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An Exploratory Study of Minor League Baseball Statistics

Author

Listed:
  • Chandler Gabriel

    (Pomona College)

  • Stevens Guy

    (Pomona College '13)

Abstract

We consider the problem of projecting future success of Minor League baseball players at each level of the farm system. Using tree based methods, in particular random forests, we consider which statistics are most correlated with Major League success, how Major League teams use these statistics differently in handling prospects, and how prior belief in a players ability, measured through draft position, is used throughout a players Minor League career. We show that roughly the 18th round prospect corresponds to being draft neutral for a team, whereas teams essentially make decisions based strictly on performance. We use for our data all position players drafted between 1999 and 2002.

Suggested Citation

  • Chandler Gabriel & Stevens Guy, 2012. "An Exploratory Study of Minor League Baseball Statistics," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 8(4), pages 1-28, November.
    • Handle: RePEc:bpj:jqsprt:v:8:y:2012:i:4:n:4
    DOI: 10.1515/1559-0410.1445
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    References listed on IDEAS

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    1. Neil Longley & Glenn Wong, 2011. "The speed of human capital formation in the baseball industry: the information value of minor‐league performance in predicting major‐league performance," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 32(3), pages 193-204, April.
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    Cited by:

    1. Lock Dennis & Nettleton Dan, 2014. "Using random forests to estimate win probability before each play of an NFL game," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 10(2), pages 197-205, June.
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    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.

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