Evidence from Spanish football," FEG Working Paper Series 03/11, Faculty of Economics and Business (University of Granada).
    9. J. James Reade & Dominik Schreyer & Carl Singleton, 2022. "Eliminating supportive crowds reduces referee bias," Economic Inquiry, Western Economic Association International, vol. 60(3), pages 1416-1436, July.
    10. Hlasny, V. & Kolaric, S., 2015. "Catch Me If You Can - Referee–Team Relationships and Disciplinary Cautions in Football," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 74994, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    11. Ulrike Holder & Thomas Ehrmann & Arne König, 2022. "Monitoring experts: insights from the introduction of video assistant referee (VAR) in elite football," Journal of Business Economics, Springer, vol. 92(2), pages 285-308, February.
    12. Vladimir Hlasny & Sascha Kolaric, 2017. "Catch Me If You Can," Journal of Sports Economics, , vol. 18(6), pages 560-591, August.
    13. J. James Reade & Dominik Schreyer & Carl Singleton, 2020. "Echoes: what happens when football is played behind closed doors?," Economics Discussion Papers em-dp2020-14, Department of Economics, University of Reading.
    14. Stijn Baert & Simon Amez, 2018. "No better moment to score a goal than just before half time? A soccer myth statistically tested," PLOS ONE, Public Library of Science, vol. 13(3), pages 1-17, March.
    15. Steven B. Caudill & James E. Long & Franklin G. Mixon, 2012. "Female athletic participation and income: evidence from a latent class model," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(3), pages 477-488, June.
    16. Barry Reilly & Robert Witt, 2016. "Disciplinary Sanction and Social Pressure in English Premiership Soccer," Working Paper Series 8816, Department of Economics, University of Sussex.
    17. Ryan Dansby & R Todd Jewell, 2014. "A Violent Response to Changing the Rules of the Game: The Case of “The Split” in Scottish Premier League Soccer," Eastern Economic Journal, Palgrave Macmillan;Eastern Economic Association, vol. 40(4), pages 473-487, September.
    18. Dawson, Peter & Dobson, Stephen, 2010. "The influence of social pressure and nationality on individual decisions: Evidence from the behaviour of referees," Journal of Economic Psychology, Elsevier, vol. 31(2), pages 181-191, April.
    19. Mike Hsu, 2024. "Umpire Home Bias in Major League Baseball," Journal of Sports Economics, , vol. 25(4), pages 423-442, May.
    20. Karol Kempa & Hannes Rusch, 2019. "Dissent, sabotage, and leader behaviour in contests: Evidence from European football," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 40(5), pages 500-514, July.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jecnmx:v:2:y:2014:i:1:p:1-19:d:32999. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.