Sports Science Monthly – September 2021

Every month we take a deep dive into the latest research in sports science. In this month’s edition we look at what coaches can learn from computer games, the importance of randomness in training, injury prevention, and eccentric training.

As always, the full Sports Science Monthly is available exclusively to HMMR Plus Members. You can browse the past topics on our archive page. The first topic below is free to everyone, but sign up now to read about all the latest research. To get an idea of what Sports Science Monthly is all about, the April 2016 edition is available in its entirety for free.

This Month’s Topics

What coaches can learn from computer games

Quick Summary – Computer game designers needs to make their games playable and addictive, whilst also allowing players to improve their playing ability as they progress through the game. As sports coaches, we can learn from some their principles when it comes to designing the environments our athletes train in, as a means of better supporting their learning and development.

An increasingly-important skill within the sporting world is that of interdisciplinary thinking; instead of having narrow and deep knowledge, there is increased importance on having broad (and, as a result, fairly shallow) knowledge across a variety of domains. This can be really useful when it comes to linking ideas together; having broad knowledge means you can potentially see links which those with narrower scopes would potentially miss. Alongside this, and specifically within the sports coaching sphere, there has been increased interest in how to best design learning environments to optimize athlete performance. In some cases, the principles underpinned by research do not necessarily reflect the traditional actions of a coach; for example, direct instruction from a coach when practicing an open skill that is subject to high levels of complexity is often an ineffective method of developing athletes, and yet I’d argue it’s very common, especially in the area of youth development.

Based on the recognition that more historic coaching methods may not be reflective of best practice, and given the increased focus on interdisciplinary thinking, there has been a recent focus on coaches to view themselves as learning designers. In the learning designer model, the interaction between the performer and their environment is the target area of influence, with coaches aiming to modify the environment to drive athlete learning. Through this method, athletes learn almost through exploration; they are provided with a problem (e.g. “score a goal”), given some constraints (e.g. can only take three touches with the ball, or run 5 steps), and then given the opportunity to attempt different solutions.

There’s another, very different, industry that has to do similar things. Computer games designers need to make their games highly playable and addictive; on of the ways they do this is to promote a feeling of mastery of the part of the gameplayer. This can be achieved through the player actually improving their skill; in essence, they learn how to play the game better. Back in 2005, James Paul Gee wrote a paper that outlined 13 key principles for designing video games to enhance learning; for 2021, leading sports scientists Sam Robertson and Carl Woods have taken Gee’s principles for game design, and attempted to adapt them into a guide for sports coaches to design optimized practice. This isn’t the first time researchers have attempted to apply Gee’s principles to a sports setting; in 2016, Amy Price and Shane Pill authored a paper exploring a Games Based Approach to sports coaching, drawing heavily on Gee’s work. Robertson and Woods aim to build on this paper by exploring all of Gee’s principles and how the apply to sport, as opposed to the selected few utilised by Price and Pill.

So what are Gee’s principles? They’re centered around three key themes; namely Empowered Learners, Problem Solving, and Understanding. Let’s take a closer look at these, along with Robertson and Woods’ ideas are how to build these into our coaching practice:

Empower Learners:

  • Co-design – this principle is built around the idea that the goal of learning design is to develop intelligent performers, and this is certainly true in sport, where we want athletes to be adaptive, engaged, and motivated, learning via their perceptions, emotions, thoughts, and actions to solve movement challenges. To develop intelligent performers, we should include athletes in the design of their learning activities. This could be through asking questions around what the athlete wants to work on to support their development, and use feedback from the practice session to modify how it is delivered in future.
  • Customise – a further goal of sports training is to develop performers who are able to self-regulate, both during competition, but also in practice. Self-regulating performers drive their own development, and, to do this, they need to be empowered to make decisions around how, where, and when their learning will take place. As a coach, you could customize your practice sessions by only providing feedback when asked by the athlete; in this case, the performer controls when they receive the information they think they need.
  • Identity – performers have an identity within their training environment and amongst their training group. We can use the principle of identify to encourage athletes to take some responsibility around their learning environment and the behaviours they exhibit at training. In team sports, this identity can be shared; for example, Manchester United are renowned for attacking football that is played both creatively and at pace. These principles form part of their identity, and so players coming into the club are taught these principles through aspects such as team culture, with senior players providing the informal learning during training sessions. As the team hold this identity, during training (and matches), they seek opportunities to exhibit the behaviours associated with this identity, driving their performance.
  • Manipulation and distribution of knowledge – learning, as a process, is exploratory in nature; we try something, see what happens, then refine our actions based on the outcome. Providing athletes with a variety of problems or scenarios within training sessions allows them to try different actions, developing skills and better understanding the context in which to apply them. Athletes could be encouraged to make their own suggestions around manipulations that happen during practice sessions (building on the principles of co-design and customization), or could be placed into uncomfortable situations, such as a change in playing position, to provide a broader depth of knowledge.

Problem Solving:

  • Well-ordered problems – practice sessions need to be aligned with the performer’s current ability; challenges in training that are too difficult, or too easy, may either stagnate learning or lower motivation, harming future performance. This principle requires coaches to keep a close eye on practice sessions, responding to changes in performance at either increasing or decreasing the challenge offered accordingly.
  • Pleasantly frustrating – in computer games, learning tends to occur when new and pleasantly frustrating problems are encountered by the game-player. The specific problem the player has to overcome should be a challenge, but achievable—a stretch task. Examples of this in computer games include the big boss at the end of the level; challenges such as this show the player that they are progressively getting better, and, as a result, getting closer to completing the challenge. As a coach, being able to create a safe but uncertain training environment, where failure is without an increased risk of injury or negative judgement, allows the athlete to fail, make changes, and get progressively closer to a successful outcome.
  • Cycles of expertise – as we become better at carrying out a skill, we need to be increasingly challenged as a way of continually improving; as such, ensuring the practice environment progressively challenges stability, the athlete should experience new adaptations. In 100m running, for example, we could use competitions to develop an optimized practice environment for a developing 100m runner, placing them in competitions of varying quality. Racing against more experienced or faster athletes creates a stretch challenge; pitting the athlete against their peers creates a different challenge, and then providing a slightly easier race means the athlete experiences a different challenge—being expected to win.
  • Information “on demand” and “just in time” – the timing and frequency of both feedback and information provided to athletes can be manipulated to support development. The best-practice principles, for both computer games and sport, are that both information and feedback should be provided to performers when they feel they need it (i.e., on demand), and when they can put it to use (i.e., just in time). This allows the athlete to be engaged, whilst reducing their reliance on the coach to drive practice.
  • Fish Tanks – a fish tank is a simplified version of the ocean, a complex ecosystem. Fish tanks are useful as they enable us to see how the various fish interact, both with each other and with other aspects of their environment. A fish tank is a simplified version of reality, but not a deconstructed version. In computer games, tutorial levels provide an example of a fish tank; the “normal” game difficulty has been reduced, and players are encouraged to explore as a means of learning how to play the game—crucially however, the important part of the “normal” levels are maintained, allowing players to adequately prepare for the real game. In sport, this might be reducing the intensity or speed of a skill for example, whilst maintaining the full sequence; as the athlete improves, the intensity or speed can increase, allowing them to continue their progression within the whole skill.
  • Sandboxes – a sandbox is a metaphor for an authentic environment that provides safety for exploration. In sport, a potential example is that of augmented and/or virtual reality training; technologies that can be utilised to support an athlete in their skill development.
  • Skills as strategies – in complex sports, such as many team sports, performers are provided with a problem (e.g., score a goal) that has a range of potential solutions. During a match, there are specific contexts that arise; there is a goalkeeper trying to stop you scoring, for example. Practicing shooting on goal without a goalkeeper, then, is not a true representation of what happens in a match; as such, this practice would not allow the performer to develop their skill as a strategy that supports the solving of their problem. This underscores the importance of ensuring practice conditions mimic, as much as is feasible, the competition environment.


  • Systems Thinking – when developing a skill, it’s important for the performer to understand how both the skill, and the method of practice, fit into the bigger picture. For example, a soccer goalkeeper practicing their goal kicks could just kick the ball upfield; however, if we add teammates and opponents, they can start to understand how their kick affects the next action of the game, potentially allowing them to better place their kicks to ensure their team retain possession.
  • Meaning as an action image – metaphors can be a powerful way to support learning; they allow us to place the new skill within a context that has a meaning to us. Being able to create an action image in the mind of the athlete is, therefore, important. Often, we do this through cueing; for example, early on in my development when working on my hand positioning for sprinting, I was cued to run as if I was holding a potato chip between my thumb and index finger. This cue was useful; too much tension and I would break the chip, which focused my efforts on being relaxed during sprinting.

There’s a lot of information contained within these principles, but they’re well worth reading through and digesting. Within sports, there is often a prevailing model of understanding performance; 100m running tends to be looked at through a biomechanical lens, for example, whilst middle distance running has a physiological focus. Given the research in the area of skill acquisition, there is the opportunity for a true competitive advantage for those who try to maximize the learning of their athletes. The 13 principles of learning by design in computer games, developed by Gee and viewed through a sporting lens by Robertson & Woods in this article, go some way to forcing us to consider how we can design and deliver our training sessions as a means of promoting athlete learning—and hence optimizing their performance during competition—through delivering practice environments that encourage problem solving and exploration; two aspects that can enable us to drive future success.

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