Emergent Strategies for Strength Development

Abstract

In the realm of strength training, I often hear the question “What is the best training block?” There’s often an implicit clause in that question: “What is the best training program (for me, right now)?” At its heart, this question is untennable. Optimizing individual training response requires optimizing the use of individual data, and individual data takes time to collect (making the “right now” part of the question impossible). Mike Tuscherer has developed a training paradigm called “emerging strategies”, and it seeks to answer a different question: “How can I structure a training block and use that block’s data in order to make sufficient progress during said block, gather insights that will lead to sufficient progress in a future block, or both?” It is a system of training that requires training to be closely monitored by keeping track of a few relevant variables. Using this paradigm, I put 30 pounds on my bench press (310 to 340 lbs), 50 pounds on my squat (405 to 455), and about 65 pounds on my deadlift (430 to 495) after nearly a year of stagnation.

Introduction: motivating the problem

In 2018, my squat, bench press, and deadlift one rep maxes were stagnating. I figured it was time to start planning my training more intelligently. I was using good “programming principles” beforehand, but these principles fail to capture individual variability to a training stimulus. They are a good starting point, but a good training regimen should be based on solid principles and an individual’s prior training data. My methodology is described futher below. Traditional statistical approaches didn’t suffice in this project for the following reasons: (1) It takes far too long to obtain the data necessary to find optimal training parameters for an individual; (2) I wasn’t forecasting time series trends, because I experimentally altered the process which governs that trend;(3) exposing myself to various experimental blocks wouldn’t work, because strength increased under some experimental block at one time does not increase my confidence that it will work for more than a few exposures in a row.

Methodology: An Emerging Strategy

To overcome the aformentioned problems, I decided to use adopt a paradigm similar to one constructed by Mike Tuscherer known as “emerging strategies”. (If you’re interested, here’s his lecture on the topic). The plan to gain strength is as follows:

• 1.) Create a microcycle of training. My microcycle is one week long. It contains a training regimen that will be repeated multiple times. For example, if I perform the lowbar squat on Monday for 5x6@8 (5 sets of 6 reps at RPE8), highbar squat on Wednesday for 4x4@7, and the front squat on Friday for 4x8@8, I will repeat that protocol each week.
• 2.) Establish a goal metric. For myself, these metrics are the e1rm on the squat, bench press and deadlift.
• 3.) Establish a way to monitor these metrics. In this case, the e1rm were monitored by performing one set of one rep at RPE 8 on the target exercises one time per week. This roughly corresponds to 92% of an e1rm.
• 4.) Repeat the microcycle until the goal metric stagnates for n weeks in a row, I selected 2.
• 5.) If the program achieved the desired outcome, repeat the protocol. If not, then repeat steps 1-5. When reparameterizing the microcycle and perhaps the goal metric, use prior knowledge and the information gained in the microcycle. This is a vague description, because I’m not quite sure how to formalize this yet. In the future, I really want to update the parameters using a bayesian model, where the prior probability is the probability of obtaining sufficient results from a sequence of microcycles, and the updated parameter estimates are the parameters described in step one.

My implementation of emerging strategies

Let’s look at how I parameterized my microcycle in the first block… I wanted to squat 13 sets per week, deadlift 8 sets per week , and bench press 17 sets per week. The average intensity was also fairly high, because my reps per ranged from 2 to 7.

My e1rm stagnated around week 4 on all exercises. I decreases the volume on the final weeks in an attempt to decay any fatigue that could be masking strength increase.

 d1<- ggplot(weeklyStatsDB1Long %>% filter(metric %in% c("e1rmZ","avgIntZ","setsZ")),aes(x=week,y=value)) + geom_line(aes(color = metric)) + geom_point(aes(color=metric)) +facet_wrap(~exercise,scales = "free_y") + ggtitle("Deadlift")
#plotly::ggplotly(d1)
d1

Potential insights:

• 1.) natural “peak” occurs approximately 4-5 weeks into the block across these lifts.

• 2.) The stall/decrease may have occurred for one of two reasons (most likely)

  • too much volume –> need to drop volume for a week or two and recover. (I tried this, and it didn’t work)

  • too little volume –> not enough of a stimulus, increase volume. (Since option 1 didn’t work, I tried this in the subsequent block)

Leading into the next block, I decided to increase the sets/week and reps/week. I also decreased the average intensity accordingly. In the 3rd block, I tried to keep most variables similar to the second except I “front loaded” volume on the squat (performed most of the high volume weeks in the first portion of the block). I also increased the bench volume. Let’s see the results of adjusting the training parameters in a meta-block review. I’ll examine the only the squat for the sake of brevity…

Comparing block results

Now, I want to really look at the differences in training parameters as well as target metrics across 4 blocks. To keep it brief, I’m just going to focus on the squat, though I will show the change in e1rm for all lifts.

We can see that blocks 2 and 3 were the most successful based on the charts below.

So, what changed? Let’s look at the reps per week, sets per week, and average intensity per week. It’s clear that moderate intensity and higher volume worked better than high intensity with lower volumes.

Insights from comparing blocks 1 to 2 and 3…

• Block 1 had insufficient volume and there was no need to keep intensity so high.

Insights from comparing blocks 2 and 3…

• Both blocks were very successful (both projected e1rms increase by ~30 lbs).
• Block 3 was successful AND predictable in that I hit the highest e1rm of the program on the exact day I planned to do so. The “peak” was precise.
• Sets per week were fairly similar (just shift block 3 two weeks to the right and they line up almost perfectly… this was by design)
• intensity stayed above its average for most weeks in block 2, while block 3 stayed below its average for most weeks.
• I managed to successfully slightly increase volume and intensity over the whole of the block while recovering adequately in block 3.

Going forward…

• Be patient, wait until the last few weeks and then spike intensity sharply.
• It works well when volume & reps remain higher “most the time”, and intensity remains lower until it’s time for the final peak. This might be a hint to stay at lower intensity ranges for a longer period of time in the future, or attempt to drop the intensity even further and have it climb from 72 percent unstead of from 77 percent.
• It’s okay to keep squatting into the “max-out” week. I actually managed to perform better on the final week with a less severe drop in volume.
• 1 experiment: try to keep the same avg intensity,reps, and sets per week, but instead of front-loading volume (high week to week variance, low within the week variance), try to increase the within week variance and decrease week to week variance.