ABSTRACT
Since isometric training is gaining popularity, some devices are being developed to test isometric force as an alternative to the more expensive force plates (FPs); thus, the aim of this study was to test the reliability and validity of "GSTRENGTH" for measuring PF in the isometric belt squat exercise. Five subjects performed 24 contractions at three different knee angles (90°, 105° and 120°) on two occasions (120 total cases). Peak force data were measured using FPs and a strain gauge (SG) and analyzed by Pearson's product-moment correlation coefficient, ICCs, Cronbach's alpha, a paired sample t-test and Bland-Altman plots. Perfect or almost perfect relationships (r: 0.999-1) were found with an almost perfect or perfect level of agreement (ICCs: 0.992-1; α: 0.998-1). The t-test showed significant differences for the raw data but not for the predictions by the equations obtained with the SG values. The Bland-Altman plots, when significant, showed trivial to moderate values for systematic bias in general. In conclusion, "GSTRENGTH" was shown to be a valid alternative to FPs for measuring PF.
Subject(s)
Isometric Contraction , Humans , Isometric Contraction/physiology , Male , Adult , Knee Joint/physiology , Reproducibility of Results , Biomechanical Phenomena/physiology , Female , Young Adult , Exercise/physiologyABSTRACT
Since the analysis of most demanding scenarios (MDS) in basketball has improved the practical knowledge about match demands and possible impacts for the training process, it seems important to summarize the scientific evidence providing useful information and future directions related to MDS. This review assesses the results reflected in the available literature about the MDS in basketball, synthesizing and discussing data from scientific papers, and then providing relevant insights about terminology, sex and sample size, competition category, workload variables recorded, technology used, method of calculation, time windows analyzed, and activities evaluated related to MDS. Therefore, the present narrative review would be of practical use for coaches, scientists, athletes as well as strength and conditioning trainers exploring the current trends and future directions related to MDS in basketball.
ABSTRACT
[This corrects the article DOI: 10.3389/fphys.2022.949445.].
ABSTRACT
Basketball in performance depends on numerous factors, where a stable trend was identified by winning teams with better performances in shooting effectiveness and rebounding. However, there is a need for a better understanding of pre-shot combinations that lead to these performance trends. This study aimed to analyze NBA teams' game-related statistics, pre-shooting combinations, and pick-and-roll differences between winning and losing teams (considering the context: playing at home, away, or in a neutral court) during the COVID-19 season. A retrospective cross-sectional study on the 2019-2020 NBA season (906 games) was carried out. Game-related statistics were gathered from the private company InStat (https://basketball.instatscout.com/). The discriminant analysis and binary logistic regression models were run in order to discriminate the most important features of winning teams depending on the game location. The results showed that defensive rebounds and three-point shooting percentage remained the most important variables that best discriminated winners and losers independently of the game location context. The main results showed that winning teams had a better shooting percentage based on three-pointers, catch-and-shot actions, cuts, pick-and-roll efficacy, and uncontested shots based on a better collective behavior after a successful space creation dynamic through a tactical functional unit. At the same time, teams would need players with the ability to clear those possessions in which the opponents force to an isolation or a contested shot. From a practical application perspective, coaches should focus on composing a team with good shooters, skilled players in isolations, and a good game-time pick-and-roll strategy.
ABSTRACT
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