Abstract
This chapter deals with identifying the characteristics of athletes in training. According to the theory of the sports training, this identification is conducted after an evaluation phase, where goals set prior to the training cycle are compared with the achieved results. The purpose of this process is to discover those characteristics of the athlete that have the greatest positive impact on performance. Improving these characteristics needs to be more strongly emphasized in the planning the training sessions in next training cycles.
The characteristics are identified using association rule mining. On the basis of the comparative analysis, progress in the performance of a specific athlete under specific training conditions is identified. These conditions affect the behavior of the athlete and highlights the quality of a realization process. The athlete’s characteristics during the training sessions are recorded in a transaction database as attributes specifying the features. In order to discover the relations among the features in the transaction databases, algorithms for association rule mining are proposed based on computational intelligence. In the future, these rules could enable athletes to select the proper training sessions without the aid of professional coaches.
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The results published in this chapter are based on the Ph.D. dissertation of Iztok Fister Jr. defended at the University of Maribor in 2017 [16].
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Fister, I., Fister Jr., I., Fister, D. (2019). BatMiner for Identifying the Characteristics of Athletes in Training. In: Computational Intelligence in Sports. Adaptation, Learning, and Optimization, vol 22. Springer, Cham. https://doi.org/10.1007/978-3-030-03490-0_9
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DOI: https://doi.org/10.1007/978-3-030-03490-0_9
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