Issue No. 03 - May-June (2016 vol. 36)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/MCG.2015.25
David H.S. Chung , Swansea University
Matthew L. Parry , Swansea University
Iwan W. Griffiths , Swansea University
Robert S. Laramee , Swansea University
Rhodri Bown , Welsh Rugby Union
Philip A. Legg , University of the West of England
Min Chen , University of Oxford
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. https://youtu.be/Cs6SLtPVDQQ.
Sorting, Analytical models, Visual analytics, Predictive models, Data models, Knowledge discovery, Game theory
D. H. Chung et al., "Knowledge-Assisted Ranking: A Visual Analytic Application for Sports Event Data," in IEEE Computer Graphics and Applications, vol. 36, no. 3, pp. 72-82, 2016.