MeetOpt: A multi-event coaching decision support system

Highlights • We use real data to illustrate the value of a decision support system for a track and field coach to assign athletes to event in a variety of meet types. • This DSS has the ability to consider multiple opponent scenarios or teams. • We illustrate the sensitivity of a meet outcome to a selected lineup and the complexity associated with this decision process. • This DSS outperforms an alternative model that is commonly used by coaches throughout the U.S. Track and field is one of the most popular and fastest growing high school sports in the United States. The teams' coaching staffs are often part-time employees that lack the time, budgets, and/or ability for the pre-competitive tactical analysis required for optimal assignment of athletes to events. In response to this often overlooked aspect of coaching, we present a new open-source spreadsheet-based DSS, MeetOpt, that offers coaches a decision making tool for the optimal athlete-to-event assignment. MeetOpt utilizes mixed-integer programming to create a lineup that maximizes the expected team score under a variety of common meet formats and assignment constraints. In addition, this framework is flexible enough to allow for the inclusion of opponent performance scenarios through the use of stochastic mixed-integer programming. We use real data to illustrate the value of this DSS in comparison with a commonly used decision aid utilizing a simple ranking criteria. In addition, we discuss the additional complexities when competing against teams in double duals - a meet with three teams scored as two head-to-head meets. Our results illustrate the sensitivity of a meet outcome to a selected lineup and the complexity associated with this decision process.
© Copyright 2018 Decision Support Systems. Elsevier. All rights reserved.

Subjects: coach coaching track and field competition co-operation athlete technology auxiliary device software
Notations: training science technical and natural sciences
Tagging: Trainer-Berater-System
DOI: 10.1016/j.dss.2018.06.007
Published in: Decision Support Systems
Published: 2018
Volume: 112
Issue: Aug
Pages: 60-75
Document types: article
Language: English
Level: advanced