In our previous article, we provided a theoretical basis and framework for developing multi-directional speed (MDS) in adolescent soccer players. In part-two, we will expand on this with an applied perspective on how to monitor and progress MDS development in an academy setting. The proliferation of new technologies (e.g., satellite tracking systems, optical tracking, accelerometers, etc.) are becoming more readily available and their adoption is now common practice in soccer clubs. Resultantly, the assessment, training and monitoring of selected key performance indicators (e.g, KPIs; total distance, speed-time, relative speed intensities, accelerometery) can be readily measured to determine the external workload prescribed in the training of MDS. Framed through a performance and training progression mindset, this next post will discuss how the regular monitoring of growth, maturation, performance, and training data within a youth setting can be harnessed to guide our ongoing decision-making processes and provide a cutting-edge programme for our youth athletes.
Characterising Key Performance Indicators to Support Training Themes and Periodisation Strategies
MDS actions such as high-speed running (Bowen et al., 2017; Gabbett et al., 2014, 2016; Gaudino et al., 2015; Jaspers et al., 2018; Malone et al., 2017, 2018), accelerations (Bowen et al., 2017; Gastin et al., 2019; Gaudino et al., 2015) and decelerations (Gastin et al., 2019; Jaspers et al., 2018) have been considered key actions to monitor in recent times. With that said, scientists and practitioners should understand that the physiological and biomechanical load-adaptation pathways underpinning these key performance indicators (KPIs) have unique response rates, which may need to be characterised, trained, and progressively overloaded differently within training cycles (Figure 1; McBurnie, Parr, et al., 2021; McBurnie & Dos’Santos, 2021; Vanrenterghem et al., 2017). For example, the utility of kinematic measures, such as total distance and high-speed running distance, may be characterised by the metabolic and cardiorespiratory adaptations they stimulate (Vanrenterghem et al., 2017). Alternatively, accelerometry-derived variables, such as PlayerLoad (Barrett et al., 2014) or total mechanical work (Delaney et al., 2018), provide an evaluation of summative body-impacts that a player has been exposed to during training, which are examples of ‘whole-body’ load measures that aim to approximate the external forces the body is exposed to and subsequently reflect the biomechanical loading demands placed on the musculoskeletal system (Vanrenterghem et al., 2017; Verheul et al., 2020). This is key to consider within a training cycle, as training themes and subsequent exercise prescription can be planned at the micro- and meso-level to sequence appropriate physiological and biomechanical stressors at different timepoints and optimise subsequent adaptation (McBurnie, Harper, et al., 2021; McBurnie, Parr, et al., 2021; Vanrenterghem et al., 2017).
Although the monitoring of these high-intensity KPIs are now widely utilised within the industry (Akenhead & Nassis, 2016), it should be acknowledged that even these ‘whole-body’ measures of biomechanical load fail to account for the highly variable nature of mechanical loading that occurs at the structural level (e.g., joints, segments, limbs, tissue) between these different measures (Verheul et al., 2020). For example, although practical, a summative metric, such as PlayerLoad (Barrett et al., 2014), fails to differentiate between high-intensity accelerations, decelerations and change-of-directions, which are known to possess unique mechanical profiles (Dalen et al., 2016; Dos’Santos et al., 2018; DosʼSantos et al., 2019; McBurnie, Harper, et al., 2021; McBurnie, Parr, et al., 2021; McBurnie & Dos’Santos, 2021; Verheul et al., 2019) and metabolic demands (Martin Buchheit & Simpson, 2017; Hader et al., 2016). In a different example, the composition of ‘sprinting distance’ (i.e., typically distance accumulated above an arbitrary speed threshold of 25.2 km.h-1) accumulated during training or match play may not exclusively be the product of linear sprinting actions, but instead, will likely comprise of multi-directional sprints with varying degrees of curvature or acute cutting manoeuvres (i.e., < 45° CODs) each of which are highly task-dependent and produce asymmetrical mechanical loading patterns within the different joint structures (McBurnie, Parr, et al., 2021; McBurnie & Dos’Santos, 2021). Ultimately, there is a need for a greater consideration of tissue specificity when evaluating the stress, strain, and subsequent response of each physiological and musculoskeletal subsystem as a result of the loading imposed by different types of activity (Kalkhoven et al., 2020; Vanrenterghem et al., 2017; Verheul et al., 2020). To date, the currently available tracking technologies do not provide this information with high degrees of precision (McBurnie, Harper, et al., 2021; Verheul et al., 2020) and generally focus on linear tasks (acceleration and deceleration), but provide limited information pertaining to COD and curvilinear sprints. Subsequently, those tasked with monitoring and evaluating the associations between direct load-response pathways should be eagerly anticipating new developments in this area (Verheul et al., 2020).
Sports scientists and coaches should also acknowledge the acute and chronic implications of the training stressors they impose on their youth athletes. As mentioned previously, injury patterns in youth athletes follow a specific aetiology according to their stage of maturation (Monasterio et al., 2021), which will have implications for how these stressors interact with the youth athlete in both the short- and long-term (Towlson et al., 2020). In addition, the intermittent and multi-directional nature of soccer means the variation in stimuli and subsequent response will differ in degrees at the physiological and musculoskeletal level (McBurnie, Harper, et al., 2021; McBurnie, Parr, et al., 2021; McBurnie & Dos’Santos, 2021; Vanrenterghem et al., 2017). For example, neuromuscular fatigue driven by high-intensity activity may cycle more transiently within the physiological system (Seiler et al., 2007) and numerous investigations have found associations between acute 1-weekly ‘spikes’ in high-speed running loads and increased risk of soft tissue injury (Bowen et al., 2017; Duhig et al., 2016; Jaspers et al., 2018; Malone et al., 2017, 2018). Conversely, high cumulative loads (i.e., 2- to 4-week accumulative totals) of total distance covered (Colby et al., 2014; Jaspers et al., 2018) and number of decelerations (<-1 m.s-2) (Jaspers et al., 2018) have been linked to overuse injury. Overuse injuries (e.g., tendinopathies and stress fractures) are defined by the concept of an injury occurring in the absence of a singular, identifiable traumatic cause (Chéron et al., 2017), and can result from the failure of the musculoskeletal system to withstand repetitive, submaximal forces over much longer time frames (Tenforde et al., 2016). This may be explained by a ‘mechanical fatigue failure’ phenomenon, in which the mechanical fatigue of tissue is perpetuated by accumulated damage as a result of summative and repetitive loading events, subsequently surpassing the remodelling rate of the biological tissue (Edwards, 2018). This may be exacerbated with inappropriate training volumes, coupled with sub-optimal physical capacities and movement quality. The heightened responsiveness of the musculoskeletal structures to joint loading in the rapidly growing athlete points towards the importance of monitoring chronic external volume indicators, alongside individual growth and maturation data (Figure 3), to mitigate these potential long-term consequences of training.
Delivering Data-Informed Training Insights
Youth soccer match play is an essential part of a player’s physical development across all stages of development and is an opportunity to gain a cross-sectional evaluation of a young athlete’s physical capabilities. Importantly, attaining match play performance data (i.e., from tracking technology) can provide age-appropriate insights into the athletic and technical requirements of the game, allowing for bespoke training methods that promote long-term athletic development (LTAD) (M. Buchheit et al., 2010). When considered in absolute terms, this data can also be used to inform when talented young players are physically capable of demonstrating performance outputs that are sufficient to compete when moving up to play in older age brackets (Palucci Vieira et al., 2019). Such data facilitates the effective ‘scaffolding’ of age group requirements around a long-term framework, allowing practitioners to reverse-engineer their weekly training programmes at the micro- and meso-level to guide developmentally appropriate training prescription. As demonstrated in Figure 2, a general trend is apparent in absolute high-intensity physical outputs as players progress through the age groups; however, there is high variability between playing positions. This information can serve as a reference for age- and position-specific training prescription and periodisation strategies, return to training, as well as facilitate conversations around whether an individual is ‘ready’ to be challenged in an older age bracket. It is, however, important to appreciate that the relative physiological demands for young soccer players playing the game within the same age category may be hugely variable within different context and are greatly influenced by growth and maturation. It is, therefore, advised that data specific to the individuals in question be collected to inform practitioners in this regard, where possible. These more nuanced elements should be considered when evaluating match performance capabilities at the individual level and efforts should be made to ensure comparisons within chronological age bands are coupled with standardised scores relative to their maturity (Parr et al., 2021).
In contrast to the senior game, those working in academy settings are afforded the opportunity to evaluate a training programme with a long-term vision in mind. Although environment-dependent, rather than a focus on winning, an emphasis can be placed on LTAD in line with the club’s philosophy. Therefore, the use of periodisation strategies that permit the structure of a training programme in phases and cycles, following specificity and progressive overload principles, can be harnessed to optimise LTAD. With the correct balance of workload and recovery, training strategies can be used to prepare the youth soccer player for the increasing high-intensity demands as they progress towards the senior game (Figure 2). For this management to be effective, an understanding of the longitudinal structure of the adolescent player’s programme is important to define suitable training doses and risk thresholds which can optimise performance and recovery (Figure 3). Furthermore, being able to establish a player’s training history, expected exposure and load trajectory will enable training decisions to be made from a progressive standpoint. More detailed investigations in youth match play and training demands (i.e., analysis of both external and internal load) and their interaction with growth and maturation are certainly warranted to suitably contextualise training methods in different academy environments.
An Applied Example in Soccer
As an applied practitioner, I have always tried to guide my practise by research and objective targets. In this new era of information, athlete management data streams can now be collated, manipulated and visualised in much more dynamic ways to support the development of training concepts and objectives. The enhanced functionality this brings can inform the components of training I am trying to impact and the ongoing decisions that need to be made with far greater analytical detail. In this final section, I will take you through the process I have undertaken when developing MDS qualities in elite youth soccer players and how the concepts discussed in this two-part series can be applied to practise. This narrative will be supported through the various figures referenced in this two-part series to support the working examples.
With particular reference to Figure 3, Player 6 can be considered an individual who would be placed under the microscope for presenting multiple ‘flags’ in relation to their growth and maturation data. They are in the peak-height velocity territory (e.g., 90 to 92% predicted adult height; PAH), which is also supported by their heightened growth rate (e.g., 10.7 cm/year). Moreover, this player’s maturity (e.g., 91.9% of predicted adult height) relative to what is typically observed for their chronological age means this player can be considered a ‘late-developer’. Playing with their more biologically mature and physically advanced peers, the physical demands of training and match play may be greater than what this individual can tolerate during this ‘sensitive’ period. As well as utilising bio-banding as a method to align this player with their maturity-matched peers, this can be seen as a key opportunity to refine and develop movement quality in supplement to a reduced soccer-specific training volume (see Part One, Figure 3) This is something that should be maintained over numerous weeks to ensure that the stages of the MDS framework are re-introduced to optimise skill acquisition and retention (see Part One, Figure 2). As a result, a balance may be reached between mitigating the risk of overuse injury, while appropriately exposing the player to a varied and individualised physical stimulus which is not merely ‘pulling a player out’ of training. This can be further guided over the long-term by monitoring external load KPIs in the ‘3-week Accumulated Total’ chart, where Player 6’s chronic training volume of high-intensity decelerations can be maintained within their normal threshold levels (Figure 3).
From a more acute perspective, with reference to Figure 4, Player 1 would be flagged due to sustaining > 14 days without a maximum speed exposure > 95% of their maximum velocity. This is an important flag due to the associations between maximal sprinting speed exposure and reduced injury risk (Malone et al., 2017) meaning these qualities should be maintained from week-to-week. Due to the individual still being in the outer bands of heightened growth (e.g., 89 to 96% PAH), alongside chronic high-intensity decelerations being above their normal (Figure 3), I would be focusing on some high-intensity, low-volume MDS training, using > 95% maximum sprinting speed as an objective for this during their session. This approach will emphasise high-quality work performed at high movement speeds, but will hopefully strike a balance between ‘risk’ and ‘reward’.
In a final example, again with reference to Figure 3, Player 3 would be highlighted with a slightly different focus from the other two players. Immediately what sticks out here is that their ‘3-week Accumulated Total’ for high-intensity decelerations is low relative to both the individual and the group (Figure 3). The low-risk status of Player 3’s growth rate (e.g., 3.5 cm/year) and maturity status (e.g., 96.6% PAH) indicates that this athlete can be pushed physically (Figure 3). Movement capacity and movement specialisation would likely be a key focus here (see Part One, Figure 3). Position-specific drills may be performed to refine the athlete’s specialised movement capabilities, and this can be guided by their match play MDS profile to inform the prescription of loading (Figure 2). In this particular instance, given the player is an U14 who has an advanced maturity status, but also appears to be behind with regards to physical outputs, I would be striving to push their loading capabilities into the U15 bracket so they can cope with the demands of their maturity-matched peers (Figure 2).
In this two-part series, we have provided a practical reference guide to support the development of MDS in your team sport setting. The over-arching framework that has been presented here should allow any practitioner working in team sport to decide what content to deliver and when best to deliver it. Indeed, as mentioned in part-one, although this series has been presented within the context of youth soccer players, the concepts and principles can be extrapolated to most team sport contexts of any age that have multi-directional speed requirements. Over the past 8 months, the Science of Multi-Directional Speed team has provided you with a number of practical references to guide your speed training. It is hoped that this current article further bridges the gap and allows you to make more theoretically-driven and data-informed decisions on your training practises with team sport athletes. Going forwards, our goals are to expand on our provision of resources, attain wider outreach, and hopefully further support you in your goals of creating fast, efficient and robust, 360-degree athletes. Stay tuned for more updates by following us on our various social media channels!
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