How does that model work? I'd like to see more before giving it too much weight.
(but, as a biased fan, I'll happily believe Mr. Cane and get Morrissey locked up for 8x4)
Matt holds a Bachelor’s degree in Engineering and Management from McMaster University, and a Master of Science in Applied Math from Ryerson University, and is currently employed in Risk Management at a Canadian bank. Same numbers game used in risk management is being applied.
That looks a little off to me - to put it mildly.
What successes, other than Kane does he have?
I can see a fairly high probability of 1 year. That would be an arb award. 2 years should be very close to 0% chance, 3 years not much more.
All the results are in the spreadsheet. If you had looked you'd have noticed that all signings are listed showing predicted AAV/length vs. signed AAV/length.
Kane's contract was off by only 1.5%, Domi 16% and correct on term, Caggiula 13% & correct on term, Hinostroza 23% & correct on term, Niemi 8.6% and correct on term, Paajarvi 0.1% and correct on term. If you read through the whole document his margin of error has been 10% thus far in 2018.
Other than Evander Kane in terms of length of the other 11 contracts so far signed in the off season he's been correct 9 of 11 times, twice off by a single year.
The model has been quoted by a number of veteran posters here as well as in The Athletic by numerous hockey journalists including the ever popular Murat Ates.
Matt Cane is an editor for Hockey Graphs. If that name sounds familiar it's because it was founded by our own Garret Hohl.
It's not meant to be absolute, but certainly a great tool, particular when playing around with a secondary website like CapFriendly. Using the two hand in hand will give the average user here tremendous insight into the summer signings for the Jets & the rest of the NHL.