Application of Regression and Neural Models to Predict Competitive Swimming Performance

Abstract

This research problem was indirectly but closely connected with the optimization of an athlete-selection process, based on predictions viewed as determinants of future successes. The research project involved a group of 249 competitive swimmers (age 12 yr., SD = 0.5) who trained and competed for four years. Measures involving fitness (e.g., lung capacity), strength (e.g., standing long jump), swimming technique (turn, glide, distance per stroke cycle), anthropometric variables (e.g., hand and foot size), as well as specific swimming measures (speeds in particular distances), were used. The participants (n = 189) trained from May 2008 to May 2009, which involved five days of swimming workouts per week, and three additional 45-min. sessions devoted to measurements necessary for this study. In June 2009, data from two groups of 30 swimmers each (n = 60) were used to identify predictor variables. Models were then constructed from these variables to predict final swimming performance in the 50 meter and 800 meter crawl events. Nonlinear regression models and neural models were built for the dependent variable of sport results (performance at 50m and 800m). In May 2010, the swimmers' actual race times for these events were compared to the predictions created a year prior to the beginning of the experiment. Results for the nonlinear regression models and perceptron networks structured as 8-4-1 and 4-3-1 indicated that the neural models overall more accurately predicted final swimming performance from initial training, strength, fitness, and body measurements. Differences in the sum of absolute error values were 4:11.96 (n = 30 for 800m) and 20.39 (n = 30 for 50m), for models structured as 8-4-1 and 4-3-1, respectively, with the neural models being more accurate. It seems possible that such models can be used to predict future performance, as well as in the process of recruiting athletes for specific styles and distances in swimming.

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.…”

“…There are many applications of artificial neural networks in sports (Schöllhorn, Jäger & Janssen, 2008). As an example of performance prediction, Maszczyk, Rocznoik, Waskiewicz, Czuba, Mikolajec, Zajac and Stanula (2012) modeled competitive swimming performance in 50 m front crawl with multilayer perceptron neural models using anthropometric data or specific swimming skills as input parameters. 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.…”

A web-oriented expert system for planning hurdles race training programmes

“…Numerous studies have shown that the ANN is a means of predicting sports results which has a good predictive ability [13,59]. Thus, the ANN enables a coach to model the future level of athletes performance and supports the process of sports selection [34,40,48,52]. For example, Silva et al [52] presented high realistic models of swimming performance prediction based on multilayer perceptron.…”

“…Evaluation of the system showed high reliability and high correlation with top experts in the field. While analyzing the literature, it can be also noticed that mathematical models frequently used in sports are artificial neural networks [34,35,40,44,48,50,52,58]. Numerous studies have shown that the ANN is a means of predicting sports results which has a good predictive ability [13,59].…”