Knowledge-assisted ranking: A visual analytic application for sports event data

Chung, David H.S.; Parry, Matthew L.; Griffiths, Iwan W.; Laramee, Robert S.; Bown, Rhodri; Legg, Philip A.; Chen, Min

Abstract

© 2016 IEEE. Organizing sports video data for performance analysis can be challenging, especially in cases involving multiple attributes and when the criteria for sorting frequently changes depending on the user's task. The proposed visual analytic system enables users to specify a sort requirement in a flexible manner without depending on specific knowledge about individual sort keys. The authors use regression techniques to train different analytical models for different types of sorting requirements and use visualization to facilitate knowledge discovery at different stages of the process. They demonstrate the system with a rugby case study to find key instances for analyzing team and player performance. Organizing sports video data for performance analysis can be challenging in cases with multiple attributes, and when sorting frequently changes depending on the user's task. As this video shows, the proposed visual analytic system allows interactive data sorting and exploration.

Citation

Chung, D. H., Parry, M. L., Griffiths, I. W., Laramee, R. S., Bown, R., Legg, P. A., & Chen, M. (2016). Knowledge-assisted ranking: A visual analytic application for sports event data. IEEE Computer Graphics and Applications, 36(3), 72-82. https://doi.org/10.1109/MCG.2015.25

• Journal Article Type: Article
• Acceptance Date: Jan 1, 2015
• Online Publication Date: Jan 26, 2015
• Publication Date: May 1, 2016
• Deposit Date: Nov 9, 2015
• Publicly Available Date: Jul 7, 2016
• Journal: IEEE Computer Graphics and Applications
• Print ISSN: 0272-1716
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• Volume: 36
• Issue: 3
• Pages: 72-82
• DOI: https://doi.org/10.1109/MCG.2015.25
• Keywords: sorting; analytical models; visual analytics; predictive models; data models; knowledge discovery; game theory; computer graphics; sports video data; visual analytic system; regression techniques; visualization
• Public URL: https://uwe-repository.worktribe.com/output/914901
• Publisher URL: http://dx.doi.org/10.1109/MCG.2015.25
• Additional Information: Additional Information : c) 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.