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Ideally an athlete would continue to improve performance indefinitely over time, however improvement slows as the athlete approaches their genetic limits. Measuring performance is complex—performance may be temporarily depressed following aggressive training for multiple reasons, physiological and psychosocial. This reality may be vexing to the strength and conditioning coach, who, as a service provider, must answer to sport coaches about an athlete’s progress. Recently an evaluation mechanism for strength and conditioning coaches was proposed, in part to help coaches establish their effectiveness within the organization. Without formal guidance and realistic expectations, if an athlete is not bigger, leaner, stronger, etc. as a result of training within a specified timeframe, blame is often placed upon the strength and conditioning coach. The purpose of this article is to explore possible causes of what may be perceived as athlete non-responses to training and to provide guidance for the coach on how to handle those issues within their domain. A process of investigation is recommended, along with resources to assist coaches as they consider a broad range of issues, including enhancing existing testing methods, improving athlete behaviors, and adjusting processes designed to bring about performance improvement.

Some controversy exists as to the most efficacious method of training to achieve enhanced levels of sport performance. Controversy concerning the efficacy of periodization and especially block periodization (BP) likely stems from the use of poorly or untrained subjects versus trained who may differ in their responses to a stimulus. The purpose of this study was to investigate the effect of training status on performance outcomes resulting from 11 weeks of BP training. Fifteen males were recruited for this study and placed into strong (age = 24.3 ± 1.9 years., body mass (BM) = 87.7 ± 8.7 kg, squat: body mass = 1.96 ± 0.16), moderate (age = 25.3 ± 2.7 years., body mass = 100.2 ± 15.5 kg, squat: body mass = 1.46 ± 0.14), or weak (age = 23.2 ± 3.9 yrs., body mass = 83.5 ± 17.1 kg, squat: body mass = 1.17 ± 0.07) groups based on relative strength. Testing was completed at baseline, and after each block which consisted of 1 repetition maximum (1RM) squat, 0 kg static jump (SJ), 0 kg countermovement jump (CMJ), 20 kg SJ, and 20 kg CMJ. Absolute and relative strength were strongly correlated with rates of improvement for absolute strength, relative strength, 0 kg, and 20 kg vertical jumps. All subjects substantially improved back squat (p < 0.001), relative back squat (p < 0.001) with large–very large effect sizes between groups for percent change favoring the weak group over the moderate and strong group for all performance variables. All subjects showed statistically significant improvements in 0 kg SJ (p < 0.001), 0 kg CMJ (p < 0.001), 20 kg SJ (p = 0.002), and 20 kg CMJ (p < 0.001). Statistically significant between group differences were noted for both 20 kg SJ (p = 0.01) and 20 kg CMJ (p = 0.043) with the strong group statistically greater jump heights than the weak group. The results of this study indicate BP training is effective in improving strength and explosive ability. Additionally, training status may substantially alter the response to a resistance training program.

Prior to major competitions, athletes often use a peaking protocol such as tapering or training cessation to improve performance. The majority of the current literature has focused on endurance-based sports such as swimming, cycling, and running to better understand how and when to taper or use training cessation to achieve the desired performance outcome. However, evidence regarding peaking protocols for strength and power athletes is lacking. Current limitations for peaking maximal strength is that many studies do not provide sufficient details for practitioners to use. Thus, when working with athletes such as powerlifters, weightlifters, throwers, and strongman competitors, practitioners must use trial and error to determine the best means for peaking rather than using an evidence-based protocol. More specifically, determining how to peak maximal strength using data derived from strength and power athletes has not been established. While powerlifting training (i.e., back squat, bench press, deadlift) is used by strength and power athletes up until the final days prior to a competition, understanding how to peak maximal strength relative to powerlifting performance is still unclear. Thus, the purpose of this study was to review the literature on tapering and training cessation practices relative to peaking powerlifting performance.

The purposes of this study were to investigate effects of partial block periodized strength training on physical performance and to examine relationships between initial muscle strength measured with isometric mid-thigh pull (IMTP) and performance changes after 7 weeks of strength training. Seventeen collegiate male soccer players participated. Initial muscle strength was determined using IMTP while physical performance included 10 m and 20 m sprints and static vertical jump with a polyvinyl chloride pipe (SJ0), 20 kg barbell (SJ20), and barbell loaded to 40 kg bar (SJ40). Performance testing was performed at three points: before first week (baseline), fourth week (T1), and seventh week (T2). Statistically small to moderate changes were found from baseline to T2 in peak power (PP; p < 0.001, ES = 0.49), net impulse (NI; p < 0.001, ES = 0.49), peak velocity (PV; p < 0.001, ES = 0.62), allometrically scaled PP (PPa; p < 0.001, ES = 0.62) in SJ20 and jump height (JH) in SJ40 (p < 0.001, ES = 0.36). Moderate to large correlations were found between isometric peak force and the changes from baseline to T2 in SJ20 PP (p = 0.04, r = −0.49), SJ20 PF (p = 0.03, r = −0.52), PPa (p = 0.04, r = −0.50), and SJ20 allometrically scaled peak force (p = 0.04, r = −0.49). Properly structured strength training maximizes task-specific physical performance. Initial muscle strength negatively affects the magnitudes of adaptations to physical performance.

Analysis of elite performances is important to elucidate the characteristics of effective weightlifting technique contributing to the highest level of achievement. The general technique of the weightlifting movements is well established. However, it is also apparent that weightlifting technique can differ based on athlete characteristics. Thus, existing technical models may not accurately reflect current technique of top performers or be applied generically to athletes of different skill, size, sex, or ability. Therefore, the purpose of this descriptive study was to update the scientific knowledge of snatch technique of top international weightlifters. This study used video analysis to determine barbell trajectory and kinematics of 319 successful snatch attempts from two major international competitions. Relative frequencies of barbell trajectory types differed based on competition, sex, category, and ranking. No statistical differences were observed among the top-three performers for either sex for most kinematic variables, and there were no overall discernible patterns of effect size differences for individual or clusters of kinematic variables. The results of this study indicate that weightlifting success can be achieved with a variety of technique profiles.

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 × 4 RM ANOVA, Cohen’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 = 0.78–0.95). 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.

The purpose of the study was to compare the physiological responses of skeletal muscle to a resistance training (RT) program using repetition maximum (RM) or relative intensity (RISR). Fifteen well-trained males underwent RT 3 d·wk−1 for 10 weeks in either an RM group (n = 8) or RISR group (n = 7). The RM group achieved a relative maximum each day, while the RISR group trained based on percentages. The RM group exercised until muscular failure on each exercise, while the RISR group did not reach muscular failure throughout the intervention. Percutaneous needle biopsies of the vastus lateralis were obtained pre-post the training intervention, along with ultrasonography measures. Dependent variables were: Fiber type-specific cross-sectional area (CSA); anatomical CSA (ACSA); muscle thickness (MT); mammalian target of rapamycin (mTOR); adenosine monophosphate protein kinase (AMPK); and myosin heavy chains (MHC) specific for type I (MHC1), type IIA (MHC2A), and type IIX (MHC2X). Mixed-design analysis of variance and effect size using Hedge’s g were used to assess within- and between-group alterations. RISR statistically increased type I CSA (p = 0.018, g = 0.56), type II CSA (p = 0.012, g = 0.81), ACSA (p = 0.002, g = 0.53), and MT (p < 0.001, g = 1.47). RISR also yielded a significant mTOR reduction (p = 0.031, g = −1.40). Conversely, RM statistically increased only MT (p = 0.003, g = 0.80). Between-group effect sizes supported RISR for type I CSA (g = 0.48), type II CSA (g = 0.50), ACSA (g = 1.03), MT (g = 0.72), MHC2X (g = 0.31), MHC2A (g = 0.87), and MHC1 (g = 0.59); with all other effects being of trivial magnitude (g < 0.20). Our results demonstrated greater adaptations in fiber size, whole-muscle size, and several key contractile proteins when using RISR compared to RM loading paradigms.

The purpose of this study was to investigate the kinetic and morphological adaptations that occur during distinct phases of a block periodized training cycle in weightlifters. Athlete monitoring data from nine experienced collegiate weightlifters was used. Isometric mid-thigh pull (IMTP) and ultrasonography (US) results were compared to examine the effects of three specific phases of a training cycle leading up to a competition. During the high volume strength-endurance phase (SE) small depressions in rate of force development (RFD) but statistically significant (p ≤ 0.05) increases in vastus lateralis cross-sectional area (CSA), and body mass (BM) were observed. The lower volume higher intensity strength-power phase (SP) caused RFD to rebound above pre-training cycle values despite statistically significant reductions in CSA. Small to moderate increases only in the earlier RFD time bands (

Seventeen strength and power athletes (n = 11 males, 6 females; height: 177.5 ± 7.0 cm, 165.8 ± 11.4 cm; body mass: 90.0 ± 14.1 kg, 66.4 ± 13.9 kg; age: 30.6 ± 10.4 years, 30.8 ± 8.7 years), who regularly performed weightlifting movements during their resistance training programs, were recruited to examine the effect of altering body posture and barbell position on the between-session reliability of force-time characteristics generated in the isometric mid-thigh pull (IMTP). After participants were familiarised with the testing protocol, they undertook two testing sessions which were separated by seven days. In each session, the participants performed three maximal IMTP trials in each of the four testing positions examined, with the order of testing randomized. In each position, no significant differences were found between sessions for all force-time characteristics (p = >0.05). Peak force (PF), time-specific force (F50, F90, F150, F200, F250) and IMP time-bands (0⁻50, 0⁻90, 0⁻150, 0⁻200, 0⁻250 ms) were reliable across each of the four testing positions (ICC ≥ 0.7, CV ≤ 15%). Time to peak force, peak RFD, RFD time-bands (0⁻50, 0⁻90, 0⁻150, 0⁻200, 0⁻250 ms) and peak IMP were unreliable regardless of the testing position used (ICC = 15%). Overall, the use of body postures and barbell positions during the IMTP that do not correspond to the second pull of the clean have no adverse effect on the reliability of the force-time characteristics generated.

Monitoring the resistance training volume load (VL) (sets × reps × load) is essential to managing resistance training and the recovery⁻adaptation process. Subjects: Eight trained weightlifters, seven of which were at national level, participated in the study. Methods: VL was measured both with (VLwD) and without (VL) the inclusion of barbell displacement, across twenty weeks of training, in order to allow for comparisons to be made of these VL calculating methods. This consisted of recording the load, repetition count, and barbell displacement for every set executed. Comparisons were made between VL and VLwD for individual blocks of training, select training weeks, and select training days. Results: Strong, statistically significant correlations (r ≥ 0.78, p < 0.001) were observed between VL and VLwD between all training periods analyzed. t-tests revealed statistically significant (p ≤ 0.018) differences between VL and VLwD in four of the seven training periods analyzed. Conclusion: The very strong relationship between VL and VLwD suggest that a coach with time constraints and a large number of athletes can potentially spare the addition of displacement. However, differences in percent change indicate that coaches with ample time should include displacement in VL calculations, in an effort to acquire more precise workload totals.

The purpose of this analysis was to construct a preliminary scale of reference values for reactive strength index-modified (RSImod). Countermovement jump data from 151 National Collegiate Athletic Association (NCAA) Division I collegiate athletes (male n = 76; female n = 75) were analyzed. Using percentiles, scales for both male and female samples were constructed. For further analysis, athletes were separated into four performance groups based on RSImod and comparisons of jump height (JH), and time to takeoff (TTT) were performed. RSImod values ranged from 0.208 to 0.704 and 0.135 to 0.553 in males and females, respectively. Males had greater RSImod (p < 0.001, d = 1.15) and JH (p < 0.001, d = 1.41) as compared to females. No statistically significant difference was observed for TTT between males and females (p = 0.909, d = 0.02). Only JH was found to be statistically different between all performance groups. For TTT no statistical differences were observed when comparing the top two and middle two groups for males and top two, bottom two, and middle two groups for females. Similarities in TTT between sexes and across performance groups suggests JH is a primary factor contributing to differences in RSImod. The results of this analysis provide practitioners with additional insight as well as a scale of reference values for evaluating RSImod scores in collegiate athletes.

The purpose of this study was to examine the force-time differences between concentric-only half-squats (COHS) performed with ballistic (BAL) or non-ballistic (NBAL) intent across a range of loads. Eighteen resistance-trained men performed either BAL or NBAL COHS at 30%, 50%, 70%, and 90% of their one repetition maximum (1RM) COHS. Relative peak force (PF) and relative impulse from 0–50 ms (Imp50), 0–90 ms (Imp90), 0–200 ms (Imp200), and 0–250 ms (Imp250) were compared using a series of 2 × 4 (intent × load) repeated measures ANOVAs with Bonferroni post hoc tests. Cohen’s d effect sizes were calculated to provide measures of practical significance between the BAL and NBAL COHS and each load. BAL COHS produced statistically greater PF than NBAL COHS at 30% (d = 3.37), 50% (d = 2.88), 70% (d = 2.29), and 90% 1RM (d = 1.19) (all p < 0.001). Statistically significant main effect differences were found between load-averaged BAL and NBAL COHS for Imp90 (p = 0.006, d = 0.25), Imp200 (p = 0.001, d = 0.36), and Imp250 (p < 0.001, d = 0.41), but not for Imp50 (p = 0.018, d = 0.21). Considering the greater PF and impulse observed during the BAL condition, performing COHS with BAL intent may provide a favorable training stimulus compared to COHS performed with NBAL intent.

The current investigation was an examination of the repetition-to-repetition magnitudes and changes in kinetic and kinematic characteristics of the back squat using accentuated eccentric loading (AEL) and cluster sets. Trained male subjects (age = 26.1 ± 4.1 years, height = 183.5 ± 4.3 cm, body mass = 92.5 ± 10.5 kg, back squat to body mass ratio = 1.8 ± 0.3) completed four load condition sessions, each consisting of three sets of five repetitions of either traditionally loaded straight sets (TL), traditionally loaded cluster sets (TLC), AEL cluster sets (AEC), and AEL straight sets where only the initial repetition had eccentric overload (AEL1). Eccentric overload was applied using weight releasers, creating a total eccentric load equivalent to 105% of concentric one repetition maximum (1RM). Concentric load was 80% 1RM for all load conditions. Using straight sets (TL and AEL1) tended to decrease peak power (PP) (d = −1.90 to −0.76), concentric rate of force development (RFDCON) (d = −1.59 to −0.27), and average velocity (MV) (d = −3.91 to −1.29), with moderate decreases in MV using cluster sets (d = −0.81 to −0.62). Greater magnitude eccentric rate of force development (RFDECC) was observed using AEC at repetition three (R3) and five (R5) compared to all load conditions (d = 0.21–0.65). Large within-condition changes in RFDECC from repetition one to repetition three (∆REP1–3) were present using AEL1 (d = 1.51), demonstrating that RFDECC remained elevated for at least three repetitions despite overload only present on the initial repetition. Overall, cluster sets appear to permit higher magnitude and improved maintenance of concentric outputs throughout a set. Eccentric overload with the loading protocol used in the current study does not appear to potentiate concentric output regardless of set configuration but may cause greater RFDECC compared to traditional loading.

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.

Daily undulating periodization (DUP), using daily alterations in repetitions, has been advocated as a superior method of resistance training, while traditional forms of programming for periodization (Block) have been questioned. Nineteen Division I track and field athletes were assigned to either a 10-week Block or DUP training group. Year and event were controlled. Over the course of the study, there were four testing sessions, which were used to evaluate a variety of strength characteristics, including maximum isometric strength, rate of force development, and one repetition maximum (1RM). Although, performance trends favored the Block group for strength and rate of force development, no statistical differences were found between the two groups. However, different (p ≤ 0.05) estimated volumes of work (VL) and amounts of improvement per VL were found between groups. Based upon calculated training efficiency scores, these data indicate that a Block training model is more efficient in producing strength gains than a DUP model. Additionally, alterations in testosterone (T), cortisol (C) and the T:C ratio were measured. Although there were no statistically (p ≤ 0.05) different hormone alterations between groups, relationships between training variables and hormone concentrations including the T:C ratio, indicate that Block may be more efficacious in terms of fatigue management.

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.

The purpose of this monitoring study was to investigate how alterations in training affect changes in force-related characteristics and weightlifting performance. Subjects: Seven competitive weightlifters participated in the study. Methods: The weightlifters performed a block style periodized plan across 20 weeks. Force plate data from the isometric mid-thigh pull and static jumps with 0 kg, 11 kg, and 20 kg were collected near the end of each training block (weeks 1, 6, 10, 13, 17, and 20). Weightlifting performance was measured at weeks 0, 7, 11, and 20. Results: Very strong correlations were noted between weightlifting performances and isometric rate of force development (RFD), isometric peak force (PF), peak power (PP), and jump height (JH). Men responded in a more predictable manner than the women. During periods of higher training volume, RFD was depressed to a greater extent than PF. JH at 20 kg responded in a manner reflecting the expected fatigue response more so than JH at 0 kg and 11 kg. Conclusions: PF appears to have been more resistant to volume alterations than RFD and JH at 20 kg. RFD and JH at 20 kg appear to be superior monitoring metrics due to their “sensitivity.”

The purpose of this study was to examine the relationships between muscle cross-sectional area (CSA), maximal strength, power output, and maximum potentiation characteristics. The vastus lateralis and biceps femoris CSA, one repetition maximum (1RM) back squat, 1RM concentric-only half-squat (COHS) strength, static jump power output, and maximum potentiation characteristics of 17 resistance-trained men was assessed during several testing sessions. Pearson’s correlation coefficients were used to examine the relationships between CSA, strength, power output, and maximum potentiation measures. Moderate-to-strong relationships existed between CSA and strength measures (r = 0.462–0.643) as well as power output (r = 0.396–0.683). In addition, moderate-to-strong relationships existed between strength and power output (r = 0.407–0.548), while trivial relationships existed between strength and maximum potentiation (r = −0.013–0.149). Finally, small negative relationships existed between CSA and maximum potentiation measures (r = −0.229–−0.239). The results of the current study provide evidence of the interplay between muscle CSA, strength, power, and potentiation. Vastus lateralis and biceps femoris CSA may positively influence an individual’s back squat and COHS maximal strength and squat jump peak power; however, muscle CSA and absolute strength measures may not contribute to an individual’s potentiation capacity. Practitioners may consider implementing resistance training strategies that improve vastus lateralis and biceps femoris size in order to benefit back squat and COHS strength. Furthermore, implementing squatting variations—both full and partial—may benefit jumping performance.

The purpose of the current study was two-fold: (1) To examine the variation in velocity and power with increasing intensity in the back squat among subjects; and (2) To explore individual subject characteristics as possible explanations for variations of velocity in the back squat. Fourteen recreationally trained male subjects with experience in the back squat agreed to participate in the study (age = 25.0 ± 2.6 years, height = 178.9 ± 8.1 cm, body mass = 88.2 ± 15.8 kg). One-repetition maximums (1RM) were performed for each subject on force platforms with four linear position transducers attached to the barbell. The 1RM assessment was immediately preceded by warm-up sets at 65%, 75%, 85%, and 95% of estimated 1RM for 5, 3, 2, and 1 repetitions, respectively. Mean concentric velocity (MCV) and mean power were recorded for each intensity condition and were analyzed using Pearson correlation to determine the relationship between each variable and relative intensity (%1RM). Statistically significant negative relationships existed between %1RM and MCV (r = −0.892) and mean power (r = −0.604). Between-subject coefficient of variation tended to increase as %1RM increased for both MCV and mean power. These results suggest that MCV is superior to mean power as an indicator of relative intensity in the back squat. Additionally, the between-subject variation observed at higher intensities for MCV and mean power support the use of velocity ranges by strength and conditioning coaches.