I still don't agree. An attribute at any given value should be the same at all weights. If a 270-lb player runs a 4.4 40 yard dash, he runs it in the same 4.4 seconds any other player would run it. 27 reps of 225 lbs is 27 reps of 225 lbs regardless of whether a 190-lb HB or a 320-lb DT does it.
Weight should factor into the soft caps for that attribute, making it more difficult to attain that same value (i.e., working much harder at it) instead of altering the value itself. My general response to scaling anything by heights and weights is to use normal distribution and change it by a particular amount for the distance from the mean.
Assume that for the set of all weights in GLB, mean ( μ ) is 250 and standard deviation ( σ ) is 30. That would mean that 68% of all players had weight between 220 and 280 ( μ±σ ) and 95% of all players would have weight between 190 and 310 ( μ±2σ ). For each σ/2 away from the mean, assess a ±2 penalty/bonus to soft caps dependent on position, e.g. heavy players would get bonuses to strength and penalties to speed.
Weight should factor into the soft caps for that attribute, making it more difficult to attain that same value (i.e., working much harder at it) instead of altering the value itself. My general response to scaling anything by heights and weights is to use normal distribution and change it by a particular amount for the distance from the mean.
Assume that for the set of all weights in GLB, mean ( μ ) is 250 and standard deviation ( σ ) is 30. That would mean that 68% of all players had weight between 220 and 280 ( μ±σ ) and 95% of all players would have weight between 190 and 310 ( μ±2σ ). For each σ/2 away from the mean, assess a ±2 penalty/bonus to soft caps dependent on position, e.g. heavy players would get bonuses to strength and penalties to speed.
Last edited May 4, 2009 09:44:05






























