Part 1 of our exploration into the sprint force-velocity-power (FVP) profiling method highlighted both promise and confusion within the field of linear sprint performance analysis. We demonstrated the method’s emergence alongside its potential for offering individualised training insights. Indeed, the recent popularisation of the FVP method has shown that velocity-time data serves as an effective tool for evaluating sprinting mechanics and tailoring training regimes. Yet, equally, perhaps it is a method that holds unnecessary complexity and some questionable mechanical principles.
In part 2 of this series, we aim to explore some simpler, alternative analytical avenues that are apparent when analysing velocity profiles of linear sprints. This article will be discussed through the lens of collecting sprint data from standardised testing (e.g evaluating a linear sprint effort). We will perform analysis to deliver insights into acceleration profiles, some of which will require no need for convoluted velocity trace transformations, merely using split times. We will also move beyond timing gates and evaluate the value of instantaneous velocity measurements to ascertain more granular and precise insights. This will involve discussing the importance of identifying maximum instantaneous velocity, alongside investigating the utility of velocity and acceleration data at key instances, all of which may be crucial for individualised training strategies related to characteristically different sprinting profiles.
Back to Basics
For most team sport practitioners, sprint assessment methods have previously been restricted to measuring using split times that allow us to quantify performances over discrete periods in time (e.g., 5, 10, 15, 20, 30 m). Various insights can then be derived from these data points using various derivations of distance, speed or time over these predetermined intervals. Yet limitations to sprint testing using split times do exist, since we cannot ascertain instantaneous information pertaining to velocity-time data. For example, we can only infer where someone’s maximum sprinting speed is using the average velocity between two splits.
With that said, the tide has started to shift, and there has been an accelerated evolution of technology in recent times. This has facilitated the widespread use of advanced measuring systems, saving time (and becoming cheaper), while affording deeper levels of analysis that were once only available in lab-based environments. New technology is on the horizon, such as computer vision, LiDAR, and wearable IMUs, all of which may offer higher frequency and higher resolution data to allow us to analyse sprint performance in a more granular way, and this will become increasingly more accessible to practitioners. Albeit with mixed results, various attempts have in fact been made to evaluate the validity and reliability of GPS systems to capture sprint force-velocity profiles (Clavel et al., 2022; Cormier et al., 2023) and also split times (Sasek et al., 2024).
As such, the ‘lab-to-field’ concept may indeed be taking off. Yet since we will now be able to analyse higher resolution data, shouldn’t we be gaining more insight? Therefore, before progressing to these ‘advanced’ sprint profiling methods, we need to distinguish between what we currently have (i.e., split time analysis) versus what we gain from new approaches (Figure 1).
Introducing a ‘Not-So-New’ Way to Evaluate Linear Speed…
As discussed in our previous article, the FVP is merely derived from kinematic data. Although the data is ‘instantaneous’, no ground reaction force (GRF) information can be ascertained, and in theory, those with identical body mass and acceleration profiles would appear to have the same horizontal force outputs (Jon Goodwin & Dan Cleather, 2023). Thus, any mechanical inferences need to be interpreted through the limited lens of what the data is (i.e., horizontal velocity data). Therefore, in this instance, a FVP measure, such as theoretical maximum force (i.e., F0), is derived from aggregating horizontal acceleration data, which means that these interpretations can be easily ascertained from evaluating split times, without the need for convoluted logic. In fact, when comparing what insights we can gain from split times against what we attain from the traditional FVP method, depending on how many ‘gates’ one has access to, there could be an argument that more information can be collected on an individual pertaining to specific sprint phases (Figure 1).
Track and field disciplines have long integrated splits and outcomes into their training, yet this practice is seemingly less common among field sport athletes, who often prioritise total times for 10 m and 40 m total times to infer acceleration and maximum velocity speed qualities, respectively. The reliance on overall times, particularly ‘outcome’ times, such as 40m time, can be problematic as it may not accurately capture an athlete’s maximum velocity capabilities. Practitioners may incorrectly infer that a total 40-metre time represents top speed, whereas split times (e.g., 30-40 m) are realistically more indicative of this phase. Instead of relying solely on outcome measures, it’s important to analyse split times to understand specific sprint phases. For instance, an athlete may excel in short-acceleration but be average at top speed, which a traditional 40 m output time might mask. Team sport practitioners may rank athletes using these times, often causing misleading insights due to the ordinal nature of rankings that fail to reflect the magnitude of differences. Combining outcome and split times using z-scores, which standardise performance based on group averages, offers a more nuanced analysis. This method, termed sprint variability profiling, enables coaches to identify strengths and weaknesses by examining both the “what” and the “how” of performance. By pooling data, calculating group means and standard deviations, and using the fastest sprints to ensure connected splits, coaches can derive valuable insights, ultimately enhancing training strategies and performance benchmarking (Figure 2).
Individualised Training of Acceleration Profiles
As stated previously, the further we manipulate the information we are training to ascertain from a speed-time curve, the more difficult it becomes to grasp where these metrics sit within an athlete’s sprint profile and thus ascertain practical value. The resulting summary information tends to oversimplify an athlete’s entire acceleration curve, effectively categorising sprint profiles into two broad groups: “force dominant” (high F0) or “velocity-dominant” (high V0) athletes (Figure 3). It is important to recognise that these abstract classifications, while informative, do not capture the nuanced abilities of athletes across specific distances. For instance, understanding an athlete’s proficiency in sprinting over distinct ranges (e.g., offensive linemen excelling in 0–2.5 m versus wide receivers performing 10-20 m) holds practical significance and can be grounded in tangible training methods applied in both the gym and field (i.e., training strategies that develop ‘early acceleration’ within 0-2.5 m).
Approach A
Approach B
Bridge – same as above figure – can format text (i.e., approach a/b)
In grouping sprinting distances into specific zones, we may ascertain important phase-specific information that enables more targeted training and evaluation of a player’s linear speed capabilities. Notably, the kinematic and spatiotemporal characteristics of the acceleration phase have been shown to undergo a progressive series of alterations preceding the attainment of peak sprint velocity (Bellon et al., 2019; von Lieres und Wilkau et al., 2020). This progression is likely mirrored in successive modifications observed in key sprint parameters such as sprint velocity, step length, step frequency, flight time, ground contact time, and centre of mass elevation during the stance phase (Table 1). As such, authors have proposed the sub-division of the acceleration phase into distinct distances based on these variances (Bellon et al., 2019). Resultantly, by characterising acceleration and velocity profiles at and within these specific specific distances, we can establish performance standards that correspond to each sub-phase of a linear sprint and offer more granularity into our acceleration analysis (Figure 2).
As has been underlined already, a pivotal determinant of acceleration performance is the production of propulsive impulse. In essence, athletes need to produce and apply a high level of anteroposterior (horizontal) force under increasingly abbreviated time constraints. Therefore, the component parts of impulse, namely, force and time, should be the predominant variables in the manipulation of programming strategies for optimising linear sprint performance. As such, ground contact times coupled with external resistance can be used to objectively guide training prescription of strength- and power-oriented exercises facilitating the development of neuromuscular qualities pertaining to each sprint phase (Table 2).
This more targeted approach to assessing and training acceleration sub-phases may more readily harness training paradigms where specific training methods can be applied to each distance. This approach advocates for vertical integration, whereby, depending on the training priority, a primary emphasis may be placed on early acceleration, with mid- and late-acceleration phases considered as secondary and tertiary objectives. By integrating block periodisation, conjugate sequential integration, and the short-to-long approach, this training model aims to enhance acceleration capabilities through developing strength-related attributes underpinning each phase (Bellon, 2016). Rather than uniformly emphasising ‘force-dominant’ and ‘velocity-dominant’ approaches, this sequenced approach suggests targeting specific sub-sections of acceleration that may be characterised by specific strength characteristics, thus, offering a more concrete, tailored training strategy that may be effectively sequenced throughout training cycles.
Applied Applications to Acceleration Zones
In the ‘early-acceleration’ phase, or the first 2-3 steps of acceleration, training methodologies should prioritise the enhancement of applying propulsive GRFs, which is a critical determinant of progressing sprint velocity. Because the time-frame needed to express maximal strength (e.g., 300 ms) exceeds that of most athletic movements, the utility of core compound lifts that facilitate the expression of maximal forces over longer time-frames should be considered a pivotal conditioning strategy during this phase. In particular, through greater ranges of motion, where there is greater hip and knee flexion, and a greater reliance on concentric strength. This should be coupled with sled towing and incline sprinting (e.g., 9° running angle), which also support extended contact durations, and thus, facilitating the application of propulsive impulse that is essential for early acceleration (Gottschall & Kram, 2005; Spinks et al., 2007). Progressing through to mid-acceleration, there may be a continued focus on maximising step frequency, reducing ground contact time, and steadily increasing stride length. Adjustments including the reduction of sled towing resistance and in the running angles of inclined sprint training (e.g., 3 – 6° running angle) may be recommended, which align with the impact loading rates that become more apparent in later sprint phases (Bellon, 2016). As we progress further, high-load velocity exercises, encompassing weightlifting movements, may assume more significance in late-acceleration training, facilitating maximal rate of force development that is crucial for attaining elevated sprint velocities and reduced contact times (e.g., <130 ms). This may supplement a variety of field-based drills like “ins and outs” that optimise the more abbreviated ground contact and velocity adaptations tailored to the late-acceleration phases, whilst also potentially minimising fatigue by removing early-acceleration requirements.
As such, practitioners may still harness the load-velocity relationship and ‘surf the curve’ with varied training methods (Haff & Nimphius, 2012; Suchomel et al., 2017). Yet the above mentioned approach also addresses the challenges posed by abstract concepts such as the ‘FVP slope’ or HF0/HV0 ratio (SFV), which polarise athlete profiles into ‘force dominance’ or ‘velocity dominance’, neglecting nuanced speed-time curve information and have limited tangible application. By focusing on specific adaptations for each sprint phase, practitioners can better tailor training methods to individual sprint profiles, optimising performance across various sprint distances and player positions, thus enhancing practical value in programme design and player profiling (Figure 4). What’s more, the same concept may be applied to deceleration training strategies, whereby ‘early’ and ‘late’ braking phases can be identified and targeted with performance interventions (Harper et al., 2020).
Going Deeper: Can We Get More Insights From Instantaneous Data?
In contrast to the conventional use of performance times captured from timing gates, as mentioned above, other technology that has become more widely available that can facilitate the capture of instantaneous velocity data. In theory, this should support the development of more granular sprint analysis methods through understanding how velocity evolves over time. One by-product of this technological advancement that is now widespread in performance monitoring is the quantification of maximum velocity, albeit, whether captured through traditional speed assessment means or through less standardised means, such as team sport match play and training. This has allowed practitioners to move beyond split times where only estimations of an athlete’s maximum speed capabilities could be achieved through averaging the speeds between gate intervals. Practitioners utilise these peak speed values to inform a wide variety of performance needs, ranging from speed development, load management, return to play progression and match analysis (Buchheit & Simpson, 2017; Burgess, 2017; Malone et al., 2017).
Sticking with this example, the identification of an athlete’s maximum velocity using a velocity-time trace can support further individualisation of training prescription by determining more appropriate sprint distances. A common training objective within team sport microcycles is to ensure that athletes are achieving frequent exposures to near-maximal sprinting intensities (i.e., > 90% maximum velocity) (Malone et al., 2017) and this may be achieved by performing sprint drills that are greater than 30 m in length. However, if one player is achieving > 90% of their maximum speed at 15 m and another at 20 m, should they both perform the same 30 m sprint effort (Figure 5)?
In a different example, velocity at key instances can be viewed as another important measure to evaluate with the use of instantaneous data. If we review Table 3 below, we can see that Player A has a faster performance time and thus average velocity over over 15 m; however, as highlighted, Player B has a higher final velocity at the 15 m mark. This suggests that Player B may still be accelerating at this point. What is sometimes lost in sprint split time analysis is that velocity at a specific instance may be more informative than overall time to get there.
In the context of team sports where performance indicators, such as impact momentum (i.e., velocity × mass), can play a key role (e.g, rugby, American football), identifying key distances where individuals may have advantages in the field has clear performance implications. This approach highlights that athletes can achieve the same performance times (i.e., 0-15 m) but have different instantaneous velocities at that point.
Closing Thoughts
In the context of linear sprint analysis, if we refer back to a figure we produced in a previous blog, the entirety of this discussion so far revolves around the first tier (performance) through evaluating performance times, and to some degree the second tier (mechanics) through analysing displacement and deriving velocity / acceleration (Figure 6). Mechanics represent both kinematics and kinetics, the latter in which will only be incompletely and less precisely assessed using horizontal displacement data. For example, to identify the vertical GRF with the FVP method, we are assuming that mean net vertical acceleration of the centre of mass during the sprint effort is “quasi-null” and therefore equal to body weight (Samozino et al., 2016). These approximated vertical GRFs seemingly produce strong correlations with the vertical GRFs produced from force platforms (r = 0.826; Samozino et al., 2016), although it is not perfect, and miss insights into ‘how’ these GRF profiles are produced (Clark & Weyand, 2014). To truly get into deeper layers of this hierarchical framework (Figure 6), we need access to force data through the athlete’s interaction with the ground, technical analysis through capturing spatiotemporal and qualitative movement technique data, and physical evaluations through less sprint-specific means, such as isolated strength tests.
Therefore, sprint analysis methods discussed here are simply rearranged horizontal displacement-time traces so only really scratch the surface of this hierarchical framework. Simply put, we are evaluating how fast an athlete travels from A to B, and instantaneous assessment methods merely give us more data points to look at through this velocity-time curve.
Summary
- Traditional performance times still remain some of the most common assessment methods to evaluate sprint performance in team sports, but advancements like computer vision, LiDAR, and IMUs now provide high-resolution data for detailed performance analysis.
- The FVP, derived from horizontal velocity data, lacks direct kinetic information; thus, split times can often offer more comprehensive insights into sprint phases without convoluted analysis.
- Categorising athletes as “force dominant” or “velocity dominant” ignores their nuanced abilities across specific distances, necessitating targeted training for specific sprint ranges in gym and field settings.
- Grouping sprint distances into zones can guide training strategies by focusing on key parameters like velocity and ground contact time, and the same may even be performed on deceleration assessments.
- Technology that can capture instantaneous velocity data can enhance sprint analysis. For example, the quantification of maximum velocity supports the identification of key distances for near-maximal sprinting intensities to further individualise training or impact momentum at key instances.
- This entire discussion primarily centres around assessing sprint performance through measuring displacement, velocity, and acceleration, but to fully understand mechanics and deeper aspects of performance, detailed force data, technical analysis, and physical evaluations are necessary.
References
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