There are many comments about inter-individual differences in peaking time. I have heard it mentioned a few times that it depends on how advanced you are, with the reasoning that the more you lift the longer you need to deload before peaking. It sounds reasonable. So lets see how well it stacks up.
I put together this survey and asked some simple, related questions.
https://www.surveymonkey.com/r/PXF88NX
I then took the data and looked for correlations. There is a strong correlation (tight diagonal cluster) between Wilks and Total, as expected. But peaking time (Weeks2Pk) is not correlated with any of the variables.
So it seems the theory that peaking time being dependent on how advanced you are isn't supported by
this data. So what to do then? Over the years, Boris has found that 3 weeks for the deload works well for most people. So that is his default setup. Indeed, according to the data 3 weeks is right in the middle of where most people fall.
So 3 weeks is a good place to start. Now if you run through the programs and find that you need either more or less time you can adjust from there. Boris has put together some guidance on how you can make the adjustments yourself. He typically uses Variant 2 (Table 1). If you need 4 weeks to peak, switch the 1st and 2nd weeks. Likewise if you need less, move the weeks around to match one of the patterns that has been shown to work for people.
Table 1
Variants For Weekly Load Distribution In A Competition Mesocycle (B. Sheiko, 2011)
Variants |
% Monthly Volume |
|
|
|
Number of Lifts |
|
1st Week |
2nd |
3rd |
4th |
1st Week |
2nd |
3rd |
4th |
TOTAL |
1 |
40% |
27% |
20% |
13% |
108 |
73 |
54 |
35 |
270 |
2 |
29% |
38% |
22% |
11% |
101 |
134 |
77 |
38 |
350 |
3-1 |
28% |
24% |
34% |
14% |
120 |
103 |
147 |
60 |
430 |
1-3 |
38% |
20% |
28% |
14% |
190 |
100 |
140 |
70 |
500 |