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Mining Trackman Golf Data
Department of Information Technology, University of Borås, Sweden. (Skövde Artificial Intelligence Lab (SAIL))
Department of Information Technology, University of Borås, Sweden.
Department of Information Technology, University of Borås, Sweden.
University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre. (Skövde Artificial Intelligence Lab (SAIL))
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2015 (English)In: 2015 International Conference on Computational Science and Computational Intelligence (CSCI) / [ed] Hamid R. Arabnia, Leonidas Deligiannidis & Quoc-Nam Tran, Los Alamitos, CA: IEEE Computer Society, 2015, 380-385 p.Conference 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
Los Alamitos, CA: IEEE Computer Society, 2015. 380-385 p.
National Category
Computer Science
Research subject
Natural sciences
Identifiers
URN: urn:nbn:se:his:diva-12251DOI: 10.1109/CSCI.2015.77ISI: 000380405100068Scopus ID: 2-s2.0-84964425566ISBN: 978-1-4673-9795-7 OAI: oai:DiVA.org:his-12251DiVA: diva2:928208
Conference
2015 International Conference on Computational Science and Computational Intelligence (CSCI), Las Vegas, Nevada, USA, 7-9 December 2015
Projects
TIKT 1- GOATS
Note

Project funded by VGR, TIKT collaboration Univ. Skövde and Univ. Borås.

Available from: 2016-05-15 Created: 2016-05-15 Last updated: 2016-08-19Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
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  • modern-language-association-8th-edition
  • vancouver
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More styles
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