Quantifying the Outfield Shift Using K-Means Clustering
DOI:
https://doi.org/10.37266/ISER.2020v8i1.pp18-23Keywords:
Baseball, Sabermetrics, Defensive Shifting, K-Means ClusteringAbstract
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.References
Baumer, B. (2014). An Overview of Current Sabermetric Thought II: Defense, WAR, and Strategy. The Sabermetric Revolution. (pp. 57–84).
Becker, K. W. (2009). Optimizing Defensive Alignments in Baseball through Integer Programming and Simulation. Kanas State University. Retrieved from https://krex.k-state.edu/dspace/handle/2097/2345?show=full.
Beneventano, P., Berger, P.D., & Weinberg, B.D. (2012). Predicting Run Production and Run Prevention in Baseball: The Impact of Sabermetrics. International Journal of Business, Humanities and Technology. 2(4), 67-75.
Hawke, C. J. (2017). Quantifying the Effect of the Shift in Major League Baseball. In Kansas State Senior Projects. Spring 2017. 191. Retrieved from http://digitalcommons.bard.edu/senproj_s2017/191.
Helfand, Z. (2015, July 19). Use of Defensive Shifts in Baseball is Spreading - because it works. Retrieved October 21, 2019, from https://www.latimes.com/sports/la-sp-baseball-defensive-shifts-20150719-story.html.
James, G., Witten, D., Hastie, T., & Tibshirani, R. (2017). An Introduction to Statistical Learning: with applications in R. New York: Springer.
Lewis, M. & Bailey, R. (2015). Batted Ball Spray Charts: a system to determine infield shifting. In 2015 Systems and Information Engineering Design Symposium, 206-211.
MLB Advanced Media. (n.d.). Catch Probability. Retrieved from http://m.mlb.com/glossary/statcast/catch-probability
Pankin, M.D. (1978). Evaluating Offensive Performance in Baseball. Operations Research. 26(4), 610-619.
Valero S. (2016). Predicting Win-Loss outcomes in MLB regular season games – A comparative study using data mining methods. International Journal of Computer Science in Sport, 15(2), 91-112.
Becker, K. W. (2009). Optimizing Defensive Alignments in Baseball through Integer Programming and Simulation. Kanas State University. Retrieved from https://krex.k-state.edu/dspace/handle/2097/2345?show=full.
Beneventano, P., Berger, P.D., & Weinberg, B.D. (2012). Predicting Run Production and Run Prevention in Baseball: The Impact of Sabermetrics. International Journal of Business, Humanities and Technology. 2(4), 67-75.
Hawke, C. J. (2017). Quantifying the Effect of the Shift in Major League Baseball. In Kansas State Senior Projects. Spring 2017. 191. Retrieved from http://digitalcommons.bard.edu/senproj_s2017/191.
Helfand, Z. (2015, July 19). Use of Defensive Shifts in Baseball is Spreading - because it works. Retrieved October 21, 2019, from https://www.latimes.com/sports/la-sp-baseball-defensive-shifts-20150719-story.html.
James, G., Witten, D., Hastie, T., & Tibshirani, R. (2017). An Introduction to Statistical Learning: with applications in R. New York: Springer.
Lewis, M. & Bailey, R. (2015). Batted Ball Spray Charts: a system to determine infield shifting. In 2015 Systems and Information Engineering Design Symposium, 206-211.
MLB Advanced Media. (n.d.). Catch Probability. Retrieved from http://m.mlb.com/glossary/statcast/catch-probability
Pankin, M.D. (1978). Evaluating Offensive Performance in Baseball. Operations Research. 26(4), 610-619.
Valero S. (2016). Predicting Win-Loss outcomes in MLB regular season games – A comparative study using data mining methods. International Journal of Computer Science in Sport, 15(2), 91-112.
Published
2021-03-06
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
Issue
Section
Articles
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
The copyediting stage is intended to improve the flow, clarity, grammar, wording, and formatting of the article. It represents the last chance for the author to make any substantial changes to the text because the next stage is restricted to typos and formatting corrections. The file to be copyedited is in Word or .rtf format and therefore can easily be edited as a word processing document. The set of instructions displayed here proposes two approaches to copyediting. One is based on Microsoft Word's Track Changes feature and requires that the copy editor, editor, and author have access to this program. A second system, which is software independent, has been borrowed, with permission, from the Harvard Educational Review. The journal editor is in a position to modify these instructions, so suggestions can be made to improve the process for this journal.
