Training Agility in Team-Based Sports; A Review

Background

Much of what we do within Strength and Conditioning (S&C) environments is focused on improving sport-specific actions involving agility. Competitive, Team-Based Sports (TBS) in particular, frequently see unique moments or phases of play which are unpredictable in nature – placing a decision-making demand upon the athlete (1). Consequently, the athlete must then execute the next skill upon perceiving the external cue and then the process repeats – this is commonly known as perception-action coupling. Therefore, in TBS, agility is a combination of two skills; the ability to perceive the environment correctly and the ability to change direction efficiently (1, 9).

Regarding training agility, a frequently used method is the Constraints-Led Approach (CLA) which looks at applying the Dynamic Systems Theory of motor learning (17). Typically, drills can be either blocked or random, utilizing varying levels of contextual interference. Recognizing which method is best demands good skill and awareness of the coach – considering the task complexity, athlete ability and technical demands of the sport.

This review will aim to assess the relevant literature around blocked and random programming and how it links with contextual interference – ultimately asking the question; can sport-specific training alone, provide an adequate agility stimulus? S&C practitioners are often given little time to work with athletes on such characteristics so this review will also aim to provide some practical takeaways for practitioners regarding how they might program agility within TBS and what level of contextual interference is considered optimal.

Team-Based Sports – A brief analysis

TBS typically involve attacking and defending periods of play, usually with a team defending one goal and trying to score in another. The games are random, intermittent and dynamic in nature, usually lasting between 50-90 minutes. Due to the chaotic nature of play, exact repeats in play are rarely seen. Where they can be predictable, they can often still be extremely varied. So you may see a 1v1 in one area of the pitch/court but 5 minutes later, see a 1v4 in the same area of the pitch. The mental, physical, technical and tactical components will all influence the perceptions an athlete faces which creates a very random and non-linear playing environment.​ Teams can range in size from 5-15 players and pitch/courts vary greatly from sport to sport (1).

Constraints-Led Approach

The CLA is a coaching approach, formed according to the theory that humans are dynamic, complex and self-organising systems. In skill acquisition environments, coaches commonly place constraints upon athletes to force them into certain positions or into desired habits (3). Newell’s model identifies 3 main constraints that can be manipulated in order to encourage certain behavior outcomes – the task, the environment and the player. This lends itself well to perception-action coupling approach which proposes that actions will occur based on cues or stimulus within the environment (17). An action will follow and thus the perception alters and the cycle repeats. Therefore, in order for agility to be specifically developed, training needs to replicate the actions that an athlete would encounter in a competitive environment. This allows them to make decisions and apply the movement skill in a realistic manner. However, considerations need to be made based on the athlete’s technical ability before getting too specific.

Practice Design: Random or Blocked and Contextual Interference

Random and Blocked practice design – both have benefits and drawbacks. Blocked practice is simple, repetitive and predictable often having a in a sole focus but can be limited in-terms of specificity to competitive-cues. Random, on the other hand is non-linear, may include multiple focuses and is more complex often being more like competition (5). Random practice poses more of a challenge for a performer both perceptually and technically (16). Across the two types of practice design, contextual interference moves from low, in blocked training to high, in random training. Contextual Interference can be defined as “the effect of learning of the degree of functional interference found in a practice situation when several tasks must be learned and practiced together” (7).

Wulf et al. (2010) found that tasks with low levels of contextual interference saw better acute skill performances whereas tasks with high contextual interference levels saw greater long-term skill retention – as the number of tasks increases, so does the level of contextual interference. This is the effect on the performer when an athlete must practice multiple tasks, simultaneously. The response to this is heavily dependent on the skill level of the performer with novice athletes typically struggling to cope with high levels of contextual interference (21). It may therefore be appropriate to use blocked practice when looking to train change-of-direction skills, particularly with novice athletes, and utilize more random practice with more advanced athletes looking to train perceptual-scanning skills. This should be an important consideration for practitioners when programming agility training (11).

Figure 1. Relationship between Contextual Interference and the Number of tasks.

Blocked Agility Training Literature

Table 1: Study Characteristics – Blocked Practice

These two studies both found greater improvements in closed change-of-direction tasks from blocked programming. It should be noted that both studies utilized novice participants. Bloomfield also used sport-specific training to provide the random dose of agility practice. It should be noted that both studies have limitations, as both used closed methods when testing their participants, failing to accommodate for the perceptual element of agility. Both studies highlight potential advantages for improving change-of-direction skills in novices using blocked practice.

Random Agility Training Literature

Table 2: Study Characteristics – Random Practice

Literature which has focused on random programming has identified the need for training to be specific for agility training to be effective. However, the level of technical proficiency required for this to be optimal is unclear. This provides a challenge for as it requires a subjective coaching judgement on the ability of the athlete. Porter and Magill (2010) suggested progressive methods be used with task difficulty matching the ability of the performer. Both random and blocked literature here is limited somewhat by the use of novice athletes.​ According to the research presented previously, sport-specific training may provide an adequate agility stimulus, but only in athletes who have technically mastered the necessary change-of-direction skills.

Skill-Level + Contextual Interference

Based on the information around random and blocked practice and contextual interference, we can start to form a model to inform practice:

Figure 2: Training Considerations for agility practice

This is a simple guide practitioners may choose to use when prescribing training based on the ability of their athletes.

Returning to the concept of contextual interference, Guadagnoll & Lee, (2004) came up with a model (Figure 3.) to try and identify the optimal challenge point for skill learning:

It suggests that there is a sweet spot of training relative to the practice performance and the potential learning benefit. As shown, these areas vary dependent on the skill level of the athlete – in a practical setting, this provides quite a challenge for a coach. As mentioned earlier, could a sport-specific session provide an adequate agility session? This would likely require collaboration between the skills-coach and S&C practitioner. This process may be aided by the use of GPS technology to add quantitative information to subjective observation. ​Practically speaking, it may be challenging to prescribe small-sided-games or similar as agility practice unless the entire squad of performers were technically proficient with their change-of-direction movement strategies.

Agility in TBS

Further to our central question, the model below can be used to prescribe practices with some different focuses. For example, our unopposed practice is probably where we are training change of direction skills in isolation, looking at mechanics and technique. But as we move along to an increasing number of players, the behavior and movement demands of the performers will have to change. Here we can increase contextual interference and start to see what constraints we may place on certain athletes.

Figure 4. Practice Design Model

The above model may be useful for practitioners looking to prescribe agility practice considering the ability of the athletes as well as the desired practice outcomes: technical/change-of-direction or perceptual/decision making.

Conclusion

In summary, both blocked and random programming have their advantages. As with many scenarios in S&C, things are contextual and where possible, training interventions should be kept as individualized and specific as possible. With this in-mind, using small-sided games as adequate agility training may provide a practical challenge, given the varied abilities of performers, desired outcomes from the skills coach and conditioning elements effecting repeated efforts of movement. The models provided in this review will hopefully provide practitioners with some takeaways to help prescribe practice, considering the athlete ability and the desired training outcomes – change-of-direction skill or perceptual scanning skill.

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