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Estimating the effects of age on NHL player performance

Author

Listed:
  • Brander James A.
  • Yeung Louisa

    (Sauder School of Business, University of British Columbia, 2053 Main Mall, Vancouver, BC, V6T 1Z2 Canada)

  • Egan Edward J.

    (National Bureau of Economic Research (NBER), 1050 Massachusetts Avenue, Cambridge, MA 02138, USA)

Abstract

Using NHL data for the 1997–1998 through 2011–2012 seasons, we examine the effect of age on scoring performance and plus-minus for NHL skaters (non-goalies) and on save percentage for goaltenders. We emphasize fixed-effects regression methods that estimate a representative age-performance trajectory. We also use a method based on the best performances over time, a method based on the age distribution of NHL players, and a “naïve” specification that does not correct for selection bias. In addition we estimate individual age-performance relationships to obtain a distribution of peak ages. All methods provide similar results (with small but understandable differences) except the naïve specification, which yields implausible results, indicating that correcting for selection bias is very important. Our best estimate of the scoring peak age is between 27 and 28 for forwards and between 28 and 29 for defencemen. Both forwards and defencemen exhibit near-peak performance over a wide range, going from about 24 to 32 and 24 to 34, respectively. Goaltenders display little systematic performance variation over most of the age range from the early 20s to late 30s.

Suggested Citation

  • Brander James A. & Yeung Louisa & Egan Edward J., 2014. "Estimating the effects of age on NHL player performance," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 10(2), pages 1-19, June.
    • Handle: RePEc:bpj:jqsprt:v:10:y:2014:i:2:p:19:n:6
    DOI: 10.1515/jqas-2013-0085
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    References listed on IDEAS

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    Cited by:

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    2. Araki, Kenji & Hirose, Yoshihiro & Komaki, Fumiyasu, 2019. "Paired comparison models with age effects modeled as piecewise quadratic splines," International Journal of Forecasting, Elsevier, vol. 35(2), pages 733-740.
    3. Michael Schuckers & Michael Lopez & Brian Macdonald, 2023. "Estimation of player aging curves using regression and imputation," Annals of Operations Research, Springer, vol. 325(1), pages 681-699, June.
    4. Jorge Lorenzo-Calvo & Alfonso de la Rubia & Daniel Mon-López & Monica Hontoria-Galán & Moises Marquina & Santiago Veiga, 2021. "Prevalence and Impact of the Relative Age Effect on Competition Performance in Swimming: A Systematic Review," IJERPH, MDPI, vol. 18(20), pages 1-19, October.
    5. Assanskiy, Artur & Shaposhnikov, Daniil & Tylkin, Igor & Vasiliev, Gleb, 2022. "Prove them wrong: Do professional athletes perform better when facing their former clubs?," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 98(C).
    6. Katarzyna Gabrys & Antoni Wontorczyk, 2023. "Sport Anxiety, Fear of Negative Evaluation, Stress and Coping as Predictors of Athlete’s Sensitivity to the Behavior of Supporters," IJERPH, MDPI, vol. 20(12), pages 1-14, June.

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