Abstract
Extraction of meaningful information by using artificial neural networks, where the focus is upon developing new insights for sports performance and supporting decision making, is crucial to gain success. The aim of this article is to create a theoretical framework and structurally connect the sports and multi-layer artificial neural network domains through: (a) describing sports as a complex socio-technical system; (b) identification of pre-processing subsystem for classification; (c) feature selection by using data-driven valued tolerance ratio method; (d) design predictive system model of sports performance using a backpropagation neural network. This would allow identifying, classifying, and forecasting performance levels for an enlarged data set.
Highlights
The prediction of sports performance is carried out using different methodological approaches
As the present paper focuses on the study of neural network modelling, the data mining method classification has been chosen
The results have shown that the neural networks can provide valuable decision support in a team selection process
Summary
The prediction of sports performance is carried out using different methodological approaches. The first and most common approach found in the literature has to do with the use of traditional statistical methods, such as linear discriminant analysis, multivariate discriminant analysis [1], multiple linear regressions [2], and probit and logit model [3]. The current trend in the intelligent nonlinear system modelling research is concerned with the integration of artificial intelligence (AI) tools: intelligent agents, neural networks, evolutionary algorithms, fuzzy technology, and support vector machines [5] in a framework for solving complex adaptive problems [6]. There are not a lot of studies referring to the evaluation of sports performance, especially, based on a socio-technical system by putting into practice artificial neural networks (ANNs). This method has been used and proven useful in other fields, such as medicine, engineering, and economics
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