IDEAS home Printed from https://ideas.repec.org/a/bpj/jqsprt/v10y2014i2p19n6.html
   My bibliography  Save this article

Estimating the effects of age on NHL player performance

Author

Listed:
  • Brander James A.

    (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)

  • Yeung Louisa

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

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. & Egan Edward J. & Yeung Louisa, 2014. "Estimating the effects of age on NHL player performance," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 10(2), pages 241-259, June.
    • Handle: RePEc:bpj:jqsprt:v:10:y:2014:i:2:p:19:n:6
    DOI: 10.1515/jqas-2013-0085
    as

    Download full text from publisher

    File URL: https://doi.org/10.1515/jqas-2013-0085
    Download Restriction: For access to full text, subscription to the journal or payment for the individual article is required.
    File URL: https://libkey.io/10.1515/jqas-2013-0085?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Fair Ray C, 2008. "Estimated Age Effects in Baseball," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 4(1), pages 1-41, January.
    2. Macdonald Brian, 2011. "A Regression-Based Adjusted Plus-Minus Statistic for NHL Players," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 7(3), pages 1-31, July.
    3. Arkes Jeremy, 2010. "Revisiting the Hot Hand Theory with Free Throw Data in a Multivariate Framework," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 6(1), pages 1-12, January.
    4. Broadie Mark & Rendleman Richard J., 2013. "Are the official world golf rankings biased?a," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 9(2), pages 127-140, June.
    5. Henderson, Daniel J. & Carroll, Raymond J. & Li, Qi, 2008. "Nonparametric estimation and testing of fixed effects panel data models," Journal of Econometrics, Elsevier, vol. 144(1), pages 257-275, May.
    6. Gramacy Robert B. & Jensen Shane T. & Taddy Matt, 2013. "Estimating player contribution in hockey with regularized logistic regression," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 9(1), pages 97-111, March.
    7. Addona Vittorio & Yates Philip A, 2010. "A Closer Look at the Relative Age Effect in the National Hockey League," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 6(4), pages 1-19, October.
    8. Tiruneh Gizachew, 2010. "Age and Winning Professional Golf Tournaments," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 6(1), pages 1-16, January.
    9. Kovalchik Stephanie Ann & Stefani Ray, 2013. "Longitudinal analyses of Olympic athletics and swimming events find no gender gap in performance improvement," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 9(1), pages 15-24, March.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    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. Griffin Jim E. & Hinoveanu Laurenţiu C. & Hopker James G., 2022. "Bayesian modelling of elite sporting performance with large databases," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 18(4), pages 253-268, December.
    4. 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.
    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.

    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. Yurko Ronald & Ventura Samuel & Horowitz Maksim, 2019. "nflWAR: a reproducible method for offensive player evaluation in football," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 15(3), pages 163-183, September.
    2. Ehrlich Justin & Sanders Shane & Boudreaux Christopher J., 2019. "The relative wages of offense and defense in the NBA: a setting for win-maximization arbitrage?," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 15(3), pages 213-224, September.
    3. Sabin R. Paul, 2021. "Estimating player value in American football using plus–minus models," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 17(4), pages 313-364, December.
    4. Hosan, Shahadat & Rahman, Md Matiar & Karmaker, Shamal Chandra & Saha, Bidyut Baran, 2023. "Energy subsidies and energy technology innovation: Policies for polygeneration systems diffusion," Energy, Elsevier, vol. 267(C).
    5. Lee, Yoonseok & Mukherjee, Debasri & Ullah, Aman, 2019. "Nonparametric estimation of the marginal effect in fixed-effect panel data models," Journal of Multivariate Analysis, Elsevier, vol. 171(C), pages 53-67.
    6. Huang, Bai & Lee, Tae-Hwy & Ullah, Aman, 2020. "Combined estimation of semiparametric panel data models," Econometrics and Statistics, Elsevier, vol. 15(C), pages 30-45.
    7. Su, Liangjun & Lu, Xun, 2013. "Nonparametric dynamic panel data models: Kernel estimation and specification testing," Journal of Econometrics, Elsevier, vol. 176(2), pages 112-133.
    8. Arkes Jeremy & Martinez Jose, 2011. "Finally, Evidence for a Momentum Effect in the NBA," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 7(3), pages 1-16, July.
    9. Geoffrey N Tuck & Athol R Whitten, 2013. "Lead Us Not into Tanktation: A Simulation Modelling Approach to Gain Insights into Incentives for Sporting Teams to Tank," PLOS ONE, Public Library of Science, vol. 8(11), pages 1-10, November.
    10. Yazan Oroud & Md. Aminul Islam & Tunku Salha Tunku Ahmad & Anas Ghazalat, 2019. "Does Audit Quality Moderate the Relationship between Accounting Information and the Share Price? Evidence from Jordan," International Business Research, Canadian Center of Science and Education, vol. 12(3), pages 58-65, March.
    11. Linton, Oliver & Xiao, Zhijie, 2019. "Efficient estimation of nonparametric regression in the presence of dynamic heteroskedasticity," Journal of Econometrics, Elsevier, vol. 213(2), pages 608-631.
    12. Joshua B. Miller & Adam Sanjurjo, 2015. "Is it a Fallacy to Believe in the Hot Hand in the NBA Three-Point Contest?," Working Papers 548, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    13. Macdonald Brian, 2012. "Adjusted Plus-Minus for NHL Players using Ridge Regression with Goals, Shots, Fenwick, and Corsi," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 8(3), pages 1-24, October.
    14. Jia Chen & Jiti Gao & Degui Li, 2013. "Estimation in Single-Index Panel Data Models with Heterogeneous Link Functions," Econometric Reviews, Taylor & Francis Journals, vol. 32(8), pages 928-955, November.
    15. Lee, Jungyoon & Robinson, Peter M., 2015. "Panel nonparametric regression with fixed effects," Journal of Econometrics, Elsevier, vol. 188(2), pages 346-362.
    16. Fumarco, Luca & Gibbs, Benjamin & Jarvis, Jonathan & Rossi, Giambattista, 2016. "The Relative Age Effect Reversal among NHL Elite," MPRA Paper 75691, University Library of Munich, Germany.
    17. Jean-Baptiste Vilain, 2018. "Three essays in applied economics [Trois essais en économie appliquée]," SciencePo Working papers Main tel-03419493, HAL.
    18. Su, Liangjun & Jin, Sainan, 2012. "Sieve estimation of panel data models with cross section dependence," Journal of Econometrics, Elsevier, vol. 169(1), pages 34-47.
    19. Craig A. Depken II & Matthew Hood & Ernest King, 2017. "Consistency and Momentum in NASCAR," Journal of Sports Economics, , vol. 18(6), pages 601-621, August.
    20. Chu, Chi-Yang & Henderson, Daniel J. & Parmeter, Christopher F., 2017. "On discrete Epanechnikov kernel functions," Computational Statistics & Data Analysis, Elsevier, vol. 116(C), pages 79-105.

    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:bpj:jqsprt:v:10:y:2014:i:2:p:19:n:6. 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: Peter Golla (email available below). General contact details of provider: https://www.degruyter.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.