Strength Training Manual

The Agile Periodization Approach

Volume Two

Mladen Jovanović

Published by:

Complementary Training

Belgrade, Serbia

2019

For information: www.complementarytraining.net

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Jovanović, M.
Strength Training Manual. The Agile Periodization Approach. Volume Two

ISBN: 978-86-900803-1-1
978-86-900803-3-5 (Volume Two)

Copyright © 2019 Mladen Jovanović

Cover design by Ricardo Marino

Cover image used under license from Shutterstock.com

E-Book design by Goran Smiljanić

All rights reserved. This book or any portion thereof may not be reproduced or used in
any manner whatsoever without the express written permission of the author except
for the use of brief quotations in a book review.

Published in Belgrade, Serbia

First E-Book Edition

Complementary Training
Website: www.complementarytraining.net

3
 Table of Contents
Preface to the Volume Two . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
5a Planning. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
Training dose . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
Intensity, Volume and Density . . . . . . . . . . . . . . . . . . . . . 10
Within quality saturation-distribution . . . . . . . . . . . . . . . . . 11
“Saturate and Separate” heuristic and “complex/parallel and
unidirectional” continuum. . . . . . . . . . . . . . . . . . . . . . . 14
Phase Potentiation is bullshit - but sequencing might not be . . . . . .20
Improve - Extend . . . . . . . . . . . . . . . . . . . . . . . . . . .22
Extensive - Intensive and the birth of linear periodization/progression. 23
The “Other” category . . . . . . . . . . . . . . . . . . . . . . . . .26
External vs Internal and subjective ratings of dose . . . . . . . . . . .30
External-Objective Dose . . . . . . . . . . . . . . . . . . . . . 31
Internal-Objective Dose . . . . . . . . . . . . . . . . . . . . . 31
External-Subjective . . . . . . . . . . . . . . . . . . . . . . . 31
Internal-Subjective . . . . . . . . . . . . . . . . . . . . . . . 31
Training dose summary . . . . . . . . . . . . . . . . . . . . . . . 40
Dose -> Response . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
Simple pharmacological model. . . . . . . . . . . . . . . . . . . . . 41
Dose zones. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .42
Upside and Downside effects (responses). . . . . . . . . . . . . . . .43
3F Model: Fitness, Fatigue and Facilitation . . . . . . . . . . . . . . .45
Circular model . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
External-Objective . . . . . . . . . . . . . . . . . . . . . . 49
External-Subjective . . . . . . . . . . . . . . . . . . . . . . .50
Internal-Objective . . . . . . . . . . . . . . . . . . . . . . . .50
Internal-Subjective . . . . . . . . . . . . . . . . . . . . . . .50

4
 Hero’s Journey and Push and Pull concepts . . . . . . . . . . . . . . . . . . 53
Push and Pull . . . . . . . . . . . . . . . . . . . . . . . . . . . . .56
Three purposes of monitoring training dose . . . . . . . . . . . . . 60
Decision matrix . . . . . . . . . . . . . . . . . . . . . . . . . . . .62
Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .63
5b Planning (continued) . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64
Set and Rep Schemes: The Basis• . . . . . . . . . . . . . . . . . . . . . . 64
Anatomy of a set and rep scheme . . . . . . . . . . . . . . . . . . . .65
Warm-up sets . . . . . . . . . . . . . . . . . . . . . . . . . .65
Pre-work sets . . . . . . . . . . . . . . . . . . . . . . . . . 68
Main or Working sets . . . . . . . . . . . . . . . . . . . . . .70
After sets . . . . . . . . . . . . . . . . . . . . . . . . . . . .70
Organization of exercises in the workout• . . . . . . . . . . . . . . . . . .74
Blocked approach . . . . . . . . . . . . . . . . . . . . . . . . . . .75
Super-sets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .75
Hub and Spoke . . . . . . . . . . . . . . . . . . . . . . . . . . . . .82
Combinations . . . . . . . . . . . . . . . . . . . . . . . . . . . . .83
Classification of Set and Rep schemes . . . . . . . . . . . . . . . . . . . 84
Classification based on Objectives or Qualities . . . . . . . . . . . . 84
Classification based on Prescriptiveness . . . . . . . . . . . . . . . .87
Classification based on Volume . . . . . . . . . . . . . . . . . . . .88
Classification based on Toughness . . . . . . . . . . . . . . . . . . .88
Classification based on Methodology . . . . . . . . . . . . . . . . . 89
Decoupling Progressive Overload from Adaptation . . . . . . . . . . . . . 90
Progression vs Variation•. . . . . . . . . . . . . . . . . . . . . . . . . . 94
Vertical and Horizontal planning . . . . . . . . . . . . . . . . . . . . . . .95
Horizontal planning . . . . . . . . . . . . . . . . . . . . . . . . . 96
Vertical Planning . . . . . . . . . . . . . . . . . . . . . . . . . . 100
Perc Drop approach . . . . . . . . . . . . . . . . . . . . . . 102
RIR Inc approach . . . . . . . . . . . . . . . . . . . . . . . 104
Difference between Perc Drop and RIR Inc methods . . . . . . 105
Adjusting for Lower Body lifts . . . . . . . . . . . . . . . . . 106
Adjusting using individualized Load-Exertion table . . . . . . 106
Vertical planning methods . . . . . . . . . . . . . . . . . . . 108

5
 Constant . . . . . . . . . . . . . . . . . . . . . . . . 111
Linear . . . . . . . . . . . . . . . . . . . . . . . . . 112
Reverse Linear . . . . . . . . . . . . . . . . . . . . . 112
Block . . . . . . . . . . . . . . . . . . . . . . . . . . 112
Block Variant . . . . . . . . . . . . . . . . . . . . . . 112
Rep Accumulation . . . . . . . . . . . . . . . . . . . 112
Set Accumulation . . . . . . . . . . . . . . . . . . . . 113
Undulating . . . . . . . . . . . . . . . . . . . . . . . 113
Reverse Undulating . . . . . . . . . . . . . . . . . . . 113
Volume-Intensity . . . . . . . . . . . . . . . . . . . 113
Mladen’s Methodological System of classifying Set and Rep schemes . . . . 113
Plateau method . . . . . . . . . . . . . . . . . . . . . . . . . . . 116
Step method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118
Reverse Step method . . . . . . . . . . . . . . . . . . . . . . . . . 121
Ascending Wave method . . . . . . . . . . . . . . . . . . . . . . . 123
Descending Wave method . . . . . . . . . . . . . . . . . . . . . . 127
Ascending Ladder method . . . . . . . . . . . . . . . . . . . . . . 129
Descending Ladder method . . . . . . . . . . . . . . . . . . . . . 131
Traditional Pyramid method . . . . . . . . . . . . . . . . . . . . . 133
Reverse Pyramid method. . . . . . . . . . . . . . . . . . . . . . . 135
Light-Heavy method. . . . . . . . . . . . . . . . . . . . . . . . . 137
Cluster method . . . . . . . . . . . . . . . . . . . . . . . . . . . 139
Rest-Pause method . . . . . . . . . . . . . . . . . . . . . . . . . 143
Cluster Wave method . . . . . . . . . . . . . . . . . . . . . . . . 144
Summary of the set and rep schemes . . . . . . . . . . . . . . . . . 146
Horizontal and Vertical planning cycles (Sprints and Phases)• . . . . . . . 148
Divisible and Indivisible Combinatorics. . . . . . . . . . . . . . . . . . . 150
Applying horizontal and vertical planning to a training slot . . . . . . . . . 153
Robust vs Optimal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 160
Barbell Strategy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 162
Microdosing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 165
Don’t Break the Chain . . . . . . . . . . . . . . . . . . . . . . . . . . . 166
Barbell Chain and Randomization . . . . . . . . . . . . . . . . . . 168
Markov Chain and Probabilistic Programming . . . . . . . . . . . . . . . 170

6
 Advanced uses of Markov Chains . . . . . . . . . . . . . . . . . . . 176
Multi-level Markov Chain and dynamic transition matrix . . . 176
Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 179
6 Review and Retrospective . . . . . . . . . . . . . . . . . . . . . . . . . . . 189
Bias-Variance Tradeoff. . . . . . . . . . . . . . . . . . . . . . . . . . . 181
Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 190
Dose - Response models . . . . . . . . . . . . . . . . . . . . . . . . . . 192
Conclusion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 203
Review and Retrospective in different time frames . . . . . . . . . . . . . 204
Review and Retrospective at the Repetition Level . . . . . . . . . . . 207
Review and Retrospective at the Set Level . . . . . . . . . . . . . . 209
Interlude: On Individualization . . . . . . . . . . . . . . . . . . . 210
Back to the Set Level . . . . . . . . . . . . . . . . . . . . . . . . . 214
Fatigue Percentages . . . . . . . . . . . . . . . . . . . . . . 216
Daily Auto-Regulatory Progressive Resistance Exercise . . . . 223
Review and Retrospective at the Exercise Level . . . . . . . . . . . . 229
Review and Retrospective at the Session/Day Level . . . . . . . . . . 230
Dan Pfaff’s 3-Day Rollover program . . . . . . . . . . . . . . 235
Review and Retrospective at the Sprint Level . . . . . . . . . . . . . 236
Review and Retrospective at the Phase Level . . . . . . . . . . . . . 237
Phase to Phase Variation . . . . . . . . . . . . . . . . . . . . 235
Updating the planning 1RM . . . . . . . . . . . . . . . . . . 239
Retesting 1RM . . . . . . . . . . . . . . . . . . . . . 239
Plus sets . . . . . . . . . . . . . . . . . . . . . . . . 240
Adjusting 1RM . . . . . . . . . . . . . . . . . . . . . 241
Adjusting percentages in the next phase . . . . . . . . 242
Doing nothing . . . . . . . . . . . . . . . . . . . . . 244
Decreasing 1RM for 10-20% . . . . . . . . . . . . . . 244
Change in Phase Objectives . . . . . . . . . . . . . . . . . . 245
Review and Retrospective at the Release Level . . . . . . . . . . . . 250
Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 251
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 252
About. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 263

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STRENGTH TRAINING MANUAL Volume Two

Preface to the Volume Two

When I started writing the Strength Training Manual, I wanted it to be a simple
and short book with heuristics and reference tables. As I began to write, I soon realized
that the readers will have hard time understanding how to actually apply those heuristics
and tables, as well as understand the whys behind them. Additionally, writing is not a
simple act of dumping material on paper for me, but rather an act of exploration and
discovery. Therefore, as I wrote, new things emerged and I wanted to play with them,
attack them from multiple perspectives to see how robust they are. In the end, this made
the Strength Training Manual much larger and much slower to write than I originally
intended.

The reasons why the Strength Training Manual e-book comes in volumes are
as follows. First, I can split it in chunks, which, for those who embark on any writing
adventure, is much more manageable. Second, I wanted this to be available to the
readers as soon as possible, so that I can collect the feedback and improve the text for the
potential paperback/hardback edition. Third, reading 600-page e-book is much harder
than reading 200-something e-book. Fourth, the profit. E-book version of the Strength
Training Manual published in volumes is available for free for the Complementary
Training members, which makes it an additional benefit of the membership. In a
nutshell, publishing in volumes seemed like a good idea and a solution. Only time will
tell if I was right or wrong.

In this Volume Two, I am continuing with more practical application compared

to more “philosophical” Volume One. Chapter 5a discusses topics of dose and response,
while Chapter 5b continues with more practical application and reviews multiple set
and rep schemes. Chapter 6 covers Review and Retrospective element of the Agile
Periodization.

As always, I am looking forward to your critiques and feedback. Please do not

hesitate to contact me if you have any questions or spot any kind of bullshit.

Mladen Jovanović

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 MLADEN JOVANOVIĆ

5a Planning
Before jumping into deep waters of strength planning, it is important to cover a
few “Small World” models of the training dose and training response that are lurking
behind all of our planning decisions.

Training dose
To make it distinctive from the term training load, which I have used to refer to
the weight on the barbell (see Figure 4.1), I will use the term training dose to indicate
a construct that represent some type of stress and/or stimuli that athlete experiences
when training for strength (or training in general). That being said, it is really hard to
have a precise definition of a training dose and to quantify it. It is particularly short
sighted to represent training dose with a single metric. Thus, I will represent pluralist
viewpoint by using multiple “Small World” models as potential tools.

Figure 5.1 contains hypothetical components of the training dose construct (keep
in mind that this is also a “Small World” representation).

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STRENGTH TRAINING MANUAL Volume Two

Intensity

Volume
Distributed -
Saturated
Density
Dose
Complex -
Unidirec�onal

Specificity -
Variability

Interac�ons
Other
Expecta�ons

Pleasure -
Displeasure
Figure 5.1. Training dose components

Intensity, Volume and Density

Simply, Intensity as a component of training dose represents quality, while
Volume represents quantity. As explained in Chapter 4, intensity represents a complex
interaction of (1) Load, (2) Exertion, and (3) Intent. For example, lifting 100kg
(here 100kg could be considered “intensity”) for 5 reps with maximal intent versus
submaximal intent would represent qualitatively different training intensity and thus
qualitatively different training dose. The same can be said for lifting 100kg for 3 with
3 reps in reserve, versus 1 rep in reserve. Intensity is usually represented with the
average relative intensity metric (aRI) introduced in Chapter 4. Using relative intensity
allows for comparison between exercises and individuals with different 1RM. For intra-
individual monitoring of intensity, one can use average intensity (aI), which represents
absolute load (in kilograms or pounds).

Volume represents quantity or amount of work done. As explained in Chapter 4,

volume is usually expressed through number of lifts (NL), tonnage, impulse and INOL
metric (as well as with novel metrics such as exertion load). In an ideal world, volume

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 MLADEN JOVANOVIĆ

would be represented with work done in Joules, but mentioned metrics are satisficing
proxies.

Intensity and volume can be combined by using “zoning”, or providing volume

metrics per intensity zone. For example, NL in 70-80%, 80-90% and 90%+ 1RM zones.
But, a workout consisting of 3 x 10 @70% will have the same volume indices (and volume
distribution) as workout consisting of 10 x 3 @70%, although we know experientially
that these are qualitatively different. Chapter 4 introduced the novel “exertion load”
(XL) metric which gives non-linear weighting of the reps depending on their proximity
to failure (using RIR).

Volume metrics tend to use intensity cut-off point (e.g., not counting reps below
certain %1RM) , which is usually around 50% of 1RM for grinding lifts. This depends if
one uses dynamic effort method and wants to keep counting reps under 50% 1RM. It is
thus important to clarify what this threshold is. For example, if someone says that the
weekly NL for bench press was 50 lifts, it is natural to ask “What counts as a lift?” or
in other words asking about the intensity cut-off point. The same is true for any other
volume metric.

What about finishing 5x5 @80% workout in 10 minutes versus 15 or 20 minutes?

These would have the same volume and intensity, but they would have different density.
Mathematically or physically expressed, density can be considered a proxy to average
power, since it is work done (or proxy metric to work done) divided by the time it takes to
complete the work. As mentioned in Chapter 4, density metrics are not really common,
but they could be particularly used in the Mongoose Persistence methods (e.g., muscular
endurance, power endurance). The concept of density is also crucial element in Charles
Staley’s Escalatory Density Training (ESD) method (Staley, 2005).

Within quality saturation-distribution

Besides the above use, density is an interesting component of a training dose,
particularly because it depends on the time frame, and thus expands into the concept of
distribution of the training dose as well as frequency of training sessions (e.g., among how
many training sessions a certain training dose is distributed). For example, in a given
session one can distribute all particular lifts of one exercise into one time block (e.g.,
5x5 @75% of Squats), or combine multiple exercises in a superset or a circuit fashion.
In motor learning and skill acquisition, this is termed blocked practice versus random
practice (Davids, Button & Bennett, 2008; Renshaw, Davids & Savelsbergh, 2012; Chow et al.,
2016; Farrow & Robertson, 2017). Blocked practice involves solving one particular task or

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STRENGTH TRAINING MANUAL Volume Two

performing single particular skill for blocked period of time. Random practice refers to
randomly solving or performing multiple skills and tasks. It has been shown that from
skill retention perspective1, random practice is better. This could be a useful tip when
coaching someone the basic skills of lifting (i.e., some type of superset, quad-set or
even circuit could be better from motor learning perspective).

Across multiple days, training dose aimed at the given quality (or method) can
be distributed or saturated (see Figure 5.2). I like to refer to this as distribution-saturation
continuum or complementary pair.

Total NL 40.00
Distributed

NL per day 5.71

SD NL 0.00

CV 0%
Monotony Inf
6 6 6 6 6 6 6 Strain Inf
Monday Tuesday Wednesday Thursday Friday Saturday Sunday Gini 0.00

Total NL 40.00
NL per day 5.71
SD NL 5.35

CV 94%
Monotony 1.07
10 10 10 10
Strain 42.76
Monday Tuesday Wednesday Thursday Friday Saturday Sunday Gini 0.50

Total NL 40.00
NL per day 5.71
SD NL 6.73

CV 118%
15 15 Monotony 0.85
5 5 Strain 33.98
Monday Tuesday Wednesday Thursday Friday Saturday Sunday Gini 0.67

Total NL 40.00
NL per day 5.71
SD NL 9.76

CV 171%
20 20
Monotony 0.59
Strain 23.42
Monday Tuesday Wednesday Thursday Friday Saturday Sunday Gini 0.83

Total NL 40.00
Saturated

NL per day 5.71

SD NL 15.12
40
CV 265%
Monotony 0.38
Strain 15.12
Monday Tuesday Wednesday Thursday Friday Saturday Sunday Gini 1.00

Figure 5.2. Saturated-Distributed continuum of distributing training dose (using NL metric)

of a single quality

1 Retention and performance needs to be differentiated. Good performance at training doesn’t necessarily
imply good retention, and vice versa. That is why sometimes one performs exercise perfectly in practice,
comes back a few days later and it looks like he has never done it before (Davids, Button & Bennett, 2008).

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STRENGTH TRAINING MANUAL Volume Two

Phase Potentiation
is bullshit - but sequencing might not be
Normal continuation of the “saturate and separate” reasoning is the fallacy
of following a particular sequence which is “optimal”. Figure 5.5 depicts common
sequences, usually referred to as phase potentiation (DeWeese et al., 2015a,b).

Phase #1 Phase #2 Phase #3 Phase #4

Quality/Method Quality/Method Quality/Method Quality/Method

#1 #2 #3 #4

Anatomic Maximal
Example #1 Hypetrophy Power
Adapta�on Strength

Strength Maximal
Example #2 Strength Speed Speed Strength
Endurance Strength

Double leg Single leg

Example #3 Landing skills Depth jumps
jumps jumps

Figure 5.5. Phase potentiation is bullshit. You do not need to follow predetermined
sequence of blocks as an ideology

The idea behind phase potentiation is that each block sets the stage and facilitates
the effects of the block to come. I do agree that this is true, but the real world training
is not a single sequence of phases, but sequence repeated multiple times over the years.
Thus, marginal improvements when following a single “optimal” sequence are lost or
negligible when sequence is repeated multiple times. It is thus stupid to force following
a particular sequence just for the sake of “phase potentiation” and marginal gains.
Besides, why would someone do, say hypertrophy phases if one doesn’t need to bulk
up? To potentiate the next maximum strength phase? It is stupid.

I had been following certain strength training phases as a young S&C coach in
soccer, but then I had few athletes missing for a week or two (either due to injury or
due to national training camp). This fucked up my sequence and I had a hard time
“reintroducing” them to the group training (for example, they missed max strength
phase, and had to jump to power phase with the rest of the group).

The “phase potentiation” proponents will bitch on me and argue that they had
results following the particular sequence. First of all, I am not negating or questioning
their results, but the rationale behind the phase potentiation. Second, the explanation
of the mechanism behind “phase potentiation” could be simpler. Here is a simple game.

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 MLADEN JOVANOVIĆ

Assume I have a rule in my head that generates triplets (sequence of three numbers)9.
Here is one triplet: 3 - 5 - 7. You need to figure out the rule that generated the triplets by
generating samples, while I will be telling you if they comply with the rule (i.e., correct
or incorrect). Because you have seen 3 - 5 - 7 triplets, you assume that the rule is “N, N
+ 2, N + 4”. This is called prior belief, or hypothesis. Let’s test that belief (Table 5.3)

You guessed Correct?

1, 3, 5 ✓
4, 6, 8 ✓
11, 13, 15 ✓
7, 9 , 11 ✓
18, 20, 22 ✓
Table 5.3. You try to figure out the rule by generating triplets

Since my feedback complies with you hypothesis, you might conclude that the
hypothesis is true and ask me “The rule must be N, N+2, N+4. Am I correct?”. And I will
answer: “No, you are not correct!.”

In this simple example we can see Karl Popper’s idea of falsification and your
suffering from confirmation bias. Rather than trying to find examples that falsify your
hypothesis, you continued looking for examples that confirms your hypothesis. This is
not how science works and this is demarcation between science versus pseudoscience:
science is always looking to falsify its theories and hypothesis. No matter how many
correct triplets you generated, you are never certain that your hypothesis is correct, but
one single incorrect example is enough to falsify your hypothesis. This is why I stated
in Chapter 2 that Negative knowledge is more robust than Positive knowledge. Knowing
what doesn’t work is much more robust, than knowing what might work.

So what was the rule? The rule was “All different positive numbers!”. Thus,
all those claiming that results are better because they followed a particular sequence
are suffering from the confirmation bias. Also, the Occam’s Razor can be applied here,
which states that the simplest explanation is the most probable one. In the case of
phase potentiation, explanation is that variability is the probable cause rather than
some optimal sequencing identified by the Russian scientist that was kept in secrecy
during the Cold War.

I do think that certain sequences might be more suitable, although not due to
phase potentiation. The particular sequence might be followed because one wants to (1)
minimize the Downside, and (2) gain upside with least stress or complexity. For example,

9 This is usually called DGP or Data Generating Process in the statistical analysis. One of the goals of
statistics is to ‘re-create’ DGP from the sample and estimate uncertainty around estimates.

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STRENGTH TRAINING MANUAL Volume Two

training, particularly when training novice athletes, we tend to start with say 3x10
@60% and proceed to 3x5 @75%, because we believe it is “progression” (in this case
in intensity of load)12. But 3x10 can be more demanding on the client or the athlete due
to higher exertion load (XL) and higher volume, which can make someone bloody sore
and report higher levels of discomfort. This can cause your average soccer mom never
to show in training anymore. And apparently you wanted to minimize the downside
by following a progressive program (assuming the %1RM is a metric that needs to be
“progressed”). Boom - busted! It is thus recommended for beginners to use Ballistic
Load-Exertion table (see Chapter 4), which can promote quality reps and minimize
fatigue and soreness and discomfort.

External vs Internal and

subjective ratings of dose
Opening up the pleasure-displeasure can of worms needs additional elaboration.
In theory, training dose can be represented with external and internal components.
Things are becoming a bit blurry here, because internal dose can actually be considered
acute psychophysiological response occurring during the execution of the exercise
(i.e., external dose). This again reminds us that we are dealing with the “Small World”
models. But anyway, let’s assume external and internal are both components of a
training dose.

Additional components of the training dose can be objective and subjective

(Figure 5.6). Please note that “objective” component also have a bunch of “subjective”
assumptions.

External
Internal
Dose

�ve
�ve
Figure 5.6. External and internal components of training dose

12 As you will soon read in this chapter, “progression” means making things “harder” and this tends to
involve one or more training dose variables. In this particular example, making things harder is assumed
to be %1RM. But that doesn’t need to be the case...

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 MLADEN JOVANOVIĆ

Objective-subjective and external-internal dose components can form quadrants.

Figure 5.7 contains example metrics for each quadrant.

External Internal

EMG of the muscles, HR, skin

NL, Tonnage, Impulse, aRI, INOL,
conductance, bLA accumula�
Objec�ve XL, Velocity, Velocity Loss,
ammonia and other metabolites
Power, Force…
or hormones…

Someone else’s, like coach’s, RIR, Ex � ra� g, E ort

Subjec�ve
�ve judgment of the dose ra� , Discomfort ra�

Figure 5.7. Training dose quadrant

External-Objective Dose

This quadrant represents metrics already explained in this and previous chapter.

Internal-Objective Dose

This quadrant consists of metrics that represent internal dose (or acute response)
in the body. This could involve your common lab coat measurements, like EMG, blood
lactate (bLA) and so forth. This represents how the body responds during the execution
of the exercise (i.e. external-objective metrics).

External-Subjective

This quadrant consists of someone else’s subjective ratings of the training

dose. For example a coach or your training partner. Coach can observe the velocity
of the barbell and make inferences about how tough was a given set or so forth. This
dose estimation is usually not much discussed in the lab coats journals, but it is very
important in the real world.

Internal-Subjective

Welcome to the lab coat shit show (Smirmaul, 2012; Halperin & Aviv, 2019;
Jovanovic & Halperin, 2019). Seriously. This has been a source of confusion and useless
research papers for years. And things are far from sorted out. I will not waste much
paper here by reviewing all the mess. I suggest you check the recent unpublished paper
(Halperin & Aviv, 2019) for a great and concise overview. I will rather provide some
of my rationale and potential solutions (although by no means I consider this a finite
model).

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the guys from Juggernaut Training Systems also utilize the following thresholds and
zones: (1) Maintenance Volume (MV), (2) Minimum Effective Volume (MEV), (3) Maximum
Adaptive Volume (MAV), (4) Maximum Recoverable Volume (MRV). MV is related to MRD,
MEV is equal to MED, MAV is equal to DED (although this can be defined as a point where
plateau starts, or where there is the biggest difference between upside and downside),
MRV is equal to MTD.

A B
Response

Upide − Downside
MRD MED DED MTD MRD MED

DED MTD

Upside

Downside

Dose Dose

Figure 5.13. Hypothetical relationship between upside and downside. Panel A contains upside and
downside as a separate curve, while panel B contains their difference. Vertical lines indicated different
dose thresholds (see text for explanation)
Dose

Overtraining

Maximum Tolerated Dose (MTD)

Diminishing E ect Dose (DED)

Development

Minimum E ec�ve Dose (MED)

Reten�on
Minimum Reten� n Dose (MRD)
Restora�on

Detraining

Figure 5.14 contains visual depiction of MRD, MED, DED and MTD, as well as recovery, retention and
development zone.

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 MLADEN JOVANOVIĆ

peaking for a competition. Doesn’t work (at least not for a long time). Which doesn’t
mean that this shouldn’t be done, but it needs to be used sparingly and with a great
awareness.

Another complementary pair that goes well with Push and Pull is develop
vs express, or sharpen/saw. This is very similar to Habit 7 from “7 Habits of Highly
Successful People” by Stephen Covey (Covey, 2013). We want our strength training to
‘sharpen our saw’, or to develop strength qualities, rather than to test them. We can
look at pull approach as more ‘developing’ and push approach as more ‘testing’ or
expressing what the athlete might have possessed already. But things are not black and
white, and we utilize this develop/express complementary pair thorough our training
program - we develop strength most of the time, but we occasionally ‘test’ (e.g. using
plus sets) to check where we are. We do not want to test too frequently, and we do not
want to avoid testing at all. They are embedded and interrelated.

Figure 5.23 contains the summary of the push and pull concepts discussed so far.

Dose 1RM
Maximum Tolerated Dose Compe�� n Maximum

Push the Ceiling

Pull the Floor

Minimum E ec�ve Dose Every Day Maximum

- Loose prescrip� - Strict prescrip�

- Conserva�ve progression - Aggressive progression
- Adapta� n over progression - Progression over adapta�
- Adap�ng to the rate of change - Forcing adapta�
- Oblique goal se ng - Direct goal se ng
- Working forwards - Working backwards
- Adap�ng program to the athlete - Adap�ng athlete to the program
- Can be repeated frequently - Cannot be repeated o en
- Can be held forever - Cannot be held long
- Bo om-up - Top-down
- Develop - Express
- Substance - Form
- Explore - Exploit
- Play - Work
- AGT/A+A - Glycoly�
- Protect from the downside - Pursue the upside
- Performance stabiliza� - Performance peaking
- Homeostasis Maintenance - Homeostasis Disrup�
- Expand the “known” - Embark into the “unknown”
- “Park Bench” - “Bus bench”
- “Feel Good” - “No Pain, No Gain”
- Variance (variability, exibility) - Bias (structure, stability)

Figure 5.23. Pushing vs pulling concept

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5b Planning (continued)
Previous chapter introduced theoretical concepts behind planning, using the
dose-response “Small Worlds” and multiple complementary aspects of planning,
culminating with the concepts of pull the floor and push the ceiling. In this chapter,
these will be put into more concrete and pragmatic form for strength training.

The building block of this chapter will be set and rep schemes, that together with
exercise (or mean) represent a prescription unit (Figure 5.26), or the smallest planning
unit (i.e., strength training atom).

Exercise Set and Rep Scheme

Prescrip� n Unit

Figure 5.26. Prescription unit consists of exercises and set and rep schemes

Set and Rep Schemes: The Basis

Chapter 3 covered exercises and their classification. This chapter will delve
more into set and rep schemes and combinatorics used in planning (e.g., vertical and
horizontal planning as well as divisible and indivisible strategies and other novel
planning strategies that will be discussed shortly). Before we even start with more
advanced topics, let’s cover the anatomy of a set and rep scheme.

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Anatomy of a set and rep scheme

Figure 5.26 contains anatomy of a set and rep scheme. This is of course a
simplification (“Small World”), but quite frequent and useful model. Every set and rep
scheme consists of multiple components (i.e., sets), but what you find in most if not
all strength training material are the main sets. This is unfortunate, since set and rep
scheme is much more complex and richer construct.

Warm-Up Sets

- Daily Max
Pre-Work Sets - Over-Warm-Up

Main Sets

- Plus Sets
- Joker Sets
- Back-O Sets
A er Sets - Myo Reps
- Dynamic E ort
- Isometric
…
Figure 5.27. Anatomy of a set and rep scheme

Warm-up sets

Similarly to the discussion on active recovery means and methods in the previous
chapter, I approach warm-ups differently. Rather than looking at warm-up as means
to reach working temperature of the body and priming the nervous system only, one
can look at warm-ups as affordance to practice and develop particular quality at the
current state of the organism or athlete. I know this is a mouthful, but it simply means
addressing what can be addressed while athlete is warming up. During the warm-up

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Main or Working sets

These are bread-and-butter of the set and rep scheme. As such, they are
considered in greater depth in this chapter and I won’t delve much in them here. For
the sake of completeness, Figure 5.28 consists of common prescription formats when
it comes to set and rep schemes, particularly main sets.

85 x 5
90 x 3
95 x 1
%1RM Reps

“Five reps at 85% 1RM”

“Three reps at 90% 1RM”
“One rep at 95% 1RM”

5 x 6 @80% w/2RIR
Sets Reps %1RM Reps In Reserve

“Five sets of six reps at 80% 1RM with 2 reps in

reserve”

3-5 x 6-8 @70-80% w/2-4RIR 30X1 in 15’

Time
Sets Zone Reps Zone %1RM Zone RIR Zone Tempo
constraint

“Three to five sets of six to eight reps at 70-80% 1RM with 2 to 4 reps in reserve, done using 30x1 tempo
within 15 minutes”

Figure 5.28. Common examples of the prescription format of the set and rep schemes

After sets

After sets represent additional opportunity and affordance in the workout and
there are different implementations here. Let’s cover the most common:

Plus sets. Plus sets involve finishing main sets with a set to failure (or to a
particular ceiling, e.g., 10 reps max). Here is an example:

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Classification of Set and Rep schemes

There are numerous criteria to classify set and rep scheme. Figure 5.33 contains
the most common criteria to classify set and rep schemes.

Set and Rep

Schemes

Objec�ves/Quali� Prescrip�veness Volume Toughness Methodology

Figure 5.33. Classification criteria for Set and Rep Schemes

Classification based on Objectives

or Qualities
As explained in Chapter 2, we tend to have simplistic models of which loading
parameters target particular qualities (see Figure 2.4). Unfortunately, as depicted in the
Figure 2.5 in Chapter 2, things are not that simple, and different methods can achieve
the same objective, while single method can hit different training qualities. The causal
network is quite complex, but it doesn’t mean we can’t have some guiding heuristics.
The Agile Periodization utilizes an iterative approach which consists of: frequent
reviews and embedded testing, MVPs and experimenting, as well as making sure that
all the major ‘buckets’ are targeted. In short, one never puts all eggs in one basket, but
rather experiments with multiple options to see which one works at any particular time.

Lab coats tend to favor simplistic proxy metric as a stimulus, which later influences
the whole program design (i.e., to optimize for that particular metric, e.g., the optimal
NL in a session, in a week, the optimal frequency, intensity zones and so forth). What is
a stimulus for hypertrophy? Or to put it more clearly, what “Small World” dose metrics
(see Dose -> Response in the previous chapter) are proxy to hypertrophy stimuli? What
are for maximal strength development? I am not going to go into research behind this,
but I highly recommend blog posts by Lyle McDonald on these topics (McDonald, 2007,
2008, 2009a,b,c, 2014a,b, 2015a,b, 2016, 2019a,b).

Besides listening to lab coats, one also (or probably more so) needs to listen to
bros and coaches who have been tinkering in the field with their skin in the game to

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create best practices (refer to Figure 2.13). The material that follows is my attempt to
collect some of those practices that can serve as a prior for your own experimentation.
I again recommend very critical and thoughtful articles by Lyle McDonald on the topic.

Figure 5.34 contains highly speculative guidelines for a single session for targeting
different qualities. How these are repeated and distributed in a week (or sprint) can vary
highly based on preferences, time limitations and so forth. There are some guidelines
for frequency and weekly NL, but for this I direct you to Lyle McDonald’s blog posts.
As already pointed multiple times in this manual, one should use these guidelines as
priors and experiment around them.

Anaconda Strength Armor Building Arrow Vanilla Training Mongoose Persistence

%1RM

80+% 1RM 65-85% 1RM <70% 1RM <40% 1RM <40-60% 1RM

15-30 reps
Reps

1-5 reps 5-12 reps 1-10 reps (usually <6) 10-20 reps (or more)
(highly variable)
Volume

10-20 total reps 25-50 total reps 10-30 total reps 50-100 total reps 100+ total reps

Figure 5.34. Suggested HIGHLY-SPECULATIVE loading parameters per training objective

Recommendations in the Figure 4.34 are very crude rules of thumb. For example,
in the Arrow category (explosive strength), one can perform 10-20 reps per set when
it comes to KB Swings, with the total volume of 100-300 reps. Also, one can perform
Olympic lifting with 80%+ 1RM, while the Arrow category suggests <70%. So take these
recommendations with a grain of salt. A lot of it.

Based on these recommendations, set and reps schemes can be classified

depending on which quality they (predominantly) target. This is of course hard, but
can serve as a general guideline.

As opposed to classifying set and rep schemes based on the target quality, one
can classify them based on methodology21 (I will present my own system later in this

21 I wasn’t sure whether to call this methodology or phenomenology, but the point is classifying set and
rep schemes based on observable and controllable qualities, rather than target qualities.

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chapter). Tables 5.9 and 5.10 contains Joe Kenn’s Tier System (Kenn, 2003) guidelines
for different intensity cycles (which can be considered methodology).

Strength Cycle Training Range Olympic Li ing Olympic Li ing Upper/Lower Body Upper/Lower Body
Reps per Set* Volume* Reps per Set Volume
General
60% - 67.5% 6 18 10 30
Condi�oning
Strength 60% - 67.5% 5-6 30 12 - 15 60 - 90
Endurance 70% - 77.5% 4-6 24 8 - 10 40 - 60
Developmental 70% - 77.5% 4-6 24 6 - 10 20 - 48
Strength 80% - 87.5 2-4 20 3-6 12 - 30
Metabolic 80% - 87.5% Cluster 20 Cluster 15 - 30
Strength 90% - 95% Cluster 10 Cluster 10 - 15
Explosive 55% - 65% 3-6 18 - 30 3-6 18 - 30
Strength* 70% - 75% 3-6 12 - 24 3-6 12 - 24
Maximum 1-2 1-3
90+% 10-Apr 3 - 12
Strength or Mul� Rep Max or Mul� Rep Max

Table 5.9. Joe Kenn’s basic intensity cycles for foundation exercises (main exercises).
Reproduced with permission by Joe Kenn.

Strength cycle from Table 5.9 can be considered target quality, although Joe
Kenn also provides different methodological set and rep schemes (see “Stable 3”,
“Descending”, “Advanced”, “Progressive”, “Clusters”, “Prilepin” in Table 5.10) that
are utilized for a given strength cycle (quality).

p
General Conditioning Strength Endurance Strength Maximum Strength Metabolic Strength Explosive Strength
Stable 3 Descending Advanced Progressive Clusters Prilepin
65% x 10 65.0% x 12 82.5% x 4 67.5% x 3 82.5% x (1+1+1+1) 75% x 3
65% x 10 62.5% x 12 82.5% x 4 72.5% x 3 82.5% x (1+1+1+1) 75% x 3
65% x 10 60.0% x 12 82.5% x 4 77.5% x 3 82.5% x (1+1+1+1) 75% x 3
57.5% x 12 82.5% x 4 82.5% x 5 82.5% x (1+1+1+1) 75% x 3
55.0% x 12 82.5% x 4 82.5% x 5 82.5% x (1+1+1+1) 75% x 3
52.5% x 12 82.5% x 4 82.5% x 5 82.5% x (1+1+1+1) 75% x 3
75% x 3
75% x 3

Table 5.10. Joe Kenn’s sample set and rep schemes for particular intensity cycle.
Reproduced with permission by Joe Kenn.

Joe Kenn also relies heavily on the Prilepin Table (see Table 4.35) that gives
volume recommendations for Olympic lifting, which can be also useful for all ballistic
movements and when one wants to focus on quality execution of the grinding lifts and
limiting unnecessary fatigue (without the novel fancy velocity drop metrics). Please
note that Prilepin Table is quite similar to Ballistic Load-Exertion table (see Table 4.37),
but also offers workout volume recommendation guidelines.

Table 5.11 contains Matt Jordan’s (Canadian Sport Institute in Calgary) excellent
classification of strength training methods. Please note the distinction between
“Strength Method” (i.e., method) and “Target Strength Ability” (i.e., quality).

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Adap�ve
Response
Dose

Varia� Progression

Figure 5.37. Variation and Progression are hardly distinguishable but necessary
components of the dose construct.

Vertical and Horizontal planning

Let’s assume you have two days a week (bottom-up approach) to train the bench
press (or upper body horizontal pushing movement): Monday and Thursday. For the
sake of example, let’s assume that the Monday workout is bench press 3x10 @60%
(Table 5.14).

Monday Tuesday Wednesday Thursday Friday Saturday Sunday

Bench Press ?Bench Press?

Week 1
3x10 @60% ???

?Bench Press? ?Bench Press?

Week 2
??? ???

?Bench Press? ?Bench Press?

Week 3
??? ???

?Bench Press? ?Bench Press?

Week 4
??? ???

Table 5.14. Two days a week to train the bench press. The question is how to plan?

The question is how to vary this workout on Thursday, and how to progress both
of them across weeks? As already explained, this progress vs. variation is very tricky,

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so it is better to refer to this problem as horizontal and vertical planning (Table 5.15).
Horizontal planning is more leaning towards variation, while vertical planning is
leaning more towards progression. This doesn’t need to be the case all the time, but
a general rule (and might be easier to grasp the concept of horizontal and vertical
planning) that can be, and usually is, broken.

Monday Tuesday Wednesday Thursday Friday Saturday Sunday

Bench Press ?Bench Press?

Week 1
3x10 @60%
Horizontal Planning
???
("Varia�on")

?Bench Press? ?Bench Press?

Week 2
??? ???
Planning
Ver�cal

?Bench Press? ?Bench Press?

Week 3
??? ???

?Bench Press? ?Bench Press?

Week 4
??? ???

Table 5.15. Horizontal and vertical planning

Horizontal planning
Horizontal planning, usually represents varying a workout that is within the
same progression stage (although, as will be seen in a few paragraphs, doesn’t need to be
the case). Dan Baker (Baker, 2007) provided an example table of variations that could
be utilized when two similar workouts are performed in one week (Table 5.16)

Method of varia� n Day 1 example Day 2 example

1. Same exercises, same RIR, increase in number of reps Bench Press 3x10 @70% w/3RIR Bench Press 3x15 @63% w/3RIR

2. Same exercises, same RIR, decrease in number of sets Bench Press 4x10 @70% w/3RIR Bench Press 2x10 @70% w/3RIR

3. Same exercises, sets, and repe�� ns, reduce the li ing speed Bench Press 3x10 @70% w/3RIR 20X1 Bench Press 3x10 @60% w/10RIR 42X1
and load.
4. Same exercises and other variables, decrease rest periods Bench Press 3x10 @70% w/3RIR R:3min Bench Press 3x10 @60% w/10RIR R:1min
and resistance
5. Same exercises and other variables, decrease resistance. Bench Press 3x10 @70% w/3RIR Bench Press 3x10 @60% w/10RIR

6. Same exercises and other variables, decrease repe�� ns. Bench Press 3x10 @70% w/3RIR Bench Press 3x6 @70% w/7RIR

7. Di erent strength exercises, but same for all other variables Bench Press 3x10 @70% w/3RIR Incline Press 3x10 @70% w/3RIR
(same %1RM).
8. Perform a strength and power version of aligned exercises on Bench Press 3x5 @80% w/3RIR Bench Throw 3x5 @40%
di erent days.
9. Perform heavier and lighter versions of aligned power Power Clean 3x5 @70% Power Snatch 3x5 @70%
exercises on di erent days.

Table 5.16. Few examples of altering training workout within a week.

Reproduced with permission by Dan Baker (Baker, 2007)

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contains example of vertical planning by Dan Baker (Baker, 2007) (although he calls is
“different variants or patterns of strength training periodization”).

Week 1 2 3 4 5 6 7 8 9 10 11 12

Subtle linear 3x13 @63% 3x12 @66% 3x11 @69% 3x10 @72% 3x9 @75% 3x8 @78% 3x7 @81% 3x6 @84% 3x5 @87% 3x4 @90% 3x3 @93% 3x2 @96%

Block with linear intensi ca�on 4x10 @60% 4x10 @64% 4x10 @68% 4x10 @70% 4x5 @78% 4x5 @81% 4x5 @83% 4x5 @85% 3x3 @88% 3x3 @90% 3x3 @92% 3x3 @94%

Block with nonlinear

4x10 @64% 4x10 @68% 4x10 @70% 4x10 @66% 4x5 @80% 4x5 @83% 4x5 @85% 4x5 @75% 3x3 @90 3x3 @92% 3x3 @94% 3x3 @80%
intensi ca�on

Undula�ng 4x10 @64% 4x10 @68% 4x6 @76% 4x6 @80% 4x8 @72% 4x8 @76% 4x4 @84% 4x4 @88% 3x6 @82% 3x6 @85% 3x3 @92% 3x3 @94%

Wave-like 4x10 @64% 4x8 @70% 4x6 @76% 4x4 @82% 4x9 @70% 4x7 @76% 4x5 @82% 4x3 @88% 3x8 @78% 3x6 @84% 3x4 @90% 3x3 @94%

Accumula�on/Intensi ca�on 6x3 @80% 6x4 @80% 6x5 @80% 6x6 @80% 5x5 @85% 4x4 @90% 3x3 @95% 2x2 @100% - - - -

Table 5.23. Dan Baker’s different variants or patterns of strength training periodization.
Modified with permission by Dan Baker (Baker, 2007)

Over the years, Dan Baker leaned more towards “wave” approach to vertical
planning (Baker, 2013). Table 5.24 contains Dan’s most common wave set and rep
schemes.

Vertical planning pretty much revolves around making things harder or tougher
across time. Having said that, this is done by adjusting certain dose parameters or
planning components (see Table 5.12 for more examples). The point is that both
horizontal and vertical planning can utilize different set and rep scheme classification
criteria (see Figure 5.33) , different dose components (e.g. saturated - distributed,
complex - unidirectional), as well as different exercises. Vertical planning and horizontal
planning represent “bottom-up” applications (as well as “forum for action”) of “top-
down” principles covered so far.

Although there are numerous approaches to vertical planning, in this manual I’ve
used the two progression approaches, where either %1RM is what is being progressed
across weeks, or proximity to failure (RIR). This is “Small World” model, and I am more
than aware of it, so please be free to modify it to suit your needs, or use something else
altogether.

Using Load-Exertion Table (Table 4.3), and Ballistic Load-Exertion Table (Table
4.37), two approaches that I named Perc Drop and RIR Inc are implemented as methods
for vertical planning.

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Week 1 Week 2 Week 3 Week 4 Week 5 Week 6 Week 7 Week 8

Wave 1 55 x 15 62.5 x 12 70 x 10 77.5 x 8 55 x 15 62.5 x 12 70 x 10 77.5 x 8
Hypertrophy exercises and/or Low 55 x 15 62.5 x 12 70 x 10 77.5 x 8 55 x 15 62.5 x 12 70 x 10 77.5 x 8
level athletes
55 x 15 62.5 x 12 70 x 10 77.5 x 8+ 55 x 15 62.5 x 12 70 x 10 77.5 x 8+

Wave 2 60 x 12 67.5 x 10 75 x 8 82.5 x 6 60 x 12 67.5 x 10 75 x 8 82.5 x 6

Hypertrophy exercises and/or 60 x 12 67.5 x 10 75 x 8 82.5 x 6 60 x 12 67.5 x 10 75 x 8 82.5 x 6
Intermediate level athletes
60 x 12 67.5 x 10 75 x 8+ 82.5 x 6+ 60 x 12 67.5 x 10 75 x 8+ 82.5 x 6+

Wave 3 65 x 10 72.5 x 8 80 x 6 85 x 5 65 x 10 72.5 x 8 80 x 6 85 x 5

Secondary Strength exercises 65 x 10 72.5 x 8 80 x 6 85 x 5 65 x 10 72.5 x 8 80 x 6 85 x 5
Intermediates & Advanced
athletes
65 x 10 72.5 x 8 80 x 6+ 85 x 5+ 65 x 10 72.5 x 8 80 x 6+ 85 x 5+

Wave 4 70 x 8 70 x 8 72 x 6 76 x 5 70 x 8 70 x 8 72 x 6 76 x 5
Primary strength exercises 70 x 8 75 x 6 80 x 5 84 x 3 70 x 8 75 x 6 80 x 5 84 x 3
Intermediates & Advanced
70 x 8 80 x 5 88 x 3+ 92 x 2+ 70 x 8 80 x 5 88 x 3+ 92 x 2+

Wave 5 70 x 8 70 x 5 72 x 3 76 x 2 70 x 8 70 x 5 76 x 3 80 x 2
Primary strength exercises 70 x 8 75 x 5 80 x 3 84 x 2 70 x 8 75 x 5 84 x 3 88 x 2
Intermediates & Advanced, using
band/chains for ME weeks
70 x 8 80 x 5 88 x 3+ 92 x 2+ 70 x 8 80 x 5 90 x 3+ 94 x 2+

Wave 6 70 x 5 70 x 4 72 x3 76 x 2 70 x 5 70 x 4 76 x3 80 x 2
Primary strength & Olympic 70 x 5 75 x 4 80 x 3 84 x 2 70 x 5 75 x 4 84 x 3 88 x 2
exercises, more advanced
athletes
70 x 5 80 x 4 88 x 3+ 92 x 2+ 70 x 5 80 x 4 90 x 3+ 94 x 2+

Reference: Baker D. 2013. The E ec� eness of the Wa e-Cycle for In-Season Training: 20 Years of ence on the In-Season Maintenance
of Strength a Power in Professional Athletes.
Table 5.24. Dan Baker’s different variants of wave set and rep schemes.. Modified with permission by
Dan Baker (Baker, 2013)

Perc Drop approach

Perc Drop stand for percent (%1RM) drop. Table 5.25 contains two tables, top and
bottom. Top table is used to calculate percent drop when going through four progression
stages (where the Progression #4 is the toughest and Progression #1 is easiest). There
are few heuristics implemented here. The first heuristic is that percent drop increases
from low reps (-2.5% for 1 rep in a set) to high reps (-5% for 12 reps in a set). The second
heuristic is that extensive set and rep schemes (i.e., those with more that 5 working
sets) need higher percent drop than more intensive set and rep schemes26.

Once the top table is calculated, the established percentage drops are applied
to Load-Exertion table, which results in the bottom reference table on Table 5.25.
The bottom table is then utilized to vertically plan (i.e., progress) various set and rep
schemes through three to four progression steps.

26 This can be more of an art than science though. It is up to you to decide what is extensive and what is
intensive given your context. When in doubt, lean on the lower percentage.

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Progression #4 Progression #3 Progression #2 Progression #1

Reps %1RM Intensive Normal Extensive Intensive Normal Extensive Intensive Normal Extensive Intensive Normal Extensive Perc Dec
1 100% 0.0% -2.5% -5.0% -2.5% -5.0% -7.5% -5.0% -7.5% -10.0% -7.5% -10.0% -12.5% -2.50%
2 94% 0.0% -2.7% -5.5% -2.7% -5.5% -8.2% -5.5% -8.2% -10.9% -8.2% -10.9% -13.6% -2.73%
3 91% 0.0% -3.0% -5.9% -3.0% -5.9% -8.9% -5.9% -8.9% -11.8% -8.9% -11.8% -14.8% -2.95%
4 88% 0.0% -3.2% -6.4% -3.2% -6.4% -9.5% -6.4% -9.5% -12.7% -9.5% -12.7% -15.9% -3.18%
5 86% 0.0% -3.4% -6.8% -3.4% -6.8% -10.2% -6.8% -10.2% -13.6% -10.2% -13.6% -17.0% -3.41%
6 83% 0.0% -3.6% -7.3% -3.6% -7.3% -10.9% -7.3% -10.9% -14.5% -10.9% -14.5% -18.2% -3.64%
7 81% 0.0% -3.9% -7.7% -3.9% -7.7% -11.6% -7.7% -11.6% -15.5% -11.6% -15.5% -19.3% -3.86%
8 79% 0.0% -4.1% -8.2% -4.1% -8.2% -12.3% -8.2% -12.3% -16.4% -12.3% -16.4% -20.5% -4.09%
9 77% 0.0% -4.3% -8.6% -4.3% -8.6% -13.0% -8.6% -13.0% -17.3% -13.0% -17.3% -21.6% -4.32%
10 75% 0.0% -4.5% -9.1% -4.5% -9.1% -13.6% -9.1% -13.6% -18.2% -13.6% -18.2% -22.7% -4.55%
11 73% 0.0% -4.8% -9.5% -4.8% -9.5% -14.3% -9.5% -14.3% -19.1% -14.3% -19.1% -23.9% -4.77%
12 71% 0.0% -5.0% -10.0% -5.0% -10.0% -15.0% -10.0% -15.0% -20.0% -15.0% -20.0% -25.0% -5.00%

Progression #4 Progression #3 Progression #2 Progression #1

Reps %1RM Intensive Normal Extensive Intensive Normal Extensive Intensive Normal Extensive Intensive Normal Extensive
1 100% 100% 98% 95% 98% 95% 93% 95% 93% 90% 93% 90% 88%
2 94% 94% 91% 89% 91% 89% 86% 89% 86% 83% 86% 83% 80%
3 91% 91% 88% 85% 88% 85% 82% 85% 82% 79% 82% 79% 76%
4 88% 88% 85% 82% 85% 82% 78% 82% 78% 75% 78% 75% 72%
5 86% 86% 83% 79% 83% 79% 76% 79% 76% 72% 76% 72% 69%
6 83% 83% 79% 76% 79% 76% 72% 76% 72% 68% 72% 68% 65%
7 81% 81% 77% 73% 77% 73% 69% 73% 69% 66% 69% 66% 62%
8 79% 79% 75% 71% 75% 71% 67% 71% 67% 63% 67% 63% 59%
9 77% 77% 73% 68% 73% 68% 64% 68% 64% 60% 64% 60% 55%
10 75% 75% 70% 66% 70% 66% 61% 66% 61% 57% 61% 57% 52%
11 73% 73% 68% 63% 68% 63% 59% 63% 59% 54% 59% 54% 49%
12 71% 71% 66% 61% 66% 61% 56% 61% 56% 51% 56% 51% 46%

Table 5.25. Perc Drop approach for planning grinding set and rep schemes

Here is an example of 3x10 and 3x3 schemes, using intensive and extensive
variants calculated utilizing Perc Drop approach (Table 5.26).

Progression
Scheme max %1RM Variant #1 #2 #3 #4
1x10 75% Intensive 61% 66% 70% 75%
3x10 75% Normal 57% 61% 66% 70%
6x10 75% Extensive 52% 57% 61% 66%
1x3 91% Intensive 82% 85% 88% 91%
3x3 91% Normal 79% 82% 85% 88%
6x3 91% Extensive 76% 79% 82% 85%
Table 5.26. Example vertical planning (progressions) for 3x10 and 3x3 scheme using Perc Drop method

Perc Drop is also applied on the Ballistic Load-Exertion table (Table 5.27) which is
used for planning ballistic lifts or high-quality sets (without too much drop in velocity
and quality of reps) as well as for beginners or in-season athletes (to avoid soreness).

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Figure 5.39a and Figure 5.39b depict conceptual changes in %1RM, number of
reps and number of sets across four progression steps implemented in each vertical
planning method.

%1RM Reps Sets

Constant
Linear
Reverse Linear
Block
Block Variant

1 2 3 4 1 2 3 4 1 2 3 4
Step
Figure 5.39a. Conceptual changes in %1RM, number of reps and number of sets
across four progression steps implemented in each vertical planning method

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Step 1 Step 2 Step 3 Step 4

Set and Rep Scheme %1RM Reps %1RM Reps %1RM Reps %1RM Reps

59% 10 61% 10 64% 10 66% 10

Plateau 59% 10 61% 10 64% 10 66% 10
59% 10 61% 10 64% 10 66% 10
59% 10 61% 10 64% 10 66% 10

36% 10 39% 10 42% 10 45% 10

Step 46% 10 49% 10 52% 10 55% 10
56% 10 59% 10 62% 10 65% 10
66% 10 69% 10 72% 10 75% 10

66% 10 69% 10 72% 10 75% 10

Reverse Step 56% 10 59% 10 62% 10 65% 10
46% 10 49% 10 52% 10 55% 10
36% 10 39% 10 42% 10 45% 10

Ascending Wave 59% 10 61% 10 64% 10 66% 10

63% 8 65% 8 68% 8 71% 8
68% 6 70% 6 73% 6 75% 6

Descending Wave 68% 6 70% 6 73% 6 75% 6

63% 8 65% 8 68% 8 71% 8
59% 10 61% 10 64% 10 66% 10

62% 2 65% 2 68% 2 70% 2

Ascending Ladder 62% 3 65% 3 68% 3 70% 3
62% 5 65% 5 68% 5 70% 5
62% 10 65% 10 68% 10 70% 10

62% 10 65% 10 68% 10 70% 10

Descending Ladder 62% 5 65% 5 68% 5 70% 5
62% 3 65% 3 68% 3 70% 3
62% 2 65% 2 68% 2 70% 2
59% 10 61% 10 64% 10 66% 10
63% 8 65% 8 68% 8 71% 8
Tradi�onal Pyramid 68% 6 70% 6 73% 6 75% 6
63% 8 65% 8 68% 8 71% 8
59% 10 61% 10 64% 10 66% 10
68% 6 70% 6 73% 6 75% 6
63% 8 65% 8 68% 8 71% 8
Reverse Pyramid 59% 10 61% 10 64% 10 66% 10
63% 8 65% 8 68% 8 71% 8
68% 6 70% 6 73% 6 75% 6
62% 10 65% 10 68% 10 70% 10
52% 5 55% 5 58% 5 60% 5
Light-Heavy 62% 10 65% 10 68% 10 70% 10
52% 5 55% 5 58% 5 60% 5
62% 10 65% 10 68% 10 70% 10

66% 8 (5x3) 69% 8 (5x3) 72% 8 (5x3) 75% 8 (5x3)

Cluster 66% 8 (5x3) 69% 8 (5x3) 72% 8 (5x3) 75% 8 (5x3)
66% 8 (5x3) 69% 8 (5x3) 72% 8 (5x3) 75% 8 (5x3)
66% 8 (5x3) 69% 8 (5x3) 72% 8 (5x3) 75% 8 (5x3)

Cluster Wave 66% 8 (5x3) 69% 8 (5x3) 72% 8 (5x3) 75% 8 (5x3)
69% 7 (4x3) 71% 7 (4x3) 74% 7 (4x3) 77% 7 (4x3)
71% 6 (6x2) 74% 6 (6x2) 76% 6 (6x2) 79% 6 (6x2)
Table 5.35. Mladen’s Methodological System of classifying Set and Rep schemes.

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Cluster method
Cluster and Rest-Pause methods represent multitude variations and approaches
and I suggest checking a paper by Tufano et al. for a great overview (Tufano, Brown &
Haff, 2017). In short, Clusters and Rest-Pause involve intra-set pause (Figure 5.40).

5 5 5

“Tr � Set 3min 3min

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

ster Set 15 15 15 15 3min 15 15 15 15 3min 15 15 15 15

5 2 1 5 2 1 5 2 1

Rest-P Set 15 15 3min 15 15 3min 15 15

Figure 5.40. Traditional set, cluster set and rest-pause set. Modified based on the graphs in (Tufano,
Brown & Haff, 2017).

Aside from the agreement between coaches and lab coats, that the Clusters and
the Rest-Pause involve intra-set rest, everything else is murky water and differs from
coach to coach, lab coat to lab coat. For example, take 3x5 @80% as a traditional set
performed with various Cluster and Rest-Pause variations (Table 5.51).

Set Type Prescrip�on Visual Note

Tradi�onal set 3x5 @85% RR:3min |||||------------|||||------------||||| Tradi�onal set, in this case 3 sets of 5 to failure (0RIR)
Cluster #1 3x[5x1 R:15sec] @85% RR:3min |-|-|-|-|------------|-|-|-|-|------------|-|-|-|-| Singles for equal number of reps (i.e. 5 reps)
Cluster #2 3x[8x1 R:15sec] @85% RR:3min |-|-|-|-|-|-|-|------------|-|-|-|-|-|-|-|------------|-|-|-|-|-|-|-| Singles for 1.5 - 2x number of reps (i.e., 7 to 10 reps) in a set
Cluster #3 3x[4x2 R:15sec] @85% RR:3min ||-||-||-||------------||-||-||-||------------||-||-||-|| Doubles or tripples for 1.5 - 2x number of reps in a set
Cluster #4 15x1 @85% RR:30sec |--|--|--|--|--|--|--|--|--|--|--|--|--|--| Rest redistribu�on (see Time and Rep constraints method)
Cluster #5 7x2 @85% RR:60sec ||----||----||----||----||----||----|| Rest redistribu�on (see Time and Rep constraints method)
Cluster #6 5x3 @85% RR:90sec |||------|||------|||------|||------||| Rest redistribu�on (see Time and Rep constraints method)
Cluster #7 15 reps @85% in 8min |||---||--|-------|||---|--|--|---||--| Time and Rep constraints method (athlete self selects)
Rest-Pause #1 3x[5F+2F+1F+1F+F R:15sec] @85% RR:3min |||||-||-|-|------------|||||-|-|-|------------||||-|-| Every set taken to (near) failure, un� 1.5 to 2x reps achived (i.e., 7 to 10 reps)
Rest-Pause #2 3x[3+2+1 R:15sec] @85% RR:3min |||-||-|------------|||-||-|------------|||-||-| Keep 1-2 RIR (quality) un� hi ng total reps, but not more
Rest-Pause #3 3x[4+2+2+1 R:15sec] @85% RR:3min ||||-||-||-|------------||||-||-||-|------------||||-||-||-| Keep 1-2 RIR (quality) un� hi ng 1 rep
Rest-Pause #5 3xmax @[85%, 65%, 45%] RR:3min |||||-|||||-|||||------------|||||-|||||-|||||------------|||||-|||||-||||| Drop sets (reduce weight). Drop should allow around equal number of reps
Rest-Pause #6 3x[1+2+3 R:15sec] @85% RR:3min |-||--|||------------|-||--|||------------|-||--||| Similar to Ladders, but with a intra-set rest

Table 5.51. Various Cluster and Rest-Pause methods.

The basic Cluster method (Cluster #1 from Table 5.51) involves performing
equal reps as traditional set with a short break between each rep (e.g., 15sec) during
which the weight is racked. Clusters and Rest-Pause are usually evaluated comparing
acute and chronic effects compared to the traditional sets. Acute effects often refer to
the mechanical characteristics of the repetitions (performance), as well as external
and internal characteristics of the dose, such as subjective feelings, EMG, hormonal
responses and so forth (see previous chapter for more info). Chronic effects refer to
response or changes in performance after a few weeks of training intervention (e.g.,
do Clusters generate more increase in strength or hypertrophy compared to regular
traditional sets). I will not go into analysis of these effects or benefits here, so I suggest
checking the aforementioned paper by Tufano et al. (Tufano, Brown & Haff, 2017).

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Repetitions performed with the Cluster #1 method will result in less velocity drop
than traditional set (acute effects), but it is questionable if these will result in improved
chronic effects (the similar Is/Ough gap jump is done with the contrast super sets). Since
these are done with more quality and velocity, more reps can be performed, either as
singles (Cluster #2) or doubles or triples (Cluster #3). The number of repetitions in
this type of cluster is around 1.5 - 2 times than the repetitions in the traditional set
(e.g., traditional set calls for 5 reps @85%, then the Cluster method might involve
performing 7 to 10 reps in a set). But if you compare Cluster #1 to Cluster #3 method
to traditional set (Table 5.51), you will notice that (1) it takes longer to perform cluster
sets and (2) one performs more volume (NL) (in #2 and #3 Cluster method). Comparing
acute and chronic effects of Cluster method to traditional method is thus not fair28, and
the effects might be related to a third variable (in this case more total rest time, or more
reps) rather than the method itself.

For this reason, lab coats entered numerous mental masturbations to try creating
equal playing field to allow more ceteris paribus (Angrist & Pischke, 2015) comparison
between Clusters and traditional sets. One such approach is to perform rest redistribution
clusters (Cluster #4, #5 and #6 in Table 5.51), where we make sure that the total rest
time is same (as well as the reps) as in the traditional sets. This is quite similar to the
Time and Rep constraint method from Chapter 4 (Cluster #7).

Although I am the first to agree that we cannot claim that one method is better
than the other if the underlying mechanisms or variables are not equal and thus
comparison is not fair, I am also the first to admit that it doesn’t matter in the real life
and that equal playing field is lab coats’ (and SJWs) wet dream. We perform Clusters
because they are different - because we can do more quality volume, because we practice
racking and re-racking, and so forth. Are the effects inherently due to cluster method,
or due more quality volume or what have you? As a pragmatist I don’t really care. It
is up to us, the practitioners to consider appropriate time to implement Clusters as a
variation when needed.

Having said that, in this manual I will approach clusters as a way to perform more
quality reps. Table 5.52 contains “Small World” model when converting traditional
reps to Clusters. You are always free to utilize any other method of course.

28 In statistics we want to create ceteris paribus (lat.) comparison or “other things equal” (Angrist &
Pischke, 2015), which is not the case if we simply compare traditional sets with clusters.

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above 50 session sequence, which can be considered long run, there are 16 Lower and 17
Upper sessions.

Markov Chain and Probabilistic

Programming
Imagine we have a training program that adheres to the following sequence of
workouts:

Upper Body A

Lower Body A

Upper Body B

Lower Body B

This sequence is fixed - after Upper Body A, always follow Lower Body A, after
which always follow Upper Body B, after which always follow Lower Body B, after which
follows Upper Body A and so forth. This sequence can be represented with a transition
matrix (Table 5.75).

Next Session
Upper Body A Lower Body A Upper Body B Lower Body B Sum
Upper Body A 0 1 0 0 1
Current
Session

Lower Body A 0 0 1 0 1
Upper Body B 0 0 0 1 1
Lower Body B 1 0 0 0 1
Table 5.75. Transition matrix. Rows indicate current session, and columns next session. Number inside
the matrix indicate probability of the next session (0 equals no probability, and 1 equals certainty).
Rows need to sum to 1.

Another way to visualize transition matrix is to use network diagram (Epskamp

et al., 2012; Schmittmann et al., 2013; Epskamp, Borsboom & Fried, 2016) (also see
discussion on network models in Chapter 2) (Figure 5.48).

The sequence in this example is fixed or certain. If the current session is Lower
Body A, the next session will always be Upper Body B. This indicated by the probability
of transition equal to one in the Table 5.75. Probability of transition of zero equals to
“never”, while probability of one equal “always”. Thus the probability is the continuum
between never and always. For example, if the transition probability from Lower Body
A to Upper Body B is 0.5, it means that out of 100 situations in the long run, 50 will
result in that transition. In the transition matrix, rows needs to sum to one.

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U −

− −

U −

Figure 5.48. Visual representation of the transition matrix using network.

Rather than having “always” or certain sequence, the sequence can be probabilistic.
That is exactly what we have done with the Barbell Chain example above, where Lower
and Upper have 50:50 chance, as long as there are no missed sessions. That scenario can
be represented with the following transition matrix (Table 5.76) and network diagram
(Figure 5.49). The thickness and darkness of edges in the network (arrows connecting
two nodes, or in this case, sessions) indicate transition probability.

Next Session
Missed Total Upper Lower Sum
Missed 0 1 0 0 1
Current
Session

Total 0 0 0.5 0.5 1

Upper 0 0 0.5 0.5 1
Lower 0 0 0.5 0.5 1
Table 5.76. Transition matrix for the Barbell Chain example.

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particular data set. Variance refers to the amount by which model parameters would
change if we estimated it using a different training data set (James et al., 2017). On
the other hand, bias refers to the error that is introduced by approximating a real-life
problem, which may be extremely complicated, by a much simpler model (James et al.,
2017). How are bias and variance estimated? One again needs to rely on the simulation,
since in the simulation we can re-sample data from the known data generating process
(DGP) (which is represented with the black line on figures 6.1 and 6.2). For a particular
value of x, we have true value (black line), and also , which is predicted by the model.
Over multiple simulations, for each particular model and tuning parameter (in this
case polynomial degree) we can estimate the absolute error between true and , which
represents bias, and variance in the in itself, which represents variance. This is done for
every x value. The expected prediction error (MSE, which is same as RMSE but without
root) for every x can be written as:

MSE=Bias2+Variance+Irreducible Error

Figure 6.4 depicts estimated bias and variance of the polynomial model for this
particular problem, averaged over all x values.
Error (MSE)

MSE
4

2
Bias

Variance

5 10 15 20
Polynomial Degree
Figure 6.4. Estimated model bias and variance averaged across all x values for every polynomial de-
gree. This is done using 20 simulations. Horizontal line represents irreducible error, which is equal to 2,
but since we are using MSE it needs to be squared.

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As can be expected, bias is high for the polynomial degrees 1 and 2, while variance
raises with raising the flexibility of the model. This is the trade-off between bias and
variance, and polynomial degree 3 represent the optimal relationship between the
two that gives the best predictive performance. In other words, by changing or tuning
polynomial degree (the model flexibility parameter) we can find the best ratio between
model stability (or bias) and variability (or variance) for a particular problem at hand
that gives the best predictive performance.

I’ve utilized two other models on the same data set: (1) closest neighbor model
(KNN), and (2) exponential moving average (EMA).

KNN model used k closest neighbors for a particular x value and takes the average
of the associated y value. In the KNN case, k value represents model tuning parameter,
where increasing k increase to model bias, while lowering k values increase the model
variance. Figures 6.5, 6.6 and 6.7 depicts performance of the KNN model for this
particular data set.

KNN 1 KNN 2 KNN 3 KNN 4 KNN 5

120
est1RM (kg)

115

110

105

100

KNN 6 KNN 7 KNN 8 KNN 9 KNN 10

120

115

110

105

100

KNN 11 KNN 12 KNN 13 KNN 14 KNN 15

120

115

110

105

100

KNN 16 KNN 17 KNN 18 KNN 19 KNN 20

120

115

110

105

100

0 1 2 3 4 5 0 1 2 3 4 5 0 1 2 3 4 5 0 1 2 3 4 5 0 1 2 3 4 5
Months

Figure 6.5. Predictions of the KNN model on the training data set.

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0.75
y^

y^
0.6

0.50

0.4

0.25

0.2

0.00 0.0

5 10 15 20 0 10 20 30
LBNL.Chronic.Lag.01 UBNL.Chronic.Lag.09

Figure 6.15. PDP + ICE plot

From Figure 6.15, we can state that increasing “LBNL.Chronic.Lag.01” variable

(this is chronic lower body number of lifts with a lag of 1, which represents previous
day effect) we observe drop in 1RM change. But, we cannot make causal inference
stating that increasing this variable causes 1RM improvements to slow down (since 1RM
change is draping). This is only descriptive analysis, which is still useful to a degree
and represents the first step in the causal ladder (Pearl, Glymour & Jewell, 2016; Pearl
& Mackenzie, 2018; Pearl, 2019). Variable “UBNL.Chronic.Lag.09” (chronic upper body
number of lifts with a lag of 9 days) seems to affect 1RM change positively. The higher
the “UBNL.Chronic.Lag.09”, the higher the improvements in 1RM. But again, we cannot
make a causal claim here, only associative claim. Besides PDP + ICE assume there is no
interaction between variables (Molnar, 2018), and that might be a huge assumption
(for example increasing LBNL might cause UBNL to drop due to fatigue generated and
so forth).

You probably noticed that not all PDP lines on Figure 6.15 are parallel to each other
and might be even in the opposite direction. This could be a few individuals (in this case
these are rows of data, or instances and we do not know if these are specific individuals)
that show different patterns of dose - response. The above model is aiming to find
general patterns, regardless of the athlete. What we can do is to add information about
the athlete into the model, but in this case we have a potential issue of how to generalize

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to unseen and new athlete42. Figure 6.16, 6.17 and 6.18 depicts model performance when
additional variable indicating athlete is introduced into the model.

Ann Ann Bill Bill

1RM 1RM Change 1RM 1RM Change

115

110 215

105
210

100

205 −

Chris Chris Donald Donald

1RM 1RM Change 1RM 1RM Change

200

175

190
170

180

170

George George John John

1RM 1RM Change 1RM 1RM Change

205

200
155
predicted
195 true

150
190

185

Mark Mark Mike Mike

1RM 1RM Change 1RM 1RM Change

150 255

250

Thomas Thomas Wesley Wesley

1RM 1RM Change 1RM 1RM Change

220

155

150

212

208
25 50 75 25 50 75 25 50 75 25 50 75
day

Figure 6.16. True and predicted daily 1RMs using the model with included athletes as a variable

Figure 6.17. Variable importance using the model with included athletes as a
variable

42 Potential solution might be to use the main effects in this case, which represents the average effect
across all different athletes.

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2014; Layton & Ostermiller, 2017; Layton & Morrow, 2018). If you check Figure 2.13 from
Chapter 2, Review and Retrospective would correspond to check and adjust components
of the Deming’s cycle (Figure 6.23). Review is about demonstrating the performance
(which can be considered testing or the analysis of the response) while comparing it to
expected performance or outcome/performance goals or objectives. Retrospective is
about understanding and improving the process underlying performance (which can be
considered analysis of the dose, current state and plan and how it affects the response).
Retrospective is also trying to answer following questions:

What worked well?

What can be improved?

What will we commit doing in the next iteration?

Review Retr �ve

• Demoing the work • Iden�fying areas of

just completed improvement to
• Performance make next itera�
• Response be er
• Process
• Dose
Figure 6.22. Review and Retrospective

Review and Retrospective are like fractals: self repeating iterative process that
is self-similar at all scales. Figure 6.24 depicts involvement of these two inspect and
adapt components an all levels of strength training and planning.

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STRENGTH TRAINING MANUAL Volume Two

Outcome/Performance Process

Plan Objec�ves Plan

Do Response Dose

Check Compare Compare

Adjust Adjust

Review Retrospec�ve
Figure 6.23. Review and Retrospective are complementary aspects of “inspect and adapt” or check
and adjust components of the Deming’s cycle. Review is mostly concerned with demonstrable perfor-
mance or response, while Retrospective tries to understand the underlying process.

Rep Rep Rep Rep

Set Set Set Set

Exercise Exercise Exercise Exercise

Session Session Session Session

Day Day Day Day

Sprint Sprint Sprint Sprint

Phase Phase Phase Phase

Release Release Release Release

Figure 6.24. Fractal-like nature of the Review and Retrospective

This concept has already been introduced in Chapter 5 (Figure 5.20) and it is
highly related to the concept of Bias-Variance tradeoff introduced in this chapter. If the
program is too responsive and we adjust sprint and phase based on a single set, exercise
or a day, we will introduce too much variance into the program and probably jump to
noise. There is nothing wrong with inspecting and adapting at small levels, au contraire
it is beneficial to take into account day-to-day variation in the current state or the

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Individualiza� is crea� “equal pla ”,

re every is tr at similar individual
ten�

Individualiza� sur what it

takes t reach his SATISFICING ten� av
the
Figure 6.30. Better definition of individualization.

Back to the Set Level

Besides individualization by utilizing relative prescription in trying to match
athlete’s current ability (stable level of adaptation and current state), Review and
Retrospective also deals in making sure that what is actually prescribed is being realized.
For example, if a hard workout is planned, one wants to make sure hard workout is
actually done. This doesn’t mean following a program to the letter, but acknowledging
program constraints and bias, while providing for some variance to take into account
errors in the prescription and current ability of the athletes.

For example, if program calls for 80% 1RM, one way to make sure that actually
80% is used is to either use predicted or estimated 1RM (done with LV profile or using
RIR equation), VBT prescription by using velocity associated with 80% 1RM from the
individual LV profile (e.g., 80% 1RM is around 0.7 m/s48), or daily nRM49. Then the
training percentages can be based off that current performance rather than pre-phase
1RMs50.

48 Research seems to point to the fact that LV profile done using %1RM and velocity seems to be more
stable than 1RM (Jovanovic & Flanagan, 2014). For example, if 1RM changes, velocity at particular % will
tend to stay more or less stable, at least during the current phase. What does this mean practically? It means
that prescribing using velocity takes into account current ability (current state plus the rate of adaptation).
More research is needed to make this claim more valid, but also to explore chronic changes in absolute and
relative LV profile across time.
49 In my experience some athletes manifest higher variability of the current state (current ability) and
demand looser prescription, or a way to estimate daily max.
50 Another solution is using more loose prescription using rep or load zones

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this initial faster convergence to nRM, we would be jumping to noise or daily current
state, so the nRM can fluctuate unnecessarily around a hypothetical true value. Figure
6.31 depicts hypothetical example of updating planning nRM using DAPRE, Iterative
and Long Phase methods.

DAPRE Iterative Long Phase

Training RM (kg)

90

60

30

4 8 12 16 4 8 12 16 4 8 12 16
Session
Figure 6.31. Updating planning 1RM using DAPRE, Iterative and Long Phase approaches

The shaded areas represent the hypothetical true nRM, which is equal for all
three approaches. As can be seen from Figure 6.31, since updating and adjusting nRM
on the daily basis, DAPRE approach converges to true nRM sooner. After this there is
fluctuation in training nRM due to current state effects as well as noise.

Iterative approach that I am proponent of, and which will be discussed in greater
detailer later in this chapter, adjusts planning 1RM at the end of the phase. This
adjustment can be “slowly cooking” by using fixed interval, or can utilize “plus set”
and it’s estimated 1RM, similarly to DAPRE approach. In Figure 6.31 slower approach is
depicted, which results in slower conversion and longer “slow cooking” which is in my
opinion better for “pull the floor” type of programs.

Long Phase is your classical long program that uses initial nRM test and plans for
the weight for longer periods of time (e.g. 8-20 weeks). As depicted in Figure 6.31, Long
Phase estimated true nRM from the get go, but it kept the same planning nRM across
the duration of the program, which eventually resulted in big differences between true
and estimate nRM used for planning.

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About

Mladen Jovanović is a Serbian Strength and Conditioning Coach and Sport

Scientist. Mladen was involved in the physical preparation of professional, amateur
and recreational athletes of various ages in sports, such as basketball, soccer,
volleyball, martial arts, tennis and Australian rules football. In 2010, Mladen started
the Complementary Training website and in 2017, developed the scheduling and
monitoring application, AthleteSR. He is currently pursuing his PhD at the Faculty of
Sports and Physical Education in Belgrade, Serbia.

Twitter: @physical_prep

Instagram: @physical_prep

Facebook: www.facebook.com/complementarytraining/

Website: www.complementarytraining.net

Email: coach.mladen.jovanovic@gmail.com

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