| Feature | No.43 January 2005 |
From Simple to Complex understanding of performance by Alternative Modelling Natalie Balagué, Spain |
This presentation was made during the 2004 Pre-Olympic Congress in Thessaloniki, Greece, August 6-11th. The full text of the article is currently under review and will be available in the IJCSS in the near future. Natalie Balagué & C. Torrents INEFC. University of Barcelona, Spain Non-linear relations and interactions between variables characterise and explain main phenomena occurring in exercise and sport performance. Nevertheless, the research tools commonly applied are based almost exclusively upon a simple and linear understanding. Current training theory and common methods applied by coaches in practice are also based on the classical science paradigm, that has dominated and is still influencing sport science very deeply. The poor and often confusing explanations or solutions for practitioners and scientists given by the available research results are not only limiting the development of sport science but also endangering the well-being of the athlete [1]. The posed question is: is it possible to handle complexity? Besides showing the limitations of the classical paradigm the aim of this presentation is to highlight how tools and concepts from computer science can face the complex nature of sport performance. Especially alternative modelling and the biologically motivated paradigms (fuzzy logic, neural networks -ANN, genetic algorithms) allow to: integrate interdisciplinary research adopting a system view [2], avoiding the increasing analytical tendency in sport sciences, getting qualitative information of dynamic processes, allowing thinking before computing, compressing and handling with imprecise information, instead of simplifying or avoiding it, studying interactions among non linear variables and non linear processes (instead of transforming them in linear or simple ones) [3]. develop systems able to learn and generalise the learn matter (supervised ANN), learn, recognise unknown structures and classify pattern responses (unsupervised ANN -KFM) [5], describe, analyse and evaluate continuous adaptation processes (dynamic controlled networks) [4]. In conclusion, the application of modelling techniques and mainly the so-called soft computing are suitable tools for understanding the non-linear complex dynamic processes involved in performance. References [1]. Balagué, N. et al. (2002). Acta Ac. Ol. Estonia, 9, 51-63. [2]. Lames, M. (2003). IJCSS, Vol2,Ed.1 [3]. Perl, J. & Mester, J. (2001). Leistungssport, 2, 54-62. [4]. Perl, J. (2001). Sport & Informatik, Köln: Straus. [5]. Schöllhorn, W. &Bauer, H-U. (1998). Informatik im Sport, Köln: Straus. Natalie Balagué nbalague@gencat.net http://www.icsspe.org/portal/bulletin-january2005.htm From Simple to Complex understanding of perofrmance by Alternative Modelling Natalie Balagué, Spain