1 Introduction 

Historically, strength training intensity has often been prescribed using fixed percentages of one-repetition maximum (1RM), with training loads calculated from a previously tested maximal lift (Berger, 1965). This approach fits logically within periodised planning because coaches can organise heavier and lighter exposures across training phases (Thompson et al., 2020). However, the increasing use of autoregulatory strategies, particularly repetitions-in-reserve (RIR)-based rating of perceived exertion (RPE) and velocity-based training (VBT), has challenged the assumption that fixed percentages are sufficiently responsive to daily readiness, technical variation and individual adaptation (Greig et al., 2020; Hickmott et al., 2022).

This essay argues that percentage-based programming and autoregulation should not be viewed as mutually exclusive. Rather, percentage-based loading provides structure and progression, while autoregulation can improve individualisation and fatigue management when implemented with appropriate athlete education and coaching oversight. 

2 Historical and Theoretical Background

Percentage-based programming developed within broader periodisation models, where volume and intensity are planned over time to promote progressive adaptation. In powerlifting, this logic is appealing because competition performance is expressed through maximal external load. A coach can prescribe, for example, several weeks of work at 70–85% 1RM before progressing towards heavier singles during a peaking phase. The method is transparent, quantifiable and easy to communicate. Autoregulation developed as a response to the practical limitation that fixed loading does not always reflect the athlete’s current capacity (Tuchscherer, 2008). It can be understood as a feedback-based approach in which training variables are modified when current performance indicators or athlete perceptions suggest that the planned stimulus no longer matches the athlete’s present state (Greig et al., 2020). Within powerlifting, RIR-based RPE was strongly influenced by Tuchscherer’s Reactive Training System, which adapted perceived exertion to the practical question of how many repetitions remained before failure (Tuchscherer, 2008). Kastner’s (2022) thesis provides useful contextual evidence here, showing that powerlifting coaches use RIR-based RPE for planning, training control and athlete feedback.

3 Critical Evaluation of Percentage-Based Programming

Percentage-based programming has clear strengths. It is objective in appearance, simple to administer and compatible with long-term planning. Thompson et al. (2020), in a systematic review of load-prescription methods, concluded that both percentage-based and repetition-maximum approaches can improve maximal strength, with percentage-based loading appearing favourable in some contexts because it may manage residual fatigue more effectively than repeated training to failure. For novice and intermediate lifters, percentages may also reduce cognitive demand because athletes do not need to accurately judge proximity to failure, although this should be viewed as an applied inference rather than a direct finding from powerlifting-specific trials. Nevertheless, the precision of percentage-based loading is limited by several assumptions. First, it depends on the accuracy of the 1RM value from which training loads are calculated. If the test was unusually good, unusually poor or technically inconsistent, subsequent training loads may be inappropriate (Zourdos et al., 2016). Secondly, a fixed percentage does not guarantee a comparable internal stimulus between athletes. Hackett et al. (2012) argued that effort in resistance training is better understood in relation to repetitions remaining before failure than external load alone. This is important because two lifters using the same percentage of 1RM may experience very different proximities to failure. Hickmott et al. (2022) summarised this limitation clearly, noting that percentage-based training is vulnerable to daily fluctuations and short-term changes in 1RM, and that repetitions performed at a given intensity are lift-specific and highly variable between individuals. In powerlifting, where technical skill, anthropometry, lift-specific fatigue and psychological readiness influence performance, this is practically important. Percentage-based programming is therefore best understood as a planning framework rather than a precise representation of daily training stress. The limitation is not the use of structure itself, but the uncritical application of predetermined loads when observed performance suggests that the planned stimulus is no longer appropriate. 

4 Critical Evaluation of Autoregulation

Autoregulation attempts to address these limitations by adjusting training to the athlete’s current state. RIR-based RPE is the most common subjective method in powerlifting. In this model, higher RPE values correspond to fewer estimated repetitions remaining before failure; practically, RPE 10 represents a maximal effort, whereas RPE 9 and RPE 8 are commonly interpreted as one and two repetitions in reserve, respectively (Helms et al., 2016; Zourdos et al., 2016). VBT provides a more objective alternative by using barbell speed to infer intensity or fatigue, although its practical value depends on reliable measurement, consistent technique and individualised interpretation (Helms et al., 2017; Hickmott et al., 2022). The theoretical advantage of RPE/RIR is that it allows planned training stress to be matched to current capacity. If an athlete is fatigued, load can be reduced while preserving the intended effort; if the athlete is performing better than expected, load can be increased to maintain the desired stimulus. Zourdos et al. (2016) supported the validity of RIR-based RPE in the squat, reporting strong inverse relationships between RPE and average velocity in both experienced and novice squatters. Helms et al. (2017) tested competitive powerlifters across the squat, bench press and deadlift and reported that RPE tracked relative intensity closely across all three competition lifts. These findings are especially relevant because they concern the actual lifts used in powerlifting competition. However, RPE/RIR is not automatically accurate. Hackett et al. (2012) found that 17 competitive male bodybuilders could estimate repetitions to failure with high correlations across bench press and squat sets, but estimates were less accurate in earlier sets and improved as sets progressed. Ormsbee et al. (2019) found that experienced benchers reported higher RPE than novice benchers at 1RM, suggesting that training experience may influence perceptual accuracy. Zourdos et al. (2021) further demonstrated that athletes judged proximity to failure more accurately as the set approached failure, whereas ratings made earlier in longer sets were more prone to errors. Thus, RPE/RIR is useful, but it is a skill requiring education, calibration and coaching oversight.

5 Comparative Evidence: Autoregulation vs Percentage-Based Programming

Direct comparisons between autoregulation and percentage-based loading are limited but informative. Helms et al. (2018) compared percentage-based loading with RPE-based loading during an eight-week daily undulating programme in resistance-trained men. Participants trained the squat and bench press three times per week, with sets and repetitions matched between groups. Both groups improved squat 1RM, bench press 1RM and muscle thickness, and there were no statistically significant between-group differences. However, effect-size estimates suggested possible small advantages for the RPE group in strength outcomes. This suggests that RPE-based loading may offer a small advantage, but it does not demonstrate clear superiority. Graham and Cleather (2021) provide stronger evidence favouring autoregulation, although interpretation remains nuanced. In their 12-week study, 31 resistance-trained men completed a twice-weekly squat programme using either fixed percentage loading or RIR-based autoregulation. Both groups improved front squat and back squat performance, but the autoregulated group improved significantly more. Importantly, the autoregulated group also trained at a higher average intensity. This supports the practical argument that autoregulation may help athletes adjust load upward as strength improves across a block. However, it also complicates interpretation because the superior strength gains may partly reflect greater realised intensity rather than autoregulation as an independent mechanism. The broader review literature supports a cautious synthesis. Larsen et al. (2021) concluded that both subjective and objective autoregulation methods can enhance maximal strength when integrated into a periodised plan. At review level, Hickmott et al. (2022) temper stronger claims for autoregulation, as their meta-analysis did not show a clear overall advantage of autoregulated load prescription over standardised percentage-based loading for 1RM strength. More recently, Huang et al. (2025) reported in a network meta-analysis that autoregulatory methods such as APRE, RPE and velocity-based resistance training ranked favourably compared with percentage-based resistance training for maximal strength. Because network meta-analytic rankings depend on included studies and indirect comparisons, this evidence should support but not dominate the argument. Overall, the evidence suggests that autoregulation is useful, but not categorically superior. Many studies are short, male-dominated and conducted in resistance-trained rather than elite competitive powerlifting samples (Helms et al., 2018; Hickmott et al., 2022; Larsen et al., 2021). Ecological validity remains limited because powerlifting performance depends on combined management of squat, bench press and deadlift fatigue, competition peaking and technical consistency under maximal loads.

6 Applied Recommendations for Coaching Practice 

The most defensible recommendation for powerlifting coaches is a hybrid model. Percentages should provide the structure of the programme, while autoregulation should refine daily execution. For example, a coach might prescribe 5 × 5 at approximately 75% 1RM with an RPE cap of 8, or allow a ±2.5–5% load adjustment depending on perceived effort, bar speed and technical quality. This recommendation follows from evidence that percentage-based and autoregulated loading can both improve strength, but that fixed percentages may fail to reflect current capacity when daily readiness or adaptation changes (Helms et al., 2018; Graham & Cleather, 2021; Hickmott et al., 2022). For novice lifters, percentage-based loading or simple linear progression may be preferable because technical consistency and training habits are still developing. This is an applied inference from evidence suggesting that RIR/RPE accuracy is influenced by training experience, proximity to failure and repetition range (Hackett et al., 2012; Ormsbee et al., 2019; Zourdos et al., 2021). RPE can still be introduced descriptively after sets to build awareness, but it should not necessarily determine all loading decisions. For intermediate lifters, hybrid prescriptions are especially useful because the athlete has enough experience to interpret effort but still benefits from clear structure. For advanced powerlifters, autoregulation may become more central because small readiness fluctuations can have larger implications for heavy training, and because powerlifting-specific evidence supports meaningful relationships between RPE, percentage 1RM and barbell velocity across the competition lifts (Helms et al., 2017). Practical models include top singles at RPE 7–8 followed by percentage-based back-off work, back-off sets calculated from the day’s top set, RPE caps on high-fatigue days, or velocity thresholds when reliable technology is available. These should be interpreted as applied coaching strategies derived from the evidence rather than as directly tested universal prescriptions. Coaches should also apply autoregulation differently across exercises. RPE/RIR is likely to be more interpretable in competition lifts and close variants than in unfamiliar accessory exercises because skill familiarity and task specificity improve the athlete’s ability to judge effort (Kastner, 2022). However, this remains a coaching inference supported indirectly by evidence on RIR accuracy and powerlifting-specific RPE relationships (Helms et al., 2017; Zourdos et al., 2021). Therefore, autoregulation should be taught, calibrated and monitored through video feedback, coach observation, estimated 1RM trends and occasional rep-max or velocity checks.

7 Conclusion

Percentage-based programming and autoregulation both have legitimate roles in powerlifting and should be understood as complementary tools. Percentage-based models provide structure and long-term progression, but they can misrepresent the actual training stimulus when 1RM estimates are outdated, daily readiness fluctuates or individual repetition capacity differs. Autoregulation, particularly through RPE, can improve individualisation and fatigue management, but it depends on athlete skill, exercise context and rating accuracy. Powerlifting coaches should use percentage-based loading to structure training over time, while using RPE/RIR, velocity, and coaching observation to individualise training programmes.