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Visualizing Rugby Game Styles Using Self-Organizing Maps

Overview of attention for article published in IEEE Computer Graphics and Applications, November 2016
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24 Mendeley
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Article details
Title
Visualizing Rugby Game Styles Using Self-Organizing Maps
Published in
IEEE Computer Graphics and Applications, November 2016
DOI 10.1109/mcg.2016.115
Pubmed ID
Authors
Abstract

Rugby coaches and analysts often use notational data describing match events to assess their team's performance and to devise strategic plans for upcoming matches. However, given the volume and complexity of the data available, it is difficult for them to recognize high-dimensional relationships among the available performance variables. A nonlinear approach using self-organizing maps (SOM) can help visualize the performance of a team and its opponents as well as the subsequent suitability of certain game styles, given the style of the opponent.

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X Demographics

X Demographics

The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley demographics

Mendeley demographics

The data shown below were compiled from readership statistics for 24 Mendeley readers of this research output. Click here to see the associated Mendeley record.
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Geographical breakdown

Geographical breakdown
Country Count As %
Unknown 24 100%

Demographic breakdown

Readers by professional status
Readers by professional status Count As %
Student > Ph. D. Student 5 21%
Lecturer 3 13%
Librarian 2 8%
Student > Doctoral Student 2 8%
Lecturer > Senior Lecturer 1 4%
Other 4 17%
Unknown 7 29%
Readers by discipline
Readers by discipline Count As %
Sports and Recreations 6 25%
Computer Science 4 17%
Engineering 3 13%
Business, Management and Accounting 2 8%
Arts and Humanities 1 4%
Other 2 8%
Unknown 6 25%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 26 November 2016.
All research outputs
#16,048,318
of 25,377,790 outputs
Outputs from IEEE Computer Graphics and Applications
#1,105
of 1,259 outputs
Outputs of similar age
#236,392
of 415,191 outputs
Outputs of similar age from IEEE Computer Graphics and Applications
#11
of 20 outputs
Altmetric has tracked 25,377,790 research outputs across all sources so far. This one is in the 34th percentile – i.e., 34% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,259 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.2. This one is in the 11th percentile – i.e., 11% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 415,191 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 40th percentile – i.e., 40% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 20 others from the same source and published within six weeks on either side of this one. This one is in the 40th percentile – i.e., 40% of its contemporaries scored the same or lower than it.