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Modeling golf player skill using machine learning
University of Borås, Borås, Sweden.
Jönköping University, School of Engineering, JTH, Computer Science and Informatics. Jönköping University, School of Engineering, JTH. Research area Computer Science and Informatics. University of Borås, Borås, Sweden.ORCID iD: 0000-0003-0412-6199
University of Skövde, Skövde, Sweden.
University of Borås, Borås, Sweden.
2017 (English)In: Machine Learning and Knowledge Extraction, Springer, 2017, 275-294 p.Conference paper, Published paper (Refereed)
Abstract [en]

In this study we apply machine learning techniques to Modeling Golf Player Skill using a dataset consisting of 277 golfers. The dataset includes 28 quantitative metrics, related to the club head at impact and ball flight, captured using a Doppler-radar. For modeling, cost-sensitive decision trees and random forest are used to discern between less skilled players and very good ones, i.e., Hackers and Pros. The results show that both random forest and decision trees achieve high predictive accuracy, with regards to true positive rate, accuracy and area under the ROC-curve. A detailed interpretation of the decision trees shows that they concur with modern swing theory, e.g., consistency is very important, while face angle, club path and dynamic loft are the most important evaluated swing factors, when discerning between Hackers and Pros. Most of the Hackers could be identified by a rather large deviation in one of these values compared to the Pros. Hackers, which had less variation in these aspects of the swing, could instead be identified by a steeper swing plane and a lower club speed. The importance of the swing plane is an interesting finding, since it was not expected and is not easy to explain. © 2017, IFIP International Federation for Information Processing.

Place, publisher, year, edition, pages
Springer, 2017. 275-294 p.
Series
Lecture Notes in Computer Science, ISSN 0302-9743 ; 10410
Keyword [en]
Classification, Decision trees, Golf, Machine learning, Swing analysis, Artificial intelligence, Classification (of information), Decision theory, Doppler radar, Extraction, Forestry, Personal computing, Sports, Area under the ROC curve, Large deviations, Machine learning techniques, Predictive accuracy, Quantitative metrics, True positive rates, Learning systems
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:hj:diva-38113DOI: 10.1007/978-3-319-66808-6_19Scopus ID: 2-s2.0-85029009266ISBN: 9783319668079 (print)OAI: oai:DiVA.org:hj-38113DiVA: diva2:1163953
Conference
1st IFIP TC 5, WG 8.4, 8.9, 12.9 International Cross-Domain Conference on Machine Learning and Knowledge Extraction, CD-MAKE 2017; Reggio; Italy; 29 August 2017 through 1 September 2017
Available from: 2017-12-08 Created: 2017-12-08 Last updated: 2018-01-13Bibliographically approved

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Johansson, Ulf

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