Cluster Sets Edge Out Rest-Redistribution for Preserving Lifting Velocity, but the Gap May Be Too Small to Matter

Every strength coach knows the feeling of watching bar speed die mid-set. A lifter starts a set of ten back squats with crisp, explosive repetitions, and by the eighth rep the bar is grinding upward at a fraction of its initial velocity. That decline is not a motivational problem; it is neuromuscular fatigue written in real time on the velocity trace. A new systematic review and Bayesian network meta-analysis published in Sports Medicine – Open has now synthesized 37 studies to answer a deceptively simple question: when the goal is to keep athletes moving fast under load, which way of structuring a training set works best?

The research team, led by Tsuyoshi Nagatani of Edith Cowan University alongside colleagues including Christopher Latella, Paul Comfort, Eric Drinkwater and G. Gregory Haff, compared three alternatives to the traditional set, in which repetitions are performed consecutively with rest only between sets. Cluster sets insert brief rest periods between individual repetitions or small groups of repetitions within a set, adding total rest and lengthening the session. Rest-redistribution strategies take a different route: instead of adding rest, they rearrange the rest already in the program. Intra-set rest-redistribution carves a slice out of the between-set rest and moves it inside the set, while inter-set rest-redistribution splits a large set into many smaller ones, for example turning three sets of ten into six sets of five, keeping total rest constant.

Because only a handful of studies have directly pitted cluster sets against rest-redistribution, the authors turned to a network meta-analysis, a statistical framework that combines direct comparisons with indirect ones linked through a common comparator, in this case the traditional set. They searched PubMed and Web of Science, screened 1,121 records, and ultimately included 37 studies encompassing 611 unique participants, 557 of them male and 54 female. The outcomes of interest were mean velocity, mean propulsive velocity and peak velocity, the kinematic signatures that velocity-based training practitioners use to gauge fatigue and readiness. Analyses were run in a Bayesian framework using Markov Chain Monte Carlo simulation, with posterior standardized mean differences and 95 percent credible intervals, and treatments were ranked with SUCRA scores, which express the probability that each set structure is the most effective.

The headline finding is that all three alternative structures beat the traditional set at preserving mean velocity. Cluster sets showed a standardized mean difference of 0.60 (95 percent credible interval 0.42 to 0.80), intra-set rest-redistribution 0.41 (0.08 to 0.75) and inter-set rest-redistribution 0.42 (0.26 to 0.61). For peak velocity, cluster sets (0.48, 0.20 to 0.76) and intra-set rest-redistribution (0.39, 0.04 to 0.73) again outperformed traditional sets, but the credible interval for inter-set rest-redistribution crossed zero, leaving its effect on peak velocity uncertain. In the SUCRA rankings, cluster sets held the highest probability of being the best structure for both mean velocity (99.7 percent) and peak velocity (95.6 percent), followed by intra-set rest-redistribution and then inter-set rest-redistribution.

Yet the more consequential result is what the analysis did not find. When the alternative structures were compared directly with one another, the credible intervals for mean and peak velocity crossed zero in every case. Cluster sets versus intra-set rest-redistribution produced a credible interval of −0.19 to 0.58 for mean velocity, and cluster sets versus inter-set rest-redistribution ranged from −0.05 to 0.41. In plain terms, although cluster sets carry the highest probability of ranking first, the evidence does not support a practically meaningful advantage over either redistribution strategy. The physiological logic behind the cluster advantage is straightforward: brief intra-set pauses allow partial resynthesis of phosphocreatine, blunting the metabolic disruption, lactate accumulation and neural drive decrements that cause velocity to crater in uninterrupted sets. But rest-redistribution achieves a similar effect without adding a single second to the session, which is precisely why the question of whether cluster sets are meaningfully better matters so much to practitioners.

The authors also tested whether the benefits depend on context, using network meta-regressions. Exercise type, comparing lower-body movements such as squats and clean pulls with the bench press, did not significantly moderate the effects, with a regression coefficient of 0.10 and a credible interval spanning zero, although there was a tendency toward larger benefits in lower-body exercise, plausibly because larger muscle masses and longer ranges of motion generate greater metabolic stress. Relative training intensity, split between moderate loads of 60 to 79 percent of one-repetition maximum and high loads of 80 percent or more, likewise failed to moderate the effects (coefficient 0.15, credible interval −0.12 to 0.43). The practical implication is that alternative set structures appear robust across exercise selection and loading zones, rather than being niche tools for specific lifts or intensity zones.

Peak velocity told a more nuanced story. The uncertain effect of inter-set rest-redistribution was driven partly by the small number of studies and by the exercises examined. The one study reporting a clear peak-velocity benefit examined the clean pull from the floor, a full-range movement with large barbell displacement and high metabolic demand, whereas the two studies showing no benefit used partial weightlifting derivatives, the hang pull and countermovement shrug, which involve shorter displacement and less time under tension and may simply not generate enough fatigue for redistributed rest to matter. The authors caution that the apparent superiority of cluster sets and intra-set redistribution for peak velocity may therefore be inflated by these study characteristics, and they call for more research on full-range, multi-joint movements.

For coaches, the practical takeaway may be the most valuable part of the paper. Because intra-set rest-redistribution produced velocity outcomes statistically comparable to cluster sets without extending session duration, it emerges as a time-efficient alternative for athletes training under tight schedules. Inter-set rest-redistribution, while effective for mean velocity, carries logistical friction: some implementations redistribute rest between every single repetition, forcing athletes to rack and un-rack a barbell dozens of times, and in team environments one athlete must finish all their scattered repetitions before partners can rotate through. The authors suggest that cluster sets and intra-set redistribution are the more practical choices in real-world settings, with the final decision guided by session time, equipment and training context rather than by any large performance differential.

The review is not without limitations, which the authors acknowledge candidly. Several studies comparing intra-set rest-redistribution with traditional sets failed to equalize total rest duration, inadvertently giving the intervention more recovery and potentially inflating its benefit. Evidence for peak velocity rested on far fewer studies than mean velocity, some consistency checks could not be performed for certain comparisons, and the participant pool was overwhelmingly male, preventing any analysis of sex as a moderator. Publication bias was detected for the mean velocity model, though not for peak velocity. Registered prospectively on the Open Science Framework, the review nevertheless represents the first analysis to treat the two rest-redistribution variants as distinct constructs, and its central message is likely to reshape how velocity-focused programming is discussed: the way rest is arranged matters far more than the label attached to it, and almost any thoughtful departure from the traditional set will keep the bar moving faster.

Subject of Research: A systematic review and Bayesian network meta-analysis comparing cluster set and rest-redistribution set configurations for maintaining movement velocity during resistance training.

Article Title: The Effectiveness of Cluster vs. Rest-Redistribution Set Configurations to Maintain Movement Velocity During Resistance Training: A Systematic Review and Bayesian Network Meta-analysis

Article References: Nagatani, T., Latella, C., Yang, J., Kendall, K. L., Vial, S., Comfort, P., Drinkwater, E. J., & Haff, G. G. (2026). The Effectiveness of Cluster vs. Rest-Redistribution Set Configurations to Maintain Movement Velocity During Resistance Training: A Systematic Review and Bayesian Network Meta-analysis. Sports Medicine – Open, 12(1), Article 141. https://doi.org/10.1186/s40798-026-01106-w

Image Credits: AI Generated

DOI: 10.1186/s40798-026-01106-w

Keywords: resistance training, cluster sets, rest redistribution, movement velocity, velocity loss, neuromuscular fatigue, network meta-analysis, strength and conditioning, velocity-based training, set structure, phosphocreatine, sports science