Exodus Strength

(Draft for @LoudMuffin - Not sure if this is succinct enough...)

When we were kids in middle school and high school we were introduced to a simple mathematical equation:

[equation]y = mx + b[/equation]

We learned how to plot this line, find intercepts, and whatever other fun stuff. We learn to use this as a model for things like pricing, distance, and even use it with other linear functions to find simple intersects that optimize the system.

If/when we got to a first course in calculus, we learn to differentiate this equation, finding that the differential is a constant. This tells us that the underlying function never varies in growth.

We all know that training isn't like this. Not at all. But yet many of us have used linear programming and often even recommend it to certain people, like novice trainees. But why?

Well, just like in the applications of a linear model in school, we come to find that this is a simplified view of reality. Like those old questions about driving from point A to B at some constant rate, we know that this doesn't usually happen in reality, especially with other people/traffic (i.e. variables) involved. Yet the linear progression for training exists. That's because it's an approximation deemed suitable for beginners. Beginners/novices have so many variables at play that trying to figure them all out upfront is usually a waste of resources. Start them off with an easy weight, and suggest they add a little weight each session until things stall. It's easy! However, it has a short shelf life.

Novice trainees will acquire skills at different rates. In reality, none of the movements of the slower barbell lifts is overly complex, but there is a spectrum of skill acquisition. Those who acquire proficiency of skill quickly will feel stronger faster. But this doesn't necessarily correlate with the trainee being prone to hypertrophy or maximal display of strength. These could very well be independent variables. The opposite can happen also: someone very prone to getting strong is simply awkward with moving. This could delay progress. Nonetheless, each novice will likely see a somewhat different length of effective linear progression, simply because the model is wrong. And that's okay.

What is being seen now is a separation of variables with respect to training. This is because the data set of people lifting with barbells has become sufficiently large to tease out responses.

The data seem to say that skill acquisition in a lift is likely a short term event in comparison. Sure, form tweaks can be found and made, and those likely create a fairly short-lived response. Cycling accessories is useful to keep the mind fresh and engaged. Even this is an individualistic response because some people are better focused than others.

Hypertrophy is the long-term adaptation most people look to make. This is a slow adaptation and requires increasing training volume of sufficent but recoverable volume to acquire. Data coming available shows that this can be done at fairly submaximal (~50-70%) levels provided the dosing is large enough. Even more interesting is that the length of the sets doesn't seem to matter as much as total reps per session. In other words, working light weights for many sets of few reps can allow for a lot of dose without the per session fatigue cost and deviation in form.

Maximal display of strength is something of interest, but likely dependent on other factors. However, it needs to be trained with some frequency in order to keep the skill fresh, but without impairing training consistency. For most trainees, these displays of maximal strength need to be spaced further and further apart. This isn't a new concept.

While the LP is touted as simple and effective, it's merely effective at being wrong and pushing the trainee to failure modes in the quickest order. Remember, the slope of that function is constant. Some will say, "Of course, we know that, and strength gain is logarithmic/asymptotic!" I'd argue that those are overly simplistic as well. There's likely variance over several windows of time with regards to strength gains that these models would overshoot. In reality, we see people hit a failure mode in lifts even with fractional plates and microloading. When you've shifted training goals to the right of the expected response, probability of success will surely fall. Moreover, the framework most training is built upon that of constantly moving the (e)1RM upwards.

Yet, when you shift training goals to the left (below) the expected response, probability of success increases, but at a risk of hypertrophy. To offset that, you'd simply increase the volume of training. This is a common theme detailed in Data Driven Strength publications and is also found in developments within RTS/BBM programming.

Addiing to this are other external factors like other sources of physical activity, nutrition, rest, stress, motivation, injury, etc. and we know that "simple" programming is ill-equipped to provide an effective training model. Relying on feedback in the loop (e.g. RPE, RIR), fatigue between sets, session video, and other data becomes needed to effectively train.

Overall, LP "works" as in it provides a simple approach to training that has adequate goals and sufficent skill building to bring a trainee to the failure point (maximum display of strength in the situation at hand) in a planned manner. However, none of this makes it the "best" method or even a "correct" method for training. Rather, like most linear models applied to complex systems, it's easy to implement, but undeniably imprecise and wholly inaccurate.