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Merrigan, JJ, Stone, JD, Wagle, JP, Hornsby, WG, Ramadan, J, Joseph, M, and Hagen, JA. Using random forest regression to determine influential force-time metrics for countermovement jump height: a technical report. J Strength Cond Res 36(1): 277-283, 2022-The purpose of this study was to indicate the most influential force-time metrics on countermovement jump (CMJ) height using multiple statistical procedures. Eighty-two National Collegiate Athletic Association Division I American football players performed 2 maximal-effort, no arm-swing, CMJs on force plates. The average absolute and relative (i.e., power/body mass) metrics were included as predictor variables, whereas jump height was the dependent variable within regression models (p0.05). Best subsets regression (8 metrics, R2 = 0.95) included less metrics compared with stepwise regression (18 metrics, R2 = 0.96), while explaining similar overall variance in jump height (p = 0.083). Random forest regression (RFR) models included 8 metrics, explained ∼93% of jump height variance, and were not significantly different than best subsets regression models (p0.05). Players achieved higher CMJs by attaining a deeper, faster, and more forceful countermovement with lower eccentric-to-concentric force ratios. An additional RFR was conducted on metrics scaled to body mass and revealed relative mean and peak concentric power to be the most influential. For exploratory purposes, additional RFR were run for each positional group and suggested that the most influential variables may differ across positions. Thus, developing power output capabilities and providing coaching to improve technique during the countermovement may maximize jump height capabilities. Scientists and practitioners may use best subsets or RFR analyses to help identify which force-time metrics are of interest to reduce the selectable number of multicollinear force-time metrics to monitor. These results may inform their training programs to maximize individual performance capabilities.

Pop time (POP) is the measure of how long it takes a catcher to throw to 2nd base calculated from when the ball arrives in his mitt to when it arrives in the fielder’s glove at 2nd base. It has recently been suggested that greater emphasis should be placed on throwing velocity development instead of the ball exchange and throwing motion. The present study determined if the differences in POP characteristics between high school (HS) and Major League Baseball (MLB) catchers indicate a greater contribution from throwing velocity. HS catchers had slower POP characteristics in both exchange and throw phases. Exchange and throw times relative to pop times were nearly identical for HS and MLB, 36.6% and 36.0%, and 63.4% and 64.0%, respectively. The exchange phase exhibited the greatest variability and the most room for improvement. POP percent contribution from the exchange and throw phases between HS and MLB catchers did not change and their absolute values both improved equally. Therefore, it may be beneficial for coaches to design programs that not only strengthen the arm, but also develop efficient ball exchange and catcher throwing mechanics prior to ball release. Article visualizations

Wagle, JP, Cunanan, AJ, Carroll, KM, Sams, ML, Wetmore, A, Bingham, GE, Taber, CB, DeWeese, BH, Sato, K, Stuart, CA, and Stone, MH. Accentuated eccentric loading and cluster set configurations in the back squat: a kinetic and kinematic analysis. J Strength Cond Res 35(2): 420-427, 2021-This study examined the kinetic and kinematic differences between accentuated eccentric loading (AEL) and cluster sets in trained male subjects (age = 26.1 ± 4.1 years, height = 183.5 ± 4.3 cm, body mass = 92.5 ± 10.5 kg, and back squat to body mass ratio = 1.8 ± 0.3). Four load condition sessions consisted of traditionally loaded (TL) "straight sets," TL cluster (TLC) sets, AEL cluster (AEC) sets, and AEL "straight sets" where only the first repetition had eccentric overload (AEL1). An interrepetition rest interval of 30 seconds was prescribed for both TLC and AEC. Concentric intensity for all load conditions was 80% 1 repetition maximum (1RM). Accentuated eccentric loading was applied to repetitions using weight releasers with total eccentric load equivalent to 105% of concentric 1RM. Traditionally loaded cluster had statistically greater concentric outputs than TL. Furthermore, statistically greater eccentric and concentric outputs were observed during AEC compared with TL with the exception of peak power. Statistically greater concentric characteristics were observed in TLC compared with AEL1, but statistically greater eccentric outputs were observed in AEL1. In the 2 cluster set conditions, statistically greater concentric rate of force development (RFDCON) (d = 0.470, p0.001) and average velocity (vavg) (d = 0.560, p0.001) in TLC compared with AEC were observed. However, statistically greater eccentric work (WECC) (d = 2.096, p0.001) and eccentric RFD (RFDECC) (d = 0.424, p0.001) were observed in AEC compared with TLC. Overall, eccentric overload demonstrated efficacy as a means of increasing eccentric work and RFD, but not as a means of potentiating concentric output. Finally, interrepetition rest seems to have the largest influence on concentric power output and RFD.

Merrigan, JJ, Rentz, LE, Hornsby, WG, Wagle, JP, Stone, JD, Smith, HT, Galster, SM, Joseph, M, and Hagen, JA. Comparisons of countermovement jump force-time characteristics among NCAA division I american football athletes: use of principal component analysis. J Strength Cond Res XX(X): 000-000, 2021-This study aimed to reduce the dimensionality of countermovement jump (CMJ) force-time characteristics and evaluate differences among positional groups (skills, hybrid, linemen, and specialists) within National Collegiate Athletic Association (NCAA) division I American football. Eighty-two football athletes performed 2 maximal effort, no arm-swing, CMJs on force plates. The average absolute and relative (e.g., power/body mass) metrics were analyzed using analysis of variance and principal component analysis procedures (p < 0.05). Linemen had the heaviest body mass and produced greater absolute forces than hybrid and skills but had lower propulsive abilities demonstrated by longer propulsive phase durations and greater eccentric to concentric mean force ratios. Skills and hybrid produced the most relative concentric and eccentric forces and power, as well as modified reactive strength indexes (RSIMOD). Skills (46.7 ± 4.6 cm) achieved the highest jump height compared with hybrid (42.8 ± 5.5 cm), specialists (38.7 ± 4.0 cm), and linemen (34.1 ± 5.3 cm). Four principal components explained 89.5% of the variance in force-time metrics. Dimensions were described as the (a) explosive transferability to concentric power (RSIMOD, concentric power, and eccentric to concentric forces) (b) powerful eccentric loading (eccentric power and velocity), (c) countermovement strategy (depth and duration), and (d) jump height and power. The many positional differences in CMJ force-time characteristics may inform strength and conditioning program designs tailored to each position and identify important explanatory metrics to routinely monitor by position. The overwhelming number of force-time metrics to select from may be reduced using principal component analysis methods, although practitioners should still consider the various metric's applicability and reliability.

The purpose of this longitudinal, descriptive study was to observe changes in maximal strength measured via isometric clean grip mid-thigh pull and home runs (total and home runs per game) across three years of training and three competitive seasons for four National Collegiate Athletic Association (NCAA) Division 1 baseball players. A one-way repeated measures analysis of variance (ANOVA) was performed, revealing significant univariate effects of time for peak force (PF) (p = 0.003) and peak force allometrically scaled (PFa) (p = 0.002). Increases in PF were noted from season 1 to season 2 (p = 0.031) and season 3 (p = 0.004), but season 2 was not significantly different than season 3 (p = 0.232). Additionally, increases in PFa were noted from season 1 to season 2 (p = 0.010) and season 3 (p < 0.001), but season 2 was not significantly different than season 3 (p = 0.052). Home runs per game rose from the 2009 (0.32) to 2010 season (1.35) and dropped during the 2011 season (1.07). A unique aspect of the study involves 2010 being the season in which ball-bat coefficient of restitution (BBCOR) bats were introduced to the NCAA competition.

Sams, ML, Wagle, JP, Sato, K, DeWeese, BH, Sayers, AL, and Stone, MH. Using the session rating of perceived exertion to quantify training load in a men's college soccer team. J Strength Cond Res 34(10): 2793-2799, 2020-The purpose of this study was to examine the training load (TL) programming of 3 training groups of a National Collegiate Athletic Association Division I men's college soccer team across a season. Thirty athletes were classified as starters (S), substitutes (NS), or redshirts (RS) based on the percentage of possible minutes played during the season. Training load was quantified at the season, phasic, and weekly levels by the session rating of perceived exertion. Between-group differences were observed at the season level (p < 0.001), and group-by-time interactions for average weekly TL were found at both the phasic (p = 0.04) and weekly levels (p < 0.001). In general, S accumulated greater TL than NS (effect size range for all analyses: -0.59 < d < 1.91) and RS (0.17 < d < 3.67), and NS accumulated greater TL than RS (-0.54 < d < 2.34). Within-group variation at the phasic level was less apparent (-0.35 < d < 1.44); however, all 3 groups displayed training variation at the weekly level (-1.80 < d < 3.24). The session rating of perceived exertion can serve as a low-cost, valid means of quantifying TL in men's college soccer athletes. Practitioners should be especially aware of the possibility of insufficient loading in NS.

Lates, AD, Greer, BK, Wagle, JP, and Taber, CB. Accentuated eccentric loading and cluster set configurations in the bench press. J Strength Cond Res XX(X): 000-000, 2020-This study was designed to examine the kinetic and kinematic differences between an Accentuated eccentric loading (AEL), traditional loading, and cluster sets in trained male subjects (age: 23.7 ± 4.0 years, height: 176.4 ± 2.8 cm, mass: 93.6 ± 7.0 kg) with lifting experience (training age: 7.2 ± 2.4 years, 1-repetition maximum (1RM) bench press: 125.0 ± 14.8 kg, relative strength ratio: 1.3 ± 0.1) in the bench press. Subjects reported for a total of 5 sessions which consisted of a 1RM testing session and 4 experimental trials. The 4 experimental conditions consisted of a traditional load (TRAD), traditional load with inter-repetition rest (TRDC), accentuated eccentric loading with inter-repetition rest (AELC), and Accentuated eccentric loading for the first repetition only (AEL1). Concentric load was 80% of subjects' 1RM for all conditions. An eccentric overload of 105% of 1RM was applied using weight-releasing hooks during the AEL conditions. TRDC demonstrated superior concentric outputs for mean velocity and mean power compared with TRAD, AELC, and AEL1 (p < 0.001). In addition, AEL1 produced significantly greater effects for rate of force development compared with TRDC (p < 0.001). These findings suggest that inter-repetition rest had an influence on concentric performance, specifically mean power and mean velocity, and may be favorable when using higher loads and when sustained power outputs are desired. In addition, AEL1 may provide a unique eccentric stimulus that alters loading parameters compared with traditional loading conditions.

The aim of this study was to evaluate the level of agreement in measuring back squat kinematics between an inertial measurement unit (IMU) and a 3D motion capture system (3DMOCAP). Kinematic variables included concentric peak velocity (CPV), concentric mean velocity (CMV), eccentric peak velocity (EPV), eccentric mean velocity (EMV), mean propulsive velocity (MPV), and POP-100: a proprietary variable. Sixteen resistance-trained males performed an incrementally loaded one repetition maximum (1RM) squat protocol. A series of Pearson correlations, 2 &times, 4 RM ANOVA, Cohen&rsquo, s d effect size differences, coefficient of variation (CV), and standard error of the estimate (SEE) were calculated. A large relationship existed for all variables between devices (r = 0.78&ndash, 0.95). Between-device agreement for CPV worsened beyond 60% 1RM. The remaining variables were in agreement between devices with trivial effect size differences and similar CV magnitudes. These results support the use of the IMU, regardless of relative intensity, when measuring EMV, EPV, MPV, and POP-100. However, practitioners should carefully select kinematic variables of interest when using the present IMU device for velocity-based training (VBT), as certain measurements (e.g., CMV, CPV) do not possess practically acceptable reliability or accuracy. Finally, the IMU device exhibited considerable practical data collection concerns, as one participant was completely excluded and 13% of the remaining attempts displayed obvious internal error.

Recent reviews have attempted to refute the efficacy of applying Selye's general adaptation syndrome (GAS) as a conceptual framework for the training process. Furthermore, the criticisms involved are regularly used as the basis for arguments against the periodization of training. However, these perspectives fail to consider the entirety of Selye's work, the evolution of his model, and the broad applications he proposed. While it is reasonable to critically evaluate any paradigm, critics of the GAS have yet to dismantle the link between stress and adaptation. Disturbance to the state of an organism is the driving force for biological adaptation, which is the central thesis of the GAS model and the primary basis for its application to the athlete's training process. Despite its imprecisions, the GAS has proven to be an instructive framework for understanding the mechanistic process of providing a training stimulus to induce specific adaptations that result in functional enhancements. Pioneers of modern periodization have used the GAS as a framework for the management of stress and fatigue to direct adaptation during sports training. Updates to the periodization concept have retained its founding constructs while explicitly calling for scientifically based, evidence-driven practice suited to the individual. Thus, the purpose of this review is to provide greater clarity on how the GAS serves as an appropriate mechanistic model to conceptualize the periodization of training.

The present article addresses issues within U.S. collegiate sport as it pertains to the physical preparation and health and well-being of intercollegiate athletes. Specifically, the sport coach is often perceived as “all knowing” about every facet of their sport when, in fact, they typically are not formally educated or well-trained in current methods of enhancing sport performance. Often strength and conditioning coaches, who may also be poorly trained, are tied directly (financially and administratively) to the sport coach—a situation which has led to a subservient role heavily influenced by the wishes of the sport coach. This has unfortunately resulted in the multidimensional well-being of the athlete clearly not being a primary objective in many programs.

There is a need for reliable analysis techniques for kinetic data for coaches and sport scientists who employ athlete monitoring practices. The purpose of the study was: (1) to determine intra- and inter-rater reliability within a manual-based kinetic analysis program; and (2) to determine test-retest reliability of an algorithm-based kinetic analysis program. Five independent raters used a manual analysis program to analyse 100 isometric mid-thigh pull (IMTP) trials obtained from previously collected data. Each trial was analysed three times. The same IMTP trials were analysed using an algorithm-based analysis software. Variables measured were peak force, rate of force development from 0 to 50 ms (RFD50) and RFD from 0 to 200 ms (RFD200). Intraclass correlation coefficients (ICC) and coefficient of variation (CV) were used to assess intra- and inter-rater reliability. Nearly perfect reliability was observed for the manual-based (ICC 0.92). However, poor intra- and inter-rater CV was observed for RFD (CV 16.25% and CV 32.27%, respectively). The algorithm-based method resulted in perfect reliability in all measurements (ICC = 1.0, CV = 0%). While manual methods of kinetic analysis may provide sufficient reliability, the perfect reliability observed within the algorithm-based method in the current study suggest it is a superior method for use in athlete monitoring programs.

Carroll, KM, Wagle, JP, Sole, CJ, and Stone, MH. Intrasession and intersession reliability of countermovement jump testing in Division-I volleyball athletes. J Strength Cond Res 33(11): 2932-2935, 2019-The countermovement jump (CMJ) is a reliable and noninvasive test of lower-body neuromuscular performance. Many of the investigations used to establish the reliability of CMJ have been conducted in a controlled environment (i.e., laboratory setting). To better inform coaches, reliability of key CMJ variables should be examined in a practical environment. This study assessed intrasession and intersession reliability of CMJ variables in NCAA D-I volleyball athletes. Eleven female volleyball players (age = 19.8 ± 0.8 years, height = 1.75 ± 0.07 m, body mass = 71.6 ± 8.9 kg) performed twice weekly CMJ testing on force platforms across 14 weeks of training. Dependent variables were CMJ height (CMJH), reactive strength index modified (RSIMOD), relative peak power (rPP), and countermovement depth (CM depth). Intraclass correlation coefficients (ICCs), coefficient of variation (CV), and typical error (TE) were calculated, along with Pearson correlation coefficients. Intrasession reliability revealed excellent reliability values for CMJH (ICC = 0.94, CV = 2.9 ± 2.4%, TE = 1.25) and RSIMOD (ICC = 0.93, CV = 4.7 ± 4.9, TE = 0.03). Good reliability values were observed for rPP (ICC = 0.79, CV = 6.1 ± 10.9%, TE = 4.48), with only moderate reliability for CM depth (ICC = 0.61, CV = 7.1 ± 10.9%, TE = 16.9). Intersession reliability indicated excellent reliability for CMJH (ICC = 0.92, CV = 3.2 ± 2.8%, TE = 1.4) and RSIMOD (ICC = 0.92, CV = 5.4 ± 4.3%, TE = 0.03). However, poor reliability was observed for rPP (ICC = 0.41, CV = 10.4 ± 15.4%, TE = 7.95) and CM depth (ICC = 0.39, CV = 7.7 ± 10.8%, TE = 6.42). These results suggest that in the context of weekly athlete monitoring, measures such as CMJH and RSIMOD exhibit the greatest reliability. Conversely, measures such as CM depth and rPP exhibit high levels of variability. Practitioners seeking to track jump performance over time should be cognizant of CM depth variability and its potential impact on CMJ variables.

The purpose of this review is to provide strength and conditioning practitioners with recommendations on how best to implement tempo eccentric training (TEMPO), flywheel inertial training (FIT), accentuated eccentric loading (AEL), and plyometric training (PT) into resistance training programs that seek to improve an athlete’s hypertrophy, strength, and power output. Based on the existing literature, TEMPO may be best implemented with weaker athletes to benefit positional strength and hypertrophy due to the time under tension. FIT may provide an effective hypertrophy, strength, and power stimulus for untrained and weaker individuals; however, stronger individuals may not receive the same eccentric (ECC) overload stimulus. Although AEL may be implemented throughout the training year to benefit hypertrophy, strength, and power output, this strategy is better suited for stronger individuals. When weaker and stronger individuals are exposed to PT, they are exposed to an ECC overload stimulus as a result of increases in the ECC force and ECC rate of force development. In conclusion, when choosing to utilize ECC training methods, the practitioner must integrate these methods into a holistic training program that is designed to improve the athlete’s performance capacity.

The purpose of this review was to provide a physiological rationale for the use of eccentric resistance training and to provide an overview of the most commonly prescribed eccentric training methods. Based on the existing literature, there is a strong physiological rationale for the incorporation of eccentric training into a training program for an individual seeking to maximize muscle size, strength, and power. Specific adaptations may include an increase in muscle cross-sectional area, force output, and fiber shortening velocities, all of which have the potential to benefit power production characteristics. Tempo eccentric training, flywheel inertial training, accentuated eccentric loading, and plyometric training are commonly implemented in applied contexts. These methods tend to involve different force absorption characteristics and thus, overload the muscle or musculotendinous unit in different ways during lengthening actions. For this reason, they may produce different magnitudes of improvement in hypertrophy, strength, and power. The constraints to which they are implemented can have a marked effect on the characteristics of force absorption and therefore, could affect the nature of the adaptive response. However, the versatility of the constraints when prescribing these methods mean that they can be effectively implemented to induce these adaptations within a variety of populations.

Wetmore, A, Wagle, JP, Sams, ML, Taber, CB, DeWeese, BH, Sato, K, and Stone, MH. Cluster set loading in the back squat: Kinetic and kinematic implications. J Strength Cond Res 33(7S): S19-S25, 2019-As athletes become well trained, they require greater stimuli and variation to force adaptation. One means of adding additional variation is the use of cluster loading. Cluster loading involves introducing interrepetition rest during a set, which in theory may allow athletes to train at higher absolute intensities for the same volume. The purpose of this study was to investigate the kinetic and kinematic implications of cluster loading as a resistance training programming tactic compared with traditional loading (TL). Eleven resistance-trained men (age = 26.75 ± 3.98 years, height = 181.36 ± 5.96 cm, body mass = 89.83 ± 10.66 kg, and relative squat strength = 1.84 ± 0.34) were recruited for this study. Each subject completed 2 testing sessions consisting of 3 sets of 5 back squats at 80% of their 1 repetition maximum with 3 minutes of interset rest. Cluster loading included 30 seconds of interrepetition rest with 3 minutes of interset rest. All testing was performed on dual-force plates sampling at 1,000 Hz, and the barbell was connected to 4 linear position transducers sampling at 1,000 Hz. Both conditions had similar values for peak force, concentric average force, and eccentric average force (p = 0.25, effect size (ES) = 0.09, p = 0.25, ES = 0.09, and p = 0.60, ES = 0.04, respectively). Cluster loading had significantly higher peak power (PP) (p < 0.001, ES = 0.77), peak and average velocities (p < 0.001, ES = 0.77, and p < 0.001, ES = 0.81, respectively), lower times to PP and velocity (p < 0.001, ES = -0.68, and p < 0.001, ES = -0.68, respectively) as well as greater maintenance of time to PP (p < 0.001, ES = 1.57). These results suggest that cluster loading may be superior to TL when maintaining power output and time point variables is the desired outcome of training.

Wagle, JP, Carroll, KM, Cunanan, AJ, Wetmore, A, Taber, CB, DeWeese, BH, Sato, K, Stuart, CA, and Stone, MH. Preliminary investigation into the effect of ACTN3 and ACE polymorphisms on muscle and performance characteristics. J Strength Cond Res 35(3): 688-694, 2021-The purpose of this investigation was to explore the phenotypic and performance outcomes associated with ACTN3 and ACE polymorphisms. Ten trained men (age = 25.8 ± 3.0 years, height = 183.3 ± 4.1 cm, body mass = 92.3 ± 9.3 kg, and back squat to body mass ratio = 1.8 ± 0.3) participated. Blood samples were analyzed to determine ACTN3 and ACE polymorphisms. Standing ultrasonography images of the vastus lateralis (VL) were collected to determine whole muscle cross-sectional area (CSA-M), and a percutaneous muscle biopsy of the VL was collected to determine type I-specific CSA (CSA-T1), type II-specific CSA (CSA-T2), and type II to type I CSA ratio (CSA-R). Isometric squats were performed on force platforms with data used to determine peak force (IPF), allometrically scaled peak force (IPFa), and rate of force development (RFD) at various timepoints. One repetition maximum back squats were performed, whereby allometrically scaled dynamic strength (DSa) was determined. Cohen's d effect sizes revealed ACTN3 RR and ACE DD tended to result in greater CSA-M but differ in how they contribute to performance. ACTN3 RR's influence seems to be in the type II fibers, altering maximal strength, and ACE DD may influence RFD capabilities through a favorable CSA-R. Although the findings of the current investigation are limited by the sample size, the findings demonstrate the potential influence of ACTN3 and ACE polymorphisms on isometric and dynamic strength testing. This study may serve as a framework to generate hypotheses regarding the effect of genetics on performance.

The purposes of this investigation were to: (1) assess kinetic characteristics of overload, (2) examine eccentric and concentric muscle activations and (3) explore velocity measurement as a method of intensity prescription in inertial flywheel squat training. A series of two experiments were performed: one assessing kinetic and muscle activation characteristics of flywheel squat training using three progressive inertial loads. The second experiment assessed inertial load-velocity relationships using six progressive inertial loads. Peak force, net impulse, positive-negative impulse ratio and positive-negative impulse duration ratio were each statistically significant between all three load conditions (

Buckner et al. [1] have submitted a letter in response to our recent review [2] on the general adaptation syndrome (GAS) and its application to training periodization. As Buckner et al. state, this topic deserves fair and thorough discussion from multiple perspectives, and we thank them for the opportunity to continue such dialogue. Their letter restates many of the points in their original reviews [3, 4], which we addressed in our manuscript. Nevertheless, we will address the main points of their letter to provide further clarity on how the GAS does in fact serve as an appropriate mechanistic model to conceptualize training periodization.

The purpose of this study was to compare ground contact time between an optical measurement system and a force platform. Participants in this study included six collegiate level athletes who performed drop jumps and sprint strike steps for a total of 15 repetitions each. Ground contact data was simultaneously collected from an optical measurement system and a force platform, at a sampling frequency of 1000 Hz. Data was then analyzed with Pearson’s correlation and paired sample t-tests. The measures from the optical measurement system were found to be significantly higher (p < 0.001) than measures from the force platform in both conditions. Although significantly different, the extremely large relationships (0.979, 0.993) found between the two devices suggest the optical sensor is able to detect similar changes in performance to that of a force platform. Practitioners may continue to utilize optical sensors to monitor performance as it may provide a superior user-friendly alternative to more traditional based monitoring procedures, but must comprehend the inherent limitations due to the design of the optical sensors.

The purpose of the current study was (1) to examine the differences between standing and lying measures of vastus lateralis (VL), muscle thickness (MT), pennation angle (PA), and cross-sectional area (CSA) using ultrasonography; and (2) to explore the relationships between lying and standing measures with isometric and dynamic assessments of force production—specifically peak force, rate of force development (RFD), impulse, and one-repetition maximum back squat. Fourteen resistance-trained subjects (age = 26.8 ± 4.0 years, height = 181.4 ± 6.0 cm, body mass = 89.8 ± 10.7 kg, back squat to body mass ratio = 1.84 ± 0.34) agreed to participate. Lying and standing ultrasonography images of the right VL were collected following 48 hours of rest. Isometric squat assessments followed ultrasonography, and were performed on force platforms with data used to determine isometric peak force (IPF), as well as RFD and impulse at various time points. Forty-eight hours later, one-repetition maximum back squats were performed by each subject. Paired-samples t-tests revealed statistically significant differences between standing and lying measurements of MT (p < 0.001), PA (p < 0.001), and CSA (p ≤ 0.05), with standing values larger in all cases. Further, standing measures were correlated more strongly and abundantly to isometric and dynamic performance. These results suggest that if practitioners intend to gain insight into strength-power potential based on ultrasonography measurements, performing the measurement collection with the athlete in a standing posture may be preferred.