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Moneyball After 10 Years

Author

Listed:
  • Daniel T. Brown
  • Charles R. Link
  • Seth L. Rubin

Abstract

Michael Lewis’ Moneyball … describes how the Oakland Athletics exploited an imperfection in the way player productivity was being evaluated pre-2003. On-base percentage appeared to be more important in determining team success compared to more popular statistics. We test the hypothesis that in a competitive market, other teams will increase the weight given on-base percentage in the reward structure for their players. Our results show that in the post-Moneyball (MB) era, the return to on-base percentage has indeed increased for free agents, the group whose salaries we expect to be most affected by the MB philosophy.

Suggested Citation

  • Daniel T. Brown & Charles R. Link & Seth L. Rubin, 2017. "Moneyball After 10 Years," Journal of Sports Economics, , vol. 18(8), pages 771-786, December.
    • Handle: RePEc:sae:jospec:v:18:y:2017:i:8:p:771-786
    DOI: 10.1177/1527002515609665
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    References listed on IDEAS

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    1. Jahn K. Hakes & Raymond D. Sauer, 2006. "An Economic Evaluation of the Moneyball Hypothesis," Journal of Economic Perspectives, American Economic Association, vol. 20(3), pages 173-186, Summer.
    2. Daniel Deli, 2013. "Assessing the Relative Importance of Inputs to a Production Function," Journal of Sports Economics, , vol. 14(2), pages 203-217, April.
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    Cited by:

    1. Koji Yashiki & Yoshiyuki Nakazono, 2024. "Moneyball revisited: Some counter-evidence," Economics Bulletin, AccessEcon, vol. 44(2), pages 620-634.
    2. Shirong Zhao & Jeremy Losak, 2024. "Two-tiered stochastic frontier models: a Bayesian perspective," Journal of Productivity Analysis, Springer, vol. 61(2), pages 85-106, April.
    3. Michael A. Roach, 2022. "Career concerns and personnel investment in the Major League Baseball player draft," Economic Inquiry, Western Economic Association International, vol. 60(1), pages 413-426, January.
    4. Lloyd-Jones, Luke R. & Nguyen, Hien D. & McLachlan, Geoffrey J., 2018. "A globally convergent algorithm for lasso-penalized mixture of linear regression models," Computational Statistics & Data Analysis, Elsevier, vol. 119(C), pages 19-38.

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