2021_Pattern Recognition of a Multicomponent Model on Malaysian Youth Soccer Performance Index
| Format: | General Document |
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| collectionurl | https://intelek.unisza.edu.my/intelek/pages/search.php?search=!collection3 |
| copyright | Copyright©PWB2025 |
| country | Malaysia |
| date | 2021-02-04 |
| format | General Document |
| id | 15941 |
| institution | UniSZA |
| originalfilename | 15941_1f6b7a9aaa9dc3e.pdf |
| person | Ahmad Bisyri Husin Musawi bin Maliki |
| recordtype | oai_dc |
| resourceurl | https://intelek.unisza.edu.my/intelek/pages/view.php?ref=15941 |
| sourcemedia | Server storage Scanned document |
| spelling | 15941 https://intelek.unisza.edu.my/intelek/pages/view.php?ref=15941 https://intelek.unisza.edu.my/intelek/pages/search.php?search=!collection3 General Document Malaysia Library Staff (Top Management) Library Staff (Management) Library Staff (Support) Terengganu Faculty of Applied Social Sciences English application/pdf 1.5 Server storage Scanned document Universiti Sultan Zainal Abidin UniSZA Private Access Universiti Sultan Zainal Abidin SAMBox 2.3.4; modified using iTextSharp™ 5.5.10 ©2000-2016 iText Group NV (AGPL-version) 246 Copyright©PWB2025 2021-02-04 15941_1f6b7a9aaa9dc3e.pdf Ahmad Bisyri Husin Musawi bin Maliki Soccer Performance Pattern Recognition Multicomponent Model Malaysian Youth Soccer 2021_Pattern Recognition of a Multicomponent Model on Malaysian Youth Soccer Performance Index Studies on youth soccer player frequently emphases on comparisons between novice and elite players, categorized by competitive level or expertise. In the same hand, numerous studies have shown age, growth, maturity and anthropometric contribute significantly to variation in functional capacities using multivariate analysis but relatively slight to discrepancy in the soccer specific skills and motivation. Thus, the objectives of the current study were to identify, classify and predict the most significant parameters inclusively anthropometric, physiological, biological, psychological (motivation) and specific soccer skills on Malaysia Youth Soccer Performance Index (MYSPI) hence will be facilitating with several learning algorithm of machine learning. A total of 223 Malaysia youth soccer players (15.2 ± 1.6 years) drawn from soccer academies and state school soccer centre randomly and with exclusion criteria such as injured, and participating in the national game were exclude with an integration of battery test, anthropometric, growth and maturation, and soccer specific test were including in the current study. A principal component analysis (PCA) was used to identify the most essential performance variables, an index of Malaysia youth soccer performance was develop using output of PCA with three classification namely, high performance players (HPP), moderate performance players (MPP) and low performance players (LPP) while discriminant analysis (DA) of standard, backward stepwise and forward stepwise mode were applied on the index in the view of relative performance variation. The k-nearest neighbour (k-NN), Tree, support vector machine (SVM), random forest, artificial neural network (ANN), Naïve Bayes, and logistic regression learning algorithms functions were trained to predict and classify the HPP, MPP and LPP. PCA indicated that out of 33 performance variables evaluated only a set of 18 variables are essential with 66.6% of total variance. Most of the players were grouped on MPP classification (58.75%), followed by LPP (36.32%) and HPP (4.93%) based on the Malaysia Youth Soccer Performance Index (MYSPI). In the same vein, the standard, backward stepwise and forward stepwise mode methods for the DA proved excellent discrimination of 95.52%, 93.27% and 92.83% with backward stepwise and forward stepwise mode verified 11 and 10 the most significant variables discriminating the MYSPI groups, respectively. Finally, an ANN learning algorithm outperformed all performance metrics of machine learning with 0.883 classification accuracies, 0.878 F1 score for the model, and 0.881 precisions for all the evaluated parameters. Furthermore, a strong sensitivity and specificity are observed between the classification and the selected performance variables for the model. The study revealed the beneficial of multivariate analysis and machine learning techniques permitting the researcher to accurately identify the potential soccer players holistically, hence reduce the time consume, cost effective and reduce the man power energy on the soccer talent identification program. Importantly, recognizing potential soccer players by reliable battery test holistically cohesive by machine learning technique revealed more information insight in the corpus of soccer and science specifically. Dissertations, Academic Youth Soccer Performance Machine Learning Classification Multivariate Analysis In Sports Thesis |
| spellingShingle | 2021_Pattern Recognition of a Multicomponent Model on Malaysian Youth Soccer Performance Index |
| state | Terengganu |
| subject | Pattern Recognition Multicomponent Model Malaysian Youth Soccer Dissertations, Academic |
| summary | Studies on youth soccer player frequently emphases on comparisons between novice and elite players, categorized by competitive level or expertise. In the same hand, numerous studies have shown age, growth, maturity and anthropometric contribute significantly to variation in functional capacities using multivariate analysis but relatively slight to discrepancy in the soccer specific skills and motivation. Thus, the objectives of the current study were to identify, classify and predict the most significant parameters inclusively anthropometric, physiological, biological, psychological (motivation) and specific soccer skills on Malaysia Youth Soccer Performance Index (MYSPI) hence will be facilitating with several learning algorithm of machine learning. A total of 223 Malaysia youth soccer players (15.2 ± 1.6 years) drawn from soccer academies and state school soccer centre randomly and with exclusion criteria such as injured, and participating in the national game were exclude with an integration of battery test, anthropometric, growth and maturation, and soccer specific test were including in the current study. A principal component analysis (PCA) was used to identify the most essential performance variables, an index of Malaysia youth soccer performance was develop using output of PCA with three classification namely, high performance players (HPP), moderate performance players (MPP) and low performance players (LPP) while discriminant analysis (DA) of standard, backward stepwise and forward stepwise mode were applied on the index in the view of relative performance variation. The k-nearest neighbour (k-NN), Tree, support vector machine (SVM), random forest, artificial neural network (ANN), Naïve Bayes, and logistic regression learning algorithms functions were trained to predict and classify the HPP, MPP and LPP. PCA indicated that out of 33 performance variables evaluated only a set of 18 variables are essential with 66.6% of total variance. Most of the players were grouped on MPP classification (58.75%), followed by LPP (36.32%) and HPP (4.93%) based on the Malaysia Youth Soccer Performance Index (MYSPI). In the same vein, the standard, backward stepwise and forward stepwise mode methods for the DA proved excellent discrimination of 95.52%, 93.27% and 92.83% with backward stepwise and forward stepwise mode verified 11 and 10 the most significant variables discriminating the MYSPI groups, respectively. Finally, an ANN learning algorithm outperformed all performance metrics of machine learning with 0.883 classification accuracies, 0.878 F1 score for the model, and 0.881 precisions for all the evaluated parameters. Furthermore, a strong sensitivity and specificity are observed between the classification and the selected performance variables for the model. The study revealed the beneficial of multivariate analysis and machine learning techniques permitting the researcher to accurately identify the potential soccer players holistically, hence reduce the time consume, cost effective and reduce the man power energy on the soccer talent identification program. Importantly, recognizing potential soccer players by reliable battery test holistically cohesive by machine learning technique revealed more information insight in the corpus of soccer and science specifically. |
| title | 2021_Pattern Recognition of a Multicomponent Model on Malaysian Youth Soccer Performance Index |
| title_full | 2021_Pattern Recognition of a Multicomponent Model on Malaysian Youth Soccer Performance Index |
| title_fullStr | 2021_Pattern Recognition of a Multicomponent Model on Malaysian Youth Soccer Performance Index |
| title_full_unstemmed | 2021_Pattern Recognition of a Multicomponent Model on Malaysian Youth Soccer Performance Index |
| title_short | 2021_Pattern Recognition of a Multicomponent Model on Malaysian Youth Soccer Performance Index |
| title_sort | 2021_pattern recognition of a multicomponent model on malaysian youth soccer performance index |