Change search
Link to record
Permanent link

Direct link
BETA
Brattberg, Peter
Publications (3 of 3) Show all publications
König, R., Johansson, U., Riveiro, M. & Brattberg, P. (2017). Modeling Golf Player Skill Using Machine Learning. In: International Cross-Domain Conference for Machine Learning and Knowledge Extraction: CD-MAKE 2017: Machine Learning and Knowledge Extraction. Paper presented at International Cross-Domain Conference, Reggio Italy, August 29 – September 1, 2017. (pp. 275-294). Calabri
Open this publication in new window or tab >>Modeling Golf Player Skill Using Machine Learning
2017 (English)In: International Cross-Domain Conference for Machine Learning and Knowledge Extraction: CD-MAKE 2017: Machine Learning and Knowledge Extraction, Calabri, 2017, p. 275-294Conference 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.

Place, publisher, year, edition, pages
Calabri: , 2017
Series
Lecture Notes in Computer Science ; 10410
Keywords
Classification, Decision trees, Machine learning, Golf, Swing analysis
National Category
Computer Sciences
Research subject
Business and IT
Identifiers
urn:nbn:se:hb:diva-13938 (URN)10.1007/978-3-319-66808-6_19 (DOI)978-3-319-66807-9 (ISBN)
Conference
International Cross-Domain Conference, Reggio Italy, August 29 – September 1, 2017.
Projects
TIKT2 - GOATS - Golf Data Analytics
Funder
Region Västra Götaland
Available from: 2018-04-04 Created: 2018-04-04 Last updated: 2018-04-19Bibliographically approved
Rikard, K., Ulf, J., Lindqvist, A. & Peter, B. (2015). Interesting Regression- and Model Trees Through Variable Restrictions. In: Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management: . Paper presented at International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, Lisbon, November 12-14, 2015. (pp. 281-292).
Open this publication in new window or tab >>Interesting Regression- and Model Trees Through Variable Restrictions
2015 (English)In: Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, 2015, p. 281-292Conference paper, Published paper (Refereed)
Abstract [en]

The overall purpose of this paper is to suggest a new technique for creating interesting regression- and model trees. Interesting models are here defined as models that fulfill some domain dependent restriction of how variables can be used in the models. The suggested technique, named ReReM, is an extension of M5 which can enforce variable constraints while creating regression and model trees. To evaluate ReReM, two case studies were conducted where the first concerned modeling of golf player skill, and the second modeling of fuel consumption in trucks. Both case studies had variable constraints, defined by domain experts, that should be fulfilled for models to be deemed interesting. When used for modeling golf player skill, ReReM created regression trees that were slightly less accurate than M5’s regression trees. However, the models created with ReReM were deemed to be interesting by a golf teaching professional while the M5 models were not. In the second case study, ReReM was evalu ated against M5’s model trees and a semi-automated approach often used in the automotive industry. Here, experiments showed that ReReM could achieve a predictive performance comparable to M5 and clearly better than a semi-automated approach, while fulfilling the constraints regarding interesting models.

Keywords
Predictive Modeling, Model Trees, Interestingness, Regression, Vehicle modeling, Golf
National Category
Computer Sciences
Research subject
Business and IT
Identifiers
urn:nbn:se:hb:diva-8847 (URN)10.5220/0005600302810292 (DOI)978-989-758-158-8 (ISBN)
Conference
International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, Lisbon, November 12-14, 2015.
Projects
Big Data Analytics by OnlineEnsemble Learning (BOEL)Golf data analysis (GOATS)
Funder
Knowledge Foundation, 20120192
Available from: 2016-02-12 Created: 2016-02-12 Last updated: 2018-06-01Bibliographically approved
Reveiro, M., Dahlbom, A., König, R., Johansson, U. & Brattberg, P. (2015). Supporting Golf Coaching and Swing Instruction with Computer-Based Training Systems. In: Panayiotis Zaphiris, Andri Ioannou (Ed.), Learning and Collaboration Technologies: Second International Conference, LCT 2015, Held as Part of HCI International 2015, Los Angeles, CA, USA, August 2-7, 2015, Proceedings. Paper presented at Learning and Collaboration Technologies, Second International Conference, Los Angeles USA, August 2-7, 2015. (pp. 279-290). Los Angeles, 9192
Open this publication in new window or tab >>Supporting Golf Coaching and Swing Instruction with Computer-Based Training Systems
Show others...
2015 (English)In: Learning and Collaboration Technologies: Second International Conference, LCT 2015, Held as Part of HCI International 2015, Los Angeles, CA, USA, August 2-7, 2015, Proceedings / [ed] Panayiotis Zaphiris, Andri Ioannou, Los Angeles, 2015, Vol. 9192, p. 279-290Conference paper, Published paper (Refereed)
Abstract [en]

Golf is a popular sport around the world. Since an accomplished golf swing is essential for succeeding in this sport, golf players spend a considerable amount of time perfecting their swing. In order to guide the design of future computer-based training systems that support swing instruction, this paper analyzes the data gathered during interviews with golf instructors and participant observations of actual swing coaching sessions. Based on our field work, we describe the characteristics of a proficient swing, how the instructional sessions are normally carried out and the challenges professional instructors face. Taking into account these challenges, we outline which desirable capabilities future computer-based training systems for professional golf instructors should have.

Place, publisher, year, edition, pages
Los Angeles: , 2015
Series
Lecture Notes in Computer Science, ISSN 0302-9743 ; 9192
Keywords
Golf, Swing instruction, Computer-based training systems
National Category
Computer Sciences
Research subject
Business and IT
Identifiers
urn:nbn:se:hb:diva-8848 (URN)10.1007/978-3-319-20609-7_27 (DOI)978-3-319-20609-7 (ISBN)
Conference
Learning and Collaboration Technologies, Second International Conference, Los Angeles USA, August 2-7, 2015.
Projects
Golf data analysis (GOATS)
Available from: 2016-02-12 Created: 2016-02-12 Last updated: 2018-06-02Bibliographically approved
Organisations

Search in DiVA

Show all publications