Different change of direction tests assess different physical ability parameters: Principal component analysis of nine change of direction tests

Author(s):  
Žiga Kozinc ◽  
Nejc Šarabon

Change of direction (CoD) ability is critical for the success of athletes in many sports. The purpose of this study was to perform a principal component analysis using 9 CoD tests in order to reveal possible subcomponents of CoD ability, which could aid practitioners in test selection. Male and female kinesiology students (n = 76) performed all CoD tests and a 30-m sprint test in a quasi-randomized, counterbalanced order. Three components for males and two components for females were extracted from principle component analysis (variance explained = 82.3 and 71.4%, respectively). It seems that the CoD test should be subdivided into at least two components: a) “pure CoD tests” (such as 505 test, T-test and 180° turn) and maneuverability tests (such as AFL run, Illinois test and Figure of Eight test). Considering that different factors seem to underlie CoD and maneuverability, our findings have important practical implications for training design. If hopping/jumping CoD is important for a given athlete, it should also be tested separately.

2003 ◽  
Vol 1 (2-3) ◽  
pp. 151-156 ◽  
Author(s):  
R. L Sapra ◽  
S. K. Lal

AbstractWe suggest a diversity-dependent strategy, based on Principle Component Analysis, for selecting distinct accessions/parents for breeding from a soybean germplasm collection comprising of 463 lines, characterized and evaluated for 10 qualitative and eight quantitative traits. A sample size of six accessions included all the three states, namely low, medium and high of the individual quantitative traits, while a sample of 16–19 accessions included all the 60–64 distinct states of qualitative as well as quantitative traits. Under certain assumptions, the paper also develops an expression for estimating the size of a target population for capturing maximum variability in a sample three accessions.


2021 ◽  
Vol 23 (06) ◽  
pp. 1699-1715
Author(s):  
Mohamed, A. M. ◽  
◽  
Abdel Latif, S. H ◽  
Alwan, A. S. ◽  
◽  
...  

The principle component analysis is used more frequently as a variables reduction technique. And recently, an evolving group of studies makes use of machine learning regression algorithms to improve the estimation of empirical models. One of the most frequently used machines learning regression models is support vector regression with various kernel functions. However, an ensemble of support vector regression and principal component analysis is also possible. So, this paper aims to investigate the competence of support vector regression techniques after performing principal component analysis to explore the possibility of reducing data and having more accurate estimations. Some new proposals are introduced and the behavior of two different models 𝜀𝜀-SVR and 𝑣𝑣-SVR are compared through an extensive simulation study under four different kernel functions; linear, radial, polynomial, and sigmoid kernel functions, with different sample sizes, ranges from small, moderate to large. The models are compared with their counterparts in terms of coefficient of determination (𝑅𝑅2 ) and root mean squared error (RMSE). The comparative results show that applying SVR after PCA models improve the results in terms of SV numbers between 30% and 60% on average and it can be applied with real data. In addition, the linear kernel function gave the best values rather than other kernel functions and the sigmoid kernel gave the worst values. Under 𝜀𝜀-SVR the results improved which did not happen with 𝑣𝑣-SVR. It is also drawn that, RMSE values decreased with increasing sample size.


2014 ◽  
Vol 30 (1) ◽  
pp. 125-136 ◽  
Author(s):  
D.M. Ogah ◽  
M. Kabir

Body weight and six linear body measurements, body length (BL), breast circumference (BCC), thigh length (TL), shank length (SL), total leg length (TLL) and wing length were recorded on 150 male and female muscovy ducklings and evaluated at 3, 5, 10, 15 and 20 weeks of age. Principal component analysis was used to study the dependence structure among the body measurements and to quantify sex differences in morphometric size and shape variations during growth. The first principal components at each of the five ages in both sexes accounted between 71.54 to 92.95% of the variation in the seven measurements and provided a linear function of size with nearly equal emphasis on all traits. The second principal components in all cases also accounted for between 6.7 to 16.17% of the variations in the dependence structure of the system in the variables as shape, the coefficient for the PCs at various ages were sex dependent with males showing higher variability because of spontaneous increase in size and shape than females. Contribution of the general size factor to the total variance increase with age in both male and female ducklings, while shape factor tend to be stable in males and inconsistent in females.


2021 ◽  
Vol 6 (3) ◽  
pp. 67
Author(s):  
Carlos D. Gómez-Carmona ◽  
David Mancha-Triguero ◽  
José Pino-Ortega ◽  
Sergio J. Ibáñez

Basketball is a sport in continuous evolution, being one of these key aspects of the players’ physical fitness that has an impact on the game. Therefore, this study aimed to characterize and identify the physical fitness level and profiles of basketball players according to sex. Total of 26 semi-professional basketball players were assessed (13 male, 13 female) through inertial devices in different previously validated fitness tests. T-test for independent samples and principal component analysis were used to analyze sex-related differences and to identify physical fitness profiles. The results showed differences according to sex in all physical fitness indexes (p < 0.01; d > 1.04) with higher values in males, except in accelerometer load during small-sided games (p = 0.17; d < 0.20). Four principal components were identified in male and female basketball players, being two common ([PC1] aerobic capacity and in-game physical conditioning, [PC4 male, PC3 female] unipodal jump performance) and two different profiles (male: [PC2] bipodal jump capacity and acceleration, [PC3] curvilinear displacement; female: [PC2] bipodal jump capacity and curvilinear displacement, [PC4] deceleration). In conclusion, training design must be different and individualized according to different variables, including physical fitness profiles between them. For practical applications, these results will allow knowing the advantages and weaknesses of each athlete to adapt training tasks and game systems based on the skills and capabilities of the players in basketball.


2018 ◽  
Vol 17 (04) ◽  
pp. 1850029
Author(s):  
Mohammad Seidpisheh ◽  
Adel Mohammadpour

We consider the principal component analysis (PCA) for the heavy-tailed distributions. A traditional measure for the classical PCA is the covariance measure. Due to the non-existence of variance of many heavy-tailed distributions, this measure cannot be used for them. We will clarify how to perform PCA in heavy-tailed data by extending a similarity measure based on covariance. We introduce similarity measures based on a new dependence coefficient of heavy-tailed distributions. Using real and artificial datasets, the performance of the proposed PCA is evaluated and compared with the classical one.


Author(s):  
José Pino-Ortega ◽  
Daniel Rojas-Valverde ◽  
Carlos D. Gómez-Carmona ◽  
Markel Rico-González

Since the accelerating development of technology applied to team sports and its subsequent high amount of information available, the need for data mining leads to the use of data reduction techniques such as Principal Component Analysis (PCA). This systematic review aims to identify determinant variables in soccer, basketball and rugby using exploratory factor analysis for, training design, performance analysis and talent identification. Three electronic databases (PubMed, Web of Science, SPORTDiscus) were systematically searched and 34 studies were finally included in the qualitative synthesis. Through PCA, data sets were reduced by 75.07%, and 3.9 ± 2.53 factors were retained that explained 80 ± 0.14% of the total variance. All team sports should be analyzed or trained based on the high level of aerobic capacity combined with adequate levels of power and strength to perform repeated high-intensity actions in a very short time, which differ between team sports. Accelerations and decelerations are mainly significant in soccer, jumps and landings are crucial in basketball, and impacts are primarily identified in rugby. Besides, from these team sports, primary information about different technical/tactical variables was extracted such as (a) soccer: occupied space, ball controls, passes, and shots; (b) basketball: throws, rebounds, and turnovers; or (c) rugby: possession game pace and team formation. Regarding talent identification, both anthropometrics and some physical capacity measures are relevant in soccer and basketball. Although overall, since these variables have been identified in different investigations, further studies should perform PCA on data sets that involve variables from different dimensions (technical, tactical, conditional).


1980 ◽  
Vol 51 (2) ◽  
pp. 371-382 ◽  
Author(s):  
Adrian F. Ashman ◽  
J. P. Das

The simultaneous-successive processing battery and five tests reputed to measure planning were administered to 104 Grade 8 male and female students. Test scores were submitted to principal component analysis and a planning factor was identified which was orthogonal to the two coding dimensions. The study clearly delineates independent coding and planning dimensions and provides support for and extends the simultaneous-successive information-processing model.


2014 ◽  
Vol 11 (1) ◽  
Author(s):  
Katarina Košmelj ◽  
Jennifer Le-Rademacher ◽  
Lynne Billard

In the last two decades, principal component analysis (PCA) was extended to interval-valued data; several adaptations of the classical approach are known from the literature. Our approach is based on the symbolic covariance matrix Cov for the interval-valued variables proposed by Billard (2008). Its crucial advantage, when compared to other approaches, is that it fully utilizes all the information in the data. The symbolic covariance matrix can be decomposed into a within part CovW and a between part CovB. We propose a further insight into the PCA results: the proportion of variance explained due to the within information and the proportion of variance explained due to the between information can be calculated. Additionally, we suggest PCA on CovB and CovW to be done to obtain deeper insights into the data under study. In the case study presented, the information gain when performing PCA on the intervals instead of the interval midpoints (conditionally the means) is about 45%. It turns out that, for these data, the uniformity assumption over intervals does not hold and so analysis of the data represented by histogram-valued variables is suggested.


2014 ◽  
Vol 635-637 ◽  
pp. 997-1000 ◽  
Author(s):  
De Kun Hu ◽  
Li Zhang ◽  
Wei Dong Zhao ◽  
Tao Yan

In order to classify the objects in nature images, a model with color constancy and principle component analysis network (PCANet) is proposed. The new color constancy model imitates the functional properties of the HVS from the retina to the double-opponent cells in V1. PCANet can be designed and learned extremely, which comprises only the very basic data processing components: cascaded principal component analysis (PCA), binary hashing, and block-wise histograms. At last, a SVM is trained to classify the object in the image. The results of experiments demonstrate the potential of the model for object classification in wild color images.


2015 ◽  
Vol 27 (6) ◽  
pp. 922-939 ◽  
Author(s):  
Mouna Gazzah ◽  
Boubaker Jaouachi ◽  
Laurence Schacher ◽  
Dominique Charles Adolphe ◽  
Faouzi Sakli

Purpose – The purpose of this paper is to predict the appearance of denim fabric after repetitive uses judging the denim cloth behavior and performance in viewpoint of bagging ability. Hence, it attempts to carry out the significant inputs and outputs that have an influence on the bagging behaviors using the Principal Component Analysis (PCA) technique. In this study, the Kawabata Evaluation System parameters such as the frictional characteristics, the bending, compression, tensile and shear parameters are investigated to propose a model highlighting and explaining their impacts on the different bagging properties. To improve the obtained results, the selected significant inputs are also analyzed within their bagging properties using Taguchi experimental design. The linear regressive models prove the effectiveness of the PCA method and the obtained findings. Design/methodology/approach – To investigate the mechanical properties and their contributions on the bagging characteristics, some denim fabrics were collected and measured thanks to the Kawabata evaluation systems (KES-FB1, KES-FB2, KES-FB3 and KES-FB4). These bagging properties were further analyzed applying the method of PCA to acquire factor patterns that indicate the most important fabric properties for characterizing the bagging behaviors of different studied denim fabric samples. An experimental design type Taguchi was, hence, applied to improve the results. Regarding the obtained results, it may be concluded that the PCA method remained a powerful and flawless technique to select the main influential inputs and significant outputs, able to define objectively the bagging phenomenon and which should be considered from the next researches. Findings – According to the results, there are good relationships between the Kawabata input parameters and the analyzed bagging properties of studied denim fabrics. Indeed, thanks to the PCA, it is probably easy to reduce the number of the influent parameters for three reasons. First, applying this technique of selection can help to select objectively the most influential inputs which affect enormously the bagged fabrics. Second, knowing these significant parameters, the prediction of denim fabric bagging seems fruitful and can undoubtedly help researchers explain widely this complex phenomenon. Third, regarding the findings mentioned, it seems that the prevention of this aesthetic phenomenon appearing in some specific zones of denim fabrics will be more and more accurate. Practical implications – This study is interesting for denim consumers and industrial applications during long and repetitive uses. Undoubtedly, the denim garments remained the largely used and consumed, hence, this particularity proves the necessity to study it in order to evaluate the bagging phenomenon which occurs as function of number of uses. Although it is fashionable to have bagging, the denim fabric remains, in contrast with the worsted ones, the most popular fabric to produce garments. Moreover, regarding this characteristic, the large uses and the acceptable value of denim fabrics, their aesthetic appearance behavior due to bagging phenomenon can be analyzed accurately because compared to worsted fabrics, they have a high value and the repetitive tests to investigate widely bagged zones may fall the industrial. The paper has practical implications in the clothing appearance and other textile industry, especially in the weaving process when friction forms (yarn-to-yarn, yarn-to-metal frictions) and stresses are drastic. This can help understanding why residual bagging behavior remained after garment uses due to the internal stress and excessive extensions. Regarding the selected influential inputs and outputs relative to bagging behaviors, there are some practical implications that have an impact on the industrial and researchers to study objectively the occurrence of this aesthetic phenomenon. Indeed, this study discusses the significance of the overall inputs; their contributions on the denim fabric bagged zones aims to prevent their ability to appear after uses. Moreover, the results obtained regarding the fabric mechanical properties can be useful to fabric and garment producers, designers and consumers in specifying and categorizing denim fabric products, insuring more denim cloth use and controlling fabric value. For applications where the subjective view of the consumer is of primary importance, the KES-FB system yields data that can be used for evaluating fabric properties objectively and prejudge the consumer satisfaction in viewpoint of the bagging ability. Therefore, this study shows that by measuring shear, tensile and frictional parameters of KES-FB, it may be possible to evaluate bagging properties. However, it highlights the importance and the significance of some inputs considered influential or the contrast (non-significant) in other researches. Originality/value – This work presents the first study analyzing the bagged denim fabric applying the PCA technique to remove the all input parameters which are not significant. Besides, it deals with the relationship developed between the mechanical fabric properties (tensile, shear and frictional stresses) and the bagging properties behavior. To improve these obtained relationships, for the first time, the regression technique and experimental design type Taguchi analysis were both applied. Moreover, it is notable to mention that the originality of this study is to let researchers and industrials investigate the most influential inputs only which have a bearing on the bagging phenomenon.


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