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Estimated Age Effects in Baseball

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Abstract

Age effects in baseball are estimated in this paper using a nonlinear fixed-effects regression. The sample consists of all players who have played 10 or more "full-time" years in the major leagues between 1921 and 2004. Quadratic improvement is assumed up to a peak-performance age, which is estimated, and then quadratic decline after that, where the two quadratics need not be the same. Each player has his own constant term. The results show that aging effects are larger for pitchers than for batters and larger for baseball than for track and field, running, and swimming events and for chess. There is some evidence that decline rates in baseball have decreased slightly in the more recent period, but they are still generally larger than those for the other events. There are 18 batters out of the sample of 441 whose performances in the second half of their careers noticeably exceed what the model predicts they should have been. All but 3 of these players played from 1990 on. The estimates from the fixed-effects regressions can also be used to rank players. This ranking differs from the ranking using lifetime averages because it adjusts for the different ages at which players played. It is in effect an age-adjusted ranking.

Suggested Citation

  • Ray C. Fair, 2005. "Estimated Age Effects in Baseball," Cowles Foundation Discussion Papers 1536, Cowles Foundation for Research in Economics, Yale University, revised Mar 2007.
  • Handle: RePEc:cwl:cwldpp:1536
    Note: CFP 1255.
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    1. Ray C. Fair, 2004. "Estimated Age Effects in Athletic Events and Chess," Cowles Foundation Discussion Papers 1495, Cowles Foundation for Research in Economics, Yale University, revised Feb 2006.
    2. Fair, Ray C, 1994. "How Fast Do Old Men Slow Down?," The Review of Economics and Statistics, MIT Press, vol. 76(1), pages 103-118, February.
    3. Ray Fair, 2004. "Estimated Age Effects in Athletic Events and Chess," Yale School of Management Working Papers amz2481, Yale School of Management, revised 01 Aug 2007.
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    Citations

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    Cited by:

    1. Anthony C. Krautmann & John L. Solow, 2009. "The Dynamics of Performance Over the Duration of Major League Baseball Long-Term Contracts," Journal of Sports Economics, , vol. 10(1), pages 6-22, February.
    2. 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.
    3. Gerber Eric A. E. & Craig Bruce A., 2021. "A mixed effects multinomial logistic-normal model for forecasting baseball performance," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 17(3), pages 221-239, September.
    4. McShane Blakeley B. & Braunstein Alexander & Piette James & Jensen Shane T., 2011. "A Hierarchical Bayesian Variable Selection Approach to Major League Baseball Hitting Metrics," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 7(4), pages 1-26, October.
    5. 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.
    6. Jahn Hakes & Chad Turner, 2011. "Pay, productivity and aging in Major League Baseball," Journal of Productivity Analysis, Springer, vol. 35(1), pages 61-74, February.
    7. 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.
    8. Andrew W. Nutting, 2013. "Immediate Effects of On-The-Job Training and Its Intensity," Journal of Sports Economics, , vol. 14(3), pages 303-320, June.
    9. Nieswiadomy Michael L. & Strazicich Mark C. & Clayton Stephen, 2012. "Was There a Structural Break in Barry Bonds's Bat?," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 8(3), pages 1-19, October.
    10. Hamrick Jeff & Rasp John, 2011. "Using Local Correlation to Explain Success in Baseball," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 7(4), pages 1-29, October.
    11. Null Brad, 2009. "Modeling Baseball Player Ability with a Nested Dirichlet Distribution," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 5(2), pages 1-38, May.
    12. Tiruneh Gizachew, 2010. "Age and Winning Professional Golf Tournaments," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 6(1), pages 1-16, January.
    13. John L. Solow & Anthony C. Krautmann, 2020. "Do You Get What You Pay for? Salary and Ex Ante Player Value in Major League Baseball," Journal of Sports Economics, , vol. 21(7), pages 705-722, October.
    14. 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.

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    More about this item

    Keywords

    Aging; Baseball performance;

    JEL classification:

    • C2 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables
    • J00 - Labor and Demographic Economics - - General - - - General

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