The 16,887-Set Case Study
A one-person training log, read set by set

One training log, read for patterns.

One IFBB pro logged 16,887 sets over four years. This case study describes patterns in that log and ideas worth testing; it does not establish rules for everyone.

Scope: This is an observational case study of one enhanced athlete, not controlled research, medical advice, or a prediction of what another person will achieve. The charts describe this log; the practical ideas still require individual testing and adjustment.

16,887
Sets logged
1,167
Workouts
209
Weeks, unbroken
4
Years, one lifter
01The trigger

The 13-rep pattern in this log

13
reps, near the most common range before a logged load increase

Across 720 logged load increases, the most common preceding range was 13 to 15 reps. In this dataset, the next session was commonly 1 to 2 reps lower. That is an observed pattern in one lifter's log, not a universal progression rule.

Idea to test

One progression option is to hold a weight until the best set reaches the top of an assigned rep range, then use the smallest practical increase and reassess. The useful trigger and response will differ by exercise and person.

Reps right before the weight went up

≤7 8–10 11–12 13–15 16+ most

720 weight increases. The single most common trigger sat at 13–15 reps.

02Rep ranges

Where reps landed in this log

In this dataset, 60% of logged work sets landed between 8 and 15 reps, with a median of 10. This describes one lifter's exercise selection and training choices; it does not establish an ideal rep range.

Sets under 6 reps were about 6% of the log, while sets over 20 reps were under 1%. The distribution is descriptive, not proof that the same range is best for another lifter.

Idea to test

An 8 to 15 rep range can be one starting option, then adjusted for the exercise, goal, comfort, technique, and individual response.

Where 16,887 sets landed

1–5 5.9% 6–7 9.7% 8–10 38.6% 11–12 21.6% 13–15 16.1% 16–20 7.3% 21+ 0.8%

Crimson marks the 8 to 15 zone, where six in ten logged sets landed.

03Volume

A volume pattern in this log

±6%
observed range in the estimated back-strength measure across four years

Back was the most-trained muscle in this log, at 22 and sometimes 28 sets a week, while the estimated pulldown-strength measure stayed within a roughly ±6% band. Chest estimates rose during lower-volume periods. This association does not prove that volume caused either pattern.

Idea to test

If progress is flat while volume is already high, review exercise selection, execution, rep range, recovery, and whether a planned reduction is appropriate before assuming more sets are needed.

Estimated strength, by half-year

Chest ↑ climbing Back — flat 2022 2026

In this log, the highest-volume period coincided with stalled estimates; later lower-volume periods coincided with gains.

04Time off

Returns after layoffs in this log

In the logged returns after breaks of two months or more, upper-body lifts reached 92 to 124% of their prior estimated strength, while leg lifts reached 71 to 84%. These comparisons describe this lifter and do not establish a general muscle-memory timetable.

What this case showed

In this lifter's returns from layoffs, upper-body loads restarted near 90% and leg loads near 80% of prior values. An appropriate starting point and return timeline will differ by person, exercise, time away, health, and current tolerance.

Strength kept after 2–3 months off

100% Upper 92–124% Legs 71–84%

In these comparisons, some upper-body lifts met or exceeded prior estimates while leg lifts remained below them.

05Fatigue

Set-to-set fatigue differed in this log

Across same-weight sets in this dataset, average rep drop-off by the third set differed by muscle group. Biceps and hamstrings showed about a one-third drop, while the recorded back and chest movements showed smaller average drops.

These groupings reflect one lifter's exercises, technique, rest periods, and logging. They do not establish a fixed fatigue profile for each muscle or person.

Idea to test

If a lifter repeatedly shows large set-to-set drop-off with stable technique and rest, fewer sets per session or more recovery may be worth testing. Programming should follow the individual's data rather than these labels.

Reps lost by the third set

Biceps 33% Hamstr. 33% Triceps 31% Quads 25% Delts 25% Back 22% Chest 21%

Observed average rep drop-off by the third logged set in this dataset.

06Loading

Loading jumps followed the equipment

A fixed weekly increase did not match every movement in this log. Recorded load changes followed the increments the equipment allowed: 5 lb on cable raises, 10 to 20 on cable stations, 25 to 45 on machine stacks, and 90 on the hack squat.

Idea to test

Track the smallest available jump for each movement. If that jump is too large, holding load while adjusting reps can be one progression option, provided technique, recovery, and the overall program support it.

The smallest honest jump

Cable raise 5 lb Cable stack 10–20 Machine 25–45 Hack squat 90 lb

One "add weight" rule can't fit a 5 lb raise and a 90 lb squat.

07Structure

One block structure Bernardo uses

One Costa Fitness structure uses a six-week block in which effort rises across the first five weeks and the sixth reduces training demand. Advanced techniques, when appropriate, are limited rather than treated as a default.

Example from this framework

One deload example reduces load and reps by about 25% and uses two sets for one week. That is a programming example, not a universal prescription; the timing and size of a reduction should follow the lifter's plan and response.

The six-week effort arc

W1 W2 W3 W4 W5 W6 peak reset

Illustration of the six-week structure used in this framework.

Straight with you

I'm an enhanced athlete. Here's what that means for this data.

I compete as an IFBB pro, and I've been open about being enhanced. That matters for how you read these numbers — so let's split them honestly.

Ideas to test, not rules to copy

The ideas: when to consider adding weight, how rep ranges and equipment increments interact, and how fatigue might inform set counts. These are hypotheses drawn from this log. Whether they transfer should be tested and adjusted for the individual.

Case-specific capacities

The capacities: how much volume I tolerated, how quickly the logged lifts returned, and how much estimated strength remained after a layoff. These measures are specific to this case and may be affected by enhancement. Do not treat the set counts or timelines as expected results.

Your log can support better questions

Every number here came from one export and a defined analysis. A similar review may help identify patterns worth testing in your own training; it does not guarantee a particular finding or outcome.

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Use this if you want the same level of review applied to your own training, nutrition, and weekly decisions.