A Major League Baseball Team Uses Operations Research to Improve Draft Preparation

Noah Streib (), Stephen J. Young () and Joel Sokol ()
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Noah Streib: School of Mathematics, Georgia Institute of Technology, Atlanta, Georgia 30332
Stephen J. Young: Department of Mathematics, University of California, San Diego, La Jolla, California 92093
Joel Sokol: H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332

Interfaces, 2012, vol. 42, issue 2, 119-130

Abstract: Preparing for the annual major league baseball draft is a difficult task; with 1,500 players selected each year, teams must evaluate and rank many hundreds of potential draftees. To evaluate the players, these teams send out scouts, baseball experts who make qualitative and quantitative observations and report their opinions to the team. However, scouts often disagree significantly in their opinions. We worked with a major league team to model and solve the problem of suggesting a consensus ranking of all players scouted by the team's representatives. Our methodology can also make in-season recommendations for dynamic scout scheduling based on the level of information each scout is likely to provide on each player, and the uncertainty in the “correct” overall ranking of each player. The team has been using the optimization tool we provided for the past two years, and a second major league team has also asked us to evaluate its ranking data.

Keywords: major league baseball; voting/committees; games/group decisions; recreation/sports; decision support systems; information systems (search for similar items in EconPapers)
Date: 2012
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Citations: View citations in EconPapers (2)

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