2021_The Development of Terengganu’s Male Athlete Capability Index Based on Validation of Fitness Test Pattern in Talent Identification Program
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| collectionurl | https://intelek.unisza.edu.my/intelek/pages/search.php?search=!collection3 |
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| country | Malaysia |
| date | 2021-04-04 |
| format | General Document |
| id | 15757 |
| institution | UniSZA |
| internalnotes | Sila masukkan subject wajib Dissertations, Academic. Terima kasih... |
| originalfilename | 15757_0665c90d727b813.pdf |
| person | Nasree Najmi |
| recordtype | oai_dc |
| resourceurl | https://intelek.unisza.edu.my/intelek/pages/view.php?ref=15757 |
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| spelling | 15757 https://intelek.unisza.edu.my/intelek/pages/view.php?ref=15757 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 Health 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) 229 Copyright©PWB2025 15757_0665c90d727b813.pdf 2021_The Development of Terengganu’s Male Athlete Capability Index Based on Validation of Fitness Test Pattern in Talent Identification Program Nasree Najmi 2021-04-04 Athletic ability—Testing Studies on development athletes were frequently evaluated through the subjective opinions of coaches and scouts that can lead to repeated errors and misjudgements when they are used in isolation. Due to this, the datasets of development athletes can be very large and often containing many variables, as well as the standard univariate statistical analysis techniques which are focused on the identification of single variables to differentiate between performance levels. The objectives of the study are to; 1) identify the most significant parameters based on anthropometric and physical fitness parameters, 2) develop and verify the index based on anthropometric and physical fitness parameters, 3) develop the model based on anthropometric and physical fitness parameters using Machine learning. A total of 309 Terengganu male athletes (16.8 ± 2.3 years) were drawn randomly from individual sport categories. Principal component analysis (PCA) was used to identify the most essential performance variables, developing an index of Terengganu Athletes Capabilities Index (TACI) with five classifications namely, Excellent Performance Athletes (EPA), Good Performance Athletes (GPA), Moderate Performance Athletes (MPA), Low Performance Athletes (LPA) and Poor Performance Athletes (PPA). The discriminant analysis (DA) was applied to validate the index group. The K-Nearest Neighbour (k-NN), Tree, Support Vector Machine (SVM), Random Forest, Artificial Neural Network (ANN), Naïve Bayes and Logistic Regression learning algorithms were trained to predict and classify the EPA, GPA, MPA, LPA and PPA. PCA indicates that out of nine performance variables evaluated, only a set of six variables are essentials with 66.4% of total variance. Most of the athletes were grouped on MPA classification (30.1%), followed by LPA (28.5%), GPA (21%), PPA (15.8%) and EPA (4.5%) based on TACI. Finally, ANN learning algorithm outperformed all the performance metrics of machine learning with 0.88 classification accuracies, 0.87 F1 score for the model, and 0.88 precisions for all the evaluated parameters. A strong sensitivity and specificity are observed between the classification and the selected performance variables for the model. The study revealed that the benefits of multivariate analysis and machine learning techniques permit the researcher to identify Terengganu athletes accurately and holistically, hence reducing the time, cost and manpower on the talent identification program. Importantly, the identification of the potential Terengganu athletes through the holistically reliable battery test with the help of machine learning technique revealed more useful insights within the field of talent identification and science specifically. Dissertations, Academic Sila masukkan subject wajib Dissertations, Academic. Terima kasih... Athlete Capability Index Fitness Test Validation Talent Identification in Sports Thesis |
| spellingShingle | 2021_The Development of Terengganu’s Male Athlete Capability Index Based on Validation of Fitness Test Pattern in Talent Identification Program |
| state | Terengganu |
| subject | Athletic ability—Testing Dissertations, Academic |
| summary | Studies on development athletes were frequently evaluated through the subjective opinions of coaches and scouts that can lead to repeated errors and misjudgements when they are used in isolation. Due to this, the datasets of development athletes can be very large and often containing many variables, as well as the standard univariate statistical analysis techniques which are focused on the identification of single variables to differentiate between performance levels. The objectives of the study are to; 1) identify the most significant parameters based on anthropometric and physical fitness parameters, 2) develop and verify the index based on anthropometric and physical fitness parameters, 3) develop the model based on anthropometric and physical fitness parameters using Machine learning. A total of 309 Terengganu male athletes (16.8 ± 2.3 years) were drawn randomly from individual sport categories. Principal component analysis (PCA) was used to identify the most essential performance variables, developing an index of Terengganu Athletes Capabilities Index (TACI) with five classifications namely, Excellent Performance Athletes (EPA), Good Performance Athletes (GPA), Moderate Performance Athletes (MPA), Low Performance Athletes (LPA) and Poor Performance Athletes (PPA). The discriminant analysis (DA) was applied to validate the index group. The K-Nearest Neighbour (k-NN), Tree, Support Vector Machine (SVM), Random Forest, Artificial Neural Network (ANN), Naïve Bayes and Logistic Regression learning algorithms were trained to predict and classify the EPA, GPA, MPA, LPA and PPA. PCA indicates that out of nine performance variables evaluated, only a set of six variables are essentials with 66.4% of total variance. Most of the athletes were grouped on MPA classification (30.1%), followed by LPA (28.5%), GPA (21%), PPA (15.8%) and EPA (4.5%) based on TACI. Finally, ANN learning algorithm outperformed all the performance metrics of machine learning with 0.88 classification accuracies, 0.87 F1 score for the model, and 0.88 precisions for all the evaluated parameters. A strong sensitivity and specificity are observed between the classification and the selected performance variables for the model. The study revealed that the benefits of multivariate analysis and machine learning techniques permit the researcher to identify Terengganu athletes accurately and holistically, hence reducing the time, cost and manpower on the talent identification program. Importantly, the identification of the potential Terengganu athletes through the holistically reliable battery test with the help of machine learning technique revealed more useful insights within the field of talent identification and science specifically. |
| title | 2021_The Development of Terengganu’s Male Athlete Capability Index Based on Validation of Fitness Test Pattern in Talent Identification Program |
| title_full | 2021_The Development of Terengganu’s Male Athlete Capability Index Based on Validation of Fitness Test Pattern in Talent Identification Program |
| title_fullStr | 2021_The Development of Terengganu’s Male Athlete Capability Index Based on Validation of Fitness Test Pattern in Talent Identification Program |
| title_full_unstemmed | 2021_The Development of Terengganu’s Male Athlete Capability Index Based on Validation of Fitness Test Pattern in Talent Identification Program |
| title_short | 2021_The Development of Terengganu’s Male Athlete Capability Index Based on Validation of Fitness Test Pattern in Talent Identification Program |
| title_sort | 2021_the development of terengganu’s male athlete capability index based on validation of fitness test pattern in talent identification program |