Readers of our posts will be well-versed by now in the multi-directional requirements of team sport athletes. Between our contributors, we have extensively covered topics that have highlighted the critical need to monitor deceleration capablities, differentiate between change of direction and agility assessments, or understand the biomechanical distinctions of curved sprinting to inform bespoke training methods. These have typically been standalone pieces, and rightly so, given the detail needed to sythensise relevant but naunced information into practical application. With that said, in the performance world, our goal should be to create fast, effective and robust 360° athletes, who have the competency to accelerate, decelerate, and change direction rapidly and effectively from both limbs (McBurnie & Dos’Santos, 2021). Therefore, although information on each of these individual aspects warrant dedicated time in the spotlight, we also need to appreciate that, in the context of the multi-directional team sport athlete at least, it will ultimately be the interation of each of these high-intensity locomotor qualities that underpins athletic performance, thus differentiating an ‘elite mover’ from one that is not.
Currently, no comprehensive framework exists for holistically evaluating the above-mentioned locomotor qualities in team sports. Speed testing typically focuses on isolated components, such as sprint acceleration and maximum velocity, deceleration, or change of direction speed, without considering how these components relate to determine an athlete’s multi-directional speed (MDS) performance (McBurnie & Dos’Santos, 2021). What’s more, time (often a commodity in elite team sports) may be spent digging into recently popularised assessment methodologies that aim to uncover an athlete’s mechanical profile pertaining to an athlete’s linear speed performance. This may lead to ignoring the other critical components of MDS performance that may require greater degrees of focus for certain individuals. Not to worry, work in this space is being performed by our team, with exciting developments coming around the corner. This article aims to set the scene for these future advances and stimulate some thought around our conventional thinking across the speed assessment landscape.
The Current State of Play of Speed Assessment
In order to set the scene in evaluating our speed assessment landscape, I performed a very crude article search in PUBMED, entering the following search terms:
“team sport*” AND “test*” AND “>>> enter MDS action here <<<”
Figure 1 demonstrates the subsequent distribution of results for each term explored. Now it is to be noted that the ‘linear speed’ figures are a summation of grouped search terms, such as ‘acceleration’ and ‘maximum velocity’, whilst ‘agility’ was very broadly used without a deciphering whether authors understood the true distinction between ‘agility’ and ‘change of direction speed’. Not necessarily a search criteria that the PRISMA guidelines would be proud of, but what it does do is paint a picture of the landscape within the research literature relating to assessment of speed qualities. Resultantly, the components of MDS may be ranked in accordance with the existing hierarchy of evidence that exists within the academic literature, ordered from most evidence to least evidence:
- Linear speed
- Change of direction speed
- Curvilinear speed
Agility Testing is the ‘Holy-Grail’ of Performance Assessment, But We’re Not There Yet…
To effectively apply a scientific approach to the athletic preparation of team sport athletes, a crucial objective is to clearly identify and assess the various elements of MDS. This enables an objective evaluation of how these components evolve in an athlete as they engage in competitive match play and training. By comprehensively characterising an athlete’s fundamental technical, mechanical, physical, and perceptual-cognitive abilities on the field, practitioners can reverse-engineer their training techniques to enhance individualisation and, consequently, match play performance.
However, assessing agility, especially its perceptual-cognitive aspect, remains a contentious subject (Nimphius et al., 2018). The primary issue lies in the challenge of evaluating and monitoring agility performance due to the complexity of conducting a standardised and reliable test that is also tailored to the specific sport and athlete under consideration (Nimphius et al., 2018; Young et al., 2015). As a result, this article will explore MDS in connection with specific foundational actions (i.e., action capacity), such as acceleration, maximum velocity, deceleration, change of direction, and curvilinear sprinting (Figure 5) which ultimately provide the mechanical foundation for the various agility and gamespeed actions that athletes performance in sport. These actions are supported by a substantial body of academic research and can be objectively measured to guide training recommendations. Undoubtedly, developing an athlete’s physical, mechanical, and technical abilities to execute these MDS actions will provide them with a wide range of movement solutions to select from (McBurnie & Dos’Santos, 2021). This, in turn, enables athletes to outmaneuver their opponents and make a positive impact on their agility and overall game performance.
Acceleration & Maximum Velocity the Most Common
It perhaps to no surprise that the comprising components of linear speed testing that has the most available evidence in the literature and this is confirmed by our literature search (34.5%; Figure 1). Acceleration testing is commonplace across elite club testing batteries, and rightly so, given its ability to discriminate against playing standards and future success (Altmann et al., 2019; Hulse et al., 2013; Saward et al., 2020). Global ‘outcome’ measures such as performance times and maximum velocity values are some of the most commonly reported variables and demonstrate both high reliability and construct validity (Altmann et al., 2019). Taking up the highest proportion of our search, there is a huge variation on linear speed testing methodology, where distances investigated can range from 5 to 200 m and most frequent studied distances being 10, 20, and 30 m (Altmann et al., 2019).
Whether teams opt to include distances above or beyond 20m may be debated, given the large proportion of sprinting actions occur within this distance (Di-Salvo et al., 2010). Maximum sprinting speed (MSS) serves as a limiting factor to performance, and higher capabilities may enable a superior acceleration phase and short-distance sprint performance (Nicholson et al., 2021). As such, extending sprinting distances to ascertain MSS ability is becoming less of a debate in team sports given the now extensive list of rationale for its inclusion:
- MSS may be used for player recruitment purposes (Saward et al., 2020).
- Offers a maximum neural stimulus training effect (Mendez-Villanueva & Buchheit, 2013).
- MSS exposures may mitigate injury risk (Malone et al., 2017).
- Deriving the anaerobic speed reserve (Sandford et al., 2019).
- Extending distances may support the individualisation of gym- and -field programming (Cross et al., 2017; Morin et al., 2019).
- Prepare athletes for peak game demands (Oliva-Lozano et al., 2020).
Furthermore, even metrics such as ‘peak speed’ that can be ascertained from tracking technology now 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).
Most pertinent to this discussion are in fact the recent developments made in field-based assessment strategies which offer deeper levels of insight relating to the kinematic and technical variables which underpin linear sprinting performance (Figure 2). The popularisation of the force-velocity-power sprint mechanical profile and the ALTIS kinogram method are examples of such assessment methods. Another example of qualitative sprint analysis that perhaps has more of an empirical basis is the Sprint Mechanics Assessment Score (SMAS) (Bramah, Tawiah-Dodoo, et al., 2023), which utilises a checklist of key technique characteristics relating to upright sprinting and potential hamstring strain injury (Bramah, Mendiguchia, et al., 2023).
Crucially, however, given the increased granularity of data that these methods now offer, practitioners need to be aware of the reliability that each metric provides. Granted, a commonly referenced aspect is an athlete’s ‘switching ability’, or thigh angular velocity, which, may indeed be a determinant of linear speed, and, therefore, warrants it’s inclusion as a key performance indicator (KPI) (Clark et al., 2020). However, these KPIs need to be evaluated in within the constraints of the technology utilised rather than it’s mechanistic value; technology providers have a responsibility to be transparent about their metrics in this regard. Resultantly, the variability of given metric when captured using more practical field-based measurement technology needs to be determined and a decision needs to be made as to whether it still remains a variable that can be sensitive enough to detect “real” changes in performance. Practitioners failing to acknowledge the measurement error could be interpreting movement variability as a true change in technical performance from their alleged intervention. Subsequently, when aiming to evaluate an athlete’s underpinning mechanical and technical movement profile (Figure 3), practitioners may be better off opting for methods that demonstrate acceptable, albeit less ‘sexy’, spatiotemporal measures that adhere to the above-mentioned principles of performance testing (Wild et al., 2022).
Apart from the additional equipment and data analysis requirements, importantly, these methods of assessment require no extra work for the athlete to perform, as they can be set up in supplement to the performance of a straight line linear sprint test. In addition, these methods can be used to quantify aspects of the entire linear speed continuum (acceleration, transition, or maximum velocity). Therefore, this discussion points towards practitioners now having whole host of deeper performance insights at their disposal of which can be captured from a single sprinting effort.
Change of Direction Speed
As Figure 1 demonstrates, a huge variety of change of direction (COD) speed tests exist and have been explored in the literature (26.4%) and this is likely more given that authors may be inaccurately characterising COD tests as agility (28.9%) tests. As well as the need to differentiate between a COD in response to a stimulus, there is also a need to ensure the COD test differentiates between the different type, angle, and velocity requirement, in order to accurately characterise the biomechanical requirements and subsequent physical capabilities of the athlete. Considering even just the vast spectrum of angles that a team sport athlete may be required to COD from, perhaps an approach may be to determine the match play demands and establish key positional differences and subsequent conditioning requirements (Dos’Santos, Cowling, et al., 2022).
It is more difficult to quantify the kinematic and kinetic aspects of COD in a practical setting, due to its multi-planar nature, which present challenges in the capture of data using current technology (e.g. radar/laser gun, motorised resistance or high-speed camera) exist. For example, authors have used two laser guns (100 Hz) to capture instantaneous velocities during 45° and 90° COD tests (Hader et al., 2015). This approach allowed for phase-specific information to be captured (Table 1), allowing insights to be made as to ‘how’ and athlete may achieve their COD performance time (Figure 2). With that said, this approach is likely cost-prohibitive and too labour intensive for any practical use.
Recent developments have also been made into the technical evaluation of movement quality during COD using qualitative screening tools, such as the Cutting Movement Assessment Score (CMAS; Dos’Santos et al., 2019, 2021; Paul A Jones et al., 2017) . Given the known performance-injury risk trade-off apparent with COD (Dos’Santos, Thomas, McBurnie, Comfort, et al., 2021; McBurnie et al., 2019), whether the practitioner seeks to utilise these methods strictly for means of injury-risk mitigation or performance is down to them; I’m sure if you asked an elite athlete if they wanted to move ‘safer’ or ‘faster’, though, you would get a rather sarcastic response.
Deceleration & Curvilinear Speed
Given the disproportionately small contribution of the final two components within our literature search (Curvilinear Speed = 6.1%, Deceleration = 4.1%; Figure 1), it was decided to combine them into a joined discussion. With that said, we are fortunate to have some of the world-leading experts contributing to this space and so cutting edge content is only a click away.
You will find no bigger advocates of the work done in the deceleration field than from our own team here at Science of Multi-Directional Speed. Recently, Damian Harper provided an excellent overview of the important developments relating to deceleration assessment methods. Given the linear nature of these approaches, it’s also been possible to now quantify the mechanical demands of horizontal deceleration performance, which will, crucially, uncover some of the posterior force requirements of these tests and potentially isolate an athlete’s eccentric capabilities in a horizontal movement context. With that said, in team sports match play, rather than from a straight-line linear approach, deceleration actions will more often than not precede a COD foot plant and subsequent re-acceleration into the newly intended direction, for example when performing a defensive or attacking agility action (Dos’Santos, McBurnie, et al., 2022). Therefore, field-based assessment methods that can accurately characterise kinetic, kinematic and spatiotemporal factors underpinning maximum horizontal deceleration through a variety of multi-planar demands will be particularly relevant (Table 1), but are not currently as feasible from a practical perspective.
Recently, authors have driven developments in recent years for their work on curvilinear sprinting analysis where they have developed a test that evaluates curved sprinting ability of soccer players using the edge of the penalty arc (Fílter et al., 2019; Filter et al., 2020). It is worth noting that this test requires a comparably large curving radius (angle) relative to the curved sprints typically performed in soccer match play and only uses task completion time; however, the fact that this test was lowly correlated with a linear sprint performance R2 = ~0.34) suggests that curvilinear sprinting ability is an independent speed quality that is worth considering as a stand-alone component, with this thinking being substantiated by our own Paul Jones’s latest post.
As with COD, the non-linear nature of curved sprinting means that practical, field-based assessment methods for quantifying deeper layers of kinematic or kinetic information remains difficult. Furthermore, quantifying the precise distance and radius of a curved sprinting test requires an understanding of trigonometry and may be challenging to reliably set-up on a consistent basis. Therefore, the use of standardised pitch markings makes sense practically (Fílter et al., 2019). Another potential consideration is to utilise the same testing set-up that can be formed when evaluating a 45-degree COD (Figure 6). Rather than perform a side-step cut that involves a clear lateral foot plant involved in re-direction, instead, the athlete may curve their run utilising a cross-over cut (XOC) where the maintenance of velocity may be optimised (DosʼSantos et al., 2019).
The utilisation of a XOC instead of a side-step cut has been biomechanically evaluated (DosʼSantos et al., 2019), with the former appearing to be a faster strategy. With that said, given the association of side-step cutting actions and their link to surrogate markers of non-contact ACL injury risk, there is undeniable rationale for the qualitative evaluation of technical characteristics associated with this cutting action. Importantly, these markers may be reliabily identified and trained, in order to improve movement quality and subsequently mitigate injury risk (Dos’Santos, McBurnie, Comfort, et al., 2019; Thieschäfer et al., 2023). Whichever test is chosen, however, it is important that strict standardisation procedures are followed thereafter, due to the biomechanical disparities between the two techniques likely yielding different results (Figure 6).
All This Speed Testing, But Where To Use It?
The utility of both linear and multi-directional speed tests are certainly warranted, given the frequency and impact of such actions in team sports. Practitioners may seek to develop a more all-encompassing speed testing battery; for example, by including both a COD test alongside a linear speed test, with the intention of hollistically evaluating their athlete’s ‘COD ability’ against their ‘linear sprinting ability’, amongst other performance tests (Figure 7). We would argue that this still isn’t enough. What is needed in order to comprehensively evaluate an athlete’s high-intensity locomotive profile is an appreciation of the continuum of movements that are required to perform in match play, how they interact on a biomechanical and physiological level, and subsequently, what implications they have from a training and monitoring perspective.
Rather than applying a scattergun approach and aiming to quantify every multi-directional movement possible, however, speed testing may need to be stripped back a layer and evaluated through a lens that seeks to group actions within their overarching movement themes. From a COD speed perspective, actions may thus be grouped into ones that require a ‘velocity maintenance’ focus and one’s that require a ‘velocity reduction’ focus. Without forgetting the all-important linear speed ability (i.e., acceleration, transition and maximum velocity), we can begin to develop a methodology that highlights the physical ‘extremes’ of an individuals high-intensity locomotive profile.
With this approach, we can begin to determine relevant tests that fit into each of these buckets and drill further into the mechanical requirements of each task, and subsequently, inform the gym- and field-based physical preparation strategies required to optimise these qualities. For example, we know that performing a turn during a 505 change of direction test will impart significant eccentric demands, with the ‘plant’ step typically experiencing GCTs of >400 ms, and thus, plenty of time to deliver braking and propulsive GRFs (Dos’Santos et al., 2020). On the flip side, a curved sprinting test (Fílter et al., 2019) may require the athlete to maintain their velocity around a bend while effectively applying centripetal and horizontal propulsive GRFs in progressively more abbreviated GCTs (Filter et al., 2020). These concepts are not new, as we have now extensively covered the unique differences between acceleration, deceleration and COD (Harper et al., 2022; McBurnie et al., 2021; McBurnie & Dos’Santos, 2021).
The advent of markerless technology has facilitated the explosion of the ‘invisible monitoring’ concept. While being cognisant of standardisation requirements, testing and training may be performed simultaneously. However, we need to be careful not to sacrifice quality of assessment for ease of application. It may be the case that the ‘in-situ A-V profile’ generated from clouds of spatiotemporal data points from match and training offers comparable mechanical insights to the F-V profiling produced from standardised, maximal sprinting equivalents (Cormier et al., 2023). Does this mean that we can now do away with all other elements of speed testing? Can we ascertain similar mechanical profiles for deceleration, COD or curvilinear sprinting yet? Indeed, speed testing is no longer required to be performed in the conventional sense, where 45-minute slots need to be sheepishly booked in with permission of the head coach when the opportunity presents itself in the season calendar. This will likely be a non-starter within most elite settings. Instead, different tests can be performed on different days depending on the theme of work required, thus facilitating the development of a 360° speed profile that can continually evaluated year round. Until we can establish accurate, reliable, affordable, and practical means of quantifying more actions within the MDS continuum, it may not be optimal to ignore all of these other components of speed in favour of advanced assessment methods that are simply one-dimensional.
Introducing the 360-Degree Speed Profile
In light of this discussion, we would like to suggest a more rounded approach to evaluating the speed qualities of the multi-directional team sport athlete. This approach combines all the key MDS qualities into one profile that aims to demonstrate the locomotive balance or biases that are apparent with each individual (Figure 8 & 9). Furthermore, given the requirements of some tests to be performed from both right and left limbs, results can also highlight an athlete’s asymmetry profile in perhaps the most relevant movement context (Figure 9), of which can be explored in more detail with further interrogation (i.e., deeper kinematic or CMJ analysis).
The grouping of tests within the buckets of linear speed, velocity maintenance and velocity reduction allows for preferences of certain tests to be selected through the steer of these overarching movement themes. This approach offers a framework for evaluating an athlete’s speed qualities within the 360-degree continuum of team sport locomotion. We can of course begin to offer more detailed phase-specific analyses for each test. However, a global performance metric, such as task completion time, may suffice to categorise athletes into distinct groups, informing subsequent training programmes based on their comprehensive 360-degree speed profile. This approach aims to highlight that, before we race ahead with these highly attractive, advanced testing methodologies, we perhaps need to take a step back and consider the components that have been identified as limiting factors of a team sport athlete’s high-intensity movement profile. For speed development, the fundemental aim still remains: we want our athletes to get from “a” to “b” faster. When building our speed assessment approaches with this objective in mind, we can’t go too far wrong.
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