Introduction The efficiency of front kick is related to the kicking technique. Thus, the aim of this study was to find the kinematic determinants of front kick dynamics across different performance and loading levels (no load to 45-kg load). Materials and Methods Twenty-four elite and sub-elite professional military personnel (26.8 ± 10.1 years, 84.2 ± 5.4 kg, 181.1 ± 6.4 cm) performed six front kicks into a force plate across five different loading conditions. Three-dimensional kinematics of the kicks was quantified and included velocity of the hip (Vhip), velocity of the knee (Vknee), velocity of the shoulder (Vshoulder), velocity of the foot (Vfoot), angular velocity of the knee (AVknee), and angular velocity of the hip (AVhip). Results The main kinematic differences between the two groups were that the sub-elite group had an increased kick time for all loading conditions (P < .001) and a lower Vfoot (P = .05) and a decreased Vhip and Vshoulder (P < .05) in the highest load condition. Vhip and AVhip were the best predictors (up to R2 = 0.58; P = .020) of peak force and impact force during no-load or loaded kicking at the elite level. Typical predictors of impulse in the elite group were AVhip, Vhip, and Vshoulder and those in the sub-elite group were AVknee and Vfoot. Conclusions The kinematic variables provide good predictions of kicking dynamics; however, the best predictor varies with the loading conditions and performance levels. Hip motion is the main differentiating factor.

While tests of basic motor abilities such as speed, maximum strength or endurance are well recognized, testing of complex motor functions such as agility remains unresolved in current literature. Therefore, the aim of this review was to evaluate which main factor or factor structures quantitatively determine agility. In methodological detail, this review focused on research that explained or described the relationships between latent variables in a factorial model of agility using approaches such as principal component analysis, factor analysis and structural equation modeling. Four research studies met the defined inclusion criteria. No quantitative empirical research was found that tried to verify the quality of the whole suggested model of the main factors determining agility through the use of a structural equation modeling (SEM) approach or a confirmatory factor analysis. From the whole structure of agility, only change of direction speed (CODS) and some of its subtests were appropriately analyzed. The combination of common CODS tests is reliable and useful to estimate performance in sub-elite athletes; however, for elite athletes, CODS tests must be specific to the needs of a particular sport discipline. Sprinting and jumping tests are stronger factors for CODS than explosive strength and maximum strength tests. The authors suggest the need to verify the agility factorial model by a second generation data analysis technique such as SEM.

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Purpose: Assisted jumping can supplement resistance training and traditional plyometric training to increase vertical jump performance. However, as coaches may choose to make field-based decisions based on lab-based research, this study determined whether a field-based assisted jumping set-up results in different ground contact times (CT), take off forces (TOF), flight times (FT), and impact forces (IF) compared to a lab-based set-up.Methods: Eighteen active males (24.8 ± 3.0 yr; 178.8 ± 7.8 cm; 77.8 ± 7.8 kg) performed two sessions of assisted jumping: one with each hand holding a commercially available resistance band (1m) that was attached to a pull-up bar (FIELD), and the other with assistance from a custom-built system of ropes, pulleys, and long (3 m) elastic bands (LAB). With each set-up, subjects performed five sets of five countermovement jumps on a force plate. Each set was performed with either bodyweight (BW), 90, 80, 70, or 60% of BW, which was achieved by either grabbing higher or lower on the bands during FIELD, or by being pulled upward via a full-body harness during LAB. The order of each visit was counter-balanced, and the order of jumps within each visit was quasi-randomized. Data from the 90, 80, 70, and 60% trials for each set-up were then expressed relative to the data of BW jumps, and these relative values were then used for analysis.Results: CTFIELD was less than CTLAB at 80, 70, and 60%. FTFIELD was greater than FTLAB at 90 and 80%, but FTLAB became greater at 60%. TOF and IF remained unchanged during LAB, but TOFFIELD was consistently less than TOF during BW, with IFFIELD generally being greater than IFLAB.Conclusion: If the purpose of assisted jumping is to spend less time on the ground without decreasing force, systems with finite adjustments and longer bands like LAB should be used. However, shorter bands similar to FIELD may also be used; but due to the larger variability of assistance throughout the range of motion, such systems may alter the neuromuscular characteristics of the jump in other ways that should be investigated in future research.

the advanced capabilities of accelerometers offer a more robust data set that coaches or athletes can use to assess more than just jump height (Crewther et al., 2011; McMaster, Gill, Cronin, & McGuigan, 2013). Previous research has determined the validity and reliability of vertical jump height (Casartelli, Müller, & Maffiuletti, 2010; Castagna et al., 2013) and estimated power output (Comstock et al., 2011) derived from accelerometers, and generally agree that accelerometers offer a valid and reliable method for measuring these variables. However, other variables such as power output and velocity measures are not as valid or reliable (Choukou, Laffaye, & Taiar, 2014). Although jump height and power output are commonly used as performance measures in athletes, some coaches may also be interested in the role of force production