APPLIED COACHING RESEARCH JOURNAL 2021 , Vol . 7
Bear in mind that different athletes have varying capacities ( eg physical capabilities or perceptualcognitive abilities ). Further factors , such as maturation , learning , social-cultural influences or even recovery from injuries , may impact on an individual ’ s performance and learning . In addition to changing personal differences between and within athletes , further constraints on athletes ’ perception , actions and performance behaviours depend on tasks designed by coaches , ( eg changing playing area dimensions and numbers of players involved , or altering practice scenarios such as game scores and time available to ‘ chase ’ or ‘ defend ’ deficits ). Constraints of the external environment also shape performance behaviours ( eg a daytime game on natural grass at the height of summer versus a night game on artificial turf under wintry conditions ). Manipulation of these three constraint categories cause any training or performance environment in sport to be constantly changing and impact athletes ’ perceived opportunities for action ( affordances ). Therefore , coaches ’ understanding of which constraints to manipulate in training , and when , is important for learning and development ( Orth et al , 2018 ).
For example , which constraints may coaches manipulate in training to encourage perception , problem-solving and decision-making ? One option is : task constraints ; these , include playing time , surfaces , rules , and line markings that can be varied and adapted throughout training sessions ( Correia et al , 2019 ). Adjusting these constraints on action may allow coaches to simplify ( stabilise ) tasks or increase information complexity to challenge athletes and drive skill adaptation ( Otte et al , 2019 ). Other ways of manipulating constraints and encouraging exploration in training may involve scaling and modification of equipment , such as down-sized goals or targets , racket dimensions in tennis , and balls with different properties ( mass , colour and size ) ( see Correia et al , 2019 , for practical examples ).
Representative learning design
Training designs should stimulate and challenge learners to perceive information , use affordances and solve problems that they may face in competitive performance situations . Overall , research and theory advocate the importance of exposing athletes to learning designs that represent or simulate competition conditions in order to advance perception of most relevant affordances and information ( see Table 1 ; Pinder et al , 2011 ).
Representative learning design relates to the idea : ‘ train the way you play ’. But how do we assess the representativeness of our training designs ? To answer this question , it is useful to consider recent approaches of measuring the representativeness of training . Farrow and Robertson ( 2017 ) proposed monitoring and comparison of the specificity of training to competitive performance through using wearable tracking technologies ( eg player tracking devices ) and observational coding to record and analyse individuals ’ actions . Particularly , a week-toweek comparison between training tasks and game conditions is proposed ; they exemplify assessing football players ’ ball-passing tasks in training in terms of time for information perception ( ie time players are allowed before deciding to pass a ball ), pitch size ( ie quarter , half or full pitch ) and passing targets ( ie 1v0 , 2v1 or 3v3 task constraints ). Additionally , while Woods and colleagues ( 2020c ) consider the use of coaches ’ experiential knowledge and objective performance analysis to identify key constraints to shape representative training actions ( eg time in ball possession ), Krause and colleagues ( 2018 , 2019 ) propose a validated practice assessment tool ( ie termed RPAT ). This tool offers some insightful questions , such as “ is the athlete striking the ball and moving with intent appropriate to achieve the task goal ?” or “ does the task encourage decision making similar to what is expected during competition ?” ( 2019 ; p . 40 ). Coaches ’ answers to these ( and other ) questions can be rated on a 1-5 scale to provide a total score ( out of 70 ) that may be used when assessing the representativeness of training designs .
It has even been proposed that coaches and athletes could co-design game-representative training environments together with other support staff to facilitate individualised skill development , athlete learning and performance preparation ( see Otte et al , 2020c ). Outlining relevant ( task and environmental ) constraints and using performance analytics to support transfer of specific and varied game demands into training designs is a challenging , important task for coaches and sport scientists .
Repetition without repetition
The phrase ‘ repetition without repetition ’ was originally used in Bernstein ’ s ( 1967 ) research on the importance of variability in movement coordination . His data showed how all goaldirected movements , even apparently stable ones ( eg involved in weight lifting or archery ), show some trial-to-trial variations ( see Ranganathan et al , 2020 ). Traditional training approaches often disregard this indisputable point , instead tending to encourage the mere repetition of ‘ idealised ’ movement solutions , such as practising a golf drive on a driving range ( eg Williams and Hodges , 2005 ). Bernstein ’ s groundwork may help us to better
27