Abstract

The selection of sports talents is a problem when many variables intervene. These variables are represented by mixed data, where symbolic and numerical data are present. Many coaches still rely on empirical methods to identify talents, which can lead to subjective and biased decisions. On the other hand, the veracity of the input data to the selection process can be affected by subjective factors that increase uncertainty in decision-making. This situation is conducive to applying soft computing techniques to solve this problem. In the methods section of the work, an algorithm is proposed for the selection of sports talent that combines word computing techniques with the neutrosophic theory. Then, in the results section, the proposal is validated by comparing the proposed method against other selection methods. The results are compared using non-parametric tests and the SPSS tool. It is shown that the proposed model reports better results than traditional methods.