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Jul 19 2019 02:58pm
Ranked 3rd for team improvement, after rangers & and Florida.




Vancouver Canucks

In: J.T. Miller, Micheal Ferland, Tyler Myers, Jordie Benn, Oscar Fantenberg
Out: Ryan Spooner, Markus Granlund, Derrick Pouliot, Luke Schenn, Ben Hutton
Wins Added: 4.9 wins
Salary Added: $7.8M

The recurring theme at the top of the list: the team is better, but is it enough (and was the timing right)? It’s the exact same story for the Canucks who are still likely not a playoff team despite going into next season much stronger. That’s mainly because the team has a very steep hill to climb from where they’ve been the past few seasons.

Vancouver didn’t really add anyone special here and they paid a high price (a conditional first for J.T. Miller and way too much money and term for Tyler Myers) to do it, but at the very least what the team added was capable NHL talent. You can’t really say the same thing about who they replaced and it’s the reason the Canucks rank so high. All five of Spooner, Granlund, Pouliot, Schenn and especially Hutton were large negative influences, and their exit has a large effect. No team gained more from addition by subtraction than the Canucks who dropped nearly two wins of negative value.

This post was edited by Secksii on Jul 19 2019 03:09pm
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Jul 19 2019 07:17pm
I hate it so much that this analysis is paywalled and I have to wait for you to post it here LOL. More that I'd like to understand how these guys arrive at these numbers rather than taking them at face value.
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Jul 20 2019 08:58am
Quote (Hizkuntza @ 19 Jul 2019 18:17)
I hate it so much that this analysis is paywalled and I have to wait for you to post it here LOL. More that I'd like to understand how these guys arrive at these numbers rather than taking them at face value.


He bases his stats of evolving hockey and created his own model explained below:

How the model works
It’s mostly outlined here in this FAQ posted before our 2017-18 projections, but basically it’s built at the player level using Game Score – a stat I adapted from basketball a few years ago. Working at the player level rather than the team level is one way that my model differs from others that are scaled via team performance only. It offers some challenges in terms of allocating proper credit, but has the advantage of being able to instantly factor for injuries and trades in ways a team-level model cannot.

Game Score is a linear weight model with the weights for each stat within it being derived according to the frequency of goals occurring from them and are as such:

Goals: 0.75
Primary Assists: 0.7
Secondary Assists: 0.55
Shots: 0.075
Blocks: 0.05
Penalty Differential: 0.15
Faceoff Differential: 0.01
5-on-5 Corsi Differential: 0.05
5-on-5 Goal Differential: 0.15

It uses data from each player’s last three seasons, with each component weighted by recency and regressed to the mean individually. That means that the weight for each prior season is different for goals than it is for shots or blocks (and different for forwards and defencemen), as is the regression factor. On top of that, there’s an age adjustment (using methods outlined here) performed at the start of each year that slowly lessens until the end of the season, as well as a small usage adjustment that factors in a player’s teammates and competition based on 5-on-5 Game Score.

From there, each player has a projection for each component going forward and that’s plugged into the Game Score formula to get a projected Game Score going forward. That’s then transformed into a wins above replacement rate (with replacement level being the 372nd forward and 186th defenceman) to create Game Score Value Added, or GSVA. That value is added up for each team based on the players in their starting lineup, and voila: team strength projections.
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Jul 23 2019 06:57pm
Wonder when Boeser gets signed.

Want to see it done.
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Jul 23 2019 07:16pm
Quote (Killingyouall @ 23 Jul 2019 17:57)
Wonder when Boeser gets signed.

Want to see it done.


Probably won't be until season starts, when they can put Roussel on IR. Or if they make a trade and get 4 mil cap
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Jul 23 2019 08:27pm
Quote (Secksii @ Jul 23 2019 09:16pm)
Probably won't be until season starts, when they can put Roussel on IR. Or if they make a trade and get 4 mil cap



I love reading your stat posts
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Jul 24 2019 09:43pm
Quote (Secksii @ Jul 20 2019 07:58am)
He bases his stats of evolving hockey and created his own model explained below:

How the model works
It’s mostly outlined here in this FAQ posted before our 2017-18 projections, but basically it’s built at the player level using Game Score – a stat I adapted from basketball a few years ago. Working at the player level rather than the team level is one way that my model differs from others that are scaled via team performance only. It offers some challenges in terms of allocating proper credit, but has the advantage of being able to instantly factor for injuries and trades in ways a team-level model cannot.

Game Score is a linear weight model with the weights for each stat within it being derived according to the frequency of goals occurring from them and are as such:

Goals: 0.75
Primary Assists: 0.7
Secondary Assists: 0.55
Shots: 0.075
Blocks: 0.05
Penalty Differential: 0.15
Faceoff Differential: 0.01
5-on-5 Corsi Differential: 0.05
5-on-5 Goal Differential: 0.15

It uses data from each player’s last three seasons, with each component weighted by recency and regressed to the mean individually. That means that the weight for each prior season is different for goals than it is for shots or blocks (and different for forwards and defencemen), as is the regression factor. On top of that, there’s an age adjustment (using methods outlined here) performed at the start of each year that slowly lessens until the end of the season, as well as a small usage adjustment that factors in a player’s teammates and competition based on 5-on-5 Game Score.

From there, each player has a projection for each component going forward and that’s plugged into the Game Score formula to get a projected Game Score going forward. That’s then transformed into a wins above replacement rate (with replacement level being the 372nd forward and 186th defenceman) to create Game Score Value Added, or GSVA. That value is added up for each team based on the players in their starting lineup, and voila: team strength projections.


thanks. Interesting that they don't bake stats like zone starts and quality of competition though and only leave that as a small after-the-fact adjustment, so that might mess up evaluation of players who are systematically sheltered or have to eat difficult minutes.

This post was edited by Hizkuntza on Jul 24 2019 09:44pm
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Jul 31 2019 12:43pm
Utanen looking good, Canada getting rekt by suomi
Member
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Aug 3 2019 04:03pm
Quote (Secksii @ Jul 31 2019 11:43am)
Utanen looking good, Canada getting rekt by suomi


He always seem to score vs Canada haha.
Member
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Aug 4 2019 10:59am
Damn is it just me or this off season feel ridiculously longer than usual? NEED SOME HOCKAY

Bored af, and there's no general hockey thread so gonna post trouba's singing here



He's only 25...i thought he was like 28 for some reason

This post was edited by Secksii on Aug 4 2019 11:02am
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