Motor Learning in Action: Practical Insights for Sports Scientists

It is a pleasure to welcome Harjiv Singh to this guest post on motor learning in sports science. Harjiv is currently the Senior Performance and Development Scientist for the Charlotte Hornets in the NBA.

Motor learning refers to a set of processes associated with practice or experience leading to relatively permanent changes in the capability for skilled behavior.

As professionals tasked with helping performers acquire and refine motor skills, a key objective of this post is to briefly introduce concepts in motor learning that can aide sport scientists in optimizing player performance, health, and development.

What is Skill?

At the most basic level, Johnson (6) provides the following equation:

Skill = Speed x Accuracy x Form x Adaptability

In any sport, movements are to be performed within time (speed) with a degree of success (accuracy), as effortlessly as possible (form), under varying conditions (adaptability) (6). For example, a basketball player’s deceleration speed on any defensive closeout will depend on factors such as their starting speed and decision-making speed. That is, reading if the offensive player will drive or shoot, disrupting their flow, and not fouling. In other words, there is information that needs to be coordinated for.

It isn’t surprising that injury incidence is relatively similar year to year. Clearly something needs to change. What is known is that physical ability may decline over one’s career, but skill often has a preservation effect (2, 11, 12). Therefore, it is important as sport scientists to evaluate how to bring about the coordinated control of complex movement. For this, the first thing that needs to decided is whether we are working on learning or performance. This decision ultimately drives inquiry, understanding, and evidence for best practices.

Learning: are we focused on relatively permanent changes in behavior?

Performance: are we focused on temporary execution at a specific point in time?

Figure 1 below represents the classical motor learning paradigm where, after a period of no practice, individuals are tested to see how well they retain the skill practiced. This evaluates stability of the skill over time. Another type of test, referred to as a “transfer” test, tests whether the performer can transfer the learned skill to different but related tasks or contexts. This evaluates adaptability and generalizability.

Tests of Skills Retention and Transfer

For example, say that Athlete A - a basketball player - is practicing their catch and shoot (C&S) three-pointer off a dribble hand off (DHO) from the wing. A retention test would be a repeat of the same task after some time (e.g. 1 hour, 24 hours, 6 days later, etc.). Whereas, a transfer test would be performance in a game setting.

While the data in this figure is hypothetical, the importance of practice is demonstrated. Meaning, performance in retention or in transfer depends a lot on practice.

Let’s dig a little deeper. Our performance analysis reveals that this athlete needs to attain a higher maximal trajectory height, so that they reach a greater jump height at the point of ball release.  Assumingly, improving this capability will enhance the skill.

We can of course, aim to focus on specific lifts in the weight room and analyze force plate metrics with precision to try to develop this individual’s jump height from a physical perspective. There will likely be a positive effect. But the true question remains whether the skill itself improves.

There are several concepts in motor learning that relate to this discussion. Here, I focus on just two that may help inform practice strategies: practice variability and practice distribution.

Practice Variability

The amount of variation and diversity in movements that an athlete practices is something to consider when thinking about practice design. These can include manipulations related to contextual variables, time, pace, and anatomical positions to name a few. It has been established that when performers experience more variation in practice there is a positive effect on retention and transfer of skills (1, 10).

Conversely, less variation may offer short-term performance benefits. In the motor learning literature, we categorize higher amounts of variation as variable or random practice. Alternatively, we categorize lower amounts of variation as constant or blocked practice.

Each of these practice types are explained and illustrated below in Table 1. Please note that figuring out the specific dose and timing of different practice types is an important discussion but not the focus of this particular post.

C&S = “catch and shoot”; DHO = “dribble hand-off”

It should be noted that when thinking about “practice”, we also include other training activities such as those in the weight room. In fact, this principle is not new; variations in resistance training are known to benefit muscle activation, strength, and hypertrophy (5, 7, 8).

For example, say your athlete is to perform 10 trap bar deadlifts. One version of variable practice can be changing different parameters of the exercise every few repetitions (tempo, weight, foot placements, etc.). If we know that the player will have to perform the C&S three pointer off a DHO against different contest levels and defenders coming at different speeds, why do the same exercise for 10 repetitions? We need more research here, but it is something to ponder. Often times, capability and skill are treated as separate entities.

Taking a closer look at the table above, it can likely be inferred that higher levels of variability in this context may include more change of directions and higher overall load compared to one that was more in line with constant practice. Therefore, there are clearly implications for training load monitoring. Thinking about this through a quadrant like system might help (see figure 2).

This figure simplifies decision making. For instance, if we are on Matchday-2 and are traveling across two time zones tomorrow, how might we balance recovery and performance in practice? Traditional sport science tells us to maybe stick to low intensity and low volume training. Coaches may take this as meaning less time and less repetitions.

However, in a sport that requires the slightest of margins in terms of performance, how do we still maximize this reduced time period?  Maybe it’s the lower left quadrant where the focus is blocked and constant practice.

In a different scenario, a question that may need to be considered is if the player performed high volume lower body in the weight room prior to on-court practice, how much variability should be experienced knowing the goal is still to enhance maximal trajectory height? In this situation, I may want to stick to the lower right quadrant where there is less inter-skill variability but more intra-skill variability (refer to the examples in table 1).

Practice Distribution

Practice distribution refers to how practice sessions are scheduled over time. Similar to the concept of micro-dosing, finding the right balance of timing, length and load is yet another thing to keep track of (3, 13). 

Distributed practice, which involves breaks between sessions, generally leads to better memory consolidation, reduced fatigue, and improved learning compared to massed practice, which involves longer, continuous sessions with fewer breaks.

For example, shooting 100 repetitions of catch and shoot three dribble hand-off in a single, intense session in massed practice typically leads to short-term improvements. Conversely, distributing the same number of repetitions across multiple days or within a day (e.g., 50 shots on day 1 and 50 shots on day 3) exemplifies distributed practice. It should be noted that there are immediate performance gains for some performing massed practice when quick improvement, a warmup effect, or maybe even confidence is the focus or need (4, 9).

Interaction of Practice Variability and Distribution

Before applying a level of variability and distribution, deciding how hard or challenging the session should be is another consideration. Remember, we are still focused on the capability within the skill.

The challenge point proposes that difficulty in practice conditions is a relationship between nominal task difficulty (i.e., task difficulty regardless of who is performing the task) and functional task difficulty (i.e., how challenging the task is relative to the skill level of the individual). As one develops skill during practice, functional difficulty is reduced. This implies that the practice environment should change as the individual’s skill level changes.

In the figure below, each dot symbolizes a specific drill or exercise within a session. For the purpose of this, this can be on-field or in the weight room. A dot at Day 4 on the X-axis and higher on the Y-axis (indicating greater variability) signifies a session practiced after a four-day interval. Dot colors denote variability levels: red for high variability, blue for low.

Toggling between variability and distribution allows for certain adaptations to occur. For illustration purposes, the dotted black square is an example of the challenge point zone which represents moderate practice distribution with moderate practice variability.

Quantifying the optimal challenge point can be achieved through techniques such as machine learning models (e.g., logistic regression, decision trees). Moreover, in the context of a season, these concepts can be used to periodize skill more effectively like we do with physical training already.

Putting it all together, the figure below is an example of what this can look like. Player A in this case has accumulated higher levels of variability in practice whereas player B has accumulated lower levels of variability. In this example, Player A has generally increased their trajectory height and improved shooting accuracy on the catch and shoot three off a dribble hand-off over time. Finally, please note that these are examples and not absolutes. It can be possible that if Player A accumulated lower levels of variability in practice, they would also show the same benefits.

Final Thoughts

The purpose of this post was to introduce a few key concepts in motor learning. While the underlying mechanisms are beyond the scope of this post, I hope I have underscored the importance of understanding capability in the context of skill. I do think sport science has evolved greatly over the past decade. However, re-thinking practice to consider motor learning alongside sports science and training load is key to improving our initiatives. Some considerations include studying the demands of the game from a perceptual-motor lens, developing an infrastructure around tracking for practice, and using data to help inform practice design.

Frequently Asked Questions (FAQ’s)

What is the take home point on practice variability for sport scientists?

Blocked and constant practice is better for performance while variable and random practice is more effective for skill learning. The question of when to introduce more variability is a more nuanced question that may be immediate or over time based on things like success and motivation.

What are some considerations for effectively balancing practice variability and practice distribution to optimize skill development in athletes?

As sport scientists, consider things like internal load and external load to effectively make decisions on how practice can be structured beyond simple intensity levels. Remember, higher intensity adaptations can come with less or more variability. However this can be at the expense of learning.

What is the difference between blocked, random, massed, and distributed practice?

Blocked practice: Involves practicing one skill prior to practicing the next in a single session.

Random practice: Involves practicing multiple skills in a random order in a single session. 

Massed practice: Involves conducting practice sessions with minimal breaks. For example, three hours in a day without a break.

Distributed practice: Involves spreading practice sessions over time. For example, shorter practice sessions spread out over a longer period of time.

Recommended Reading

• Chow, J. Y., Davids, K., Button, C., & Renshaw, I. (2016). Nonlinear Pedagogy in Skill Acquisition: An Introduction. Routledge.

• Hodges, N. J., & Lohse, K. R. (2022). An extended challenge-based framework for practice design in sports coaching. Journal of Sports Sciences, 40(7), 754-768.

• Farrow, D., & Robertson, S. (2017). Development of a skill acquisition periodisation framework for high-performance sport. Sports Medicine, 47, 1043-1054.

• Williams, A. M., & Hodges, N. J. (2023). Effective practice and instruction: A skill acquisition framework for excellence. Journal of Sports Sciences, 41(9), 833-849.

References

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4.     Donovan, J.J., & Radosevich, D.J. (1999). A meta-analytic review of the distribution of practice effect: Now you see it, now you don't. Journal of Applied Psychology, 84(5), 795-805.

5.     Fonseca, R.M., Roschel, H., Tricoli, V., de Souza, E.O., Wilson, J.M., & Laurentino, G.C. (2014). The effects of training variation in resistance exercise on skeletal muscle adaptations and performance: A systematic review. Sports Medicine, 44(2), 183-194

6.     Johnson, H. W. (1961). Skill= speed× accuracy× form× adaptability. Perceptual and Motor Skills, 13(2), 163-170

7.     McNamara, J.M., & Stearne, D.J. (2010). Muscle activation and strength performance: Evidence of variability in changes due to resistance training. Journal of Strength and Conditioning Research, 24(1), 285-290

8.     Rhea, M.R., Ball, S.D., Phillips, W.T., & Burkett, L.N. (2002). A comparison of linear and daily undulating periodized programs with equated volume and intensity for strength. Journal of Strength and Conditioning Research, 16(2), 250-255

9.     Shea, J. B., Lai, Q., Black, C., & Park, J. H. (2000). "Spacing practice sessions across days benefits the learning of motor skills." Human Movement Science, 19(4), 511-524.

10.  Shea, J. B., & Morgan, R. L. (1979). Contextual interference effects on the acquisition, retention, and transfer of a motor skill. Journal of Experimental psychology: Human Learning and memory, 5(2), 179.

11.  Vaci, N., Cocić, D., Gula, B., & Bilalić, M. (2019). Large data and Bayesian modeling—aging curves of NBA players. Behavior research methods, 51, 1544-1564.

12.  Wörner, T., Kauppinen, S., & Eek, F. (2024). Injury patterns in Swedish elite female and male ice hockey–A cross-sectional comparison of past-season's injuries. Physical therapy in sport, 65, 83-89.

13.  Zinner, C., Matzka, M., & Mahlberg, J. (2021). Microdosing - Enhancing Performance in Elite Athletes Through Smaller, More Frequent Training Sessions. International Journal of Sports Physiology and Performance, 16(5), 647-653. doi:10.1123/ijspp.2019-0304.