Over about the last 2 decades, the concept of force–velocity profiling has been developed and promoted with the aim of providing practitioners with an evaluation tool to individually guide and modify training for explosive movements,1 including accelerated sprinting.2,3 Without doubt, the introduction of accurate force measurements in combination with kinematics of accelerated sprinting4 has contributed considerably to the understanding of accelerated sprint running.5 This has also made it possible to apply—or misapply—the force–velocity profiling concept to sprinting. In this commentary, I will explain this concept and discuss some important issues that occur when force–velocity profiling is applied to accelerated sprinting for the purpose of guiding training modifications.
Force–Velocity Profiling for Accelerated Sprinting
Recently, Samozino et al2 extensively described and investigated the use of force–velocity profiling as a method for guiding training for sprint performance. While I use this paper as the main reference for this commentary, the central point of this commentary is a reflection about the concept of force–velocity profiling.
For accelerated sprinting, the entire force–velocity concept is based on the commonly accepted and verified notion that in human motion force and velocity are inversely related. This is usually described by a negatively sloped (curvi-)linear force–velocity relationship of motion. The classic Hill’s force–velocity relationship for isolated skeletal muscle is one of the contributors (causes), but also other factors in the neuromusculoskeletal system play a role (eg, muscles’ length-force and force-activation dynamics, the resulting rate of force-development, neural aspects, coordination, technique). This rationale is also used in the force–velocity profiling concept in accelerated sprinting.3 In accelerated sprinting from the stillstanding, or crouch start at each consecutive step, the generated propulsive force reduces while velocity (of center of mass) increases. Based on Newton’s laws the propulsive force is directly related to the acceleration of the center of mass. Thus, the description of the time trace of center of mass velocity entails in principle all information to deduce the (behavioral) relationship between propulsive force and velocity of the center of mass that evolves during sprinting. It must be noted that such a relationship may depend (in part) on the intrinsic force–velocity properties of the musculoskeletal system, yet, these relationships are not one and the same as clearly shown in the case of vertical jumping.6
In theory, there is an optimum SFv value, that is, a Fo/vo ratio for a given P, that leads to the best sprinting performance for different distances (in Samozino et al2 tested for 5–30 m). Of course, if one enhances Po, one also will improve, but this is neither what is discussed here, nor the essence of the SFv concept. A deviation of the athlete’s SFv from this optimum is referred to as a force–velocity imbalance.2 It is argued that this may be useful information for the coach and athlete and that it allows for personalized training modification to optimize SFv, that is, change the slope of the force–velocity capacity curve and thus, performance.
Samozino et al2 recorded accelerated sprinting performance of many athletes from different sports. Even though these may not be the world’s best athletes, they can be regarded as well-trained experts in their sports. In the Samozino et al2 paper, a figure is provided where all athletes’ SFvs are compared with an optimal profile. The key findings are shown in Figure 1. It appears that athletes perform (close to) optimally at 10- to 15-m distance sprints. Small differences between sports-training backgrounds seem to be present, yet the overlap between groups is major. For shorter (<10 m) and longer (>15 m) distances, a deficit is present, that is, the athletes’ SFv is not optimal. Above 20-m distance, no athlete reaches the optimal SFv, that is, all have a velocity deficit. Hence, for accelerated sprinting more than 15 m, almost all athletes would be advised to modify training toward “velocity” oriented.
—Force–velocity profiles (SFv) relative to the theoretical optimal profile for sprint distance and sport background. The bars indicate the 100% range in which all individual athletes appear. The vertical dashed line indicates zero deviation from optimal SFv. Note that the absolute optimal SFv (in N·s·m−1) corresponding to 0 ΔSFv-o (%) depends on distance. Modified from Figure 4 of Samozino et al.2
Citation: International Journal of Sports Physiology and Performance 19, 1; 10.1123/ijspp.2023-0110
The reader should be aware that an athlete has only one SFv, and the optimal SFv will change with sprinting distance (ie, the 0% line in Figure 1 does not refer to one constant SFv).
Challenges of the Concept
- 1.Does the force–velocity profile describe the athlete’s capacity or behavior?
This question is important for understanding what to target when modifying training guided by F–v profiling. In the SFv concept, Fo and vo and SFv are depicted as capacity descriptors,2 that is, characteristics of the underlying systems that lead to performance behavior, not behavioral characteristics. For vertical jumping, this appears not to be the case, and moreover, the force–velocity characteristics are task dependent.6 In other words, each task will result in a different SFv for one and the same athlete. Furthermore, in the sprinting application, the SFv derivation is based purely on behavioral performance (sprinting) data and not on other independent tasks. It definitively is not based on theory about the properties and behavior of the underlying structures that make up the neuromusculoskeletal system, unlike as done in, for example, Lin and Pandy.8 Moreover, force–velocity profiling ignores the vertical component, which is the largest component of the generated ground reaction force.9 The SFv that is obtained from the transformation of horizontal velocity time traces and thus, definitively not a representative of the complete performance profile of the neuromusculoskeletal system. Arguing that the concept is based on a holistic, macroscopic approach2 does not help, it does not clarify what specifically to target in training for capacity optimization (eg, pure muscle strength/velocity, technique, or something else).
- 2.Does the practitioner need an—alleged—description of the force–velocity capacity?
The force–velocity profile is simply a mathematical rearrangement of the time–velocity relationship, which can easily be plotted to provide all information required to guide training. So, the question arises: why rearrange? In F–v profiling, the rationale is that the rearrangement results in variables (Fo and vo) that are easier to understand and/or interpret for the practitioner, that is, that it provides insight that cannot be obtained before this rearrangement. Yet, any practitioner can determine from the velocity–time trace how quickly velocity develops (as quantified by τ) and how high it becomes (vmax; see Figure 2A). Additionally, a reference curve can be easily added to a plot of the velocity–time curve (analog to the optimal SFv 0% line in Figure 1, see, eg, Healy et al10). The visual analysis of the time traces will be more direct and far easier to interpret than any “one-number-quantification,” for example, the τ value, which is, as mentioned, related to but not the same as acceleration and force. Rearranging information does not create new information but may still be useful. For example, the differences in initial acceleration (or force)—simply the slope of the velocity time trace—are badly shown in the velocity time trace (see Figure 2A). Indeed, by transforming signals, small, yet important details become more visible11 (compare Figure 2A and 2B). Having made such transformations, the practitioner can evaluate all relevant information, there is no need for further abstraction. In the F–v profile (Figure 2D), the time aspect of the performance behavior has been removed, making the information more abstract, harder to grasp. Please note, revisiting challenge 1, this curve is the relationship between generated propulsive force and the athlete’s velocity while sprinting, and not analog a Hill like force–velocity curve of muscle or the athlete’s “engine.”
- 3.Is the optimal SFv a relevant measure for training intervention?
The force–velocity profile for 2 simulated performances with equal Po but different force–velocity profiles are presented in Figure 2D. I argued that this way of presenting the performance information is not needed. The SFv (when compared with the optimum value) provides no relevant information. This is clarified by considering data in Figure 1, where irrespective of sports and training background, a well-trained athlete is optimally designed for 10- to 15-m sprints. Not by chance, a similar break-even distance is found in my simulations in Figure 2. Even specialized track-and-field sprinters appear to be “too slow” in their specialism after about 15 m. One wonders what the gold standard reference of a force–velocity profile is based on. The experimental data (Figure 1) indicate that whatever the SFv, but within reasonable limits, an athlete is optimized for a 10- to 15-m sprint distance. I examined this notion further by using Equation 1 and the highest vmax during the world elite 100-m sprints from a data set including the world record,10 and setting distance at 100 m at the world record time 9.58 seconds. Applying the SFv concept, performance can only be improved by increasing vo (reducing Fo). Figure 2 shows the opposite profile adaptation: the athlete deviates from the world record at about 11 m, after nearly 2 seconds into the race (Figure 2C). Who knows, Usain Bolt may have solved the conundrum; anecdotally, he was known to be a bit of a “slow starter” with extreme top velocity. The main message is that the optimal SFv for a distance outside the 10 to 15 m range must be regarded as a “Utopic” for athletes. If even the best 100-m sprinters in track and field were to be defined as “imbalanced,” “too slow” after ∼15 m, and even the strongest are “not forceful enough” up to ∼10 m, something must be off with the benchmark rather than the athlete. The claim that the concept applies to sprint distances up to 30 m (Samozino et al2) must be reappraised because the profile only applies to sprint distances of about 10 to 15 m (a serendipitous, not scientific outcome—see challenge 4).
- 4.Can we change the Fo–vo ratio independent of change in Po?
Only if the SFv can be changed independently of any change in Po, that is, Fo and vo can be changed inversely by adapting training, the SFv can be of relevance on its own account. Of course, one can change the Fo/vo ratio by enhancing either Fo or vo. In such cases, the real target is not to change the ratio itself but the performance capacity of the athlete (expressed in Po). However, in the SFv concept, the key idea is that changing the ratio by itself is beneficial2: I cite “Or could a change in its slope (ie, an increase or decrease in SFv) independently from its overall position also contribute to performance improvement?”2 (“its” refers to the force–velocity relationship; “position” to Po). Unlike force and velocity during behavior (performance), these 2 parameters describing the capacity of the athlete are physiologically independent of each other, they cannot be exchanged by training, for example, increasing Fo at a physiologically linked cost of decreasing vo. (eg, at the muscular level, Fo is related to physiological cross-sectional area, vo to fiber type and length.) This implausible notion that one can exchange the force and velocity capacities is embedded and implied in the SFv concept. Finding the reference optimal SFv by modeling is done in this way, a nice academic exercise but pointless for practice if the athlete cannot do the same thing. Suggesting that the SFv by itself is of practical importance implies the belief in that it can be modified independently of changing Po. Otherwise, any SFv change is a mere mathematical by-product of enhancing Po, a notion I advocate.
The term “imbalance” suggests that an—apparent—underperformance is not necessarily due to a deficit in one of the capacities (Fo or vo) but because the ratio of these 2 is out of order, as if an athlete can be too forceful for her velocity or too fast for his might. Changing the SFv (independently of Po) would be like changing the internal “gear” of the athlete’s “engine.” Unfortunately, or maybe fortunately, unlike gearing in cycling, humans only have one internal gear.
In team sports, athletes are not sprint specialists for one particular distance they rather need to be able to sprint over a relatively large range of distances. Oddly, the SFv concept suggests that if we were able to optimize the SFv (at constant Po) and improve performance for one sprinting distance, performance will decline at any other distance. Please, note that this idea of changing SFv independently of Po is something else than the “24/7” challenge: Every hour used on improving vo is not used on improving Fo and vice versa. The consequences on spending less training on one aspect in favor of another are not part of the SFv concept.
—Simulation of time profile of the 100-m-sprint world record compared with reduced vmax and increased Fo, unaltered Po. The world record simulation is based on reported vmax,8 τ calculated on the requirement that at 9.58 seconds distance equals 100 m. (A) Velocity against time, (B) force (normalized for body mass) against time, (C) distance against time, and (D) (mass normalized) force–velocity profile. The black dot indicates equal performance for the 2 simulations.
Citation: International Journal of Sports Physiology and Performance 19, 1; 10.1123/ijspp.2023-0110
Recently, the SFv concept was critically investigated regarding vertical jumping, and found to be of little value.6 In my view, the same applies to accelerated sprinting. It reminds me of the Jewelry salesman Rufus in the movie “Love Actually” having a Christmas present wrapped for an impatient and anxious client Harry, evidently with all best intention to make it a better present: Rufus: “Oh this isn’t a bag, sir.” [Harry: “Really?”] Rufus: “This is SO much more than a bag ... .” I’m afraid I must agree with Harry, it’s just a bag. In fact, in the case of force–velocity profiling it is worse: It is a wrapping suggesting a present inside that simply is not there.
Practical Implication
An athlete may well be found too slow or too weak for a certain sprinting distance. Obviously, it makes sense to focus on enhancing attributes that are below the required capacity. This may also imply some sacrificing of attributes that are well in order. However, this is, unlike the SFv implies, not a necessity and may be avoided.
Conclusion
Because the practical inference of force–velocity profiling is based on implausible assumptions, when applied to accelerated sprinting, it is a dead end. At best, it may be able to identify between-athletes differences. However, these can be deduced more easily from performance time traces.
Acknowledgments
I thank the reviewers and editor for their extremely useful comments and recommendations for the text. Even though in this way they have indirectly contributed to the formulation of this commentary, the author is solely responsible for the content and the way it was phrased.
References
- 1.↑
Morin J-B, Samozino P. Interpreting power-force-velocity profiles for individualized and specific training. Int J Sports Physiol Perform. 2016;11(2):267–272. PubMed ID: 26694658 doi:10.1123/ijspp.2015-0638
- 2.↑
Samozino P, Peyrot N, Edouard P, et al. Optimal mechanical force-velocity profile for sprint acceleration performance. Scand J Med Sci Sports. 2022;32(3):559–575. PubMed ID: 34775654 doi:10.1111/sms.14097
- 3.↑
Cross MR, Brughelli M, Samozino P, Morin J-B. Methods of power-force-velocity profiling during sprint running: a narrative review. Sports Med. 2017;47(7):1255–1269. PubMed ID: 27896682 doi:10.1007/s40279-016-0653-3
- 4.↑
Rabita G, Dorel S, Slawinski J, et al. Sprint mechanics in world-class athletes: a new insight into the limits of human locomotion. Scand J Med Sci Sports. 2015;25(5):583–594. PubMed ID: 25640466 doi:10.1111/sms.12389
- 5.↑
Clark KP, Weyand PG. Sprint running research speeds up: a first look at the mechanics of elite acceleration. Scand J Med Sci Sports. 2015;25(5):581–582. PubMed ID: 26374380 doi:10.1111/sms.12520
- 6.↑
Bobbert MF, Lindberg K, Bjornsen T, Solberg P, Paulsen G. The force-velocity profile for jumping: what it is and what it is not. Med Sci Sports Exerc. 2023;55(7):1241–1249. PubMed ID: 36848926 doi:10.1249/MSS.0000000000003147
- 7.↑
Ae M. Sprint running: running at maximum speed. In: Müller B, et al, eds. Handbook of Human Motion. Springer International Publishing; 2018:1583–1611. doi:10.1007/978-3-319-14418-4_119
- 8.↑
Lin Y-C, Pandy MG. Predictive simulations of human sprinting: effects of muscle–tendon properties on sprint performance. Med Sci Sports Exerc. 2022;54(11):1961–1972. PubMed ID: 35736543 doi:10.1249/MSS.0000000000002978
- 9.↑
Haugen T, Mcghie D, Ettema G. Sprint running: from fundamental mechanics to practice—a review. Eur J Appl Physiol. 2019;119(6):1273–1287. PubMed ID: 30963240 doi:10.1007/s00421-019-04139-0
- 10.↑
Healy R, Kenny IC, Harrison AJ. Profiling elite male 100-m sprint performance: the role of maximum velocity and relative acceleration. J Sport Health Sci. 2022;11(1):75–84. PubMed ID: 35151419 doi:10.1016/j.jshs.2019.10.002
- 11.↑
Samozino P, Rivière JR, Jimenez-Reyes P, Cross MR, Morin J-B. Is the concept, method, or measurement to blame for testing error? An illustration using the force-velocity-power profile. Int J Sports Physiol Perform. 2022;17(12):1760–1768. PubMed ID: 36368326 doi:10.1123/ijspp.2021-0535
