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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.
Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi. (Skövde Artificial Intelligence Lab (SAIL))
Vise andre og tillknytning
2015 (engelsk)Inngår i: 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, s. 380-385Konferansepaper, Publicerat paper (Fagfellevurdert)
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.

sted, utgiver, år, opplag, sider
Los Alamitos, CA: IEEE Computer Society, 2015. s. 380-385
HSV kategori
Forskningsprogram
Naturvetenskap; Skövde Artificial Intelligence Lab (SAIL)
Identifikatorer
URN: urn:nbn:se:his:diva-12251DOI: 10.1109/CSCI.2015.77ISI: 000380405100068Scopus ID: 2-s2.0-84964425566ISBN: 978-1-4673-9795-7 (tryckt)OAI: oai:DiVA.org:his-12251DiVA, id: diva2:928208
Konferanse
2015 International Conference on Computational Science and Computational Intelligence (CSCI), Las Vegas, Nevada, USA, 7-9 December 2015
Prosjekter
TIKT 1- GOATS
Merknad

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

Tilgjengelig fra: 2016-05-15 Laget: 2016-05-15 Sist oppdatert: 2018-03-28bibliografisk kontrollert

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