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Mining trackman golf data
Department of Information Technology, University of Borås, Sweden.ORCID iD: 0000-0003-0412-6199
Department of Information Technology, University of Borås, Sweden.
Department of Information Technology, University of Borås, Sweden.
School of Informatics, University of Skövde, Sweden.
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2016 (English)In: Proceedings - 2015 International Conference on Computational Science and Computational Intelligence, CSCI 2015, IEEE, 2016, p. 380-385Conference paper, Published paper (Refereed)
Abstract [en]

Recently, innovative technology like Trackman has made it possible to generate data describing golf swings. In this application paper, we analyze Trackman data from 275 golfers using descriptive statistics and machine learning techniques. The overall goal is to find non-trivial and general patterns in the data that can be used to identify and explain what separates skilled golfers from poor. Experimental results show that random forest models, generated from Trackman data, were able to predict the handicap of a golfer, with a performance comparable to human experts. Based on interpretable predictive models, descriptive statistics and correlation analysis, the most distinguishing property of better golfers is their consistency. In addition, the analysis shows that better players have superior control of the club head at impact and generally hit the ball straighter. A very interesting finding is that better players also tend to swing flatter. Finally, an outright comparison between data describing the club head movement and ball flight data, indicates that a majority of golfers do not hit the ball solid enough for the basic golf theory to apply.

Place, publisher, year, edition, pages
IEEE, 2016. p. 380-385
Keywords [en]
Data mining, Golf, Trackman, Artificial intelligence, Computation theory, Decision trees, Learning systems, Correlation analysis, Descriptive statistics, General patterns, Innovative technology, Machine learning techniques, Predictive models, Sports
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:hj:diva-38114DOI: 10.1109/CSCI.2015.77Scopus ID: 2-s2.0-84964425566ISBN: 9781467397957 (print)OAI: oai:DiVA.org:hj-38114DiVA, id: diva2:1163938
Conference
International Conference on Computational Science and Computational Intelligence, CSCI 2015, 7 December 2015 through 9 December 2015
Available from: 2017-12-08 Created: 2017-12-08 Last updated: 2018-01-13Bibliographically approved

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CiteExportLink to record
Permanent link

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Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf