Case Article—Baseball Analytics: Advancing to Prescriptive Analytics in the Major League Baseball Front Office

Sean L. Barnes () and Margrét V. Bjarnadóttir ()
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Sean L. Barnes: Department of Decision, Operations and Information Technologies, Robert H. Smith School of Business, University of Maryland, College Park, Maryland 20742-1815
Margrét V. Bjarnadóttir: Department of Decision, Operations and Information Technologies, Robert H. Smith School of Business, University of Maryland, College Park, Maryland 20742-1815

INFORMS Transactions on Education, 2019, vol. 19, issue 3, 143-145

Abstract: This case uses player evaluation and personnel decision making in Major League Baseball (MLB) to introduce many of the key steps of data analytics projects. The data analytics process is a unique combination of art and science, and teaching the art of data analytics is challenging to do in a standard classroom setting with small data sets. The goal of this case is to move beyond the simple “cookie cutter” data sets and introduce students to the challenges of dealing with real data to answer important questions, as well as introduce or reinforce multiple data-mining/machine-learning methods. The case builds on a very rich data set collected by the authors, which allows for students or groups of students to arrive at different answers to the same question.

Keywords: data analytics; prediction modeling; machine learning; sports analytics; baseball; descriptive analytics; predictive analytics; prescriptive analytics (search for similar items in EconPapers)
Date: 2019
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