Quantifying the Outfield Shift Using K-Means Clustering

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Jeffrey Gerlica
Izaiah LaDuke
Garrett O’Shea
Pierce Pluemer
John Dulin


Sports teams constantly search for a competitive advantage (e.g. bidding for free agents or scouting nontraditional markets). As popularized by Moneyball, we focus on advanced analytics in baseball. These sabermetrics are employed to provide objective information to management and coaches to support player management and in-game strategy decisions. Though widely used at the professional level, analytics use in college baseball is limited. Air Force Academy Baseball has been one win short of qualifying for the Mountain West tournament three straight years, resulting in the loss of potential income from media payouts and exposure for future recruiting efforts. Using a K-means clustering method for defensive shifting, we calculate an overall catch probability increase of 7.4% with a shifted outfield in a one-game case study. Based on our analysis, we provide evidence that Air Force Baseball can benefit from an outfield defensive shifting scheme that drives a competitive advantage and additional wins.

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How to Cite
Gerlica, J., LaDuke, I., O’Shea, G., Pluemer, P., & Dulin, J. (2021). Quantifying the Outfield Shift Using K-Means Clustering. Industrial and Systems Engineering Review, 8(1), 18-23. https://doi.org/10.37266/ISER.2020v8i1.pp18-23


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