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Journal of Quantitative Analysis in Sports

An official journal of the American Statistical Association

Editor-in-Chief: Mark Glickman PhD

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Scrambled experts: team handicaps and win probabilities for golf scrambles

Scott E. Grasman
  • Corresponding author
  • Industrial and Systems Engineering, Kate Gleason College of Engineering, Rochester Institute of Technology, 81 Lomb Memorial Drive, Rochester, New York 14623, USA
  • Email:
/ Barrett W. Thomas
  • Department of Management Sciences, Tippie College of Business, University of Iowa, W272 Pappajohn Business Building, Iowa City, IA 52242-1000, USA
Published Online: 2013-07-06 | DOI: https://doi.org/10.1515/jqas-2012-0024

Abstract

Golf is a popular form of competition, and it is traditional for amateur players to use a handicapping system when competing against one another in order to make the competition more interesting and perhaps more equitable. Additionally, the scramble, where each player plays a ball and the better/best of the shots is selected and played (by all players) until the ball is holed, is a popular format for team competition. However, an official handicapping system for scrambles has yet to be developed. This paper develops a model that could provide a rationale for assigning handicaps to multi-person scramble teams with the objective of yielding equitable matches, i.e., equal win probabilities for both/all teams. Probabilistic analysis is used to derive the distributions of team scores and winning probabilities, which can then be use as a mechanism for optimally assigning teams. This paper relaxes many strict assumptions of previous work; results show that equal handicaps are not the best measure of fairness and that player inconsistency may be desirable in scramble formats.

Keywords: golf handicaps; team assignment; scrambles; winning probability

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About the article

Corresponding author: Scott E. Grasman, Industrial and Systems Engineering, Kate Gleason College of Engineering, Rochester Institute of Technology, 81 Lomb Memorial Drive, Rochester, New York 14623, USA


Published Online: 2013-07-06

Published in Print: 2013-09-01


Citation Information: Journal of Quantitative Analysis in Sports, ISSN (Online) 1559-0410, ISSN (Print) 2194-6388, DOI: https://doi.org/10.1515/jqas-2012-0024. Export Citation

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