The relative roles of skill and luck within 11 different golfer populations

Rendleman Jr. Richard J. ()
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Rendleman Jr. Richard J.: University of North Carolina at Chapel Hill, Kenan-Flagler Busness School, Chapel Hill, NC, USA, Phone: +(919) 302-5639

Journal of Quantitative Analysis in Sports, 2020, vol. 16, issue 3, 237-254

Abstract: Drawing on the golf-related example of regression to the mean as presented by Kahneman in his best-selling book, Thinking Fast and Slow, this study shows how the regression-to-the-mean phenomenon is revealed in first- and second-round scoring in 11 different golfer populations, ranging from golfers with the highest level of skill (professional golfers on the PGA TOUR) to amateur groups of much lower skill. Using the mathematics of truncated normal distributions, the study introduces a new method for estimating the mix between variation in scoring due to differences in player skill and that due to luck. Estimates of the skill/luck mix are very close to those obtained using the regression-based methodology of Morrison and are nearly identical to those implied by fixed effects regression models where fixed player and round effects are estimated simultaneously. The study also sheds light on the “paradox of skill,” originally suggested by Gould and developed further by Mauboussin, as it relates to golf by showing that luck plays a more important role in determining player scores in higher-skilled golfer groups compared with lower-skilled groups.

Keywords: Golf; Regression to the mean; Skill vs. luck (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1515/jqas-2019-0028

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