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The paper presents the use of linear and nonlinear multivariable models as tools to predict the results of 400-metres hurdles races in two different time frames. The constructed models predict the results obtained by a competitor with suggested training loads for a selected training phase or for an annual training cycle. All the models were constructed using the training data of 21 athletes from the Polish National Team. The athletes were characterized by a high level of performance (score for 400 metre hurdles: 51.26±1.24 s). The linear methods of analysis include: classical model of ordinary least squares (OLS) regression and regularized methods such as ridge regression, LASSO regression. The nonlinear methods include: artificial neural networks as multilayer perceptron (MLP) and radial basis function (RBF) network. In order to compare and choose the best model leave-one-out cross-validation (LOOCV) is used. The outcome of the studies shows that Lasso shrinkage regression is the best linear model for predicting the results in both analysed time frames. The prediction error for a training period was at the level of 0.69 s, whereas for the annual training cycle was at the level of 0.39 s. Application of artificial neural network methods failed to correct the prediction error. The best neural network predicted the result with an error of 0.72 s for training periods and 0.74 for annual training cycle. Additionally, for both training frames the optimal set of predictors was calculated.

Background: The purpose of this study was to investigate the lateralization of the lead leg during special exercises and the relationship with athletic performance throughout a hurdling session. Methods: Thirty-eight physical education students participated in the study. A novel three-part “OSI” test (walking over hurdles arranged in a circle, spiral, and straight line) was performed, and various hurdle practices (jogging and running) were selected as research tools. The lead leg selected by the participants was taken into consideration, and the relationship between the chosen lead leg and athletic performance in the five tests was established. Results: The lateralization of the lead leg changed depending on the shape of the running course. The results of further analysis showed (i) no correlation between the use of the right leg as the lead leg in three tests conducted at a marching pace, and (ii) a significant positive correlation between tests performed at the marching and running paces. Conclusion: Hurdlers flexibly change the dominant leading leg depending on the shape of the running course. The results of this research could prove helpful in the training of athletes for hurdling competitions, especially young runners in 400-m hurdles involving straight and corner tracks.

This paper presents a novel approach to planning training loads in hurdling using artificial neural networks. The neural models performed the task of generating loads for athletes’ training for the 400 meters hurdles. All the models were calculated based on the training data of 21 Polish National Team hurdlers, aged 22.25 ± 1.96, competing between 1989 and 2012. The analysis included 144 training plans that represented different stages in the annual training cycle. The main contribution of this paper is to develop neural models for planning training loads for the entire career of a typical hurdler. In the models, 29 variables were used, where four characterized the runner and 25 described the training process. Two artificial neural networks were used: a multi-layer perceptron and a network with radial basis functions. To assess the quality of the models, the leave-one-out cross-validation method was used in which the Normalized Root Mean Squared Error was calculated. The analysis shows that the method generating the smallest error was the radial basis function network with nine neurons in the hidden layer. Most of the calculated training loads demonstrated a non-linear relationship across the entire competitive period. The resulting model can be used as a tool to assist a coach in planning training loads during a selected training period.