Document Detail

Neural network modelling and dynamical system theory: are they relevant to study the governing dynamics of association football players?
MedLine Citation:
PMID:  22060175     Owner:  NLM     Status:  MEDLINE    
Recent studies have explored the organization of player movements in team sports using a range of statistical tools. However, the factors that best explain the performance of association football teams remain elusive. Arguably, this is due to the high-dimensional behavioural outputs that illustrate the complex, evolving configurations typical of team games. According to dynamical system analysts, movement patterns in team sports exhibit nonlinear self-organizing features. Nonlinear processing tools (i.e. Artificial Neural Networks; ANNs) are becoming increasingly popular to investigate the coordination of participants in sports competitions. ANNs are well suited to describing high-dimensional data sets with nonlinear attributes, however, limited information concerning the processes required to apply ANNs exists. This review investigates the relative value of various ANN learning approaches used in sports performance analysis of team sports focusing on potential applications for association football. Sixty-two research sources were summarized and reviewed from electronic literature search engines such as SPORTDiscus, Google Scholar, IEEE Xplore, Scirus, ScienceDirect and Elsevier. Typical ANN learning algorithms can be adapted to perform pattern recognition and pattern classification. Particularly, dimensionality reduction by a Kohonen feature map (KFM) can compress chaotic high-dimensional datasets into low-dimensional relevant information. Such information would be useful for developing effective training drills that should enhance self-organizing coordination among players. We conclude that ANN-based qualitative analysis is a promising approach to understand the dynamical attributes of association football players.
Aviroop Dutt-Mazumder; Chris Button; Anthony Robins; Roger Bartlett
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Publication Detail:
Type:  Journal Article; Review    
Journal Detail:
Title:  Sports medicine (Auckland, N.Z.)     Volume:  41     ISSN:  1179-2035     ISO Abbreviation:  Sports Med     Publication Date:  2011 Dec 
Date Detail:
Created Date:  2011-11-08     Completed Date:  2012-03-07     Revised Date:  2013-05-16    
Medline Journal Info:
Nlm Unique ID:  8412297     Medline TA:  Sports Med     Country:  New Zealand    
Other Details:
Languages:  eng     Pagination:  1003-17     Citation Subset:  IM    
School of Physical Education, University of Otago, Dunedin, New Zealand.
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MeSH Terms
Athletic Performance
Neural Networks (Computer)*
Nonlinear Dynamics*
Task Performance and Analysis

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine

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