Current research ideologies in sport science allow for the possibility of investigators producing statistically significant results to help fit the outcome into a predetermined theory. Additionally, under the current Neyman-Pearson statistical structure, some argue that null hypothesis significant testing (NHST) under the frequentist approach is flawed, regardless. For example, a p-value is unable to measure the probability that the studied hypothesis is true, unable to measure the size of an effect or the importance of a result, and unable to provide a good measure of evidence regarding a model or hypothesis. Many of these downfalls are key questions researchers strive to answer following an investigation. Therefore, a shift towards a magnitude-based inference model, and eventually a fully Bayesian framework, is thought to be a better fit from a statistical standpoint and may be an improved way to address biases within the literature. The goal of this article is to shed light on the current research and statistical shortcomings the field of sport science faces today, and offer potential solutions to help guide future research practices.

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 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 (RI_SR). Fifteen well-trained males underwent RT 3 d·wk⁻¹ for 10 weeks in either an RM group (n = 8) or RI_SR group (n = 7). The RM group achieved a relative maximum each day, while the RI_SR group trained based on percentages. The RM group exercised until muscular failure on each exercise, while the RI_SR 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. RI_SR 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). RI_SR 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 RI_SR 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 RI_SR compared to RM loading paradigms.

Caffeine is often used in a variety of forms to enhance athletic performance; however, research regarding caffeine’s effects on strength and power in female athletes is lacking. Therefore, the purpose of this study was to analyze the acute effects of caffeine anhydrous (6 mg/kg of body mass) on jumping performance and maximal strength in female collegiate athletes. Eleven athletes (19.7 ± 0.9 yrs; 166.4 ± 10.2 cm, 67.7 ± 9.4 kg) performed two testing sessions separated by one week, and randomly received caffeine or placebo using a double-blind approach. Heart rate, blood pressure, and tympanic temperature were recorded before athletes received each condition, following 60 min of quiet sitting, and directly after performance testing. Athletes were assessed on unweighted and weighted squat jump height (SJH0, SJH20) and countermovement jump height (CMJH0, CMJH20), isometric mid-thigh pull peak force (IPF), and rate of force development from 0–200 ms (RFD200). Resting systolic blood pressure was significantly greater following caffeine administration compared to a placebo (p = 0.017). There were small, significant differences in SJH0 (p = 0.035, g = 0.35), SJH20 (p = 0.002, g = 0.49), CMJH0 (p = 0.015, g = 0.19), and CMJH20 (p < 0.001, g = 0.37) in favor of caffeine over placebo. However, there was no significant difference in IPF (p = 0.369, g = 0.12) and RFD200 (p = 0.235, g = 0.32) between conditions. Therefore, caffeine appears to enhance jumping performance, but not maximal strength in female collegiate athletes.

Monitoring tests are commonly used to assess weightlifter’s preparedness for competition. Although various monitoring tests have been used, it is not clear which test is the strongest indicator of weightlifting performance. Therefore, the purpose of this study was to (1) determine the relationships between vertical jump, isometric mid-thigh pull (IMTP) and weightlifting performance; and (2) compare vertical jumps to IMTP as monitoring tests of weightlifting performance in a large cohort of male and female weightlifters. Methods: Fifty-two competitive weightlifters (31 males, 21 females) participated in squat and countermovement jump testing (SJ, CMJ), and IMTP testing performed on force plates. All laboratory testing data was correlated to a recent competition where the athletes had attempted to peak. Results: Squat jump height (SJH) was the strongest correlate for men and women with the Sinclair Total (r = 0.686, p ≤ 0.01; r = 0.487, p ≤ 0.05, respectively) compared to countermovement jump height (r = 0.642, p ≤ 0.01; r = 0.413, p = 0.063), IMTP peak force allometrically scaled to body mass (r = 0.542, p ≤ 0.01; r = −0.044, p = 0.851) and rate of force development at 200 ms (r = 0.066, p = 0.723; r = 0.086, p = 0.711), respectively. Further, SJH was a stronger correlate of relative weightlifting performance compared to IMTP peak force in females (p = 0.042), but not male weightlifters (p = 0.191). Conclusions: Although CMJ and IMTP are still considered strong indicators of weightlifting performance, SJH appears to be the most indicative measure of weightlifting performance across a wide-range of performance levels. Thus, SJH can be used as a reliable measure to monitor weightlifting performance in male and female weightlifters.

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.