By Gaby Muller
At universities as ongoing coaches we typically get taught the various periodization models and get advised to strictly follow those types of top-down plannings. Is it really beneficial to plan out an entire year in advance? Where does individualization come into play? In a recent conversation that I had with Mike Tuchscherer, the founder of Reactive Training Systems and the man who gave bottom-up planning the name of Emerging Strategies, shared his story as a coach and his modern view on programming- and his answers were surprising.
Mike started coaching in 2004 and Emerging Strategies didn´t come into play until 2015. During that time span, he mostly followed traditional periodization models, and even taught courses on periodization.
His paradigm shift happened around 2015 after having noticed some limitations with the traditional models. Around this time, he got inspired by a podcast by Derek Evely, who described bottom-up planning. “The bones of Emerging Strategies” were formed.
Periodization models- training specific qualities
One of the core values of Emerging Strategies is to follow the trail of athlete response. Periodization models however teach us a more rigid approach, that allows us to focus on specific qualities during certain training periods. The speculation behind the positive effects of that approach is that athletes are highly sensitized to the new training stimulus.
Mike discussed his perspective on the importance of top-down and bottom-up approaches. While a top-down model allows relying on built-in sequences, a bottom-up approach requires conscious effort in decision making. Mike described Emerging Strategies as “an information processing system”. However, he highlighted that blindly following the trail of athlete response by simply observing and following the trends isn´t enough. Background understanding of top-down approaches and physiological responses to training are crucial to be able to process observations effectively, fill in any information gaps and serve as a guideline.
Planning out a macrocycle
Mike discussed the balance between bottom-up and top-down training approaches. According to him, strictly following one type of approach without considering the other is not optimal. Bottom-up training involves reacting to emerging information, while top-down training involves rigid structure and planning.
Although top-down planning establishes a training rhythm and maps out the structure of a development block, he advised against planning every little detail in advance, as the effectiveness of it can decrease with the uncertainty and complexity of real-life. A minimum of top-down work that can be beneficial is a thorough thought process during initial planning, as it can be “hard to recreate the thought process at some point in the future”.
Additionally, Mike introduced the concept of training effects categorized into three broad categories; skill development, structural adaptation and metabolic adaptation. While all training affects all three qualities, according to him, the application of a top-down approach can help provide a direction that puts more emphasis on certain qualities to avoid “hammering the same adaptations over and over again”. The success of this approach is variety and having a balanced and well-rounded plan.
Overall, Emerging Strategies advocates for a balance between top-down planning and bottom-up responsiveness in training to optimize training outcomes.
Mike described stress index as “a measure of training volume” and the difficulty of a session or exercise and the similarity to the concept of counting hard sets, but it provides more detailed and high-resolution information. The issue with counting the number of hard sets is that it can be subjective and difficult to define, therefore the stress index aims to quantify the training load more accurately. He mentioned the limitations of working solely with tonnage or relative volumes, as directly factoring the training loads can lead to distorted results when working with different training intensities. The stress index allows for better understanding of the overall difficulty of the training and the need for recovery or overreaching.
Additionally, stress index differentiates between central, peripheral (localized) and total stress. It considers the stress of each repetition within a set. Mike mentioned that the central stress “maps out quite well to intensity” and that it is “more like a centralization of the fatigue symptoms”, such as brain fog for example, while peripheral stress “tends to produce more localized effects like muscle soreness and joint stiffness”. Just like an athlete can be more sensitive to different intensities, trends can also be observed with peripheral and central stress.
Case Study- Brett Gibbs
One of Mike´s athletes, Brett Gibbs, showed a pattern of poor response to high-intensity training but thrived with lower intensities. This was considered leading up to the world championships. His last block focused on low-intensity training of around 70% and the second-to-last block had an intensity of around 75-80%. This descending intensity approach resulted in Brett performing exceptionally well at the worlds, where he became world champion in his class.
This was interesting to bring up, as a traditional competition preparation usually consists of a linear periodization type of approach, while Mike was able to individualize his athlete´s competition preparation based on previous trends in the program and not by blindly forcing a traditionally recommended approach on his athlete.
Athlete response can change over time
“The whole point of training is to create change and adaptation”. Mike talked about the review of blocks to reevaluate their success, and how the data could not be reliable after two years or less for younger athletes. Various factors including external life events and internal sensitivity can contribute to an unexpected training response. He highlighted the importance of effective communication to gain insight on an athlete´s perception and experiences during training. Athlete feedback in terms of quantitative data and qualitative experiences plays a significant role in shaping the training approach.
Limitations of Emerging Strategies
Mike mentioned that Emerging Strategies heavily relies on engagement and effective communication. As there is no preset structure in a bottom-up approach, coaches need to rationalize their decision-making and must be actively involved in the training process to provide stability. This means that a coach should strive to analyze data, identify trends and make necessary adjustments to optimize training, which are responsibilities that can already be taken care of with a top-down approach.
However, he clarified that both systems have their limitations. Data analysis in a bottom-up approach can be complicated by variability in athletes´ lives. It should be noted that these limitations exist regardless of the system used and can reinforce the significance of clear communication and engagement.
It can be difficult to establish a clear correlation and causation relationship between specific training methods and training progress. Mike mentioned several factors, including the unique individual training response, the continuous adaptations and changes over time, and the inability to conduct controlled experiments in this environment. Although it is essential to acknowledge the correlation-causation problem, coaches must take action and implement training strategies based on reasonable correlations. This requires rational judgment and observation of repeatability in training, although complete certainty is never guaranteed.
Mike has been working on a dynamic load management system, called TRAC-R, that prescribes training loads and considers an athlete’s fatigue state and recent training adjustments.
Traditionally, athletes that miss training sessions continue to stick to their original program, however, TRAC-R seeks to optimize training by making precise adjustments based on individual needs and changes in training (schedule changes, overshot RPEs, variable recovery). Mike further mentions that it acknowledges the complexity of training and the multitude of training decisions that can be made. Ultimately, it compensates for training deviations and aims to maintain structure simultaneously and provide a more individualized training approach.
TRAC-R breaks training down into lots of different ways and provides a nuanced understanding of the stress that is being imparted on the athlete. As an athlete himself, Mike explained that he had noticed over the years that his back is very sensitive to shearing stress ever since his back injury happened, which limits his tolerance in squats and deadlifts for example. From a load management standpoint, accounting for different stressors has allowed him to optimize his training while minimizing the risk of triggering pain and allowing systematic tracking. The system can allow gradual exposure therapy principles and facilitate progression at a reasonable pace. “So far, the results have been much better and I´ve had fewer back issues in this period of my training, than I have had since my back injury started”. Mike keeps working on the idea to incorporate TRAC-R into the Reactive Training System, but at the moment it is not “very user friendly”.
The talk with Mike was a good reminder that ongoing coaches should really consider the bigger picture. A broad understanding of “old-school” training models is crucial to work with a bottom-up approach in efficient ways and be able to form educated guesses on emerging information. Knowledge is power.
We get confronted with a vast array of tools that we can use in our coaching. While a precise collection of data is important, we should not get caught up in over analyzing, but learn how to use different tools to our own advantage, rationalize our decision making and listen to our intuition. Working with athletes is trial and error, and the more information we ́re able to collect of an athlete, the more individualized their coaching experience will be. Effective communication can´t be disregarded.
About the interviewer: Gaby Muller
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