The Development of Athlete Performance Capabilities Index (APCI) Model for Male U12 Player Selection using Multivariate Analysis

  • Abstract
  • Keywords
  • References
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  • Abstract

    This study develops an Athlete Performance Capabilities Index (APCI) model using multivariate analysis for selecting the best player of under twelve (U12).  Measurement of anthropometrics and physical fitness were evaluated among 178 male players aged 12±0.52 years. Factor score derived by Principal Component Analysis were used to obtain a model for APCI and Discriminant Analysis (DA) were conducted to validate the correctness of group classification by APCI. Result was found two factors with eigenvalues greater than 1 were extracted which accounted for 62.00% of the variations present in the original variables. The two factors were used to obtain the factor score coefficients explained by 35.72% and 26.67% of the variations in athlete performance respectively. Factor 1 revealed high factor loading on fitness compared to Factor 2 as it was significantly related to anthropometrics. A model was obtained using standardized coefficient of factor 1. Three clusters of performance were shaped in view by categorizing APCI ≥ 75%, 25% ≤ APCI < 75% and APCI < 25% as high, moderate and low performance group respectively. Three discriminated variables out of thirteen variables were obtained using Forward and Backward stepwise mode of DA, which were weight, standing broad jump, and 40 meters’ speed. Such variables were established as essential indicator for selecting the best player among male U12.




  • Keywords

    Athlete performance model; Capabilities Index; Male U12 player; Multivariate analysis.

  • References

      [1] Balyi, I., Way, R., & Higgs, C. (2013). Long-term athlete development. Human Kinetics.

      [2] Balyi, I., & Hamilton, A. (2004). Long-term athlete development: Trainability in childhood and adolescence: Windows of opportunity, optional trainability. Victoria, British Colombia: National Coaching Institute and Advanced Training and Performance.

      [3] Papacharisis, V., Goudas, M., Danish, S. J., & Theodorakis, Y. (2005). The effectiveness of teaching a life skills program in a sport context. Journal of Applied Sport Psychology, 17(3), 247-254.

      [4] Jones, M. I., & Lavallee, D. (2009). Exploring the life skills needs of British adolescent athletes. Psychology of sport and Exercise, 10(1), 159-167.

      [5] Lang, M., & Light, R. (2010). Interpreting and implementing the long term athlete development model: English swimming coaches' views on the (swimming) LTAD in practice. International Journal of Sports Science and Coaching, 5(3), 389-402.

      [6] Abbott, A., & Collins, D. (2002). A theoretical and empirical analysis of a'state of the art'talent identification model. High Ability Studies, 13(2), 157-178.

      [7] Abbott, A., & Collins, D. (2004). Eliminating the dichotomy between theory and practice in talent identification and development: Considering the role of psychology. Journal of Sports Sciences, 22(5), 395-408.

      [8] G. Hoare, D., & Warr, C. R. (2000). Talent identification and women's soccer: An Australian experience. Journal of Sports Sciences, 18(9), 751-758.

      [9] Maliki, A. B. H. M., Abdullah, M. R., Juahir, H., Abdullah, F., Abdullah, N. A. S., Musa, R. M., Mat-Rasid, S. M., Adnan, A., Kosni, N. A., Muhamad, W. S., & Nasir, N. A. M. (2018). A multilateral modelling of Youth Soccer Performance Index (YSPI). IOP Conference Series: Materials Science and Engineering, 342(1), 1-10.

      [10] Maliki, A. B. H. M., Abdullah, M. R., Juahir, H., Muhamad, W. S. A. W., Nasir, N. A. M., Musa, R. M., Mat-Rasid, S. M., Adnan, A., Kosni, N. A., Abdullah, F., & Abdullah, N. A. S. (2018). The role of anthropometric, growth and maturity index (AGaMI) influencing youth soccer relative performance. IOP Conference Series: Materials Science and Engineering, 342(1), 1-10.

      [11] Abdullah, M. R., Maliki, A. B. H., Musa, R. M., Kosni, N. A., Juahir, H., & Mohamed, S. B. (2017). Identification and comparative analysis of essential performance indicators in two levels of soccer expertise. International Journal on Advanced Science, Engineering and Information Technology, 7(1), 305-314.

      [12] Abdullah, M. R., Maliki, M., Husin, A. B., Musa, R. M., Kosni, N. A., Juahir, H., & Haque, M. (2016). Multi-hierarchical pattern recognition of athlete's relative performance as a criterion for predicting potential athletes. Journal of Young Pharmacists, 8(4), 463-470.

      [13] Musa, R. M., Abdullah, M. R., Maliki, A. B. H. M., Kosni, N. A., & Haque, M. (2016). The application of principal components analysis to recognize essential physical fitness components among youth development archers of Terengganu, Malaysia. Indian Journal of Science and Technology, 9(44), 1-6.

      [14] Eyduran, E., Karakus, K., Karakus, S., & Cengız, F. (2009). Usage of factor scores for determining relationships among body weight and some body measurements. Bulgarian Journal of Agricultural Science, 15(4), 374-378.

      [15] Yakubu, A., Kuje, D., & Okpeku, M. (2009). Principal components as measures of size and shape in Nigerian indigenous chickens. Thai Journal of Agricultural Science, 42(3), 167-176.

      [16] Ifeanyichukwu, U. (2012). Use of factor scores for determining the relationship between body measurements and semen traits of cocks. Open Journal of Animal Sciences, 2(1), 41-44.

      [17] Stangier, C., Abel, T., Mierau, J., Hollmann, W., & Strüder, H. K. (2016). Effects of cycling versus running training on sprint and endurance capacity in inline speed skating. Journal of Sports Science and Medicine, 15(1), 41.

      [18] Ford, P., De Ste Croix, M., Lloyd, R., Meyers, R., Moosavi, M., Oliver, J & Williams, C. (2011). The long-term athlete development model: Physiological evidence and application. Journal of Sports Sciences, 29(4), 389-402.

      [19] Miyamoto, T., Kamada, H., Tamaki, A., & Moritani, T. (2016). Low-intensity electrical muscle stimulation induces significant increases in muscle strength and cardiorespiratory fitness. European Journal of Sport Science, 16(8), 1104-1110.

      [20] Fenton, S. A., Duda, J. L., & Barrett, T. (2016). Inter-participant variability in daily physical activity and sedentary time among male youth sport footballers: Independent associations with indicators of adiposity and cardiorespiratory fitness. Journal of Sports Sciences, 34(3), 239-251.

      [21] Popovici, I. M., Popescu, L., & Radu, L. E. (2016). Evaluation of some physical fitness characteristics at age 11 to 13. Timisoara Physical Education and Rehabilitation Journal, 9(17), 24-28.




Article ID: 23589
DOI: 10.14419/ijet.v7i4.34.23589

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