Two players, same league, same position, both averaging 18 points a game. One is 22. One is 30. If you evaluate them purely on the number, you’d treat the signings identically. You shouldn’t.
Age isn’t just a biographical detail; it’s a signal about where a player’s production is likely headed next, and the direction matters as much as the current level. A 22-year-old putting up 18 points is plausibly still climbing: his role, his efficiency, and his physical tools may all still be developing. The 30-year-old putting up the same 18 is far more likely to already be at his peak, or past it. The number is identical. The trajectory is not.
This cuts both ways, and it’s easy to get lazy about it in either direction. Teams sometimes overpay for the younger player purely on “potential,” ignoring that potential is not the same as production; a 20-year-old averaging 8 points on a bad team is not automatically more valuable than a 26-year-old averaging 16 on a good one. But the opposite mistake is just as common: treating a 30-year-old’s current numbers as a stable baseline for the next three seasons, when the realistic range of outcomes for a player at that age skews down, not up.
The honest way to use age in an evaluation isn’t a hard cutoff; it’s a widening or narrowing of the range of outcomes you should expect. A younger player’s stat line carries more upside risk in both directions: he might break out, or he might plateau below where you projected. An older player’s stat line is more likely to be close to his ceiling already, which makes it more reliable as a baseline, but also means you shouldn’t expect much more.
When MelonIQ projects a player into a new league, age-curve adjustment is part of the model precisely for this reason; the same raw stat line means something different depending on whether it belongs to a player who is likely still improving or one who has likely already arrived.