scholarly journals Differences in External Load Variables Between Playing Positions in Elite Basketball Match-Play

2020 ◽  
Vol 75 (1) ◽  
pp. 257-266
Author(s):  
Hugo Salazar ◽  
Julen Castellano ◽  
Luka Svilar
Keyword(s):  
Principal Components ◽  
External Load ◽  
Principal Component ◽  
Match Play ◽  
Change Of Direction ◽  
Time Motion ◽  
External Variables ◽  
Load Monitoring ◽  
Kaiser Criterion ◽  
Motion Profile

Abstract The purpose of this study was to describe the specific demands and structure of interrelationships of external load variables in order to generate a position-related time motion profile in elite basketball. Seventeen professional players from three different playing positions (6 guards, 4 forwards, and 7 centers) were analyzed in five friendly games. Player load per minute (PLmin) was used as an indicator of intensity to compare positions. Furthermore, high and total external variables of jumping (hJUMP and tJUMP), acceleration (hACC and tACC), deceleration (hDEC and tDEC) and change of direction (hCOD and tCOD), respectively, were used for the principal component analysis (PCA). The Kaiser criterion (eigenvalue > 1) was applied, and the Varimax rotation mode was used to extract multiple principal components. PCA showed that all positions had three or four principal components, but the configuration of each factor was different: tCOD, hCOD, hDEC and hJUMP for guards, hCOD, tCOD, tACC and hDEC for forwards, and tJUMP, hJUMP, hDEC and tACC for centers were specifically demanded in match-play. For guards and forwards, a significant correlation was found between COD variables, while for centers tCOD and PLmin had the strongest correlation. When monitoring the external load via tri-axial accelerometers in basketball match-play, each playing position showed specific physical demands. Therefore, these variables must be prioritized in load monitoring programs.

Positional Differences in Elite Basketball: Selecting Appropriate Training-Load Measures

2018 ◽  
Vol 13 (7) ◽  
pp. 947-952 ◽  
Author(s):  
Luka Svilar ◽  
Julen Castellano ◽  
Igor Jukic ◽  
David Casamichana
Keyword(s):  
Principal Components ◽  
Perceived Exertion ◽  
Principal Component ◽  
Training Load ◽  
Rating Of Perceived Exertion ◽  
Change Of Direction ◽  
External Variables ◽  
Load Monitoring ◽  
External Training ◽  
Almost All

Purpose: To study the structure of interrelationships among external-training-load measures and how these vary among different positions in elite basketball. Methods: Eight external variables of jumping (JUMP), acceleration (ACC), deceleration (DEC), and change of direction (COD) and 2 internal-load variables (rating of perceived exertion [RPE] and session RPE) were collected from 13 professional players with 300 session records. Three playing positions were considered: guards (n = 4), forwards (n = 4), and centers (n = 5). High and total external variables (hJUMP and tJUMP, hACC and tACC, hDEC and tDEC, and hCOD and tCOD) were used for the principal-component analysis. Extraction criteria were set at an eigenvalue of greater than 1. Varimax rotation mode was used to extract multiple principal components. Results: The analysis showed that all positions had 2 or 3 principal components (explaining almost all of the variance), but the configuration of each factor was different: tACC, tDEC, tCOD, and hJUMP for centers; hACC, tACC, tCOD, and hJUMP for guards; and tACC, hDEC, tDEC, hCOD, and tCOD for forwards are specifically demanded in training sessions, and therefore these variables must be prioritized in load monitoring. Furthermore, for all playing positions, RPE and session RPE have high correlation with the total amount of ACC, DEC, and COD. This would suggest that although players perform the same training tasks, the demands of each position can vary. Conclusion: A particular combination of external-load measures is required to describe the training load of each playing position, especially to better understand internal responses among players.

High-intensity curvilinear movements’ relevance in semi-professional soccer: An approach from principal components analysis

2021 ◽  
pp. 175433712110483
Author(s):  
José Pino-Ortega ◽  
Filipe Manuel Clemente ◽  
Luiz H Palucci Vieira ◽  
Markel Rico-González
Keyword(s):  
Principal Components ◽  
High Intensity ◽  
External Load ◽  
Principal Component ◽  
Centripetal Force ◽  
Tracking Systems ◽  
Match Analysis ◽  
Reduction Techniques ◽  
Professional Soccer ◽  
Components Analysis

Due to the high number of variables reported from tracking systems, the interest in data reduction techniques has grown. To date, principal component analysis (PCA) has been performed in soccer, but since the results depend on the variables included, a lack of objectivity continues to be of concern. The aim of this study was to highlight the variables that compose the principal components (PC) in semi-professional soccer, including all variables extracted from tracking systems. Data were collected from a semi-professional Spanish team that participated in 10 matches. From more than 250 variables, the PCA grouped a total of 19 variables in six PCs, explaining 72% of players’ external load. All variables were related to centripetal force, high intensity running, and high-intensity efforts and short efforts. Interestingly, the first PC was composed of four variables related to centripetal force. The current exploratory analysis indicated that, in addition to traditional high-intensity displacement variables, force measures should also be considered in soccer match analysis due to their effect on a player’s external load.

scholarly journals External Load and Muscle Activation Monitoring of NCAA Division I Basketball Team Using Smart Compression Shorts

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5348
Author(s):  
David N. Saucier ◽  
Samaneh Davarzani ◽  
Reuben F. Burch V ◽  
Harish Chander ◽  
Lesley Strawderman ◽  
...  
Keyword(s):  
Principal Components ◽  
External Load ◽  
Muscle Activation ◽  
National Collegiate Athletic Association ◽  
Principal Component ◽  
Division I ◽  
Basketball Players ◽  
Worst Case ◽  
Men's Basketball ◽  
Additional Information

There is scarce research into the use of Strive Sense3 smart compression shorts to measure external load with accelerometry and muscle load (i.e., muscle activations) with surface electromyography in basketball. Sixteen external load and muscle load variables were measured from 15 National Collegiate Athletic Association Division I men’s basketball players with 1137 session records. The data were analyzed for player positions of Centers (n = 4), Forwards (n = 4), and Guards (n = 7). Nonparametric bootstrapping was used to find significant differences between training and game sessions. Significant differences were found in all variables except Number of Jumps and all muscle load variables for Guards, and all variables except Muscle Load for Forwards. For Centers, the Average Speed, Average Max Speed, and Total Hamstring, Glute, Left, and Right Muscle variables were significantly different (p < 0.05). Principal component analysis was conducted on the external load variables. Most of the variance was explained within two principal components (70.4% in the worst case). Variable loadings of principal components for each position were similar during training but differed during games, especially for the Forward position. Measuring muscle activation provides additional information in which the demands of each playing position can be differentiated during training and competition.

scholarly journals Assessing the External Load Associated With High-Intensity Activities Recorded During Official Basketball Games

2021 ◽  
Vol 12 ◽  
Author(s):  
Marco Pernigoni ◽  
Davide Ferioli ◽  
Ramūnas Butautas ◽  
Antonio La Torre ◽  
Daniele Conte
Keyword(s):  
Mixed Models ◽  
High Intensity ◽  
External Load ◽  
Injury Risk ◽  
Training Programs ◽  
Specific Activity ◽  
Activity Patterns ◽  
Basketball Players ◽  
Time Motion ◽  
Load Monitoring

Load monitoring in basketball is fundamental to develop training programs, maximizing performance while reducing injury risk. However, information regarding the load associated with specific activity patterns during competition is limited. This study aimed at assessing the external load associated with high-intensity activities recorded during official basketball games, with respect to different (1) activity patterns, (2) playing positions, and (3) activities performed with or without ball. Eleven male basketball players (six backcourt, five frontcourt, age: 20.5 ± 1.1 years, stature: 191.5 ± 8.7 cm, body mass: 86.5 ± 11.3 kg; experience: 8.5 ± 2.4 years) competing in the Lithuanian third division were recruited for this study. Three in-season games were assessed via time-motion analysis and microsensors. Specifically, the high-intensity activities including sprints, high-intensity specific movements (HSM) and jumps were identified and subsequently the external load [PlayerLoad™ (PL) and PlayerLoad™/min (PL/min)] of each activity was determined. Linear mixed models were used to examine differences in PL, PL/min and mean duration between activity pattern, playing positions and activities performed with or without ball. Results revealed PL was lower in jumps compared to sprints [p &lt; 0.001, effect size (ES) = 0.68] and HSMs (p &lt; 0.001, ES = 0.58), while PL/min was greater in sprints compared to jumps (p = 0.023, ES = 0.22). Jumps displayed shorter duration compared to sprints (p &lt; 0.001, ES = 1.10) and HSMs (p &lt; 0.001, ES = 0.81), with HSMs lasting longer than sprints (p = 0.002, ES = 0.17). Jumps duration was longer in backcourt than frontcourt players (p &lt; 0.001, ES = 0.33). When considering activity patterns combined, PL (p &lt; 0.001, ES = 0.28) and duration (p &lt; 0.001, ES = 0.43) were greater without ball. Regarding HSMs, PL/min was higher with ball (p = 0.036, ES = 0.14), while duration was longer without ball (p &lt; 0.001, ES = 0.34). The current findings suggest that external load differences in high-intensity activities exist among activity patterns and between activities performed with and without ball, while no differences were found between playing positions. Practitioners should consider these differences when designing training sessions.

Structuring Assessments of Psychopathology

2006 ◽  
Vol 27 (2) ◽  
pp. 87-92 ◽  
Author(s):  
Willem K.B. Hofstee ◽  
Dick P.H. Barelds ◽  
Jos M.F. Ten Berge
Keyword(s):  
Principal Components ◽  
Personality Assessment ◽  
Clinical Sample ◽  
Principal Component ◽  
Normal Sample ◽  
Normative Sample ◽  
Assessment Data ◽  
Obsessive Compulsive ◽  
Oblique Rotation ◽  
Two Samples

Hofstee and Ten Berge (2004a) have proposed a new look at personality assessment data, based on a bipolar proportional (-1, .. . 0, .. . +1) scale, a corresponding coefficient of raw-scores likeness L = ΢XY/N, and raw-scores principal component analysis. In a normal sample, the approach resulted in a structure dominated by a first principal component, according to which most people are faintly to mildly socially desirable. We hypothesized that a more differentiated structure would arise in a clinical sample. We analyzed the scores of 775 psychiatric clients on the 132 items of the Dutch Personality Questionnaire (NPV). In comparison to a normative sample (N = 3140), the eigenvalue for the first principal component appeared to be 1.7 times as small, indicating that such clients have less personality (social desirability) in common. Still, the match between the structures in the two samples was excellent after oblique rotation of the loadings. We applied the abridged m-dimensional circumplex design, by which persons are typed by their two highest scores on the principal components, to the scores on the first four principal components. We identified five types: Indignant (1-), Resilient (1-2+), Nervous (1-2-), Obsessive-Compulsive (1-3-), and Introverted (1-4-), covering 40% of the psychiatric sample. Some 26% of the individuals had negligible scores on all type vectors. We discuss the potential and the limitations of our approach in a clinical context.

Comparison of Principal Component Solutions in Two Populations

Methodology ◽  
2016 ◽  
Vol 12 (1) ◽  
pp. 11-20 ◽  
Author(s):  
Gregor Sočan
Keyword(s):  
Simulation Study ◽  
Principal Components ◽  
Common Factor ◽  
Principal Component ◽  
Component Model ◽  
Bootstrap Procedure ◽  
Factor Loadings ◽  
Component Loadings ◽  
Principal Component Model ◽  
Two Populations

Abstract. When principal component solutions are compared across two groups, a question arises whether the extracted components have the same interpretation in both populations. The problem can be approached by testing null hypotheses stating that the congruence coefficients between pairs of vectors of component loadings are equal to 1. Chan, Leung, Chan, Ho, and Yung (1999) proposed a bootstrap procedure for testing the hypothesis of perfect congruence between vectors of common factor loadings. We demonstrate that the procedure by Chan et al. is both theoretically and empirically inadequate for the application on principal components. We propose a modification of their procedure, which constructs the resampling space according to the characteristics of the principal component model. The results of a simulation study show satisfactory empirical properties of the modified procedure.

Stormwater inflow prediction using radar rainfall data compressed by principal component analysis

2006 ◽  
Vol 1 (1) ◽  
Author(s):  
K. Katayama ◽  
K. Kimijima ◽  
O. Yamanaka ◽  
A. Nagaiwa ◽  
Y. Ono
Keyword(s):  
Principal Component Analysis ◽  
Prediction Model ◽  
Principal Components ◽  
Prediction Method ◽  
Principal Component ◽  
Component Analysis ◽  
Rainfall Data ◽  
Radar Rainfall ◽  
Input Variables ◽  
Inflow Prediction

This paper proposes a method of stormwater inflow prediction using radar rainfall data as the input of the prediction model constructed by system identification. The aim of the proposal is to construct a compact system by reducing the dimension of the input data. In this paper, Principal Component Analysis (PCA), which is widely used as a statistical method for data analysis and compression, is applied to pre-processing radar rainfall data. Then we evaluate the proposed method using the radar rainfall data and the inflow data acquired in a certain combined sewer system. This study reveals that a few principal components of radar rainfall data can be appropriate as the input variables to storm water inflow prediction model. Consequently, we have established a procedure for the stormwater prediction method using a few principal components of radar rainfall data.

Evaluation functions for integral mapping

2017 ◽  
Vol 921 (3) ◽  
pp. 24-29 ◽  
Author(s):  
S.I. Lesnykh ◽  
A.K. Cherkashin
Keyword(s):  
Principal Component Analysis ◽  
Environmental Factors ◽  
Principal Components ◽  
Principal Component ◽  
Evaluation Function ◽  
Final Value ◽  
Evaluation Functions ◽  
Geographical Environment ◽  
Environmental Background ◽  
Integral Mapping

The proposed procedure of integral mapping is based on calculation of evaluation functions on the integral indicators (II) taking into account the feature of the local geographical environment, when geosystems in the same states in the different environs have various estimates. Calculation of II is realized with application of a Principal Component Analysis for processing of the forest database, allowing to consider in II the weight of each indicator (attribute). The final value of II is equal to a difference of the first (condition of geosystem) and the second (condition of environmental background) principal components. The evaluation functions are calculated on this value for various problems of integral mapping. The environmental factors of variability is excluded from final value of II, therefore there is an opportunity to find the invariant evaluation function and to determine coefficients of this function. Concepts and functions of the theory of reliability for making the evaluation maps of the hazard of functioning and stability of geosystems are used.

scholarly journals EXPRESS: Exploration of Principal Component Analysis: Deriving PCA Visually Using Spectra

2021 ◽  
pp. 000370282098784
Author(s):  
James Renwick Beattie ◽  
Francis Esmonde-White
Keyword(s):  
Principal Components ◽  
Principal Component ◽  
Original Data ◽  
Specific Chemical ◽  
Successive Refinement ◽  
Minimal Loss ◽  
Components Analysis ◽  
The Mathematical Model ◽  
Application Specific ◽  
Reconstructed Data

Spectroscopy rapidly captures a large amount of data that is not directly interpretable. Principal Components Analysis (PCA) is widely used to simplify complex spectral datasets into comprehensible information by identifying recurring patterns in the data with minimal loss of information. The linear algebra underpinning PCA is not well understood by many applied analytical scientists and spectroscopists who use PCA. The meaning of features identified through PCA are often unclear. This manuscript traces the journey of the spectra themselves through the operations behind PCA, with each step illustrated by simulated spectra. PCA relies solely on the information within the spectra, consequently the mathematical model is dependent on the nature of the data itself. The direct links between model and spectra allow concrete spectroscopic explanation of PCA, such the scores representing ‘concentration’ or ‘weights’. The principal components (loadings) are by definition hidden, repeated and uncorrelated spectral shapes that linearly combine to generate the observed spectra. They can be visualized as subtraction spectra between extreme differences within the dataset. Each PC is shown to be a successive refinement of the estimated spectra, improving the fit between PC reconstructed data and the original data. Understanding the data-led development of a PCA model shows how to interpret application specific chemical meaning of the PCA loadings and how to analyze scores. A critical benefit of PCA is its simplicity and the succinctness of its description of a dataset, making it powerful and flexible.

scholarly journals Associations between Well-Being State and Match External and Internal Load in Amateur Referees

2021 ◽  
Vol 18 (6) ◽  
pp. 3322
Author(s):  
Eñaut Ozaeta ◽  
Javier Yanci ◽  
Carlo Castagna ◽  
Estibaliz Romaratezabala ◽  
Daniel Castillo
Keyword(s):  
External Load ◽  
Contact Time ◽  
Well Being ◽  
Ground Contact ◽  
Internal Load ◽  
Match Play ◽  
Power Meter ◽  
Ground Contact Time ◽  
Training Impulse ◽  
External Loads

The main aim of this paper was to examine the association between prematch well-being status with match internal and external load in field (FR) and assistant (AR) soccer referees. Twenty-three FR and 46 AR participated in this study. The well-being state was assessed using the Hooper Scale and the match external and internal loads were monitored with Stryd Power Meter and heart monitors. While no significant differences were found in Hooper indices between match officials, FR registered higher external loads (p < 0.01; ES: 0.75 to 5.78), spent more time in zone 4 and zone 5, and recorded a greater training impulse (TRIMP) value (p < 0.01; ES: 1.35 to 1.62) than AR. Generally, no associations were found between the well-being variables and external loads for FR and AR. Additionally, no associations were found between the Hooper indices and internal loads for FR and AR. However, several relationships with different magnitudes were found between internal and external match loads, for FR, between power and speed with time spent in zone 2 (p < 0.05; r = −0.43), ground contact time with zone 2 and zone 3 (p < 0.05; r = 0.50 to 0.60) and power, speed, cadence and ground contact time correlated with time spent in zone 5 and TRIMP (p < 0.05 to 0.01; r = 0.42 to 0.64). Additionally, for AR, a relationship between speed and time in zone 1 was found (p < 0.05; r = −0.30; CL = 0.22). These results suggest that initial well-being state is not related to match officials’ performances during match play. In addition, the Stryd Power Meter can be a useful device to calculate the external load on soccer match officials.

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