Modeling and prediction of competitive performance in swimming upon neural networks

Abstract

The purpose of the paper is to demonstrate that the performance of an elite female swimmer in the finals of the 200-m backstroke at the Olympic Games 2000 in Sydney can be predicted by means of the nonlinear mathematical method of artificial neural networks (Multi-Layer Perceptrons). The data consisted of the performance output of 19 competitions (200-m backstroke) prior to the Olympics and the training input data of the last 4 weeks prior to each competition. Multi-Layer Perceptrons with 10 input neurons, 2 hidden neuron, and 1 output neuron were used. Since the data of 19 competitions are insufficient to train such networks, the training input and competition data of another athlete were used in the training processes of the neural networks to pre-train the neural networks. The neural models were validated by the "leave-one-out" method, then the neural models were used to predict the Olympic competitive performance. The results show that the modeling was very precise; the error of the prediction was only 0.05 s, with a total swim time of 2:12.64 min:s.

Linear and Nonlinear Prediction Models Show Comparable Precision for Maximal Mean Speed in a 4x1000 m Field Test

“…was not superior to the linear regression model in this study. Nevertheless, other studies had found nonlinear models to allow better predictions (Edelmann-Nusser, Hohmann, & Henneberg, 2002;Maszczyk, Zajac, & Ryguła, 2011;Maszczyk, Rocznoik, Waskiewicz, Czuba, Mikolajec, Zajac, & Stanula, 2012).…”

“…When it comes to comparing linear and nonlinear models with regard to prediction accuracy, the quality of prediction of neural models seems to be similar to that of regression analysis and regression models, potentially even better. This seems to hold true in VO 2 max prediction (Abut, & Akay, 2015;Akay, Zayid, Aktürk, & George, 2011) as well as in performance prediction studies (Edelmann-Nusser, Hohmann, & Henneberg, 2002;Maszczyk, Zajac, & Ryguła, 2011;Maszczyk, Rocznoik, Waskiewicz, Czuba, Mikolajec, Zajac, & Stanula, 2012). Hence, Jäger, Kurz and Müller (2016) assume that nonlinear neural network approaches possibly provide more sophisticated methods for predicting maximal mean speed in a 4x1000 m Field Test which may enhance the accuracy of their linear model.…”

Predicting Elite Triathlon Performance: A Comparison of Multiple Regressions and Artificial Neural Networks

“…Artificial neural networks (ANNs) are an alternative computational approach for performance predicition. Edelmann-Nusser, Hohmann, and Henneberg (2002) as well as Silva et al (2007) showed that artificial neural networks could be a valuable method for performance modelling, without the restrictions of distribution and independence of variables. Edelmann-Nusser et al (2002) predicted the 200 m backstroke time of an elite female swimmer in the finals of the Olympic games by using artificial neural networks (multi-layer perceptrons) based on collected training data.…”

“…Edelmann-Nusser, Hohmann, and Henneberg (2002) as well as Silva et al (2007) showed that artificial neural networks could be a valuable method for performance modelling, without the restrictions of distribution and independence of variables. Edelmann-Nusser et al (2002) predicted the 200 m backstroke time of an elite female swimmer in the finals of the Olympic games by using artificial neural networks (multi-layer perceptrons) based on collected training data. The accuracy of the results of this approach were attributed to the fact that "the adaptive behavior of the system athlete is quite a complex, non-linear problem" (Edelmann-Nusser et al, 2002).…”

“…Edelmann-Nusser et al (2002) predicted the 200 m backstroke time of an elite female swimmer in the finals of the Olympic games by using artificial neural networks (multi-layer perceptrons) based on collected training data. The accuracy of the results of this approach were attributed to the fact that "the adaptive behavior of the system athlete is quite a complex, non-linear problem" (Edelmann-Nusser et al, 2002). However, multiple linear regression analyses have been more widely used to develop prediction models.…”