Unlocking Youth Athletic Potential: Predicting Triple Jump Outcomes from Anthropometric Profiles in U-17 Male Athletes
DOI:
https://doi.org/10.22399/ijcesen.1528Keywords:
Triple Jump Performance, Anthropometric Parameters, Predictive Modeling, Body Mass Index (BMI), Regression Analysis, Sports ScienceAbstract
Understanding the role of anthropometric characteristics in athletic performance is essential for identifying and nurturing young talent. This study explores the predictive relationship between key anthropometric variables and triple jump performance among under-17 male athletes. A total of 60 participants were assessed for parameters including height, weight, leg length, arm span, thigh circumference, and body mass index (BMI). Triple jump performance was evaluated under standardized field conditions. Using multiple linear regression analysis, the study identified leg length and height as the most significant predictors of jump distance, while BMI showed a negative association. The developed model demonstrated strong predictive accuracy, accounting for 68% of the variance in performance outcomes. These findings emphasize the importance of incorporating physical profiling into youth training programs, allowing coaches and sports scientists to design data-driven strategies for athlete development. The study contributes to performance optimization and talent identification frameworks in youth athletics.
References
Fernández-Romero, J.J., Suárez, H.V., & Cancela, J.M. (2016). Anthropometric analysis and performance characteristics to predict selection in young male and female handball players. Motriz: Revista de Educação Física. 22(04);0283-0289. https://doi.org/10.1590/s1980-6574201600040011
Saavedra, J.M., Kristjánsdóttir, H., Einarsson, I.Þ., Guðmundsdóttir, M.L., Þorgeirsson, S., & Stefansson, A. (2018). Anthropometric characteristics, physical fitness, and throwing velocity in elite women's handball teams. The Journal of Strength & Conditioning Research. 32(8);2294-2301. https://doi.org/10.1519/jsc.0000000000002412
Schmitz, T.L., Fleddermann, M.T., & Zentgraf, K. (2024). Talent selection in 3× 3 basketball: role of anthropometrics, maturation, and motor performance. Frontiers in Sports and Active Living. 6(1459103). https://doi.org/10.3389/fspor.2024.1459103
Kolodziej, M., Groll, A., Nolte, K., Willwacher, S., Alt, T., Schmidt, M., & Jaitner, T. (2023). Predictive modeling of lower extremity injury risk in male elite youth soccer players using least absolute shrinkage and selection operator regression. Scandinavian Journal of Medicine & Science in Sports. 33(6);1021-1033. https://doi.org/10.1111/sms.14322
Craig, T.P., & Swinton, P. (2021). Anthropometric and physical performance profiling does not predict professional contracts awarded in an elite Scottish soccer academy over a 10-year period. European Journal of Sport Science. 21(8);1101-1110. https://doi.org/10.1080/17461391.2020.1808079
Coelho-E-Silva, M.J., Vaz, V., Simões, F., Carvalho, H.M., Valente-Dos-Santos, J., Figueiredo, A.J., et al. (2012). Sport selection in under-17 male roller hockey. Journal of Sports Sciences. 30(16);1793-1802. https://doi.org/10.1080/02640414.2012.734474
Fernández-Romero, J.J., Suárez, H.V., & Carral, J.M.C. (2017). Selection of talents in handball: anthropometric and performance analysis. Revista Brasileira de Medicina do Esporte. 23;361-365. https://doi.org/10.1590/1517-869220172305141727
Saavedra, J.M., Halldórsson, K., Kristjánsdóttir, H., Þorgeirsson, S., & Sveinsson, G. (2019). Anthropometric charachteristics, physical fitness and the prediction of throwing velocity in handball men young players. Kinesiology. 51(2);253-260. https://doi.org/10.26582/k.51.2.14
Ferraz, A., Valente-Dos-Santos, J., Sarmento, H., Duarte-Mendes, P., & Travassos, B. (2020). A review of players' characterization and game performance on male rink-hockey. International Journal of Environmental Research and Public Health. 17(12);4259. https://doi.org/10.3390/ijerph17124259
Ramos, S., Volossovitch, A., Ferreira, A.P., Barrigas, C., Fragoso, I., & Massuça, L. (2020). Differences in maturity, morphological, and fitness attributes between the better-and lower-ranked male and female U-14 Portuguese elite regional basketball teams. The Journal of Strength & Conditioning Research. 34(3);878-887. https://doi.org/10.1519/jsc.0000000000002691
Barrera-Domínguez, F.J., Almagro, B.J., Tornero-Quiñones, I., Sáez-Padilla, J., Sierra-Robles, Á., & Molina-López, J. (2020). Decisive factors for a greater performance in the change of direction and its angulation in male basketball players. International Journal of Environmental Research and Public Health. 17(18);6598. https://doi.org/10.3390/ijerph17186598
França, C., Gouveia, É., Caldeira, R., Marques, A., Martins, J., Lopes, H., ... & Ihle, A. (2022). Speed and agility predictors among adolescent male football players. International Journal of Environmental Research and Public Health. 19(5);2856. https://doi.org/10.3390/ijerph19052856
Pérez-López, A., Sinovas, M.C., Álvarez-Valverde, I., & Valades, D. (2015). Relationship between body composition and vertical jump performance in young spanish soccer players. Journal of Sport and Human Performance. 3(3). https://doi.org/10.12922/jshp.v3i3.63
França, C., Marques, A., Ihle, A., Nuno, J., Campos, P., Gonçalves, F., et al. (2023). Associations between muscular strength and vertical jumping performance in adolescent male football players. Human Movement. 24(2);94-100. https://doi.org/10.5114/hm.2023.117778
Nikolaidis, P.T., Ruano, M.A.G., De Oliveira, N.C., Portes, L.A., Freiwald, J., Lepretre, P.M., & Knechtle, B. (2016). Who runs the fastest? Anthropometric and physiological correlates of 20 m sprint performance in male soccer players. Research in Sports Medicine. 24(4);341-351. https://doi.org/10.1080/15438627.2016.1222281
Valente-dos-Santos, J., Coelho-e-Silva, M.J., Simões, F., Figueiredo, A.J., Leite, N., Elferink-Gemser, M.T., et al. (2012). Modeling developmental changes in functional capacities and soccer-specific skills in male players aged 11-17 years. Pediatric Exercise Science. 24(4);603-621. https://doi.org/10.1123/pes.24.4.603
Pienaar, C., Kruger, A., Monyeki, A.M., & Van Der Walt, K.N. (2015). Physical and motor performance predictors of lower body explosive power (LBEP) among adolescents in the North-West Province: PAHL study. South African Journal for Research in Sport, Physical Education and Recreation. 37(2);95-108. https://www.ajol.info/index.php/sajrs/article/view/123011/112552
Cejudo, A. (2022). Risk factors for, and prediction of, shoulder pain in young badminton players: a prospective cohort study. International Journal of Environmental Research and Public Health. 19(20);13095. https://doi.org/10.3390/ijerph192013095
McCluskey, L., Lynskey, S., Leung, C.K., Woodhouse, D., Briffa, K., & Hopper, D. (2010). Throwing velocity and jump height in female water polo players: Performance predictors. Journal of Science and Medicine in Sport. 13(2);236-240. https://doi.org/10.1016/j.jsams.2009.02.008
Hafez, I. Y., & El-Mageed, A. A. A. (2025). Enhancing Digital Finance Security: AI-Based Approaches for Credit Card and Cryptocurrency Fraud Detection. International Journal of Applied Sciences and Radiation Research, 2(1). https://doi.org/10.22399/ijasrar.21
Olola, T. M., & Olatunde, T. I. (2025). Artificial Intelligence in Financial and Supply Chain Optimization: Predictive Analytics for Business Growth and Market Stability in The USA. International Journal of Applied Sciences and Radiation Research, 2(1). https://doi.org/10.22399/ijasrar.18
Shajeni Justin, & Tamil Selvan. (2025). A Systematic Comparative Study on the use of Machine Learning Techniques to Predict Lung Cancer and its Metastasis to the Liver: LCLM-Predictor Model. International Journal of Computational and Experimental Science and Engineering, 11(1). https://doi.org/10.22399/ijcesen.788
Fowowe, O. O., & Agboluaje, R. (2025). Leveraging Predictive Analytics for Customer Churn: A Cross-Industry Approach in the US Market. International Journal of Applied Sciences and Radiation Research, 2(1). https://doi.org/10.22399/ijasrar.20
M. Venkateswarlu, K. Thilagam, R. Pushpavalli, B. Buvaneswari, Sachin Harne, & Tatiraju.V.Rajani Kanth. (2024). Exploring Deep Computational Intelligence Approaches for Enhanced Predictive Modeling in Big Data Environments. International Journal of Computational and Experimental Science and Engineering, 10(4). https://doi.org/10.22399/ijcesen.676
Ibeh, C. V., & Adegbola, A. (2025). AI and Machine Learning for Sustainable Energy: Predictive Modelling, Optimization and Socioeconomic Impact In The USA. International Journal of Applied Sciences and Radiation Research , 2(1). https://doi.org/10.22399/ijasrar.19
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