Developing a philosophy by questioning established practices

What is your philosophy on coaching, and how has it developed over time?

I’m a firm believer in continuous learning, emphasizing personal growth through reading and engaging in small projects. When I was younger, I was heavily influenced by some excellent coaches I worked under and was trying to replicate what I’d seen them do. I see many young coaches doing that now.

Rather than following a one size fits all template, I focus on identifying gaps in athletes, teams, or programs, then finding ways to add value that align with our goals. It’s individualization within a team context.

Our group is very diverse in their physiological make up, and our program needs to reflect this. While we have overarching themes for our squad, such as strength gain or fat loss, we overlay personalization onto these foundations. This means crafting targeted gym or field programs based on the stimulus needed, as opposed to a universal focus on traditional exercises like squatting and benching

A powerful Fijian rugby player may not require extensive weightlifting compared to a 18 year old kid from Edinburgh straight out of school.

This approach acknowledges the diversity of physiologies and gene expressions among athletes.

The cornerstone of my philosophy is the multifaceted nature of athlete development. There’s a tendency to solely concentrate on strength, conditioning, or speed. We do all this, but we also advocate for a holistic approach that encompasses nutrition, recovery, and more.

Over time, I’ve grown comfortable with deviating from traditional methods. A program’s effectiveness isn’t solely determined by adherence to established practices. Regularly measuring progress and reflecting on outcomes ensures that what we’re doing aligns with our goals and is actually yielding results.

How does this philosophy result in day to day programming and exercise selection?

I previously emphasized exercises like squats, bench presses, and pull ups to make athletes stronger. However, over time, I realized that merely improving performance in these exercises doesn’t necessarily equate to true strength gains that we need in athletes. For instance, when I experimented with force plates with Olympic weightlifters, I observed that even though athletes improved their squat performance, it didn’t always translate to increased force output.

This prompted me to delve into the science of strength development. I learned more about factors like maximal recruitment of motor units, synchronization of recruitment patterns, and co-activation of antagonistic muscles. These elements play crucial roles in determining the quality of strength training adaptations.

This shift in perspective led me away from the necessity to do certain exercises in all athletes and towards understanding the specific adaptations we were targeting.

Biomechanics became central to my approach. For example, traditional squatting might not be the best choice for taller athletes with long femurs. They cannot express high levels of force. Instead of blindly following conventional wisdom and squatting, I sought exercises that would optimize force expression while considering individual differences.

Weightlifters generated higher ground reaction forces in a horizontal leg press than in squatting, suggesting that the difference would be even greater in a 195 cm second row who struggles to squat.

This led me to reconsider power training’s impact on speed development. Track & field research emphasizes metrics like reactive strength and ground contact time to develop maximal velocity.

However, depth jumping and ground reaction force, rather than RSI, play more significant role in predicting sprinting performance in rugby players in the UK. This challenged my preconceptions and highlighted the importance of exploring sport specific adaptations.

Collecting data and conducting deeper analyses became integral to my approach. I turned to tools like R programming and statistical models. By analyzing our data, I could identify relationships between different variables and outcomes like speed. This allowed me to question existing paradigms and adopt strategies that aligned better with our athletes’ needs.

Why have depth jumps and drop jumps become a cornerstone of your programme?

In my first year here we focused on the low hanging fruit, starting with building a solid foundation of the fundamentals. The primary emphases were on improving fitness, addressing injuries, adapting to training schedules, and gradually building strength.

We anticipated quicker strength gains in the second year. We entered the season noticeably stronger compared to the previous year due to accumulated work. The primary objective, then, was to effectively translate this increased strength into enhanced speed.

This year, our training approach involves extensive use of drop and depth jumping. Weighted squat jumps emerged as valuable indicators, especially for acceleration. We use velocity based training (VBT) to prescribe specific velocities and aim to work around 80% of maximal load as a peak force stimulus, with a focus on quicker movement.

It’s important not to over complicate matters. Emphasizing fundamental aspects of fitness is vital.

For instance, maintaining optimal body composition significantly contributes to maximizing speed. There’s not much logic in focussing on producing force quickly if you’re unable to produce sufficient force irrespective of time. This fundamental groundwork must precede advanced training techniques.

While not all strong individuals are necessarily powerful, powerful individuals generally possess considerable strength (even if some struggle to express it against a barbell). Misconceptions arise from confusing expressions of strength with the ability to generate force. The combination of strength and speed is the basis for athletic success.

That brings us to where we can introduce advanced exercises like depth jumps from boxes. Starting with microdosing, athletes adapt to new exercises over time. They might have some soreness at first, but their tolerance improves.

While most athletes are encouraged to engage in advanced exercises, we’re still attuned to individual differences. Supplementary exercises address issues such as tendon pathologies that might hinder specific high speed, high load eccentric exercises or repetitive landings.

How would you recommend coaches understand more about predictors of speed in their populations?

The first step is to gather quality data. Historical data on exercises like drop jumps or squat jumps can reveal correlations based on different populations and coaching approaches.

Correlation often hinges on the athletes’ intent during these exercises. The quality of execution and the underlying neuromuscular qualities are critical to their performance and our assessment / analysis of their performance. High quality data will reflect the athlete’s intent.

Instead of treating all athletes as a single data set, categorize them into meaningful groups or “bins.” Analyze data within these bins to draw relevant conclusions. This accommodates a broad range of athletic profiles and allows you to look at how different sub groups achieve their performance within the wider population.

Clearly define the question you seek to answer through data analysis. Whether it’s about the influence of stiffness on max velocity or other aspects, having a well defined outcome variable is crucial. Statistical packages like random forests can help build predictive models, highlighting interactions and relationships within data sets.

Undertaking data analysis might seem daunting, but it’s manageable. Online resources, including tools like GPT, can assist in understanding statistical concepts. While it might take time to become proficient, the process isn’t as intimidating as it might appear.

Over time, delve deeper into the data and refine your analysis. As you become more adept at using statistical packages and exploring relationships, you’ll uncover richer insights.

How do you ensure transfer of training?

My understanding currently remains conceptual, and we’re far from having a complete solution.

While I’m not well versed in this area, I’ve studied Frans Bosch‘s work extensively. Initially critical, I engaged with Bosch’s ideas more deeply, exchanging opinions with him and his colleagues.

Bosch emphasizes the significance of the nervous system and its role in governing movement. His work touches upon understanding neural pathways, how they regulate movement based on contextual conditions, and how this determines the motor strategy the athlete uses.

Bosch’s concept of attractors is particularly intriguing. These are positions or states that lead to optimal outcomes. A technique that satisfies attractors puts athletes in the best possible position to solve movement problems when confronted with various unforeseen contexts.

While I’d love to present a magical data source that guarantees transfer, the reality is complex. Demonstrating transfer is easier with fitness related aspects due to available data streams. However, assessing strength, power, and speed transfer in rugby is intricate. Various factors, including technical components and injury history, can complicate the evaluation of transfer.

We’re analyzing work rate using data from Opta and focusing on aspects like scrum dominance. Scrums are highly technical, and even though it might appear that players have become weaker, it could be a matter of altered movement due to injuries.

We’re also exploring collision performance, both with and without the ball. Assessing momentum, kinetic energy, and other factors could predict collision quality.

Dan Tobin’s work presents an alternative perspective. He emphasizes optimizing positions for force expression, solving momentum challenges. His findings challenge existing notions, shedding light on how taller, faster players can be levelled in collisions.

Ideally, what would give you a more thorough understanding of training transfer?

We’re looking at what really matters on the rugby field and what we can physically influence.

We look at players excelling in meters made, defenders beaten, collision dominance, etc. We build a dataset and then integrate strength and power data for these players, establishing relationships between these metrics. The depth jump, for example, was a strong predictor in our Edinburgh group for defenders beaten, meters made, and post-contact meters. But it’s correlation, not necessarily causation.

We’re trying to build something from which we can make educated guesses. Opta provides dominant carries data, but we could use better data on collision quality. We integrate strength and power data, technical aspects, and, going forward, we would like to add in information about player posture. Athletes with lower athleticism might need to get all three postures right to win a collision. On the other hand, highly athletic players might win most collisions even if they don’t nail all three postures.

We might need to work on video analysis to complete the data stream.

Defensive aspects are also significant for us, especially in terms of momentum changing actions. We’re striving to get better here since it’s crucial and we believe we can improve.

Do you integrate any of Bosch’s methods at the club?

Some of his concepts, like deepening attractor wells to establish stable components for planned movements, resonate with me. For instance, the hip lock. We’ve noticed that some guys can automatically hit a hip lock, creating strong co-contractions around the hip. This autonomy in reacting to stimuli on the field leads to creating stability, which aids problem solving.

Understanding co-contractions in movement within chaotic environments is significant, as is appreciating the performance benefits of optimal muscle lengths in technical execution.

Bosch’s methods are rarely fatiguing and usually come at a low cost. They’re quite easy to integrate, and so we do integrate some of them.

There’s also an abundance of historical evidence suggesting that traditional methods of getting stronger, becoming more explosive, and sprinting regularly yield improvements in athleticism and sprinting ability. I have a decent database of evidence supporting this from my own teams in the last 10 years.

These classical training methods have stood the test of time for a reason. Shifting away from them to adopt something with limited supporting data poses challenges for me.

Bosch would likely argue that the success of his approach is challenging to measure because it’s context specific, focused on problem solving within the right context, rather than universally enhancing max velocity, for example. I can understand that perspective.

Much of my critique revolves around the fact that you often see people on social media prescribing exercises inspired by Bosch without a deep understanding of what he is actually discussing. There’s a gap between the application and his theories, and this can lead to misguided practices.

If you don’t truly comprehend the foundation behind his ideas, you might end up making things worse by targeting the wrong attractors.