In Part 1, we explored some of the historical contexts that facilitated the emergence of Top-Down Training Planning (TDTP). For you to get the most out of Part 2 (and, Part 3), I’d highly recommend reading this first. As always, a huge thank you to Mike Tuscherer, Mladen Jovanovic, Ciaran O’Regan and John Kiely who have influenced these ideas. An extra big thank you to Lachlan Wilmot — the other director of Athletes Authority — for his permission to use his models for decision making when it comes to the planning of training.

Top Down Training Planning (TDTP) makes sense in theory. It is such an attractive solution to the problem of sports planning (both because of its best-practice approach to problem-solving and, the assertion that training can be predictably planned) that it is almost never challenged as a model for decision-making. While different methodologies are debated feverishly amongst strength coaches and sports scientists (block vs linear vs undulating etc) the fundamental premise that the planning of sports can be done in advance according to a best-practice approach is almost always (and scaringly so), assumed to be true.

This is a huge problem for strength coaches because it reinforces a disconnect between what is valued in our discourse, investigation and professional development, and what should be valued in accordance with the practical realities of sports planning and what is emerging in science.

In Part 2 of this series, we will explore the science of traditional periodisation, accounting for its scientific origins in stress theory, and, the lack of emerging evidence that training planning produces anything close, to predictable outcomes. Given the extensiveness of this exploration, we’ll then discuss Integrated Periodisation at a low level of resolution — the intersect of TDTP and, decision-making in uncertainty via emergent outcomes, most commonly referred to as Bottom’s Up Training Planning.

Periodisation & Stress Theory

Periodisation has been considered by many to be the ‘management of stress’ in the sports planning context and is rooted in the early 20th century idea that all biological challenges can be reduced to three (four if you include homeostasis) phases: alarm, resistance and exhaustion. This model for understanding stress is called ‘General Adaptation Syndrome (GAS) and is the work of Hans Selye, the famous endocrinologist of the early 20th Century, who is considered by many, to be the Father of Stress. 

This model for understanding the stress response is what is taught by our Governing bodies, our textbooks and by our Universities, and is deeply embedded in ‘Modern Periodisation’ as an explanation for the response and adaptation to training. According to NCAA in their definitive guide to program design:

“GAS is one of the foundational theories from which the concept of periodization of training was developed”

National Collegiate Athletic Association

On the surface, GAS makes sense. Exposure to a training stimulus enters your body into a state of alarm — you have probably heard this referred to simply as the Fight-Flight response. Following the acute exposure to a training stimulus, the body experiences a transient reduction in performance and an increase in fatigue (anyone who has walked out of the gym after leg day knows exactly how this feels). Over the coming days, GAS proposes that the body transitions to a stage of ‘resistance’, where performance is improved and adaptation takes place. After this, two possible outcomes emerge — either the next training stimulus is applied during the time-window of ‘supercompensation’, or, the training stimulus is either applied too early, or too late, resulting in maladaptation or exhaustion.

Interestingly however and by Selye’s own admission, GAS was never meant to be extrapolated to sports planning. Instead, his research explored the stress response of rodents to toxic and chemical stimuli — not exactly a good comparison to sports training. Later in life and as the GAS model became ineffective at explaining stress as new evidence emerged, he would critique his own theory, acknowledging that stress couldn’t be reduced simply to ‘a purely physiological and medical phenomenon’ as originally hypothesised. What we know now about stress with advancements to neuroscience — is that the stress response is just as much a cognitive event as it is a biological event, and results from a discrepancy between the resources available to complete a task, and the perceived difficulty of the task. 

This new theory for explaining stress is called Allostasis and, has completely replaced GAS in the literature as the prevailing model. Rather than stress being a purely biological phenomena (the assertion of GAS), Allostasis infers that the brain operates as a master organ, first processing the perceived stress, then orchestrating a complex array of neuro-hormonal and neuro-chemical responses that we commonly refer to as the Fight-Flight response. Allostasis, at a fundamental level, can be described as the emotionally driven process by which the human system responds to imposed challenges. When applied to the context of strength & conditioning, while the physical demands imposed on the body through training are undeniably the primary instigators of the stress response, the adaptations that follow are nowhere near as predictable, and, are heavily influenced by the individual psycho-emotional response to the stimulus. This is something that is both unique to the individual (no two athletes will have the exact same response to an isolated training stress), but will also be  different between exposures of the same individual, depending on the prevailing context (an isolated training stress can be perceived as easy in the absence of an injury for example, and terrifying in the presence of one).

Taken from Periodisation Theory: An Inconvenient Truth (Kiely) https://link.springer.com/content/pdf/10.1007%2Fs40279-017-0823-y.pdf

So if GAS was never intended to be a model for sports planning, nor, even an accurate reflection of the stress response within the sports planning context, why has GAS found its way into the strength and conditioning mainstream and become universally adopted as the model to articulate the stress response to training? For that, we need to go back to our Soviet forefathers who were responsible for most of what we think we know, about ‘Modern Scientific Periodisation’.

As modern periodisation emerged out of the USSR as the ‘best-practice’ approach to sports planning, the forefathers were in dire need of a scientific basis to justify a ‘planned-in-advance’, ‘best-practice’, ‘predictable-outcomes’ approach. The Marxist strength coaches of the USSR during the mid 20th century already had the philosophical basis for long-term planning (discussed in Part 1), but, they needed a scientific basis for it, too. In the absence of Allostasis as a model for understanding stress (it emerged as an alternative model to GAS only three years before the collapse of the USSR and well after TDTP had taken hold as the prevailing model for sports planning), GAS provided that in spades — it was similarly reductionist, simplistic, and predictable. And so, TDTP in its rigidity and predictability not only had a philosophical basis, but now, a scientific basis too, and as a result, ‘Modern Scientific Periodisation’ was born.

So What Is So ‘Scientific’ About Periodisation?

One of the complications around the periodisation discourse is the absence of a formal, universally accepted definition of what it actually means. Historically, it was used to describe the planning of ‘periods’ of training, planned in advance with the intention to train a specific quality of fitness that would build on from the previous period, or block. These qualities of fitness were assumed to be developed sequentially: hypertrophy before strength, strength before power and power before speed, for example. Thought of in another way; it was typified by starting a training cycle with high volume and low intensity, and inverting these variables as you proceeded closer toward competition. 

Periodisation of Training (Matveyev)

These days however, periodisation seems to mean anything at all that relates to sports planning, whether or not they follow this historically sequential model that is most commonly referred to as ‘block’ training. Alternative models like conjugate periodisation, concurrent periodisation, vertical periodisation and non-linear periodisation have all been argued by their proponents as ‘best practice’, but ultimately, they are all wolves dressed in different outfits. Underneath their exterior, they all share the same assumptions:

• Training is planned in advance on the assumption that time frames for the maximisation of each quality have been pre-determined and predictable

• That athletes will adapt predictably to the same external training stimulus

• That training can be effectively predicted in advance.

In part one of this series, the flaws in TDTP were discussed at length, shedding light on some of the historical contexts that gave rise to its popularity despite its obvious limitations in practice. However, the superiority of periodisation, compared to non-progressive training programs, is compelling. So how do we rationalise its superiority in the research against its limitations in practice? To answer that, we’ll need to look at the research, itself.

When conducting periodisation research, the hypothesis will generally stem around a question like this:

“Will this pre-planned training (variable) have better outcomes than if we just keep training fixed (the control) for say, a 12-week duration?” 

Because of the extensive constraints, hurdles and limitations involved with conducting research, studies into periodisation don’t actually observe what we’d hope for; they don’t compare pre-planned training with emergent training (both forms of variation to the training stimulus), instead, they compare pre-planned training with training of little, or no, variation. As John Kiely recognises: 

“What the studies have demonstrated is that variation is a critical aspect of effective training, not, that periodisation methodologies are an optimal means of providing variation.”

John Kiely

However, citing a lack of evidence is not sufficient to discredit traditional periodisation models given the logistical constraints of conducting controlled research. Rather, it poses the question: 

Are traditional periodisation models more effective than any other form of providing variation in training?

This question can then consider whether the mechanistic, reductionist and planned-in-advance approach of traditional periodisation is more effective than a more dynamic, emergent strategy for the planning and implementation of training.

While I have stressed this before, one of the fundamental presuppositions of traditional periodisation is the assertion that the outcomes that result from training are predictable. After reading part 1 of this series, you’re now probably aware of some of those limitations. Take, for example, the Heritage Family Study, discussed in John Kiely’s research. Across 120 separate publications which assessed individual responses to training interventions, it was concluded that despite similar (in some cases, identical) training interventions, individual responses varied significantly from the mean. Take, for example, changes to VO2 max; while the mean change was 19% following the intervention, 5% of participants demonstrated little to no change, while another 5% demonstrated a significant change in excess of 40%.

Standard Distribution, known as a Bell Curve

These outcomes reflect your traditional bell curve — close to the mean, the response to exercise appears to be similar, but at the extremities, it is markedly different. The same outcomes can be seen as a result of strength training — in one study of over 500 male and female participants, the results of 12 weeks of periodised training demonstrated an average improvement of 54%; while the actual range of difference was between 0% and 250%. This same trend can be seen in muscle cross-sectional area; changes ranged between -2% all the way through to 59%.

This is not an isolated occurrence. Whether you look at the research, or, in the practical domain where most of us operate, the response to exercise differs for each individual across any demographic — novice or elite, young or elderly, male and female, and everything in between. This assertion has been validated extensively in research — if a cohort of individuals is exposed to the same training stimulus, a portion of that group will respond positively (+A), while another group responds negatively (-A), and another again, not at all (/A). 

Once you factor for every other source of variation that influences the training response (discussed in part 1), the following ideas can be rationally deduced:

1. The patterns and data that emerge from scientific research on groups of individuals can be misleading when applied to the individual.

2. Given the variation of each individual to training, it’s unlikely there is such a thing as a best-practice approach to training that follows predictable time frames, loading schemes and exercise progressions.

3. Each individual will respond differently to the same external training stimulus.

4. Within an individual, the same external training stimulus, will have markedly different outcomes based on their transient psychosocial and physiological state (Allostatic load).

Given these observations aren’t by any means, revolutionary, what practical inferences does this have? Enter the new paradigm of Integrated Periodisation — the interplay between the utility of TDTP and Bottoms Up Training Planning (BUTP) that we have adapted from the work of Mladen Jovanovic. In the integrated planning model, we extract what is useful from both, while simultaneously avoiding their inherent downsides, and letting emerging outcomes dictate decisions at a micro-, meso- and macrocycle level.

At the lowest resolution of interpretation, Integrated Periodisation uses frameworks, models for training, and heuristics to make more effective decisions within a domain of uncertainty like sports and competition. It collects data on the front end to ensure we meet the athlete where they are (not where we want them to be) and designs a minimally viable program — what we call a skeleton program — that is expounded upon over time as the adaptations and outcomes emerge for each individual athlete. Rather than progressing an athlete’s sports training in accordance with pre-planned blocks of training, sets and reps schemes — most commonly seen in TDTP — we let the emergent outcomes govern the progress of the individual in accordance with building their program from the bottom, up. 

Not only will this likely result in better outcomes for each individual, but it also saves you from falling prey to a sunk cost fallacy: 

Individuals commit the sunk cost fallacy when they continue a behaviour or endeavour as a result of previously invested resources (time, money or effort).

Arkes and Blumer (1985)

 We see this all the time with strength coaches; the more effort you put into your precise TDTP at the start of your training cycle, the more married you are to it, and the more you’re unconsciously going to be biased toward its implementation, regardless of the outcomes that emerge for athletes at the level of the individual (rather than, the collective)..

Integrated Planning in contrast, gives you the permission to utilise models, heuristics and frameworks (some of which are elements of TDTP) to make fast decisions in uncertainty and ultimately, provide more honest and less dogmatic sports planning for your athletes.

The rest of part 2 will take you through what Integrated Planning looks like from a high-level perspective, and the three step process we use to get our athletes going in the gym. 

In part 3, we’ll explore each level in greater detail and discuss the models, heuristics and frameworks we take into consideration to make better use of emerging outcomes.

An Introduction To Integrated Sports Planning

Step 1: Determining The Qualities Of Fitness You Want To Pursue

Within the context of TDTP, the qualities of fitness for a given time period are pre-determined; restoration and hypertrophy (accumulation) is pursued during the off-season, strength (intensification) is pursued during pre-season and power/speed (realisation) in the weeks leading to competition. This approach, pioneered by Matveyev (one of our Soviet forefathers), assumes that each athlete for a given period in time, needs to follow this mechanistic structure to maximise their upside and minimise their downside so they can perform at their best in competition. While built upon good intentions, it is incredibly naive, and for those that still defend it, ultimately a form of cognitive arrogance — it is simply impossible to predict not only where an athlete will be into the future, but also, what qualities will need to be pursued for them to compete to the best of their ability.

As such, there needs to be a more effective way to determine the qualities of fitness one wants to pursue. For that, you need data.

Before you can define goals or plan for training, we need to establish where the athlete is, not, where we assume them to be.

The collection of data — via assessment, screening, and testing — is a rabbit-hole too deep to traverse today. For now, we’ll explore the qualities of fitness we look for at Athletes Authority, and in the next part of this series, dive into this more thoroughly.

1. Movement Quality.

The first domain of exploration for each of our athletes that come into the facility begins with movement quality. For the most part, movement quality is an accepted domain of fitness for strength coaches, but, it has received criticism for those inferring that it can illuminate an athletes risk of injury. This is bollocks. Not only is this not demonstrated in the evidence, but, reducing injury risk to a score of 0-1-2-3 as most commonly used by the FMS, is once again, naive and arrogant. We simply cannot predict the risk of future injury on the basis of a subjective interpretation of how well an athlete moves.

Rather, movement quality helps us identify what we can consider, structural and motor control limitations within each athlete, and help provide a skeleton for decision making in regards to mobility drills and exercise-programming considerations. 

For this, we use our own bastardisation of the FMS and SFMA, by Gray Cook and colleagues. We assess for Overhead Squat, Hurdle Step, Shoulder Mobility and Active Straight Leg Raise with each assessment having breakout tests to determine structural or motor control/neurological interference.

2) Strength 

It is possible that you have heard that ‘training is testing, and testing is training.’ This maxim appears to be true. Whether you choose to test for strength or not is up to you because any capable strength coach can quickly assess strength competencies during the prescription of training.

With that said, we choose to, mostly because it’s helpful to demonstrate an athlete’s ability express force, in comparison to how well they express it, quickly. This is known as a DSI — and compares their maximal force expression (either through a back squat or in our case, a mid-thigh-pull) to their rate of force expression (usually through a counter-movement jump). 

With this information, we can become aware of their ‘strength balance’ and can answer the following question: is the athlete expressing all of their available potential (resulting in a ratio of >1), or, are they stronger than they are, powerful (resulting in a ratio of <1). This can help infer the quality of training to pursue.

3) Power & Speed

This is the language of athletes. When you ask an athlete what they want; they don’t ask for a bigger back squat, they ask to feel faster and more explosive. As long as an athlete has a reasonable strength reserve (and isn’t actually just horrendously weak and slow), the pursuit of power and speed usually equates to incredible buy in from the athlete. We think of power and speed across the vertical and horizontal vectors for the lower body, and primarily, the horizontal vector for upper body. This is measured via a battery of tests that can include: 

• Triple hop for distance

• Squat Jump & CMJ

• 40m sprint speed

• Bench Throw/Pull

Naturally, a lot of these measures are reliant on objective testing equipment — like force plates (in the very least contact mats), timing gates and Linear Positions Transducers for velocity. We’ll discuss the minimum viable investment for this in the next part of this series.

4) Injury Risk

Injury prevention became a buzzword about eight years ago but for an unknown reason, this notion was never dispelled despite injury prevention being a fallacy. The only way to prevent injury, is to do nothing at all, all the time. In reality, the interventions that are applied under the guise of injury prevention are much more honestly described as injury mitigation, simply because sport is risky and chaotic, and you can never really predict what will happen. The same is true for the intervention itself — there is a risk that during the act of training for injury mitigation, you get injured. It’s a tough pill to swallow, but it is true nonetheless.

With that said, there has been emerging research to indicate that low or suppressed levels of some physiological markers — like eccentric strength, isometric force and peak force output — may correlate to injury risk and indicate an athlete’s readiness to return to sport. We use the NordBord and ForceDecks (but could easily be extended to groins, quads, hip flexors, calfs etc) to collect this data and determine if relative to bodyweight, the athlete is meeting minimum requirements for their sport. Naturally, a court based athlete is probably going to need less eccentric hamstring strength given the unlikely exposure to max velocity, but will still need Groin/Hip, Quad and Calf/Achilles examined with a range of isometric and eccentric tests. Similarly, a Baseball Pitcher may not have much need to examine groin strength, but thoroughoughly benefit from better understanding peak force output during the Athletic Shoulder Test (ASH). 

Combining these tests alongside the demands of the sport, we can then apply an intervention appropriate for improving these outcomes and mitigating the risk. 

5) Fitness

The fifth and final quality is Fitness, itself. Fitness in this context explores the energy system abilities of the athlete relative to their sport. Alactic athletes (sprinters/jumpers/throwers etc) complete the Wingate Test, and field sport athletes complete a VO2 Max Test. From here, we reference normative data and get a gauge of where they sit accordingly.

Step 2: Orienting Training Around The Goal & Vision

Now that we know where the athlete is, the next step is orientating their training around their goal and vision. This isn’t what you think it is.

Goal setting historically, is defining a goal that is specific, measurable, attainable, relevant and timely; and has since been coined SMART goals. However, that is rarely the goal or vision that our athletes have in mind; the goals are far more complex. While they are perhaps specific like ‘Play For Australia’, they are certainly not timely, relevant, or easily attainable. Because we do not work with weight loss or muscle-gain clients with single dimensional goals like: ‘lose x kilos of body fat’, SMART goal frameworks are fundamentally, useless.

Complex goals like the example above are best achieved when you aren’t trying to achieve it directly. There is simply too much distance, too much uncertainty, and too much room for change to orientate training towards this objective. Going directly for the goal provides no route, map, or course of action to make the goal worthwhile.

As a result, we apply a simple rule: Complex goals require complex solutions and complex solutions only emerge over time.

Instead, we orientate our training toward indirect objectives — like improving relevant qualities of fitness, bolstering a more antifragile mindset (one which improves through setbacks), developing good habits and fine-tuning behaviours that are within an athlete’s locus of control. While these objectives are by no means direct, in fact, in John Key’s estimation in his book Obliquity, they are in fact the opposite — utterly indirect — they provide the only sane way to navigate toward a complex goal. It is the only viable course of action given the sheer uncertainty of the pursuit.

During this step what is considered ‘phase’ planning, we decide on the first course of action, knowing full well that we will have to adapt as challenges emerge.

Step 3: Designing A Skeleton Program

With the qualities of fitness we wish to pursue in mind and knowing the first course of action, we can then design a skeleton program. Factoring for considerations such as age, training history, confidence, injury status and the sports season — training can be applied in digestible blocks that strike a balance between structure and adaptability. Key lifts and objectives can be planned for and enough room left to expand on areas of interest as we begin to work with the athlete and learn more about them. While our performance testing may determine the need for an increase in strength, the actual prescription of exercises is still unknown — we won’t know what knee dominant movement will look the best until we suck it and see. While most of the time, we could be bang on with our prediction of a back squat for example, every once and a while an individual will respond better to a safety bar squat because they had a wrist issue they failed to mention (and we failed to pick up) during our assessment processes.

This is why we don’t bother with intricate and precise training plans in the first instance while we still no relatively little about the athlete’s presentation. Rather, designing a skeleton program provides us with the flexibility to iterate as we go as the outcomes emerge, and, provide the opportunity to increase athlete autonomy; by seeing the program built out and iterated over time, the athlete is a part of the process.

After this induction process – usually the first 4-8 weeks — this skeleton program can be beefed up — it can be evolved into a more complete human model, with muscle, organs and skin. Once you know more about the athlete, exercise prescription can be more targeted and everything can be a little more precise. This is where models often used in TDTP have utility — we’ll dive into what this looks like in more detail in the final instalment of this series. 

That will be it for Part 2 — that’s more than enough information for one read. In Part 3, we’ll be more descriptive with modelling and provide practical heuristics for the programming for sports under the new paradigm of Integrated Planning. 

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