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openWAR: An open source system for evaluating overall player performance in major league baseball

Author

Listed:
  • Baumer Benjamin S.

    (Smith College – Statistical and Data Sciences, Northampton, MA, USA)

  • Jensen Shane T.

    (Statistics, The Wharton School, University of Pennsylvania, Philadelphia, PA, USA)

  • Matthews Gregory J.

    (Mathematics and Statistics, Loyola University Chicago, Chicago, IL, USA)

Abstract

Within sports analytics, there is substantial interest in comprehensive statistics intended to capture overall player performance. In baseball, one such measure is wins above replacement (WAR), which aggregates the contributions of a player in each facet of the game: hitting, pitching, baserunning, and fielding. However, current versions of WAR depend upon proprietary data, ad hoc methodology, and opaque calculations. We propose a competitive aggregate measure, openWAR, that is based on public data, a methodology with greater rigor and transparency, and a principled standard for the nebulous concept of a “replacement” player. Finally, we use simulation-based techniques to provide interval estimates for our openWAR measure that are easily portable to other domains.

Suggested Citation

  • Baumer Benjamin S. & Jensen Shane T. & Matthews Gregory J., 2015. "openWAR: An open source system for evaluating overall player performance in major league baseball," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 11(2), pages 69-84, June.
    • Handle: RePEc:bpj:jqsprt:v:11:y:2015:i:2:p:69-84:n:4
    DOI: 10.1515/jqas-2014-0098
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    Citations

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    Cited by:

    1. Mallepalle Sarah & Yurko Ronald & Pelechrinis Konstantinos & Ventura Samuel L., 2020. "Extracting NFL tracking data from images to evaluate quarterbacks and pass defenses," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 16(2), pages 95-120, June.
    2. Shane Sanders & Joel Potter & Justin Ehrlich & Justin Perline & Christopher Boudreaux, 2021. "Informed voters and electoral outcomes: a natural experiment stemming from a fundamental information-technological shift," Public Choice, Springer, vol. 189(1), pages 257-277, October.
    3. Chu Dani & Reyers Matthew & Thomson James & Wu Lucas Yifan, 2020. "Route identification in the National Football League: An application of model-based curve clustering using the EM algorithm," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 16(2), pages 121-132, June.
    4. 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.
    5. Franks Alexander M. & D’Amour Alexander & Cervone Daniel & Bornn Luke, 2016. "Meta-analytics: tools for understanding the statistical properties of sports metrics," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 12(4), pages 151-165, December.
    6. Jyh-How Huang & Yu-Chia Hsu, 2021. "A Multidisciplinary Perspective on Publicly Available Sports Data in the Era of Big Data: A Scoping Review of the Literature on Major League Baseball," SAGE Open, , vol. 11(4), pages 21582440211, November.
    7. 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.
    8. Vock David Michael & Vock Laura Frances Boehm, 2018. "Estimating the effect of plate discipline using a causal inference framework: an application of the G-computation algorithm," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 14(2), pages 37-56, June.

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