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    Exploring the Relationship between Biological Maturation Level, Muscle Strength, and Muscle Power in Adolescents
    (Mdpi, 2022) Yapici, Hakan; Gulu, Mehmet; Yagin, Fatma Hilal; Eken, Ozgur; Gabrys, Tomasz; Knappova, Vera
    Simple Summary Muscle strength increases with age, and the period in which the increase in muscle mass is highest is the growth and development period in adolescents. In this context, the improvement of muscle power and muscle strength in adolescents can be achieved with the development of simple motor skills. Research on the relationship between biological maturation, muscle strength, and muscle power was limited in adolescents, and this research will make an important contribution to the literature. In this research, the relationship between biological maturation and muscle strength and power was investigated. In conclusion, biological maturation was found to be significantly associated with muscle strength and power in adolescents. The purpose of this study was to investigate the relationship between adolescents' biological maturation level and their muscle power, as well as their overall muscle strength. Overall, 691 adolescents (414 boys and 277 girls) aged 12.01-11.96 (measured for body mass, body height as well as vertical jump, muscle power, and muscle strength). There was a statistically significant difference in terms of average right and left grip strength, vertical jump, and power in the late maturation group. For the body height and vertical jump averages in male adolescents, it was observed that the body height and vertical jump averages in the late group were significantly lower than in the early and on-time maturation groups. For female adolescents' chronological age, sitting height, body mass, BMI, left and right grip strength, and power averages were found to be significantly higher compared with the on-time group (p < 0.05). It was established that biological maturation has a substantial link with vertical jump height and power, as well as grip strength on the right and left hands.
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    Using machine learning to determine the positions of professional soccer players in terms of biomechanical variables
    (Sage Publications Ltd, 2023) Yagin, Fatma Hilal; Hasan, Uday C. H.; Clemente, Filipe Manuel; Eken, Ozgur; Badicu, Georgian; Gulu, Mehmet
    This study aimed to predict professional soccer players' positions with machine learning according to certain locomotor demands. Data from 20 male professional soccer players (five defenders, eight midfielders, and seven attackers) from the same team were tracked daily with a global navigation satellite system. A total of 1910 individual training sessions were recorded. The 10-fold cross-validation method was used. Soccer player positions were predicted using predictive models created with random forest (RF), gradient boosting tree, bagging classification, and regression trees algorithms, and the results were evaluated with comprehensive performance measures. Ratios and an importance plot were used to analyze the importance of the variables according to their contributions to the estimation. The findings show that the RF model achieved 100% accuracy, which means that RF can predict all player positions (100%). Running distance (26.5%), total distance (17.2%), and player load (15.8%) were the three variables that contributed the most to the estimation of the RF model and were the most important factor in distinguishing player positions. Consequently, our proposed machine learning approach (RF model) can reduce false alarms and player mispositioning.