Classical one-repetition maximum prediction uses a single equation: if you can lift a weight for N repetitions, your maximum is that weight divided by a fixed conversion factor. Brzycki, Epley, Lombardi — different equations, same assumption. The conversion factor doesn't depend on how heavy the weight is or which exercise you're doing.
Across 388 exercises, the assumption is wrong. The conversion factor increases with load: at light weights, each additional repetition implies a larger fraction of maximal capacity than at heavy weights. A set of 15 reps at 50% of max tells you less about your true maximum than a set of 5 reps at 85% of max, and the classical equations underestimate this asymmetry.
The weight-dependent formula reduces prediction inconsistency by 17-22% compared to classical benchmarks. The improvement is largest for lighter, more diverse exercises that dominate typical training programs — exactly the exercises where the one-size-fits-all conversion performs worst. Heavy compound lifts, where classical equations were originally calibrated, show smaller improvement because the conversion factor approaches linearity at high loads.
The practical consequence: a trainer using classical equations to prescribe loads from a 10-rep test systematically misprescribes for exercises that aren't bench press or squat. The error is small for the lifts that get studied and large for the lifts that get programmed. The gap between research calibration and training-floor application is the gap between the exercises the equations were built for and the exercises people actually do.