For four years I logged essentially everything I did in the gym — 16,887 working sets across prep, offseasons, and everything between. Then I went back through the whole log looking for patterns: when progress actually happened, when it stalled, and what the weeks before a stall had in common.
The full visual ebook, The 16,887-Set Case Study, walks through the data and its limits. This article is the short version: three patterns that changed how I train and coach. They are observations from one enhanced lifter, not rules for everyone.
1. More volume did not always track more progress in this log
Across this log, higher set counts did not consistently coincide with higher estimated strength. Some high-volume periods coincided with flatter performance and more sessions rated as grinders. That association does not prove that volume caused the pattern.
The coaching idea I took from it was to earn volume rather than default to more. When progress is flat, set count is only one variable to review alongside exercise selection, execution, recovery, and the limits of the estimate. That same one-variable discipline appears in mesocycle progression.
2. Logged progression often followed repeatable conditions
In this log, many load increases followed a similar setup: top sets reached the assigned rep range with reps still in reserve and no obvious performance decline in the sessions before.
I began using “top of rep range at RIR 2+” as one condition to consider before adding load. That is a method drawn from this log, not a proven threshold for every lifter or exercise.
3. Some performance changes appeared before flat weeks
Before some flat weeks in this dataset, reps slipped at a constant load and effort scores rose at similar performance. Those observations can prompt a review, but they do not establish a universal fatigue signal or identify a medical cause.
I now use patterns like these as one input when deciding whether to hold, reduce, or continue a block. The appropriate adjustment still depends on the person, program, and context.
What an n=1 log cannot prove
Honesty matters here: this is one lifter’s log, not a controlled study. It cannot prove optimal volume for you, and it cannot separate every variable. Sleep, prep dieting, and life stress are all baked into those four years. What it can do is show the kinds of questions a detailed training log can support and offer patterns worth checking against your own data.
The practical takeaway is to log enough context to review rather than assume. Reps, loads, effort, and short notes can support that review, but a log may not produce a clear answer.
The full case study, including charts and limitations, is free to read: The 16,887-Set Case Study. Coaching can apply the same review process to the information a client provides without promising a specific finding or outcome.