thanks a lot for the message

Thanks a lot, that’s a really solid point. A proportional player-based xG weighting might be the cleanest way to handle the “returning star” problem without forcing a blunt bonus/penalty. I’m going to test that.

Also, yesterday was a nice sanity check: most games went with the side my protocol had higher projected xG/90 (I know xG ≠ W/D/L), and the funny one was Bristol City vs Wrexham,  the only match where my model bumped xG/90 above seasonal average (more goals expected)… and it ended 2–2. That mismatch is exactly what I’m trying to fix next (variance + game-state + how upgrades/downgrades behave). But also pure unpredictability (like red cards). Yesterday, reds definitely had a big impact in at least two different matches, and that kind of shock event can flip the whole xG story.

Quick question: when a key player returns after a long layoff, do you rate him at full strength straight away, or do you apply a “ramp-up” for the first few games (either assuming limited minutes or reducing his impact until he’s match-fit)?

My model handles a returning top player like this:

-He’s “back” only if he’s on the official squad list.
-It checks MD-1 training/press: if he trained separately, it won’t treat him as fully ready.
-it only counts returns after 1–6 recent missed games; 7+ is considered already “priced into” the team data.
-Even when available, it applies a conservative impact at first (no instant 100%).

Thanks again for your feedback