Here’s a real one: a guard averaging 16.0 points a game for Hapoel Galil Gilboa in Israel signs with Napoli Basket in Italy. Same player, same skill set, same summer. His new-season average: 12.0 points. Not a slump, not an injury, just a different league.
This is the single most common mistake in cross-league recruitment: treating a stat line as a fixed property of the player, when it’s actually a property of the player *and* the environment he produced it in. League strength, pace, shot volume available on a given roster, and the quality of the competition he’s facing every night all shape a raw number, and none of that travels with a player when he changes leagues.
It’s not that the player got worse. It’s that a possession he’d get an open look on in one league is a contested, help-defence-loaded possession in another. The shot volume he was getting on his old roster might not exist on the new one. A 16-point season in one context and a 12-point season in a materially tougher one can represent the *same level of player* , the number moved, the player didn’t.
This is the exact problem cross-league projection exists to solve. It’s not about predicting whether a player will “play well”; it’s about translating a real, observed level of production through the actual, measured relationship between two leagues, so a 16.0 average from Israel doesn’t get read as a decline when it becomes a well-earned, appropriately scaled 12.0 in a tougher league. In this specific case, MelonIQ’s model projected 12.03 before the season started. The real number came in at 12.0.
The takeaway for recruitment isn’t “distrust every stat line.” It’s: a number without its league context is only half the information. The other half is what that number is actually worth somewhere else.