Advancing Strength Training: A Review of Velocity-Based Training

for Performance Optimisation

Fan Yi

Imperial College London, London, U.K.

Keywords: Velocity-Based Training (VBT), Load-Velocity Profile (LVP), Fatigue Management.

Abstract: Velocity-based training (VBT) has been introduced as a data-driven approach to resistance training, providing

a precise and adaptable method for monitoring and optimising athletic performance. Unlike conventional

percentage-based training, VBT leverages movement velocity to estimate daily readiness, prescribe

individualised loads, and manage fatigue accumulations. This review explores two key aspects of VBT: (1)

the development of personalized velocity-load profiles (VLP) and their role in estimating one-repetition

maximum (1RM), and (2) the implementation of percentage velocity loss thresholds (VLT) to regulate

training volume and adaptation. While VBT presents advantages in some respects over traditional regimes,

challenges remain in its practical application, including variability in load-velocity relationships,

biomechanical constraints, and technological limitations. Future research should focus on refining

measurement techniques and integrating VBT with traditional periodization models to maximize its

effectiveness in strength and conditioning programs.

1 INTRODUCTION

Resistance training (RT) has been programmed by

strength and conditioning professionals to achieve

muscle growth (hypertrophy) and subsequent

improvements of strength and power (González-

Badillo & Sánchez-Medina, 2010; Cunanan et al.,

2018a; Suchomel et al., 2018). The rationale of sports

performance training is to efficaciously approximate

maximum power output by amplifying contraction

force and movement velocity by suing relatively light

loads in RT. In terms of athletic performance, the

force-generating capability (FGC) and relevant rate

of force development (RFD) in sport-specific

movements are considered more important than

simply absolute strength, both of which are

determined by the velocity (Maffiuletti et al., 2016).

The combination of RT and plyometric training

highlighted significantly better enhancement on FGC

and RFD due to increasing improved intermuscular

and intramuscular coordination (Young, 2006; Sáez-

Sáez de Villarreal, Requena & Newton, 2010;

Guerriero, Varalda & Piacentini, 2018). Maximal

velocity accomplished in plyometric training under

low/moderate loads is emphasized as an essential role

in increasing the reflexed motor units and facilitating

other neuromuscular adaptions. Moreover, when

comparing performance gains between a maximal

velocity group and a deliberately controlled half-

velocity group, only the maximal velocity group

showed a significant increase in countermovement

jump height, along with twice the improvement in

1RM back squat (Pareja-Blanco et al., 2014). As

such, an increasing amount of science research is

shedding light on the potential role of velocity as an

indicator of strength gaining and even a quality

marker of RTs.

Conventional RT programs refer to training

intensity and volume as two pivotal factors boosting

athletic performances. Percentage-based training

(PBT) has been prevalently exploited by coaches and

trainers owing to their load prescriptions based on

generalised 1 repetition maximum (RM) data (Tan,

1999; Rhea & and Alderman, 2004). However, a

previously recorded 1RM does not account for the

daily oscillations in athletic strength caused by factors

such as life stress, training fatigue, sleep quality and

recovery status (Mann, Ivey & Sayers, 2015).

Furthermore, progressive strength gains cannot be

examined gradually and aligned with the real-time

1RM, which furthers the erroneousness of later load

prescriptions and deviations from an athlete’s real-

time performance readiness(Guppy, Kendall & Haff,

2024). Another approach is supervising the maximum

repetitions of certain exercises an individual can

Yi, F.

Advancing Strength Training: A Review of Velocity-Based Training for Performance Optimisation.

DOI: 10.5220/0014494200004933

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 1st International Conference on Biomedical Engineering and Food Science (BEFS 2025), pages 407-411

ISBN: 978-989-758-789-4

407

perform under specific loads (nRM) during RT,

where subjects have to complete as many repetitions

as possible (Guerriero, Varalda & Piacentini, 2018).

An inappropriate training volume (repetitions × sets)

in a training regime, as one factor of exercise

intensity, also results in fatigue and non-functional

overreaching in the presence of training goals

combined with the effect of prescribed loads. Many

autoregulatory methods, including the rating of

perceived exertion (RPE) and adjustable progressive

resistance exercise (APRE), have been introduced to

adjust training volume instead of setting fixed

numbers (Knight, 1985; Mann, Ivey & Sayers, 2015).

Following more autoregulation being integrated into

modern RTs, disadvantages have been discovered and

reflected on. The use of RPE is restricted by its nature

of rating fatigue level depending on subjective

feelings, which has shown weak correlation with

exercise intensities and even variations among

different training methods (power training, super

slow training and traditional training) and training

experience (Egan et al., 2006; Ormsbee et al., 2019).

Compared to RPE, APRE replaces the subjective

ratings used in RPE with the number of completed

nRMs, involving set 3 and set 4, where subjects

perform as many consecutive repetitions as possible

until reaching failure (Mann et al., 2010). The

resulting fatigue, on the other hand, makes APRE less

effective in explosive training via slowing down the

muscle contraction speed, prolonged recovery time

and ineffective FGC (Sánchez-Medina & González-

Badillo, 2011). Moreover, with both RPE and APRE,

set-to-set adjustments on the training volume have to

be made after a training set has been completed. It is,

thus, of utmost importance to develop safer and

simpler methods for measuring accurate 1RM values

and monitoring an athlete’s fatigue level, preventing

them from overloading and unnecessary fatigue.

Velocity-based training (VBT) is an RT method

that utilizes movement velocity to optimize athletic

force development and inform improved performance

through measuring velocity during the concentric

phase of major strength exercises (squat, bench press

and power clean) with linear position transducers

(LPT) and then velocity-load profiles (VLP). The

robust relationships of velocity with several strength

and conditioning measurements, such as training

intensity, relative loads and fatigue, have laid down

the foundation for the advent of VBT (Weakley et al.,

2021). In the study carried out by Dorrell, Smith &

Gee, the VBT intervention was found to improve

maximal strength and jump height in trained men

more effectively than traditional PBT, accompanied

with a significantly lower training volume, pointing

out its benefits for fatigue management in RT. While

VBT is often promoted for its continuously and

precisely updated training prescription, research

suggests no between-group difference in

neuromuscular fatigue and perceived soreness

between VBT, RPE and APRE (Cowley et al., 2022).

Despite debates regarding VBT's functionality and

real-life applications in strength training, its ability to

use velocity as a quantitative measure to assess

training effort while simultaneously monitoring the

athletes’ physiological condition daily makes it a

promising metric for RT prescriptions.

In this paper, I will discuss the advantages and

limitations of current VBT from two key aspects: (1)

the development of personalised LVP and 1RM

predictions and (2) the use of velocity loss threshold

(VLT) to manage fatigue and achieve specific

adaptations.

2 RESULTS

2.1 Individual LVP and 1RM

Prediction

The highly linear pattern observed in polynomial and

regression models between load and velocity

provides LVP with reliability and feasibility

(Weakley et al., 2021). Furthermore, velocity will

maintain the declining trend until 1RM is reached,

which indicates the arrival of terminal velocity

threshold (V1RM) and allows sports scientists to

estimate 1RM (Izquierdo et al., 2006). The two

characteristics of VBT suggest its novel means of

load prescriptions and monitoring training intensities.

The two-point method was proposed by Garcia-

Ramos & Jaric in 2018, where two submaximal (<

1RM) could be used to construct the linear regression

model to estimate 1RM. Regarding only two load

samples used in the LVP construction, the two-point

method is much less time-consuming compared to the

direct (conventional) 1RM measurement talked about

in the introduction. Undoubtedly, the two-point

method offers the availability of periodically and

tailored updated 1RM and removes the need for a

time-consuming and potentially fatiguing maximal

strength testing.

However, with deeper investigations into the

original load-velocity mathematical models and

application of LVP, the underlying downsides start to

be excavated. The biggest problem, in my opinion, is

the choice of proper velocity variables. There are

three different velocity metrics: mean velocity (MV),

mean concentric velocity (MCV) and peak velocity

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(PV). MV is interpreted as the mean velocity during

a complete concentric phase, while MCV and PV

mean the mean velocity before the phase when the

acceleration is less than gravity and the instantaneous

greatest velocity during the concentric stage,

respectively. To discriminate MCV from MV, MCV

does not include the braking phase or stick point

encountered in specific exercises like bench press and

overhead push, in which voluntary decelerations

avoiding the body off the ground or solid surface are

likely to disobey the linear load-velocity relationship.

In a study of the LVP applied in the free-weigh prone

bench pull exercise, the absence of a braking phase

provided a similar velocity corresponding with each

1%RM for the MV and MPV sessions (García-Ramos

et al., 2019). This study also noted that between-

subject velocities always have a larger difference than

within-subject velocities, especially at lower relative

loads (faster velocity), implying a meaningful view of

utilizing individual LVPs in personal training. Future

studies should endeavour to fill the gap of variability

of general load-velocity relationship equations

among exercise intensities and types (ballistic,

concentric-only and concentric-eccentric).

Ultimately, the public should be aware of a possible

1RM overestimation, exposing athletes to loads

beyond their ability and restraining the beneficial

incorporation of autoregulatory (Macarilla et al.,

2022). Even though some innovative versions of the

two-point method (Thompson’s and LVP with a final

submaximal load close to the 1RM) have been

introduced to improve the reliability of produced

LVPs, VBT is still an underexplored area in the

demand for a more precise L-V model evolving with

phase-specific velocity and force-velocity meta-

analysis (Guppy, Kendall & Haff, 2024).

2.2 Relative Velocity Loss Threshold

(VLT) and Fatigue and Adaptation

Management

Theoretically and mathematically, fatigue positively

correlates with velocity loss because of acute

metabolic stress and stressful FPC (González-Badillo

et al., 2017). When programming variables are not

suitably organized, the resulting fatigue has a

counterproductive effect on athletes’ sport form

(physical, psychic, technical and tactical readiness)

(Bowen et al., 2017). However, fatigue is not entirely

harmful for RTs. According to the well-known

general adaptation syndrome (GAS), the central

dogma of biological adaptation is a training stimulus

(fatigue) that can disturb the normal state

(homeostasis) of an organism (Cunanan et al.,

2018b). The understanding of GAS inspired sports

scientists with an idea of periodized training (PT)

where decisions on programming variables are fluid

and identifiable depending on distinctive training

aims (Cunanan et al., 2018b). To clarify, strength-

power trainings require high velocities to stimulate

more motor units and synchronizations (i.e. neural

factors), while strength exercises can be completed in

a more controlled way, resembling more muscle

stimulus for the desired hypertrophy (Mann, Ivey &

Sayers, 2015). The combination of VLP and VLT

estimates daily 1RM and tracks velocity decline

within each set to effectively manage fatigue

accumulation. It further treats adaptational and

pathological (excessive) fatigue separately with

distinct velocity loss zones and avoiding interference

phenomenon. Additionally, Unique flexible or fixed

set and repetition schemes facilitate the transition

from a set-to-set basis in RPE and APRE to a rep-to-

rep basis in VBT. Therefore, VBT comes into play as

a practically measurable parameter to monitor

ongoing athletic responses to training, followed by

scientifically driven and evidence-based

periodisation.

More limitations have been found through the

real-life integration of autoregulation with VBT.

Firstly, it’s very hard for strength and conditioning

practitioners to make sure that athletes satisfy the

assumption of maximal voluntary efforts (strength).

Secondly, fatigue management may not be

implemented throughout a periodization cycle.

Accordingly, pre-season and in-season periods may

temporarily involve VBT as an approach to reduce

training volume while maintaining — or even

increasing — training intensity, thereby enhancing

preparedness by minimizing fatigue. Thirdly,

biomechanical restrictions of certain movements

might attenuate the correlation between exercise

fatigue accumulations and velocity loss. With the

standardized efforts and absolute strength, the MV of

squat and bench press is expected to be lower than

that of power clean owing to a larger amplitude of

motion, reinforcing the importance of analysing

specificity of training with similar exercise physique

and velocity variables (Mann, Ivey & Sayers, 2015).

Lastly, the accuracy of measuring devices also

matters. LVP has been proven to be superior to most

accelerometer-based equipment and as precise as 3D

motion capture in slow conditions (< 1m/s) (Weakley

et al., 2021). Alternatively, LVP offers vibrating data

influenced by the position of the displacement

detector on barbells or subjects (Appleby et al., 2020).

Moreover, all measuring technologies share a

limitation in that they are unable to automatically

Advancing Strength Training: A Review of Velocity-Based Training for Performance Optimisation

409

disaggregate acceleration and deceleration, or in the

case of ballistic exercise, take off phase and flight

phase (Guppy, Kendall & Haff, 2024). Until now,

LVP is still the best choice considering commercial

and practical factors.

3 CONCLUSION

The two major elements, individual LVP and

percentage velocity loss threshold, propose the

advantages of VBT over conventional RT

approaches, namely rapid quantitative and objective

measurements on reflecting athletic performance and

fatigue. Nonetheless, I want to emphasize that there

are still many practical obstacles for VBT to achieve

its maximum capacity on monitoring RTs. Before

getting more reliable experimental evidence

supporting the advantages of VBT, sports science

professionals should exploit VBT more as a

complementary tool to catalyse training outcomes

with traditional RT models in the previous sections,

in case athletes shift focus away from the intended

physical quality and trigger unintended fatigue

accumulation at inappropriate stages of the

periodization. Furthermore, the intra-set motivation

feedback ought to be given for advancing

competitiveness and consciousness, in the premise of

optimal movements of the training exercises. In

conclusion, VBT is currently a grey area, encouraging

practitioners to try more synthesis with conventional

training strategies built upon their experiences and

expert knowledge.

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