Banyard, HG, Nosaka, K, and Haff, GG. Reliability and validity of the load-velocity relationship to predict the 1RM back squat. J Strength Cond Res 31(7): 1897-1904, 2017-This study investigated the reliability and validity of the load-velocity relationship to predict the free-weight back squat one repetition maximum (1RM). Seventeen strength-trained males performed three 1RM assessments on 3 separate days. All repetitions were performed to full depth with maximal concentric effort. Predicted 1RMs were calculated by entering the mean concentric velocity of the 1RM (V1RM) into an individualized linear regression equation, which was derived from the load-velocity relationship of 3 (20, 40, 60% of 1RM), 4 (20, 40, 60, 80% of 1RM), or 5 (20, 40, 60, 80, 90% of 1RM) incremental warm-up sets. The actual 1RM (140.3 ± 27.2 kg) was very stable between 3 trials (ICC = 0.99; SEM = 2.9 kg; CV = 2.1%; ES = 0.11). Predicted 1RM from 5 warm-up sets up to and including 90% of 1RM was the most reliable (ICC = 0.92; SEM = 8.6 kg; CV = 5.7%; ES = -0.02) and valid (r = 0.93; SEE = 10.6 kg; CV = 7.4%; ES = 0.71) of the predicted 1RM methods. However, all predicted 1RMs were significantly different (p ≤ 0.05; ES = 0.71-1.04) from the actual 1RM. Individual variation for the actual 1RM was small between trials ranging from -5.6 to 4.8% compared with the most accurate predictive method up to 90% of 1RM, which was more variable (-5.5 to 27.8%). Importantly, the V1RM (0.24 ± 0.06 m·s) was unreliable between trials (ICC = 0.42; SEM = 0.05 m·s; CV = 22.5%; ES = 0.14). The load-velocity relationship for the full depth free-weight back squat showed moderate reliability and validity but could not accurately predict 1RM, which was stable between trials. Thus, the load-velocity relationship 1RM prediction method used in this study cannot accurately modify sessional training loads because of large V1RM variability.

Velocity-based training (VBT) is a contemporary method of resistance training that enables accurate and objective prescription of resistance training intensities and volumes. This review provides an applied framework for the theory and application of VBT. Specifically, this review gives detail on how to: use velocity to provide objective feedback, estimate strength, develop load-velocity profiles for accurate load prescription, and how to use statistics to monitor velocity. Furthermore, a discussion on the use of velocity loss thresholds, different methods of VBT prescription, and how VBT can be implemented within traditional programming models and microcycles is provided.

PV, MPV, and MV are reliable and can be utilized to develop LVPs using linear regression. Conceptually, LVPs can be used to monitor changes in movement velocity and employed as a method for adjusting sessional training loads according to daily readiness.

PUSH accuracy for determining MV, PV, MF, MP, and PP across all 6 relative intensities was questionable for the back squat, yet the GYM was highly valid at assessing all criterion variables, with some caution given to estimations of MP and PP performed at lighter loads.

Purpose: To compare the effects of velocity-based training (VBT) and 1-repetition-maximum (1RM) percentage-based training (PBT) on changes in strength, loaded countermovement jump (CMJ), and sprint performance. Methods: A total of 24 resistance-trained males performed 6 weeks of full-depth free-weight back squats 3 times per week in a daily undulating format, with groups matched for sets and repetitions. The PBT group lifted with fixed relative loads varying from 59% to 85% of preintervention 1RM. The VBT group aimed for a sessional target velocity that was prescribed from pretraining individualized load–velocity profiles. Thus, real-time velocity feedback dictated the VBT set-by-set training load adjustments. Pretraining and posttraining assessments included the 1RM, peak velocity for CMJ at 30%1RM (PV-CMJ), 20-m sprint (including 5 and 10 m), and 505 change-of-direction test (COD). Results: The VBT group maintained faster (effect size [ES] = 1.25) training repetitions with less perceived difficulty (ES = 0.72) compared with the PBT group. The VBT group had likely to very likely improvements in the COD (ES = −1.20 to −1.27), 5-m sprint (ES = −1.17), 10-m sprint (ES = −0.93), 1RM (ES = 0.89), and PV-CMJ (ES = 0.79). The PBT group had almost certain improvements in the 1RM (ES = 1.41) and possibly beneficial improvements in the COD (ES = −0.86). Very likely favorable between-groups effects were observed for VBT compared to PBT in the PV-CMJ (ES = 1.81), 5-m sprint (ES = 1.35), and 20-m sprint (ES = 1.27); likely favorable between-groups effects were observed in the 10-m sprint (ES = 1.24) and nondominant-leg COD (ES = 0.96), whereas the dominant-leg COD (ES = 0.67) was possibly favorable. PBT had small (ES = 0.57), but unclear differences for 1RM improvement compared to VBT. Conclusions: Both training methods improved 1RM and COD times, but PBT may be slightly favorable for stronger individuals focusing on maximal strength, whereas VBT was more beneficial for PV-CMJ, sprint, and COD improvements.