The identification of high potential archers based on relative psychological coping skills variables: A Support Vector Machine approach

Taha, Zahari; Musa, Rabiu Muazu; Majeed, Anwar P. P. Abdul; Abdullah, Mohamad Razali; Zakaria, Muhammad Aizzat; Alim, Muhammad Muaz; Jizat, Jessnor Arif Mat; Ibrahim, Mohamad Fauzi

doi:10.1088/1757-899x/319/1/012027

Cited by 19 publications

(8 citation statements)

References 12 publications

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“…Amongst the various factors contributing to the athlete's performance, the most important is the athlete's physique, but at the same time the athlete's body, technical characteristics, mental state, and external environment will affect the athlete's sports performance to some extent [36]. Besides, anthropometrics have the major advantage of studying the physique of different populations, athlete and non-athletes.…”

Section: Discussionmentioning

confidence: 99%

Determining Youth Profile using Principle Component Analysis for Identifying Talent in Sports

2019

ijrte

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This study attempts to determine youth profile in sports talent identification program. Data of anthropometric and physical fitness included power, agility, speeds, flexibility, strength and endurance were obtained from 600 participants in a sports talent identification program aged 13-15 years. Data analyses were carried out using multivariate analysis of principal component analysis (PCA). PCA revealed three principal components with 71.5% total variation for this studied group. The first component consists of high factor loading in speed variables (10 meter run, 20 meter run, 40 meter run) and endurance (predicted VO₂ max). The second component was constituted by anthropometric variables (weight, height, sitting height and armspan). While third component contains flexibility variable (sit and reach). These selected variables of anthropometric and fitness are, therefore, revealed as the essential attributes those must be prioritized for a talent scouting in sports. Present results had demonstrated youth profile that capable of providing an information that could help coaches in decision making during athlete selection in sports talent identification.

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Section: Discussionmentioning

confidence: 99%

Determining Youth Profile using Principle Component Analysis for Identifying Talent in Sports

2019

ijrte

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“…The findings of component DA also exposed three parameters (height, sitting height and weight) correlated well which then brings out a high contribution (85%) by first factor (F1); as a factor of model to be developed. Based on the analyzed data using Quartile analysis, the researcher managed to construct an athletic body somatotype model, Moreover, most of the futsal athletes are mesomorphic, which is a characteristic that is highly presented as an important factor when recruiting players [19,20,21]. Better teams were associated with much lower body fat percentages compared to the teams with a higher percentage of body fat [22].…”

Section: Discussionmentioning

confidence: 99%

The Development of Malaysian Body Somatotype Model using Comprehensive Multivariate Techniques

2019

ijrte

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This study was designed to develop Malaysian body somatotype model among U21 athletes using multivariate analysis. A total of 85 athletes (aged 20.5 ± 5.5 year) were participated in the assessment of anthropometrics measurements. They were from three types of sports which are weightlifter, futsal and Sepak Takraw. These selected sports were represented three categories of body somatotypes namely endomorph, mesomorph and ectomorph respectively. Discriminant Analysis (DA) was used to determine the model and validate the accuracy of these groups classification. As the result, DA obtained 98.8% variability with two factors. First factor (F1) revealed 85.9% discrimination while second factor (F2) revealed 12.9% discrimination as it is related to anthropometrics and the variation of three groups significantly. A body somatotype model was generated using the standardized coefficient of F1. Standard DA showed three discriminated variables (height, sitting height and weight) with 67.06% percentage of correctness. While forward and backward stepwise mode assigned two discriminated variables (weight and height) with 69.41% percentage of correctness. They were established as essential indicators for differentiating body physical category.

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“…Clustering is one of the most common exploratory data processing techniques used to study the nature and patterns of datasets. Cluster analysis has been reported to be useful in identifying subsets or samples about certain observed variables [ 45 , 46 ].…”

Section: Methodsmentioning

confidence: 99%

Association of Physical Activity with Anthropometrics Variables and Health-Related Risks in Healthy Male Smokers

Eswaramoorthi

Suhaimi

Abdullah

et al. 2022

IJERPH

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Anthropometric variables (AV) are shown to be essential in assessing health status and to serve as markers for evaluating health-related risks in different populations. Studying the impact of physical activity (PA) on AV and its relationship with smoking is a non-trivial task from a public health perspective. In this study, a total of 107 healthy male smokers (37 ± 9.42 years) were recruited from different states in Malaysia. Standard procedures of measurement of several anthropometric indexes were carried out, and the International Physical Activity Questionnaire (IPPQ) was used to ascertain the PA levels of the participants. A principal component analysis was employed to examine the AV associated with physical activity, k-means clustering was used to group the participants with respect to the PA levels, and discriminant analysis models were utilized to determine the differential variables between the groups. A logistic regression (LR) model was further employed to ascertain the efficacy of the discriminant models in classifying the two smoking groups. Six AV out of twelve were associated with smoking behaviour. Two groups were obtained from the k-means analysis, based on the IPPQ and termed partially physically active smokers (PPAS) or physically nonactive smokers (PNAS). The PNAS were found to be at high risk of contracting cardiovascular problems, as compared with the PPAS. The PPAS cluster was characterized by a desirable AV, as well as a lower level of nicotine compared with the PNAS cluster. The LR model revealed that certain AV are vital for maintaining good health, and a partially active lifestyle could be effective in mitigating the effect of tobacco on health in healthy male smokers.

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The identification of high potential archers based on relative psychological coping skills variables: A Support Vector Machine approach

Cited by 19 publications

References 12 publications

Determining Youth Profile using Principle Component Analysis for Identifying Talent in Sports

Determining Youth Profile using Principle Component Analysis for Identifying Talent in Sports

The Development of Malaysian Body Somatotype Model using Comprehensive Multivariate Techniques

Association of Physical Activity with Anthropometrics Variables and Health-Related Risks in Healthy Male Smokers

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