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Sabres Trade Alex Nylander to Chicago for D Man Henri Jokiharju

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9 hours ago, SwampD said:

Why am I not thrilled that we gave away the more talented player?

Like Grigorenko vs Girgensons?

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He says he's really good friends with Laaksonen, they have the same hometown. 

And he knows 6K. (The goalie Ukko Pekka Luukkonen - no idea if i spelled that right)

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10 minutes ago, inkman said:

Like Grigorenko vs Girgensons?

That was a push.

Before Grigorenko left Colorado for the KHL, their numbers were comparable.

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1 hour ago, TrueBlueGED said:

 

Sorry, WildCard. I hope you're sitting down. 

408889581_download(7).thumb.png.48a756852c5eb54513d290a22d0d45d5.png

Casey stuck with Okposo all year sucked, surprise!

 

Casey in his 6 game stint the year prior, not saddled with idiots:

 

download.thumb.png.99b45c1b94291d8dd22e2112eff0afd6.png

 

People like you really undermine the usefulness of these charts when you take everything in poor context and post stats just to prove a narrative.

 

 

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3 minutes ago, triumph_communes said:

People like you really undermine the usefulness of these charts when you take everything in poor context and post stats just to prove a narrative.

Explain the significance of the difference between 68 minutes TOI versus 884 minutes TOI.  I'll hang up and listen.

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9 minutes ago, triumph_communes said:

Casey stuck with Okposo all year sucked, surprise!

 

Casey in his 6 game stint the year prior, not saddled with idiots:

 

download.thumb.png.99b45c1b94291d8dd22e2112eff0afd6.png

 

People like you really undermine the usefulness of these charts when you take everything in poor context and post stats just to prove a narrative.

 

 

It's not a narrative that Casey was bad this reason. It's just the harsh reality. As to the charts...a regression with a sample of 68 minutes, such as the one you just posted, is almost certainly useless. I can't be 100% sure without seeing the full model, but I'd bet my life savings that such a model has a p-value around 3 million and a confidence interval that includes the entire range of outcomes. 

Edited by TrueBlueGED
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5 minutes ago, TrueBlueGED said:

check please restaurant GIF

I can't be 100% sure without seeing the full model, but I'd bet my life savings that such a model has a p-value around 3 million and a confidence interval that includes the entire range of outcomes.

 

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18 minutes ago, ... said:

Explain the significance of the difference between 68 minutes TOI versus 884 minutes TOI.  I'll hang up and listen.

https://arxiv.org/pdf/1006.4310.pdf

 

Pages 34-37

A snippet:  

Quote

In the introduction, and elsewhere in this paper, we noted that Henrik and Daniel Sedin have a much higher error than other players with a similar number of shifts.

One reason for this high error could be that the twin brothers spend most of their time on the ice together. Daniel spent 92% of his playing time with Henrik, the highest percentage of any other player combination where both players have played over 700 minutes. Because of this high colinearity between the twins, it is difficult to separate the individual effect that each player has on the net goals scored on the ice. It seems as though the model is giving Henrik the bulk of the credit for the offensive contributions, and Daniel most of the credit for defense. Henrik’s defensive rating is strangely low given his low goals against while on the ice. Likewise, Daniel’s offensive rating is unusually low.

i.e., when a player spends the majority of his time with another skater, the player's stats become indistinguishable from each other.

 

Casey spent all year with Okposo and in limited time with Thompson, and Thompson otherwise spent his year with Sobotka outside a few games.  As a result, their charts are going to mimic those players to a large degree, a digression explicity noted by the creator of the statistics.

 

The terms in the regression are built off of a massive dataset, so the error by smaller minutes played put into the model is relatively low.

13 minutes ago, TrueBlueGED said:

It's not a narrative that Casey was bad this reason. It's just the harsh reality. As to the charts...a regression with a sample of 68 minutes, such as the one you just posted, is almost certainly useless. I can't be 100% sure without seeing the full model, but I'd bet my life savings that such a model has a p-value around 3 million and a confidence interval that includes the entire range of outcomes. 

If this is what you think, then you really have no clue how this model was created.  Read the links above.

 

 

Edited by triumph_communes

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1 hour ago, Randall Flagg said:

This is foolish to glean from watching 20 clips from just two games...but IMO Jokiharju's ceiling is sky high. I was impressed as hell by what I saw, against the future stanley cup champions, from a 19 year old. It's summer so we're allowed to be hopeful. 

My favorite clip is his zone entry where he goes from blue line to blue line and then once he enters the zone immediately recognizes the space to his right and cuts hard into it allowing the opposing player to simply glide off towards the middle where Kaiju was originally driving too. Clean entry with the opportunity to set up teammates in space. 

 

11 minutes ago, TrueBlueGED said:

It's not a narrative that Casey was bad this reason. It's just the harsh reality. As to the charts...a regression with a sample of 68 minutes, such as the one you just posted, is almost certainly useless. I can't be 100% sure without seeing the full model, but I'd bet my life savings that such a model has a p-value around 3 million and a confidence interval that includes the entire range of outcomes. 

I was actually wonder what the CI was for the 68minutes. Either way, I think good players can make others better. Bad players make mediocre players worse. Casey was clearly bad last year and that was partly due to age, usage, and linemates. Either way we can see that he had a negative overall impact and needs to work a lot this summer to physically be ready for NHL minutes. 

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"Henri is a young, mobile defenseman who has shown he can compete at the NHL level," Sabres General Manager Jason Botterill said. "His international success last season only furthered his development and we are excited to add him to our current group of defensemen."

https://www.nhl.com/sabres/news/sabres-acquire-henri-jokiharju-from-blackhawks-for-alexander-nylander/c-308219208

2 minutes ago, LGR4GM said:

My favorite clip is his zone entry where he goes from blue line to blue line and then once he enters the zone immediately recognizes the space to his right and cuts hard into it allowing the opposing player to simply glide off towards the middle where Kaiju was originally driving too. Clean entry with the opportunity to set up teammates in space.

Link?

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3 minutes ago, triumph_communes said:

Casey spent all year with Okposo and in limited time with Thompson, and Thompson otherwise spent his year with Sobotka outside a few games.  As a result, their charts are going to mimic those players to a large degree, a digression explicity noted by the creator of the statistics.

The terms in the regression are built off of a massive dataset, so the error by smaller minutes played put into the model is relatively low.

Are you saying Casey spent 92% of his time with Okposo and TT 92% of his time with Sobotka?  

Since the league is littered with pairs, are you arguing the fancy stats aren't built to account for the effect one might have on the other within a normal context?

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@triumph_communes Natural stat trick says Casey played 500 of his 825 5v5 minutes without Okposo. Statistics sucks so I'm far less familiar with the branch as a whole than almost any other branch of mathematics, but I'm pretty comfortable with the regression competently isolating him from Kyle. 

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20 minutes ago, triumph_communes said:

https://arxiv.org/pdf/1006.4310.pdf

 

Pages 34-37

A snippet:  

i.e., when a player spends the majority of his time with another skater, the player's stats become indistinguishable from each other.

 

Casey spent all year with Okposo and in limited time with Thompson, and Thompson otherwise spent his year with Sobotka outside a few games.  As a result, their charts are going to mimic those players to a large degree, a digression explicity noted by the creator of the statistics.

 

The terms in the regression are built off of a massive dataset, so the error by smaller minutes played put into the model is relatively low.

At 5v5: 

Casey spent 326minutes (38%) with Okposo and 533minutes (62%) without Okposo. (minutes are rounded to nearest whole number) 

Kyle and Casey both see their CF% increase when they are away from eachother. 

Tage spent 354mins with Sobotka and 361 without, Tage sees a significant rise in his CF% without Sobotka and Sobotka sees a significant dip without Tage. 

Edited by LGR4GM
error

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2 minutes ago, LGR4GM said:

At 5v5: 

Casey spent 326minutes (38%) with Okposo and 533minutes (62%) without Okposo. (minutes are rounded to nearest whole number) 

Kyle and Casey both see their CF% increase when they are away from eachother. 

Tage spent 354mins with Sobotka and 361 without, Tage sees a significant rise in his CF% without Sobotka and Sobotka sees a significant dip without Tage. 

Interesting... and add another year of maturity and some better players and see what kind of changes happen.  At this point, what choice do we have but an off season hope and a prayer.

Edited by North Buffalo

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For fun, take a look at Erod with Sobotka versus without. Sobotka is an offensive black hole of sadness. Erod without Sobotka: 53.46% CF versus with 45.76%

Erod with Tage: 60% CF but only 47min TOI. 

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3 minutes ago, ... said:

Are you saying Casey spent 92% of his time with Okposo and TT 92% of his time with Sobotka?  

Since the league is littered with pairs, are you arguing the fancy stats aren't built to account for the effect one might have on the other within a normal context?

The highest errors in the model come when percentages are above 60% for players with >700 minutes.

 

image.thumb.png.a3618ec1a9f686cc9718ea496e7f3548.png

 

And yes, quite literally, the author of these fancy stats states:

 

Quote

By not including interaction terms in the model, we do not account for interactions between players. Chemistry between two particular teammates, for example, is ignored in the model. The inclusion of interaction terms could reduce the errors. The disadvantages of this type of regression would be that it is much more computationally intensive, and the results would be harder to interpret.

 

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I'm sorry, I read the number incorrectly, Tage is about the same away from Sobotka. 

Tage with Sobotka: 46.17 CF%

Tage w/out Sobotka: 47.93 CF%

(CF% - Percentage of total Corsi while that combination of players is on the ice that are for the selected team. CF*100/(CF+CA))

Edited by LGR4GM

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17 minutes ago, triumph_communes said:

Casey spent all year with Okposo

Do you have any data to back this up, because I'm pretty sure that the 2nd half of the season, Casey didn't play with Okposo at all and Okposo was with Girgs/Lars.

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3 minutes ago, LGR4GM said:

I'm sorry, I read the number incorrectly, Tage is about the same away from Sobotka. 

Tage with Sobotka: 46.17 CF%

Tage w/out Sobotka: 47.93 CF%

(CF% - Percentage of total Corsi while that combination of players is on the ice that are for the selected team. CF*100/(CF+CA))

Tage being about the same with Sobotka as without might honestly be a testament to Tage. If he has any talent it at least allowed him to avoid being sucked into the Sobotka black hole.

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12 minutes ago, LGR4GM said:

At 5v5: 

Casey spent 326minutes (38%) with Okposo and 533minutes (62%) without Okposo. (minutes are rounded to nearest whole number) 

Kyle and Casey both see their CF% increase when they are away from eachother. 

Tage spent 354mins with Sobotka and 361 without, Tage sees a significant rise in his CF% without Sobotka and Sobotka sees a significant dip without Tage. 

So in a nut shell. 

This past year the Sabres where a mix of players ( predominately the middle six) that had the worst possible impact on each other when on the same line.

Even scuffling them just mixed the bad mix for the same results. The bad mix being TT and Sobs. No matter where they where in the line up they drug down their line mates.

 

So the hope is by adding even just 2 players with *average* possession and CF% numbers will greatly elevate the middle six exponentially?

Is my simple assumption of these stats an over-simplification?

 

Edited by woods-racer

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2 hours ago, SwampD said:

Because people are drooling over themselves about what a steal this trade is, while at the same time we’re being told that Nylander is the guy with more talent, he just isn’t motivated, all while we know (rumored to know) that he had been in a toxic environment that maybe he feels the organ-eye-zation knew about. Does anybody actual talk to these guys?! We know GMTM/DB didn’t.

I dream of a day when the Sabre hold on to talent instead of this addition by subtraction method of team building that hasn’t worked out so well for us.

If he was THIS talented/motivated he would of played more games in a Sabres uniform. Until that talent shows up on NFL ice it doesn't exist. A change of scenery was perfectly fine and the return we got made it a little sweeter for Sabres fans.

You cannot keep players in your system if they don't make the jump in a pre determined amount of years. Nylander never lit it up consistently in the AHL while others have made a bigger impact. Might as well get an asdet that's a little younger and has already shown some success in the NHL.

As for the whole toxic environment. Meh not interested in hyperbole that happens when fans need to manufacture reason for the failures. Reasons for our failures are pretty straight forward. Started with Rigas, peaked with Golisano, and then exhausted all of us with Pegula's trying to tear it down and do a full head to toe rebuild. But we are on the other side of this, and fully believe the toxic environment issues are in the past, the team just needs to come of age now and grow. 

Many of you have lost patience. Mistakes were made, lessons learned. We are moving forward IMO not backward. Just got to stay patient 🤪

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1 minute ago, rakish said:

Where was Nylander during the prospects camp?

Obviously at the Chicago Merc.

Edited by North Buffalo
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30 minutes ago, triumph_communes said:

The highest errors in the model come when percentages are above 60% for players with >700 minutes.

And yes, quite literally, the author of these fancy stats states:

You did read this: https://arxiv.org/pdf/1201.0317.pdf Right?

And this: https://hockey-graphs.com/2019/01/14/reviving-regularized-adjusted-plus-minus-for-hockey/ too?

Because those more aptly apply to the metrics we're discussing.  You're using paper #1 to make your argument.

Quote

Additionally, we will use a technique called “regularization” in the linear regression (this is where “Regularized” in “Regularized Adjusted Plus-Minus” comes from). Regularization in a linear regression comes in two main forms – ridge regularization (also known as Tikhonov regularization or L2 regularization) and LASSO regularization (“Least Absolute Shrinkage and Selection Operator”, also known as L1 regularization). The main purpose is to address multicollinearity that is present in the data. Why do we care about multicollinearity? Well, when a pair of players play together for a significant amount of time (the classic example is Henrik and Daniel Sedin who spent over 90% of their career time on ice together) the coefficient estimates in a traditional OLS regression will be extremely unstable (and therefore unreliable). Regularization combats this by adding some amount of “bias” into the model (Gaussian “white-noise”) to decrease the variance in the coefficient estimates [more info here]. What this means, essentially, is unstable coefficient estimates are “penalized” (or “shrunk”) based around a Gaussian distribution where 0 is the mean. Ridge regularization will pull coefficients towards 0 (but never exactly 0). LASSO regularization will pull coefficients toward 0 and also “zero” some coefficients. 

 

Edited by ...
...edited this to make it less dense.

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