Here's what I would usedabo wrote:
These curves have nothing to do with the quality of players. Two players peaking at 18 could be completely different, one could be NHL-ready while the other could be useless and never amount to anything. This is just to estimate player career curves.
I have tweaked the peak percentages to the following:
1 (Extremely rare, 0.1%)
2 - 3 (Very rare, 2%)
4 - 5 (Rare, 7%)
6 - 8 (Somewhat common, 15.9%)
9 - 12 (Very common, 50%)
13 - 15 (Somewhat common, 15.9%)
16 - 17 (Rare, 7%)
18 - 19 (Very rare, 2%)
20 (Extremely rare, 0.1%)
I tweaked all the combinations of atrophy percentages too.
Peaked at
1: 1%
2 - 3: 5%
4 - 5: 10%
6 - 8: 20%
9 - 12: 39.5%
13 - 15: 14%
16 - 17: 8%
18 - 19: 2%
20: .5%
Regression starts
1: 4% (about 75% of players should have peaked by now which is 25 yo)
2 - 3: 5% (about 90% of players peaked by now at 28 yo)
4 - 5: 10% (about 98% of players should have peaked by now at 30 yo)
6 - 8: 13% (about 99.5% of players should have peaked by now at 32yo)
9 - 12: 46% (all players should have peaked by now at 34yo)
13 - 15: 14%
16 - 17: 5%
18 - 19: 2%
20: 1%
The main problem with your curves, IMO is they do not have enough spread. This may be too much spread but I'd prefer that.
Also, I will note that it is very important that regression and progression is not CONSTANT. Not simply all attributes get better by 2 then peak, then each year regression happens all attributes get worse by 2. SImplistic example obviously, but you get the idea. For example look at Steve Yzerman. He peaked in the late 80's/early 90's, then came down from that peak but played solid for alot of years, then became a role player for several years. I would love to see career paths like that. Or Todd Bertuzzi who bounced around as a 3rd liner/2nd liner for alot of years before all at once progressing into a 1st liner all-star at 28/29 all at once. And his later career path also follows that as he regressed all at once, but now has solidied into solid 3rd liner again for several years.