‘Bridging the Gap’ – The Role of Individualisation in Managing the Physical Transition Between ‘Part-time’ and ‘Full-time’ Academy Football

FEATURE / FRANCES HUNTER & JONATHAN TAYLOR

Introduction

In English academies the transition between the under 16 (u16) and under 18 (u18) age-groups (also representative of the part-time to full-time transition) observes a ‘two-fold’ increase in required coaching hours in accordance with the Elite Player Performance Plan (EPPP). Additional coaching contact times come s with greater physical and physiological demands, and if this transition is not managed correctly, overuse injuries and under performance are highly likely as a consequence (Gabbett et al. 2017). This is significant given that injury is negatively associated with player progression in academies (Larruskain et al. 2021). The substantial differences in training loads between u16, u18 and u23 age-groups in a Category 1 EPL academy were recently demonstrated (Taylor et al. 2022). Weekly total distance, high-speed running, ‘sprint’ distance (>25.2km.hr1), mechanical load and RPE-Loads were substantially higher in u18 in comparison to u16 players (Taylor et al. 2022) further highlighting the increased physical demand.

Evidence around injury incidence age-group comparisons is equivocal in high-level youth football. However, in a recent systematic review, u17 to u21 players were reported to have the highest injury incidence per 1000/h (7.9), with the probability of sustaining a time-loss injury 51-91% in u18 players, in comparison to wider probability range of 1-96% in u9-16 players (Jones et al. 2019). Evidence around Injury burden (time-loss) is clearer, with u18 players reported to suffer the most severe injuries (Materne et al. 2021). Data on seasonal variation of injuries indicates that two ‘injury peaks’ occur (Read et al. 2017). The first peak occurs immediately post ‘pre-season’ during which physical stress is elevated (September), and the second following the ‘winter break’ where ‘deconditioning’ may occur (January).

Interventions

Effectively managing the ‘transition’ requires an individualised approach to player development. This is of particular relevance during pre-season - immediately following the transition, and other periods where training load ‘spikes’ occur. Various approaches are used to mitigate the potential negative effects of the transition phase. Here we outline some of the strategies that can be used, whilst providing case-examples.

‘Pre loading’ within the final months (i.e., Apr-May) of the U16 training programme has been highlighted as a strategy to ‘bridge the gap’ (Taylor et al. 2022). This refers to the systematic build-up of training load in the months preceding the transition. However, academic pressures and low training adherence once player scholarships are awarded, might limit effectiveness of this method. Here, two alternative methods of individualising training that may facilitate the transition are detailed. The use of individualised speed zones has received increased attention over recent years and may be of use to help solve this puzzle. However, for the purpose of this article, we are going to focus on two interventions only.

Fitness Testing Results & Training Age:

Physical testing batteries are commonplace in Academies that operate within the EPPP framework. Therefore, with appropriate test selection, testing data can be used to create sub-groups within the U18 age-group to individualise training prescription for players. It is widely accepted that players aerobic fitness (Gabbett et al, 2018; Malone et al, 2017) and the age of athlete (Gabbett et al, 2017; Blanch et al; 2015) are moderators to tolerating training load. Therefore, accounting for these factors within a given intervention is intuitive. Table 1 demonstrates how Clubs may categorise players into groups based on Aerobic Fitness and age (training age).

In this example, the 30:15 Intermittent Fitness Test termination speed (VIFT) and training age score (see below), is used:

• U17 (recruited externally as a 1st year scholar) = 1

• U17 (recruited internally as a 1st year scholar) or U18 (recruited externally from a part-time model) = 2

• U18 (Graduated from U17s; or recruited from a full time Academy model) = 3

In this example, Group 2 players could complete the standard training programme employed by the coach and MDT staff at the start of the U18 season. Players in Group 1 and 3 would complete a modified & progressed programme respectively. The example below (table 2) presents what this could look like at Middlesbrough Football Club (we appreciate the nature of these modifications are club and resource specific). It should be noted that effective communication of these training differentiators and agreement between MDT is critical to the success of this model.

Table 1. Use of training age score (see above) and Aerobic Fitness to group player.

Table 2. Possible Training modifications/ Training swaps for players identified as having training ‘gap’

Table 3. Example of Player Locomotor Profiles and compatible HIIT methods.

Player locomotor profiling - ‘Typologies’:

Recently, Sandford et al. (2021) detailed the potential of the Anaerobic Speed Reserve (ASR) in understanding athlete locomotor profiles (which provide insight into muscle fibre typology) and the potential ‘responsiveness’ of athletes to different training modalities/formats. The anaerobic speed reserve defines the difference between athlete maximal aerobic speed (often estimated from continuous or intermittent tests in football) and maximal sprint speed (Sandford et al. 2021). This construct could also facilitate the identification of sub-groups, and enhance training prescription to facilitate the transition.

At Middlesbrough Football Club, the 30:15 IFT is used to profile aerobic fitness and MAS can be estimated as 87% vIFT (Taylor et al 2022). MSS is determined using the 20-30m split from a 30-m sprint (alternatively maximal velocity derived from GPS data achieved during training and match-play can be used). The example in table 3 demonstrates the diversity of locomotor profiles seen within team sport athletes. Further to this, Figure 1a and 1b shows a true representation of a U18 ASR (Figure 1a) and MAS and MSS (Figure 1b). This profiling can be useful during the prescription of High Intensity Interval Training (HIIT), which is a frequently used tool at Middlesbrough FC. Specifically, it may allow the selection of an appropriate HIIT formats for each sub-group (Bellinger et al 2020), examples are presented in table 3.

These profiles may also be useful in prescribing game-based training to complement player profiles and maximise training benefits. For example ‘Speed’ players could complete 4v4 more SSGs e.g., 4v4 Work:Rest of 2 mins:2 mins x 6. In contrast, ‘Endurance’ players could complete more MSG and LSG e.g., 8v8 work:Rest of 8 mins: 2 mins x 3. It should be stressed, that all players should be exposed to a range of training methods/ formats, but an emphasis on the appropriate method could be used at different points in the season. With careful planning and dosing, it could be agreed and designed whereby all players complete the same training model by September. With only one conditioning day suited to their typology from this time point onwards.

On a separate note, Figure 2 nicely highlights how players tactical positions can predispose players to certain typology profiles. For 4 of the anonymised player profiles shown in Figure 2, we have added their tactical position (CF=Centre Forward, CB=Centre Back, CM=Centre Midfield & ST=Striker). Using FB as the example, positional demands require them to have sprint and endurance capabilites due to the need of them to overlap at high speed and recover at high speed, over varying distances, continually throughout the game. Therefore, this position could be classified crudely as ‘Hybrid’. For Clubs or Organisations that cannot facilitate regular testing, or need to categorise a player quickly, positional profiling may prove useful.

Conclusion

This article outlines practical solutions to the ‘gap’ in training load seen between U16 and U18 age groups in Academy Football (Taylor et al 2022). The strategies outlined could be useful for practitioners to implement within their programming to try and help successful integration of players transitioning from a part time (u16) to a full time programme (u18). It is recommended that, these, or any intervention used, should be supported by commonly used subjective training load responses such as wellbeing / session RPE.

Figure 1a.

Figure 1b

Figure 2

Figure 1a. Shows the difference in absolute ASR (m.s) in a U18 squad and how ASR can be used to anchor typology profiles: ENDURANCE:>13.5km. hr, HYBRID: 13.5-15.0km.hr and SPEED: >15.0km. hr). This needs to interpreted with caution as does not show the contribution of both MAS and MSS. Figure 1b highlights this.

Figure 1b. shows the diverse spread seen within a U18 squad when players are represented as a relationship between their MAS and MSS.

Figure 2. Player Locomotor profiles with player positions included.

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THREE P’S OF PRODUCTIVE PERFORMANCE