Anthropometric Determinants of Triple Jump Performance in Under-17 Boys: A Predictive Modelling Approach

Authors

  • S. Prakash Research Scholar, Department of Physical Education and Sports Science, College of Science and Humanities, SRM Institute of Science and Technology, Kattankulathur, Tamilnadu, India
  • S. Jayasingh Albert Chandrasekar Assistant Professor, Department of Physical Education and Sports Science, College of Science and Humanities, SRM Institute of Science and Technology. Kattankulathur, Tamilnadu, India

DOI:

https://doi.org/10.22399/ijcesen.1527

Keywords:

Triple Jump Performance, Anthropometric Parameters, Under-17 Boys, Predictive Modelling, Physical Profiling, Jump Distance

Abstract

The triple jump is a complex athletic event that demands a unique combination of speed, strength, coordination, and biomechanical efficiency. This study aims to investigate the influence of key anthropometric parameters on triple jump performance among under-17 boys and to develop a predictive model that can assist in talent identification and performance enhancement. A sample of U-17 male athletes was assessed on various anthropometric variables including height, weight, leg length, arm span, and body mass index (BMI). Triple jump performance was measured through standardized field testing. Using multiple regression analysis, the study identified significant correlations between specific body dimensions and jump distance, with leg length and BMI emerging as the strongest predictors. The resulting model demonstrated high predictive accuracy and offers a valuable tool for coaches and sports scientists in identifying promising young athletes and customizing training strategies. This research highlights the importance of anthropometric profiling in youth athletics and its potential to inform evidence-based development programs.

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 DOI: 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 DOI: 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 DOI: 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 DOI: 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 DOI: 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 DOI: https://doi.org/10.1080/02640414.2012.709262

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 DOI: 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 DOI: 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 DOI: 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 DOI: 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 DOI: 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 DOI: 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 DOI: 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 DOI: 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 DOI: 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 DOI: 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 DOI: 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 DOI: https://doi.org/10.22399/ijasrar.21

Serap ÇATLI DİNÇ, AKMANSU, M., BORA, H., ÜÇGÜL, A., ÇETİN, B. E., ERPOLAT, P., … ŞENTÜRK, E. (2024). Evaluation of a Clinical Acceptability of Deep Learning-Based Autocontouring: An Example of The Use of Artificial Intelligence in Prostate Radiotherapy. International Journal of Computational and Experimental Science and Engineering, 10(4). https://doi.org/10.22399/ijcesen.386 DOI: https://doi.org/10.22399/ijcesen.386

S. Esakkiammal, & K. Kasturi. (2024). Advancing Educational Outcomes with Artificial Intelligence: Challenges, Opportunities, And Future Directions. International Journal of Computational and Experimental Science and Engineering, 10(4). https://doi.org/10.22399/ijcesen.799 DOI: https://doi.org/10.22399/ijcesen.799

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 DOI: https://doi.org/10.22399/ijasrar.20

ZHANG, J. (2025). Artificial intelligence contributes to the creative transformation and innovative development of traditional Chinese culture. International Journal of Computational and Experimental Science and Engineering, 11(1). https://doi.org/10.22399/ijcesen.860 DOI: https://doi.org/10.22399/ijcesen.860

S. Menaka, & V. Selvam. (2025). Bibliometric Analysis of Artificial Intelligence on Consumer Purchase Intention in E-Retailing. International Journal of Computational and Experimental Science and Engineering, 11(1). https://doi.org/10.22399/ijcesen.1007 DOI: https://doi.org/10.22399/ijcesen.1007

G. Prabaharan, S. Vidhya, T. Chithrakumar, K. Sika, & M.Balakrishnan. (2025). AI-Driven Computational Frameworks: Advancing Edge Intelligence and Smart Systems. International Journal of Computational and Experimental Science and Engineering, 11(1). https://doi.org/10.22399/ijcesen.1165 DOI: https://doi.org/10.22399/ijcesen.1165

M.K. Sarjas, & G. Velmurugan. (2025). Bibliometric Insight into Artificial Intelligence Application in Investment. International Journal of Computational and Experimental Science and Engineering, 11(1). https://doi.org/10.22399/ijcesen.864 DOI: https://doi.org/10.22399/ijcesen.864

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 DOI: https://doi.org/10.22399/ijasrar.19

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Published

2025-04-13

How to Cite

S. Prakash, & S. Jayasingh Albert Chandrasekar. (2025). Anthropometric Determinants of Triple Jump Performance in Under-17 Boys: A Predictive Modelling Approach. International Journal of Computational and Experimental Science and Engineering, 11(2). https://doi.org/10.22399/ijcesen.1527

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Research Article