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A Closer Look at the Relative Age Effect in the National Hockey League

Listed author(s):
  • Addona Vittorio

    (Macalester College)

  • Yates Philip A

    (Saint Michael’s College)

Registered author(s):

    At young ages, a few extra months of development can make a big difference in size, strength, and athletic ability. A child who turns 5 years old in January will be nearly 20% older by the time a child born in December has their 5th birthday. In many sports, including hockey, children born in the early months of the calendar year get noticed by their coaches because of the superiority they demonstrate due to their age advantage. As a result, boys born early in the year are more likely to reach the professional ranks of the National Hockey League (NHL). The phenomenon just described has been labeled the relative age effect (RAE). Previous work studying the RAE in the NHL has focused on individual NHL seasons, often encompassing many of the same players across multiple seasons. We investigate the RAE using complete data on every player who has ever played in the NHL. We focus the majority of our analysis on Canadian born players and examine the RAE across hockey position and hall-of-fame status. For the first time, we provide strong evidence of when the RAE began to manifest itself in Canada. Our change point analysis indicates that the RAE began for players born since 1951. Finally, we make a case for what initiated this change in the way young hockey players develop, particularly in Canada, which produced over 90% of NHL players at that time.

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    File URL: https://www.degruyter.com/view/j/jqas.2010.6.4/jqas.2010.6.4.1227/jqas.2010.6.4.1227.xml?format=INT
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    Article provided by De Gruyter in its journal Journal of Quantitative Analysis in Sports.

    Volume (Year): 6 (2010)
    Issue (Month): 4 (October)
    Pages: 1-19

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    Handle: RePEc:bpj:jqsprt:v:6:y:2010:i:4:n:9
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    1. Zeileis, Achim & Kleiber, Christian & Kramer, Walter & Hornik, Kurt, 2003. "Testing and dating of structural changes in practice," Computational Statistics & Data Analysis, Elsevier, vol. 44(1-2), pages 109-123, October.
    2. Jushan Bai & Pierre Perron, 2003. "Computation and analysis of multiple structural change models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 18(1), pages 1-22.
    3. Perron, P. & Bai, J., 1995. "Estimating and Testing Linear Models with Multiple Structural Changes," Cahiers de recherche 9552, Universite de Montreal, Departement de sciences economiques.
    4. Jushan Bai, 1997. "Estimation Of A Change Point In Multiple Regression Models," The Review of Economics and Statistics, MIT Press, vol. 79(4), pages 551-563, November.
    5. Erdman, Chandra & Emerson, John W., 2007. "bcp: An R Package for Performing a Bayesian Analysis of Change Point Problems," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 23(i03).
    6. William Hurley & Dan Lior & Steven Tracze, 2001. "A Proposal to Reduce the Age Discrimination in Canadian Minor Hockey," Canadian Public Policy, University of Toronto Press, vol. 27(1), pages 65-75, March.
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