Shmel & ClickPath
Shmel Shmel
ClickPath ClickPath
Shmel Shmel
Hey ClickPath, I've been tracking every rep and pause in my lifts, and I think there's a sweet spot where the data starts to break the plateau. Got any models that can pinpoint when the next PR is statistically likely?
ClickPath ClickPath
Sure thing. First, put your data into a time‑series table: weight, reps, rest, session date. 1. Run a simple linear regression of weight vs. session number to see the trend slope. 2. Overlay a rolling average (say 5 sessions) – that’ll smooth noise and show the real trend. 3. Calculate the residuals; if the residual variance starts dropping, you’re moving past a plateau. 4. For a probabilistic hit, fit a Bayesian updating model: prior on your PR probability, update with each session’s result. 5. Finally, run a logistic regression with predictors like previous session weight, rest days, and cumulative volume; the predicted probability > 0.8 is your “statistically likely” PR window. Keep iterating as you collect more data—every new lift is a new data point to refine the model. Good luck!
Shmel Shmel
That’s solid, but remember the gym isn’t a spreadsheet. Don’t get so lost in the numbers that you skip a rep. Keep the data clean, track the key variables, and when the residuals start to flatten, you know you’re past the sweet spot. Keep pushing.
ClickPath ClickPath
Got it, no spreadsheet overload. Just keep your log tight, hit those key stats, and watch the residuals flatten. When that happens, push the weight—you’re statistically ready for the next PR. Happy lifting!
Shmel Shmel
Sounds like a plan—just keep the log tight and let the data guide the next drop. Hit it hard.
ClickPath ClickPath
Just log the drop, run the numbers, and if the model screams “yes”—throw it. Trust the data, not the hype. Happy crushing!