@article{article_454938, title={Talent classification of motoric parameters with support vector machine}, journal={International Journal of Sport Exercise and Training Sciences - IJSETS}, volume={4}, pages={98–104}, year={2018}, DOI={10.18826/useeabd.454938}, author={Kanat Usta, Hanife and Usta, Naci and Duru, Adil Deniz and Çotuk, Hasan Birol}, keywords={Talent selection,classification,support vector machines}, abstract={ Aim: In recent years, the methods of analysis of data science have started to be used frequently in talent selection in sports and the evaluation of athletes. Based on the motor and physical measurements of the future athletes, determining which sports branch they are prone to is important in terms of training and resource planning. Within the scope of this study, it was aimed to propose a classification system to determine which sports branches the participants are suitable for, based on motor and physical measurements. Material and Methods: Measurements of height, arm span, body weight, 20-meter sprint test, vertical jump height, 1 kg medicine ball throw, back strength, hand grip strength, flexibility test and standing long jump values <a> </a> <a class="msocomanchor"> [mk1] </a> were recorded with the contribution of 1240 participants who are 9 years old. Afterwards, grouping procedures were carried out with classification methods based on Support Vector Machines (SVM). Radial based functions are used as kernel functions of SVM. The results of evaluations made by consulting expert opinion beforehand were accepted as actual values, compared with the classification results and the performances of the classifiers were calculated. Within the scope of this study, participants were classified into four as rapidity branch (E), strength branch (F), height branch (G) and other group (H). Results: The accuracy values of classification of support vector machines were found ranging from 96% to 100% in each class, and 98% in average. Minimum value of sensitivity was found to be 93% while it was 99% in maximum. On the other hand , precision varied between 92% and 100%. Conclusion: In the light of the information provided, successful classification of the test dataset using the model that is formed by the training dataset, points out a possible high classification accuracy of big test datasets even in the use of a small dataset in the training phase. }, number={3}, publisher={İbrahim ERDEMİR}