This paper describes a fuzzy-based software tool for predicting results in the 110m hurdles. The predictive models were built on using 40 annual training cycles completed by 18 athletes. These models include: ordinary least squares regression, ridge regression, LASSO regression, elastic net regression and nonlinear fuzzy correction of least squares regression. In order to compare them, and choose the best model, leave-one-out cross-validation was used. This showed that the fuzzy corrector proposed in this paper has the lowest prediction error. The developed software can support a coach in planning an athlete's annual training cycle. It allows the athlete's results to be predicted, and in this way, for the best training loads to be selected. The tool is a web-based interactive application that can be run from a computer or a mobile device. The whole system was implemented using the R programming language with additional packages.
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