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NHL equivalency (NHLe) scoring rates have been around for a while and have morphed and changed as more data becomes available. NHLe is a system that looks at how much scoring a player retains from various leagues when he moves to the NHL the following year. It’s not meant to be a definitive evaluation by any means, but it helps tell us two things:


It gives us a basic hierarchy of feeder league difficulty. By virtue of NHLe, we can say things like “the KHL is a stronger league than Liiga.” That helps us establish, at a glance, how difficult a given player’s competition has been in a non-NHL league.
It gives us a rough idea how a prospect is performing and may be progressing.

Recently, Emmanuel Perry decided to look not just at the main NHL feeder leagues, but every single league on the Elite Prospects “leagues” page. His approach differs from those of previous NHLe calculations. Perry’s approach takes a different tact (as explained to Ryan Stimson of The Athletic, here), attempting to broaden the prospect sample size and understand how all leagues, not just the immediate major junior, college, and pro leagues churn out NHL players. Using his new calculations, we can take a peek at the players in the Sharks prospect pipeline.Here are the all of the Sharks’ forwards who have some chance of eventually making the NHL. Perry released a more in-depth prospect success model based on his NHLe adjusted scoring rates, here, for further reading.

This table shows each forward prospect’s age as of July 15, 2018. It lists his points-per-game rate in his current league (including playoffs), his league, and his NHLe were he to take his current scoring rate to the NHL over an 82-game season. Three players — Ivan Chekhovich, Sasha Chmelevski and Linus Karlsson — played games in two different leagues last season. Those players have two rows, one with the NHLe scoring rate for each league he played in. They’re added up in the “total” column, though thinking about that now it doesn’t make much sense. Rather than add the two league totals, the column should have reflected the percentage of 2017-18 season the player spent in each league then estimated for that percentage of an 82-game season. Instead, take a look at the rates for each individual league as an indication of how well he performed in that league.