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International Journal of Physical Education, Sports and Health
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P-ISSN: 2394-1685 | E-ISSN: 2394-1693 | CODEN: IJPEJB

Impact Factor (RJIF): 5.38

2024, Vol. 11, Issue 1, Part C

Quantitative analysis using an artificial neural network of the most important bio-kinematic variables to determine the effectiveness of the long jump


Author(s): Dr. Mazin Enhaier Lami

Abstract:
The purpose of this paper is to using a neural network to identify the most important bio-kinetic variables to determine the level of achievement of advanced youth in the long jump event, and identifying the differences between the bio-kinetic variables that contribute to determining the level of achievement between the categories of young people and advanced for the long jump event. The researcher used the descriptive method with correlational and comparative relationships to suit the nature of the research problem. The research community was determined from the jumpers participating in the Iraqi Clubs and Institutions Championships for the year 2023, as the researcher chose the research sample in a deliberate manner, namely the top six jumpers for the two categories of youth and advanced participating in the two championships above. 40 successful attempts for the youth category and 36 successful attempts for the advanced were analyzed, and thus the total number became there are 76 attempts for the youth and advanced categories. One of the most important results reached by the researcher is that: The starting angle variable came in first place in terms of its importance in determining achievement for the youth group, while the leaning time variable came in first place in terms of its importance in determining achievement for advanced students, and the contribution rate of the independent variables to the dependent variable (achievement competition) for young people reached (58%), while the contribution rate for advanced reached (65%). One of the most important recommendations recommended by the researchers is that: Necessity of applying theoretical models extracted using artificial neural networks in training programs for trainers to evaluate the level of achievement, and providing biomechanical feedback based on the importance of arranging the values of biomechanical variables in models for determining achievement for the effectiveness of the long jump for young people and advanced students.


DOI: 10.22271/kheljournal.2024.v11.i1c.3212

Pages: 149-154  |  135 Views  64 Downloads

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International Journal of Physical Education, Sports and Health
How to cite this article:
Dr. Mazin Enhaier Lami. Quantitative analysis using an artificial neural network of the most important bio-kinematic variables to determine the effectiveness of the long jump. Int J Phys Educ Sports Health 2024;11(1):149-154. DOI: https://doi.org/10.22271/kheljournal.2024.v11.i1c.3212

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