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Knowledge-Assisted Ranking: A Visual Analytic Application for Sport Event Data

David Chung, Philip Legg, Matthew Parry, Rhodri Bown, Iwan Griffiths, Robert Laramee, Min Chen, Bob Laramee Orcid Logo

IEEE Computer Graphics and Applications, Pages: 1 - 1

Swansea University Authors: Iwan Griffiths, Bob Laramee Orcid Logo

DOI (Published version): 10.1109/MCG.2015.25

Abstract

Organizing sport video data for performance analysis can be challenging, especially when this involvesmultiple attributes, and the criteria for sorting frequently changes depending on the user’s task. In thiswork, we propose a visual analytic system to convert a user’s knowledge on rankings to suppo...

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Published in: IEEE Computer Graphics and Applications
Published: 2015
Online Access: http://cs.swan.ac.uk/~csbob/research/
URI: https://cronfa.swan.ac.uk/Record/cronfa22327
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spelling 2021-01-28T13:08:28.1002292 v2 22327 2015-07-08 Knowledge-Assisted Ranking: A Visual Analytic Application for Sport Event Data 2ed2cc8d3dff635184def8d15afa21a9 Iwan Griffiths Iwan Griffiths true false 7737f06e2186278a925f6119c48db8b1 0000-0002-3874-6145 Bob Laramee Bob Laramee true false 2015-07-08 STSC Organizing sport video data for performance analysis can be challenging, especially when this involvesmultiple attributes, and the criteria for sorting frequently changes depending on the user’s task. In thiswork, we propose a visual analytic system to convert a user’s knowledge on rankings to support such aprocess. The system enables users to specify a sort requirement in a flexible manner without dependingon specific knowledge about individual sort keys. We use regression techniques to train different analyticalmodels for different types of sorting requirements. We use visualization to facilitate the discovery ofknowledge at different stages of the visual analytic process. This includes visualizing the parameters of theranking model, visualizing the results of a sort query for interactive exploration, and the playback of sortedvideo clips. We demonstrate the system with a case study in rugby to find key instances for analyzing teamand player performance. Journal Article IEEE Computer Graphics and Applications 1 1 data visualization, visual analytics 31 12 2015 2015-12-31 10.1109/MCG.2015.25 http://cs.swan.ac.uk/~csbob/research/ COLLEGE NANME Sport and Exercise Sciences COLLEGE CODE STSC Swansea University 2021-01-28T13:08:28.1002292 2015-07-08T15:26:11.2271740 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science David Chung 1 Philip Legg 2 Matthew Parry 3 Rhodri Bown 4 Iwan Griffiths 5 Robert Laramee 6 Min Chen 7 Bob Laramee 0000-0002-3874-6145 8 0022327-07122015102607.pdf chung15knowledge.pdf 2015-12-07T10:26:07.2500000 Output 6162703 application/pdf Author's Original true 2015-12-07T00:00:00.0000000 false
title Knowledge-Assisted Ranking: A Visual Analytic Application for Sport Event Data
spellingShingle Knowledge-Assisted Ranking: A Visual Analytic Application for Sport Event Data
Iwan Griffiths
Bob Laramee
title_short Knowledge-Assisted Ranking: A Visual Analytic Application for Sport Event Data
title_full Knowledge-Assisted Ranking: A Visual Analytic Application for Sport Event Data
title_fullStr Knowledge-Assisted Ranking: A Visual Analytic Application for Sport Event Data
title_full_unstemmed Knowledge-Assisted Ranking: A Visual Analytic Application for Sport Event Data
title_sort Knowledge-Assisted Ranking: A Visual Analytic Application for Sport Event Data
author_id_str_mv 2ed2cc8d3dff635184def8d15afa21a9
7737f06e2186278a925f6119c48db8b1
author_id_fullname_str_mv 2ed2cc8d3dff635184def8d15afa21a9_***_Iwan Griffiths
7737f06e2186278a925f6119c48db8b1_***_Bob Laramee
author Iwan Griffiths
Bob Laramee
author2 David Chung
Philip Legg
Matthew Parry
Rhodri Bown
Iwan Griffiths
Robert Laramee
Min Chen
Bob Laramee
format Journal article
container_title IEEE Computer Graphics and Applications
container_start_page 1
publishDate 2015
institution Swansea University
doi_str_mv 10.1109/MCG.2015.25
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Mathematics and Computer Science - Computer Science{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Mathematics and Computer Science - Computer Science
url http://cs.swan.ac.uk/~csbob/research/
document_store_str 1
active_str 0
description Organizing sport video data for performance analysis can be challenging, especially when this involvesmultiple attributes, and the criteria for sorting frequently changes depending on the user’s task. In thiswork, we propose a visual analytic system to convert a user’s knowledge on rankings to support such aprocess. The system enables users to specify a sort requirement in a flexible manner without dependingon specific knowledge about individual sort keys. We use regression techniques to train different analyticalmodels for different types of sorting requirements. We use visualization to facilitate the discovery ofknowledge at different stages of the visual analytic process. This includes visualizing the parameters of theranking model, visualizing the results of a sort query for interactive exploration, and the playback of sortedvideo clips. We demonstrate the system with a case study in rugby to find key instances for analyzing teamand player performance.
published_date 2015-12-31T03:26:34Z
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score 11.019224

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