Over the last few months, I have been working with coaches of various sports (Cricket, Volleyball, Rugby, AFL) and assisting them in their coaching development. Possibly the most asked question by any coach is "do you have any drills for x...?" This is one reason why coaching sessions or masterclasses are so popular - because the coach delivering normally has one or two new drills or tasks that the attending coaches can take away and apply instantaneously with their own team. Answering the above question doesn't sit well with me for two reasons:
I don't have the content knowledge in each different sport to identify or create outstanding drills that coaches can use and apply with their teams. I could answer their questions, but it would be an uninformed answer at best.
Copy and paste methods of coaching do not work. A good coach will create a training task to solve a very specific problem with a very specific player or group of players. Every problem, player and group of players are different, so care must be taken to provide them with suitably challenging tasks.
This can all be summarised as the "what to coach". My work (and that of other skill acquisition specialists around the world) is more focussed on "how to coach" (Smith et al., 2022). Specifically, how to design and implement tasks, and how to communicate and interact with players. This post will focus on the design of practice tasks, and the utilisation of a modified STEP model (figure 1) to assist coaches with the design of their practice design.
Figure 1. Modified STEP model for practice design.
Before getting onto the model, it is important to understand why training tasks should be designed with a specific group or cohort in mind, and what to look for to understand when to progress and regress an activity. For me, the answer comes through the challenge point framework (Hodges & Lohse, 2022). Errors and mistakes are a massive part of the learning process. However, there is an optimal challenge point. Why would coaches need to modify the activity in front of them – what is the key information they need to search for in their environment to guide their actions (Wood et al., 2022)?
The failute rate is very high. Remember: players learn from their failures, but are motivated by their successes (h/t Damien Farrow).
It's too dangerous. Players might get hurt.
It's too easy. Players are not learning.
The weather conditions are too extreme, so you want to work on the same skill, in a less taxing environment.
Imagine a player does a task 10 times, it is important they can succeed 6-8 times out of those 10 attempts. Some players may be able to cope with succeeding only 4 times, and they don't become disheartened. Others may need to succeed 9 times to feel competent. It is important that we meet every player where they are at. For each player, they will have an optimal challenge point, and coaches should be aware of this. See figure 2 below.
Figure 2. Goldilocks pricniple for optimally challenging players.
This highlights why copy and paste models of coaching do not work. A specific task that is “optimally challenging” for one group of players, may need to be adapted so it is “optimally challenging” for another group of players. This is a common issue. If the task is designed appropriately enough (RLD), then the task will enhance a players performance in a competitive environment. If the player only gets better at the task over time, and has no impact on competition performance, then it is a poorly designed training task in my opinion. A caveat – not all training is about skill development, and not all training has to be always fully representative. Of course, there is more to coaching than skill development, but skill development is my bias, and I would encourage coaches as much as possible to include representative aspects of performance in all training tasks. Sometimes it may not be warranted or possible, but sometimes it can be down to a lack of planning and/or creativity.
· Space: Game-Intensity Index (GII) (Chow et al., 2021)
· Task: Task Design Model (Sullivan et al., 2021)
· Environment: Environmental Design Principles (Renshaw et al., 2019)
· Players: Coadaptation (Renshaw et al., 2019)
The GII can be a useful metric to quantify the amount of space afforded to a player in each training task and can be useful to compare to a game situation or to quantify the additional challenge a coach has imposed on players over a period of time. The formula: (playing area) / (number of players). However, the changes made to a playing area can be complex, and there can be several competing demands as a result. For example, a player in a 5v5 game:
· Large area (100m x 50m) – each player will have a greater aerobic demand, the skill demands may decrease, as players have more space to execute, and cognitively they will have more time to perceive information.
· Small area (40m x 25m) – the physical demands will be less aerobic and more anaerobic, with a greater number of change of directions, players will have less time and space to execute skills and perceive information compared to the larger area.
However, to illustrate the complexity, some skills may emerge here that would not in a smaller area game. For further reading on the demands of small sided games in soccer, see (Hill-Haas et al., 2011).
To take a different example in a different sport. The tackle in rugby is such an important aspect of the game, especially for young players. With an increase in space, there is generally an increase in running speed. For example, when doing a 1v1 tackling drill and players start 15m apart, the attacker will likely build up a decent running speed before reaching the collision if he is aiming to score a try, and this can make for a heavy collision. If we are starting out on our tackle journey, this can be quite intimidating for some players. By reducing the space, we reduce the speed that the attacker is running at, making it easier for the defender to tackle. Another option to simplify the activity is to curb the competitive aspect of the task, and just ask the attacker to run straight. Alternatively, if players are comfortable and demonstrating competence, then increasing the space gradually can increase the challenge of an activity.
The task constraints are one of the most easily modifiable constraints that a coach can influence (see here). Mark O’Sullivan’s task design model (figure 3) highlights a number of key areas that a coach can modify to increase or decrease the represntativeness of a task:
Figure 3. Task design model (O'Sullivan et al, 2021)
Ball: a game of tag where each player has a ball is more representative than a game of tag without any ball.
Direction: a game of bulldog is more representative than a game of tag because players are aiming to get to a target.
Opponents: games (1v1; 2v1; 2v2; 3v1) are more representative than drills (1v0; 2v0).
Consequences: Games where attackers become defenders and defenders become attackers with every single transition of possession is more representative than working on one phase of play only.
I have discussed environmental design principles previously, and these principles are vital towards the design of practice. The 4 keys principles are:
Session intention: This is arguably the most important principle in the whole model, and in my opinion is the starting point – what is the coach trying to achieve. For example, with the manipulation of pitch dimensions, there are a number of changes that can occur. If the coach is looking to develop player’s dribbling abilities, but doesn’t challenge the player by playing a large area, then the task design is not aligned with the session intention.
Manipulate constraints (constrain to afford): Constraining to afford is important to ensure players are always searching for the information that will guide their actions. To follow on from the previous example, a coach can constrain players that they can ONLY dribble the ball, so the player’s behaviour becomes forced. Or the coach can REWARD a player who dribbles, so they are encouraged to dribble but doesn’t remove the possibility of passing etc.
Representative Learning Design: as mentioned in the previous section, representativeness can be scaled up and down, which is generally dependent on the learners a coach has in front of them.
Variability: skill can be defined as the ability to adapt goal-directed movement to the surrounding constraints. Skill is the ability to adapt, and players will be forced to adapt when there is variability in the environment i.e. repetition without repetition. This can come in many forms: different starting positions, different teammates and opponents, different angles, speed, and direction of pass.
Each player has different capabilities. In any interaction, a player’s action capabilities (faster, stronger, left-foot, right foot) will cause them to act in a certain way and will cause their opponent(s) to coadapt in a certain way. (To read about the influence of action capabilities on skilled performance, see here.) Simply by manipulating players within a task, new behaviours can emerge and can alter the training experience for everyone involved.
The other way in which coaches can alter the role of players in training to influence the emergent behaviours is by creating uneven teams. If a coach has 12 players, rather than playing 6v6, they can play 5v5+2. This can challenge the defending team to have to defend the space against a numerically advantaged opposition, or to challenge the attacking team to maximise their numerical advantage to score. Using players to create numerical mismatches can be a useful way to allow tactical creativity to emerge.
Coaching is complex and there are numerous considerations for a coach when designing tasks in training. Because to the wide variety of considerations, a copy and paste model of coaching does not work. Rather, the modified STEP model of practice design can aid coaches when designing and modifying training tasks to ensure training suitably challenge the their players.
Chow, J. Y., Davids, K., Button, C., & Renshaw, I. (2021). Nonlinear Pedagogy in Skill Acquisition (2nd Edition ed.). Routledge. https://doi.org/https://doi.org/10.4324/9781003247456
Hill-Haas, S., Dawson, B., Impellizzeri, F., & Coutts, A. (2011). Physiology of Small-Sided Games Training in Football. Sports medicine (Auckland, N.Z.), 41, 199-220. https://doi.org/10.2165/11539740-000000000-00000
Hodges, N. J., & Lohse, K. R. (2022). An extended challenge-based framework for practice design in sports coaching. J Sports Sci, 40(7), 754-768. https://doi.org/10.1080/02640414.2021.2015917
Renshaw, I., Davids, K., Newcombe, D., & Roberts, W. (2019). The Constraints-Led Approach (1st Edition ed.). Routledge. https://doi.org/https://doi.org/10.4324/97813151023519781315102351
Smith, K., Burns, C., O’Neill, C., Duggan, J. D., Winkelman, N., Wilkie, M., & Coughlan, E. K. (2022). How to coach: A review of theoretical approaches for the development of a novel coach education framework. International Journal of Sports Science & Coaching, 0(0), 17479541221136222. https://doi.org/10.1177/17479541221136222
Sullivan, M. O., Woods, C. T., Vaughan, J., & Davids, K. (2021). Towards a contemporary player learning in development framework for sports practitioners. International Journal of Sports Science & Coaching, 16(5), 1214-1222. https://doi.org/10.1177/17479541211002335
Wood, M. A., Mellalieu, S. D., Araújo, D., Woods, C. T., & Davids, K. (2022). Learning to coach: An ecological dynamics perspective. International Journal of Sports Science & Coaching, 17479541221138680. https://doi.org/10.1177/17479541221138680