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Introducing Grid WAR: rethinking WAR for starting pitchers

Author

Listed:
  • Brill Ryan S.

    (Graduate Group in Applied Mathematics and Computational Science, University of Pennsylvania 6572 , Philadelphia, PA, USA)

  • Wyner Abraham J.

    (Department of Statistics and Data Science, University of Pennsylvania 6572 , Philadelphia, USA)

Abstract

The baseball statistic “Wins Above Replacement” (WAR) has emerged as one of the most popular evaluation metrics. But it is not readily observed and tabulated; WAR is an estimate of a parameter in a vaguely defined model with all its attendant assumptions. Industry-standard models of WAR for starting pitchers from FanGraphs and Baseball Reference all assume that season-long averages are sufficient statistics for a pitcher’s performance. This provides an invalid mathematical foundation for many reasons, especially because WAR should not be linear with respect to any counting statistic. To repair this defect, as well as many others, we devise a new measure, Grid WAR, which accurately estimates a starting pitcher’s WAR on a per-game basis. The convexity of Grid WAR diminishes the impact of “blow-up” games and up-weights exceptional games, raising the valuation of pitchers like Sandy Koufax, Whitey Ford, and Catfish Hunter who exhibit fundamental game-by-game variance. Although Grid WAR is designed to accurately measure historical performance, it has predictive value insofar as a pitcher’s Grid WAR is better than Fangraphs’ FIP WAR at predicting future performance. Finally, at https://gridwar.xyz we host a Shiny app which displays the Grid WAR results of each MLB game since 1952, including career, season, and game level results, which updates automatically every morning.

Suggested Citation

  • Brill Ryan S. & Wyner Abraham J., 2024. "Introducing Grid WAR: rethinking WAR for starting pitchers," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 20(4), pages 293-329.
    • Handle: RePEc:bpj:jqsprt:v:20:y:2024:i:4:p:293-329:n:1003
    DOI: 10.1515/jqas-2023-0095
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    References listed on IDEAS

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    1. Acharya Rohit A & Ahmed Alexander J & D'Amour Alexander N & Lu Haibo & Morris Carl N & Oglevee Bradley D & Peterson Andrew W & Swift Robert N, 2008. "Improving Major League Baseball Park Factor Estimates," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 4(2), pages 1-18, April.
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