Load-velocity profile: individualized or generic to estimate 1RM?

Updated on 26 de June de 2026 by Adrián Escobar Morales

One of the first questions any trainer asks when starting to use VBT is this: Do I need to build an individualized load-velocity profile for each athlete, or can I use generic values ​​from the literature? The intuitive answer is that the individualized profile should be more precise — it is specific to the athlete, not an average of a population. But the most recent scientific evidence quite qualifies that idea.

Getting this right changes how you spend your time training and how you interpret the 1RM estimates you get from the encoder.

What is the load-speed profile and what is it for?

The relationship between load and speed in strength exercises is linear and inverse: the greater the load, the lower the speed of execution. This relationship is stable, reproducible and exercise specific. From it, the load-speed profile allows you to estimate the daily 1RM without having to reach failure — just measure the speed with two or more submaximal loads and extrapolate the curve to the minimum technical speed (the speed threshold at which the 1RM would be raised).

This is extremely useful in practice: you can know your athlete’s estimated 1RM in each session, without added fatigue, integrating it into the warm-up itself. That’s what he does ADR Encoder automatically in each session with the app ADR System.

The debate is in what profile to use To make that estimate:

• Generic profile: average values ​​from the scientific literature that relate speed and percentage of 1RM for a specific exercise. For example, in the squat, lifting at ~1.0 m/s is equivalent to approximately 60% of the 1RM according to the data of González-Badillo and Sánchez-Medina.
• Individualized profile: curve built specifically for that athlete through an incremental test, using their own speed data against known loads.

The logic behind the individualized profile

Interindividual variability in the load-speed relationship is real. Not all athletes move the same speed at the same percentage of 1RM — a very experienced powerlifter can lift his 1RM squat at 0.15 m/s, while a more explosive athlete can do it at 0.30 m/s. If you use the same minimum speed threshold for both, one of the two will have a systematic error in the 1RM estimate.

That’s why the logic of the individualized profile seems solid: if you build the curve with the athlete’s actual data, the estimate should be more precise. This was for years the dominant recommendation in the VBT literature.

What the most comprehensive meta-analysis to date says

In 2023, Greig et al. published in Sports Medicine the most ambitious meta-analysis carried out so far on the predictive validity of load-velocity profiles to estimate 1RM. They analyzed 137 models from 26 studies, with data from 434 participants in the squat, bench press, deadlift, clean and snatch.

Their findings are more nuanced than expected:

• The load-speed profiles have a moderate predictive validity to estimate the 1RM, with a standard error of estimation of 9.8% (95% CI: 7.4-12.2%). In practical terms, if the actual 1RM is 100 kg, the estimate can be off by between 7 and 12 kg on average.
• The profiles tend to overestimate 1RM systematically — on average 3.7% above the real value (about 4.5 kg in absolute terms).
• And here is the most surprising find: Using an individualized speed threshold does not produce significantly more accurate estimates than using a generic threshold. The difference between both approaches was statistically non-significant (β = -0.4%, 95% CI: -1.9% to 1.0%).

In other words: constructing a completely individualized load-speed profile for each athlete does not appreciably improve the precision of 1RM estimation compared to using generic values ​​from the literature.

How is it possible? The explanation

That individualized profiles are not clearly superior may seem counterintuitive, but it has a logical explanation when analyzed in detail.

The load-speed profile of an athlete is not stable over time. It changes with fatigue level, activation status of the day, training adaptations, and changes in muscle fiber composition. A profile built today may not be representative of what will happen in two weeks. This reduces the theoretical advantage of individualization — the “individualized” profile also has an inherent error because it is not static.

Furthermore, the biggest problem in estimating 1RM is not in the slope of the curve but in the minimum speed threshold (MVT) — the speed at which the 1RM is lifted. This value varies greatly between individuals and is difficult to measure accurately without reaching actual failure. And paradoxically, the variability in the MVT affects both individualized and generic profiles in a similar way, which equalizes their final error.

So, what is the individualized profile for?

The conclusion of the meta-analysis is not that individualized profiling is useless — it is that its advantage for predict absolute 1RM It is smaller than expected. But the individualized profile is still valuable for other reasons:

• Longitudinal monitoring: although the profile has error in absolute terms, changes in the curve throughout a training cycle reflect real adaptations. If the velocity at a fixed load goes up, the athlete has improved — regardless of how accurate the absolute 1RM estimate is.
• Detection of daily variations: The individualized profile is more sensitive to session-to-session fluctuations caused by accumulated fatigue, sleep quality, or recovery status. This makes it useful for self-regulation of loads, although the estimated 1RM has a margin of error.
• Athletes with extreme characteristics: advanced powerlifters, Olympic lifters or athletes with MVT very far from normative values ​​benefit more from their own profile, because the error of the generic profile in those cases can be greater than the average.

Practical recommendation: a phased approach

The evidence suggests that there is no single answer. The most efficient approach depends on the context:

A detail that most overlook: the intention of speed

One of the factors that most affects the accuracy of the profile, whether generic or individualized, is the maximum speed intention in each repetition. Several studies indicate that when the athlete does not apply the maximum possible intention in each submaximal repetition — something very common in practice when the load is “comfortable” — the recorded speed is lower than the real one and the 1RM estimate is inflated even more.

That is why athlete instruction is as important as the type of profile you use: each repetition must be executed with maximum speed intention, even if the load is light. That is what ensures that the recorded speed is that athlete’s true ceiling for that load, not a reduced version due to lack of motivation or effort.

Conclusion

The most recent research shows that individualized load-velocity profiles do not predict 1RM significantly more accurately than generic profiles. Both have a moderate margin of error — about 10% — and both tend to overestimate the true 1RM.

This does not mean that individualizing is useless. It means that The value of the individualized profile is not so much in predicting absolute 1RM as in detecting relative changes over time and in allowing more sensitive self-regulation of loads session by session. For that it is more valuable than the generic profile.

In practice, the smartest thing is to start with the generic values, build the individual profile with the real data that the encoder accumulates in each session and use that profile to monitor trends — not to obtain an “exact” 1RM that in reality can never be completely accurate without a real maximum test.

Literature

1. Greig, L., Aspe, R.R., Hall, A., Comfort, P., Cooper, K. & Swinton, P.A. (2023). The predictive validity of individualised load-velocity relationships for predicting 1RM: a systematic review and individual participant data meta-analysis. Sports Medicine, 53(9), 1693–1708. See study →
2. González-Badillo, J.J. & Sánchez-Medina, L. (2010). Movement velocity as a measure of loading intensity in resistance training. International Journal of Sports Medicine, 31(5), 347–352. See in PubMed →
3. Marston, K.J. et al. (2022). Load-velocity relationships and predicted maximal strength: a systematic review of the validity and reliability of current methods. PLOS ONE, 17(10), e0267937. See study →
4. Ramos, A.G. (2024). Resistance training intensity prescription based on load-velocity relationship. International Journal of Sports Medicine, 45, 257–266. See study →

Adrián Escobar Morales