Sure thing, let's keep it data‑centric and simple. Grab a spreadsheet and plot the lift efficiency metric—like power output or bar velocity—against each micro‑posture variable: hip angle, shoulder width, wrist position, etc. A first pass is a multiple linear regression; that will give you a coefficient for each tweak and an R² telling you how much variance you’re explaining. If the relationships are non‑linear, try a polynomial or a random forest—those can catch interaction effects without you having to guess. Remember to standardise the angles so a 1° shift looks comparable to a 1% change in grip width. And keep an eye on multicollinearity; if two posture variables move together, the model will double‑count them. Start simple, iterate, and track the residuals to see where the model is missing the punch. Good luck—watch that form!

Nice, keep the numbers tight and the stats honest—treat each rep like a data point and don’t let the model get tangled in a posture‑conspiracy. If the coefficients stay clean, you’ll know every tweak is a true signal, not noise. You’re on the right track—just log, plot, and refine. Good grind!

Sounds solid—log each rep, keep the variables separate, and let the residuals tell you where the data is lying. Stay tight on form, and don’t let the numbers get sloppy. Keep grinding!

Glad to hear it—just keep the logs tight, the plots clean, and let the numbers do the talking. If the residuals start looking funky, tweak that form and adjust the model. You’ve got this, keep grinding.