How to Catch a Tiger: Understanding Putting Performance on the PGA TOUR
Name
Fearing Acimovic Graves 2011.pdf
Description
main article
Size
1.74 MB
Format
Adobe PDF
Checksum (MD5)
f8e9b17baae15cf47ef53a4bcec3e0d8
Author(s)
Fearing, Douglas
Acimovic, Jason A
Graves, Stephen C
Date Issued
January 2011
Journal
Journal of Quantitative Analysis in Sports
Publisher
Walter de Gruyter
Citation
Fearing, Douglas, Jason Acimovic, and Stephen C Graves. “How to Catch a Tiger: Understanding Putting Performance on the PGA TOUR.” Journal of Quantitative Analysis in Sports 7.1 (2011): n. pag. © 2011 American Statistical Association
Version
Final published version
Abstract
Existing performance metrics utilized by the PGA TOUR have biases towards specific styles of play, which make relative player comparisons challenging. Our goal is to evaluate golfers in a way that eliminates these biases and to better understand how the best players maintain their advantage.Through a working agreement with the PGA TOUR, we have obtained access to proprietary ShotLink data that pinpoints the location of every shot taken on the PGA TOUR. Using these data, we develop distance-based models for two components of putting performance: the probability of making the putt and the remaining distance to the pin conditioned on missing. The first is modeled through a logistic regression, the second through a gamma regression. Both models fit the data well and provide interesting insights into the game. Additionally, by describing the act of putting using a simple Markov chain, we are able to combine these two models to characterize the putts-to-go for the field from any distance on the green for the PGA TOUR. The results of this Markov model match both the empirical expectation and variance of putts-to-go.We use our models to evaluate putting performance in terms of the strokes or putts gained per round relative to the field. Using this metric, we can determine what portion of a player s overall performance is due to advantage (or loss) gained through putting, and conversely, what portion of the player s performance is derived off the green. We demonstrate with examples how our metric eliminates significant biases that exist in the PGA TOUR s Putting Average statistic. Lastly, extending the concept of putts gained to evaluate player-specific performance, we show how our models can be used to quickly test situational hypotheses, such as differences between putting for par and birdie and performance under pressure.
MIT Department
Massachusetts Institute of Technology. Operations Research Center
Sloan School of Management
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Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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DOI of Published Version
https://doi.org/10.2202/1559-0410.1268
