An intelligent identification model for the selection of elite rowers by incorporating Internet-of-Things technology - Liu, J.-W.; Chen, S.-H.; Huang, Y.-C.; Wang, C.-T. (IEEE Access, 2020)
Over the last few decades, the training methods for rowers have been converging toward similar models owing to the progress in science and technology, in particular, the increased flow of information. As a result, rowing performance in competitions at the international level is the best it has ever been. However, it is possible to further enhance rowers` performance. An important first step to obtain an advantage on the race course is the selection of rowing athletes. The selection method presented here began by inviting experts and scholars in the field to complete a questionnaire, which was established through the analysis and compilation of relevant literature on rowing and athlete selection. Subsequently, the modified Delphi method is applied to achieve an expert consensus on the selection criteria to be evaluated. Five primary criteria, including athlete monitoring via internet-of-things (IoT) technology, and twenty sub-criteria were identified for the selection of elite rowers. An evaluation model for the athletes was constructed from the data using the analytic hierarchy process. The results showed that when selecting rowers the primary criterion of body factor has the highest priority, followed by IoT measurement factor, professional factor, reaction factor, and psychological factor. Furthermore, it reveals that the important sub-criteria affecting athlete selection are body composition, muscle composition, and competition scores. The framework provided by this study for the selection of elite rowers can be refined and adapted for the selection of elite athletes in related sports.