2018-19 stats and underlying metrics thread

[Maukkis]

And in case you were wondering:

Tanev and Lowry have been deployed together for basically the entire year. Their threats are going to be basically the same.

 

[Duke749]

Tanev and Lowry have been deployed together for basically the entire year. Their threats are going to be basically the same.

They’re our bottom 6 version of Scheif/Wheeler

 

[DRW204]

So last year that line essentially had a career year defensively?

i think so. i think Lowry is still good defensively, but last year it was excellent for him in both D and O.

this year vs last

they've taken a bit of a step back defensively
they're more or less the same in offensive opportunities/shot attempts
they've taken a step back in finishing/scoring ability

 

[Whileee]

Lowry's defensive stats this year have been beyond abysmal as well. He's at a -7.2 CF% and a -7.5 FF% after being well in the positives last season. Granted a lot of that has to do with being very bad on offense but you don't have numbers that terrible as a premiere defensive player. Tanev is having the "best" season of his career by those metrics at -8.2 and -8.1 respectively.

They limit shots and chances against, but don't generate shots and chances for this season. The charts below (2018/19) use metrics that are adjusted for teammates, opponents, and various context variables.

A player that has had a particularly tough season is Little, while Copp has been very strong this season.

 

[Whileee]

They’re our bottom 6 version of Scheif/Wheeler

If you want to understand why the Jets have been so much less effective at 5v5 this season than previously, the following graphs illustrate the substantial drop in outputs for Scheifele and Wheeler. I'm not sure you can blame Maurice for their drop in performance, since I doubt he's changed his system or coaching style. I've been harping on it from early in the season; Scheifele seems to be very focused on producing offense and it's resulted in very poor play by his line in their own zone. They can generate offense once they are in the offensive zone, but they are spending way too much time in their own zone. There is a big contrast in how Scheifele and Copp play in their defensive zone. Copp is intense, aggressive and physical. Scheifele plays a peripheral game, avoids board battles and instead is looking to poke and chip pucks to try to generate a break. Wheeler needs to play with more speed and aggressiveness with the puck. He hustles without the puck, but when he gets it he seems to stop skating and starts looking for a pass. He needs to be aggressive in taking the puck to the net and create problems for the defense.

Here are the stats... they are still scoring, but they aren't generating shots and are giving up too many.

Very strong performance in 2015-18...

Very disappointing in 2018/19....

 

[Duke749]

Those are brutal @Whileee . And they match my eye test pretty well too.

 

[KB1971]

Those are brutal @Whileee . And they match my eye test pretty well too.

I've said it a few times all year that 55 and 26 have been trying to "attack with slow", always slowing the play as soon as they get the puck.

Now is this a decision to try to save their bodies / efforts for the playoffs? Doesn't help PoMo plays them both too much and on the PK.

 

[AKAChip]

If you want to understand why the Jets have been so much less effective at 5v5 this season than previously, the following graphs illustrate the substantial drop in outputs for Scheifele and Wheeler. I'm not sure you can blame Maurice for their drop in performance, since I doubt he's changed his system or coaching style. I've been harping on it from early in the season; Scheifele seems to be very focused on producing offense and it's resulted in very poor play by his line in their own zone. They can generate offense once they are in the offensive zone, but they are spending way too much time in their own zone. There is a big contrast in how Scheifele and Copp play in their defensive zone. Copp is intense, aggressive and physical. Scheifele plays a peripheral game, avoids board battles and instead is looking to poke and chip pucks to try to generate a break. Wheeler needs to play with more speed and aggressiveness with the puck. He hustles without the puck, but when he gets it he seems to stop skating and starts looking for a pass. He needs to be aggressive in taking the puck to the net and create problems for the defense.

Here are the stats... they are still scoring, but they aren't generating shots and are giving up too many.

Very strong performance in 2015-18...

View attachment 203279


Very disappointing in 2018/19....

View attachment 203281

This is by far the biggest problem with the Jets right now. These two are drowning and there's no one who can pick up the slack.

 

[CaptainChef]

If you want to understand why the Jets have been so much less effective at 5v5 this season than previously, the following graphs illustrate the substantial drop in outputs for Scheifele and Wheeler. I'm not sure you can blame Maurice for their drop in performance, since I doubt he's changed his system or coaching style. I've been harping on it from early in the season; Scheifele seems to be very focused on producing offense and it's resulted in very poor play by his line in their own zone. They can generate offense once they are in the offensive zone, but they are spending way too much time in their own zone. There is a big contrast in how Scheifele and Copp play in their defensive zone. Copp is intense, aggressive and physical. Scheifele plays a peripheral game, avoids board battles and instead is looking to poke and chip pucks to try to generate a break. Wheeler needs to play with more speed and aggressiveness with the puck. He hustles without the puck, but when he gets it he seems to stop skating and starts looking for a pass. He needs to be aggressive in taking the puck to the net and create problems for the defense.

Here are the stats... they are still scoring, but they aren't generating shots and are giving up too many.

Very strong performance in 2015-18...

View attachment 203279


Very disappointing in 2018/19....

View attachment 203281

Great stuff. Matches the eye test for sure. When your leaders are having subpar years, Little & Lowry have been so-so, Laine has had quite a miserable year, and Helle has been so-so as well, its no wonder we are nowhere near the team we were last year

 

[garret9]

I get what you're saying, but can't it be a combination of those statements.

Take for example the game of chess....it is not solvable (kind of like hockey imo). HOWEVER. You can objectively say which moves are better than other moves, and you can prove it. You can't say "on move #1 you do this and you will win the game". You CAN say "on move #1 you do this and you increase your likelihood of winning the game". I think this is the crux with all of this scientifically based hockey analysis. You can't exactly deny the descriptive power of it (at least if you want to stay on valid conversational grounds). But the prescriptive power of it is limited...since you can't separate and isolate each variable and test them independently. Hope that makes sense I have been hitting the bottle tonight.

you would be correct in your assessment as it's not so absolute as one or the other. Its multivariable and random.

Yes, there is "randomness"... for two reasons:
1) These are humans and not robots. Fairly self explanatory.
2) There is randomness just simply inherent in sample sizing. For example, if we pretended there was no human nature element and each team was exactly equal in skill, work ethic, etc. the season would work out like a coin flip and we'd still have a spread in outcomes/results.

My argument wasn't that there is no randomness. My argument was that the NHL would only be "too many variables" if the variables were 100% completely random.


Going further into this, we have a fairly good handle how much randomness (or "luck") there is in hockey. Of course, the amount of randomness will depend on what you are talking about and how large the sample is.

For example, the variation in the standings is about 33% luck. There has been two ways we can estimate this. In one case we looked at how the spread of the regular season looks typically, vs how much spread you have if the league was 100% "luck." The difference being how much is "non-luck". The other was part of Josh Weissbock's masters thesis, using some machine learning, and he found the same as the previous method, to the first decimal point.

Corsi, or really shot volume, accounts for about 2/3rds of non-luck variation in the standings, or 44% of the overall variation in the standings. This tells you that ignoring Corsi is ignoring about 2/3rds of the game. I should indicate that I'm looking at overall shot volume, so this would include special teams performance and penalty differential and how that impacts shot volume.

This leaves about 23% for finishing talent, shot quality, goaltending, and other factors we cannot yet account for. Now you may wonder why those things that can cause hot streaks be such a low percentage... it's because the hot-streak portion of said varables are accounted for in the huge 33% portion chunk.

Another way to look at the last paragraph would be that it's far more common for a good goaltender to perform poorly, a good shooter to go cold, or someone that gets shots up close get kept outside, than it is for a outshooting team to get outshot.

Ex: Jets' with Laine on the ice have been about equal in xGoals RAPM (adjusting for usage) for: -0.8, -0.13, -0.12. Jets actual Goals for with Laine on the ice though has varried greatly over the past three seasons: +0.26, +0.17, -0.04.

So, any team can still be good at shot quantity and not succeed a great deal, if they are below average in the other categories unless they get some luck. Also a team can outperform their shot volume. But it is difficult to sustain such a thing.

For more, I looked at this last year:
A closer look at Corsi, how much it matters, and what it...

 

[garret9]

I should mention that I meant CF% rel. and FF% rel. which is Corsi for % relative and Fenwick for % relative. It's basically the percentage of attempts at the opponents net relative to the attempts at their own net while the player is on the ice compared to the same thing when they are off the ice. Fenwick is the same, it just includes blocked shots. It basically means that since Lowry has a FF% rel of -7.5 and Tanev has a FF% rel. of -8.1, when Lowry is on the ice, the Jets allow 7.5% more attempts at their net than they do when he's off the ice. Considering the Jets as a team are largely pretty bad in this metric, it's particularly telling that Lowry and Tanev are so much worse than average there. Again, not the be all and end all stat, but ideally you want more attempts than the other team, not significantly less.

Tells me enough!

Is that all year? Where’d you find that? Do they have a game by game basis? I’m interested in the Boston and Calgary games. Ducks and Kings are throwaways honestly.

It's on hockey-reference.com but you can find it on pretty much any half-decent hockey stats site. As far as I can tell, they don't have those stats for individual games at that particular website. I would imagine that without the data against the worst teams in the league, the numbers look even worse but that would just be speculation on my part. I'll see if I can find that data, but I'm not the best person for the job.

Yes, rel Corsi would be Corsi% with player on the ice versus off the ice (but still playing that game). So for example Player A has a 53% Corsi and the team has a 50% Corsi when he's on the bench, then he is a +3. Before we had rel TeamMate Corsi or RAPM methods, it was an archaic way to try and reduce team effects. Ultimately though relTM has been shown to be superior method than rel, and RAPM superior to both. relTM is a weighted WOWY of all the players a player skates with, while RAPM is a regularized adjusted method using ridge regression.

Both RAPM and relTM can be found on Evolving Wild twins' website:
Evolving-Hockey

 

[Saintb]

Yes, there is "randomness"... for two reasons:
1) These are humans and not robots. Fairly self explanatory.
2) There is randomness just simply inherent in sample sizing. For example, if we pretended there was no human nature element and each team was exactly equal in skill, work ethic, etc. the season would work out like a coin flip and we'd still have a spread in outcomes/results.

My argument wasn't that there is no randomness. My argument was that the NHL would only be "too many variables" if the variables were 100% completely random.


Going further into this, we have a fairly good handle how much randomness (or "luck") there is in hockey. Of course, the amount of randomness will depend on what you are talking about and how large the sample is.

For example, the variation in the standings is about 33% luck. There has been two ways we can estimate this. In one case we looked at how the spread of the regular season looks typically, vs how much spread you have if the league was 100% "luck." The difference being how much is "non-luck". The other was part of Josh Weissbock's masters thesis, using some machine learning, and he found the same as the previous method, to the first decimal point.

Corsi, or really shot volume, accounts for about 2/3rds of non-luck variation in the standings, or 44% of the overall variation in the standings. This tells you that ignoring Corsi is ignoring about 2/3rds of the game. I should indicate that I'm looking at overall shot volume, so this would include special teams performance and penalty differential and how that impacts shot volume.

This leaves about 23% for finishing talent, shot quality, goaltending, and other factors we cannot yet account for. Now you may wonder why those things that can cause hot streaks be such a low percentage... it's because the hot-streak portion of said varables are accounted for in the huge 33% portion chunk.

Another way to look at the last paragraph would be that it's far more common for a good goaltender to perform poorly, a good shooter to go cold, or someone that gets shots up close get kept outside, than it is for a outshooting team to get outshot.

Ex: Jets' with Laine on the ice have been about equal in xGoals RAPM (adjusting for usage) for: -0.8, -0.13, -0.12. Jets actual Goals for with Laine on the ice though has varried greatly over the past three seasons: +0.26, +0.17, -0.04.

So, any team can still be good at shot quantity and not succeed a great deal, if they are below average in the other categories unless they get some luck. Also a team can outperform their shot volume. But it is difficult to sustain such a thing.

For more, I looked at this last year:
A closer look at Corsi, how much it matters, and what it...

Thanks. This post is very helpful to me. It puts an analytical frame around what serious fans probably feel somewhat intuitively.

 

[Jimby]

Yes. I believe the Lowry line was matched against the oppositions' best more often this year than last which would contribute to the stats not being as good this year.

Wasn’t Lowry’s line matched up against the Bergeron and Gaudreau lines?

 

[Whileee]

Yes, there is "randomness"... for two reasons:
1) These are humans and not robots. Fairly self explanatory.
2) There is randomness just simply inherent in sample sizing. For example, if we pretended there was no human nature element and each team was exactly equal in skill, work ethic, etc. the season would work out like a coin flip and we'd still have a spread in outcomes/results.

My argument wasn't that there is no randomness. My argument was that the NHL would only be "too many variables" if the variables were 100% completely random.


Going further into this, we have a fairly good handle how much randomness (or "luck") there is in hockey. Of course, the amount of randomness will depend on what you are talking about and how large the sample is.

For example, the variation in the standings is about 33% luck. There has been two ways we can estimate this. In one case we looked at how the spread of the regular season looks typically, vs how much spread you have if the league was 100% "luck." The difference being how much is "non-luck". The other was part of Josh Weissbock's masters thesis, using some machine learning, and he found the same as the previous method, to the first decimal point.

Corsi, or really shot volume, accounts for about 2/3rds of non-luck variation in the standings, or 44% of the overall variation in the standings. This tells you that ignoring Corsi is ignoring about 2/3rds of the game. I should indicate that I'm looking at overall shot volume, so this would include special teams performance and penalty differential and how that impacts shot volume.

This leaves about 23% for finishing talent, shot quality, goaltending, and other factors we cannot yet account for. Now you may wonder why those things that can cause hot streaks be such a low percentage... it's because the hot-streak portion of said varables are accounted for in the huge 33% portion chunk.

Another way to look at the last paragraph would be that it's far more common for a good goaltender to perform poorly, a good shooter to go cold, or someone that gets shots up close get kept outside, than it is for a outshooting team to get outshot.

Ex: Jets' with Laine on the ice have been about equal in xGoals RAPM (adjusting for usage) for: -0.8, -0.13, -0.12. Jets actual Goals for with Laine on the ice though has varried greatly over the past three seasons: +0.26, +0.17, -0.04.

So, any team can still be good at shot quantity and not succeed a great deal, if they are below average in the other categories unless they get some luck. Also a team can outperform their shot volume. But it is difficult to sustain such a thing.

For more, I looked at this last year:
A closer look at Corsi, how much it matters, and what it...

Good post. I'd add the observation that the contribution of "luck" or "talent" has its own distribution, so for a given team / season the contribution of luck / talent varies around the overall estimates.

 

[WPGChief]

Yes. I believe the Lowry line was matched against the oppositions' best more often this year than last which would contribute to the stats not being as good this year.

Here’s the issue: most hockey analysts have come to the conclusion that Quality of Competition doesn’t matter anywhere near as much as people think it does.

"It matters, on average, about three or four times less than quality of teammates does,” said Micah Blake McCurdy, “even after you take into account how you have five opponents at 5v5 but only four teammates.”

[...]

“You shouldn't ever be allowed to talk about a guy's competition without making a quantitative comparison to his teammates,” said McCurdy. “I don't think you can say anything fairly unless you observe that rule of thumb.”

“Shift by shift, [Quality of Competition] is very meaningful; it's the aggregate patterns that average out,” he added. “Coaches are much more consistent with their lines than with their matchups, partly by necessity. Your linemates are all from one team (basically, not counting trades) and your opponents are from 30.”

It makes intuitive sense: you’re far more likely to play with the same quality of linemates than you are the same quality of opponents, because a coach has direct control of the former and only indirect control of the latter.

There's been lots of discussion on quality of competition and lots of it comes to the same conclusion that it isn't nearly as important as quality of teammates in terms of impacts on actual results that can be accounted for. Some more reading:

TITLE	PRIMARY AUTHOR	PUBLISHED	TOPIC	KEYWORD(S)	SOURCE
Why Quality of Competition Doesn't Matter To Analytics Experts Anymore	Daniel Wagner	2018​	Discussion	QoC, QoT, Roles	Vancouver Courier
By the Numbers: Can New Leafs' Defenceman Ron Hainsey Handle Tough Minutes?	Dom Luszczyszyn	2017​	Measures	Quality of Competition / Teammates	The Athletic
Behind the Numbers: The Issues with Binning, QoC, and Scoring Chances	Garret Hohl	2017​	Measures	Quality of Competition / Teammatess	Hockey Graphs
WoodMoney: A new way to figure out quality of competition in order to analyze NHL data	Darcy McLeod	2016​	Measures	Quality of Competition	Because Oilers
Matt Hunwick, Martin Marincin and Quality of Competition	Dom Luszczyszyn	2016​	Measures	Quality of Competition / Teammates	Hockey Graphs
Bootstrapping QoT/QoC and the Sedin Paradox	Emmanuel Perry	2016​	Measures	Quality of Competition / Teammates	Corsica
Just How Important is Quality of Competition? Very. Also, Not Much. It’s All Relative	Ryan Stimson	2016​	Measures	Quality of Competition / Teammates	Hockey Graphs
The Distribution of QoC/QoT at the NCAA Level	Ryan Stimson	2016​	Measures	Quality of Competition / Teammates	Hockey Graphs
Distribution of Quality of Competition and Teammates Metrics	Conor Tompkins	2015​	Measures	Quality of Competition / Teammates	Hockey Graphs
The Significance of Quality of Competition	Domenic Galamini	2014​	Measures	Quality of Competition / Quality of Teammates	Blue and White Brotherhood
How much does matching competition matter on a team level?	Garik16	2014​	Measures	Competition, Quality, Skaters, Teammates	Hockey Graphs
Relative Importance of a Player’s Impact on Teammate Shooting Percentage	David Johnson	2012​	Measures	Quality of Competition / Teammates	Hockey Analysis
Importance of Quality of Competition / Teammates	David Johnson	2012​	Measures	Quality of Competition / Teammates	Hockey Analysis
A Competition Metric Based on Ice Time	Eric Tulsky	2012​	Measures	Quality of Competition / Teammates	NHL Numbers
The Importance of Quality of Competition	Eric Tulsky	2012​	Measures	Quality of Competition / Teammates	NHL Numbers
Quantifying Players' Impact on Teammates Shooting Percentage	Eric Tulsky	2012​	Measures	Quality of Competition / Teammates	NHL Numbers
Impact of Last Change on Quality of Competition	Gabe Desjardins	2012​	Measures	Quality of Competition / Teammates	Arctic Ice Hockey
Does QualComp Matter?	Dirk Hoag	2011​	Measures	Quality of Competition / Teammates	Arctic Ice Hockey
Young Forwards and Tough Defensive Minutes	Eric Tulsky	2011​	Measures	Quality of Competition / Teammates	Broadstreet Hockey
Do Playmakers Drive Teammates’ Shooting Percentage?	Eric Tulsky	2011​	Measures	Quality of Competition / Teammates	Broadstreet Hockey
Further to: Does QualComp Matter?	Gabe Desjardins	2011​	Measures	Quality of Competition / Teammates	Arctic Ice Hockey
Can a Player Influence his Teammates’ Shooting Percentage?	Gabe Desjardins	2011​	Measures	Quality of Competition / Teammates	Arctic Ice Hockey
Yeah, But: QualComp	Jared L.	2011​	Measures	Quality of Competition / Teammates	Driving Play
How and Why: OZ Coke Charts	Rob Vollman	2011​	Measures	Quality of Competition	Hockey Prospectus
Slight Statistical Improvement: Corsi Qualcomp	Gabe Desjardins	2010​	Measures	Quality of Competition / Teammates	Arctic Ice Hockey
Behind the Net: Junior Hockey Quality of Competition	Gabe Desjardins	2009​	Evaluation	Quality of Competition, Prospects	Hockey Prospectus
Estimating AHL Quality of Competition by Looking at Points Per Game of Opposing Forwards	Jonathan Willis	2008​	Measures	Quality of Competition / Teammates	Copper and Blue

[TBODY] [/TBODY]

 

[garret9]

Re-reading my old article there @WPGChief and I need to proof read.

 

[Jimby]

IIRC a few days ago on HFJets someone explained that Expected Goals took into account quality of competition while CF% did not. It was said this was the reason why xGF and CF differed on a per game basis with xGF perhaps being a better indication of what actually happened in the game while CF% was better when looking at larger numbers of games and was more predictive. Did I understand that correctly? I find that xGF usually matches what I saw on the ice better than CF%.

 

[Guffman]

Caveman Guff here. Just some quick questions on xGF, since I see that tossed out on post game threads.

First question: When people post that the Jets xGF is something like 2.87, can you verify that takes into account the player taking the shot?

For example, there is quite a big difference Lowry taking a shot versus Schiefele (shooting%). If it takes into account the player taking the shot.

Seecond question: Is there some blanket formula that says “we expect a player to score X% of the time on a breakaway” that is used for purposes of expected goals but doesn’t acknowledge that the particular player on a breakaway SUCKS at breakaways?

Third question: Not so much a question but a comment. If you’re trying to apply xGF to a player like Laine, it probably doesn’t work very well. If he’s on fire, he’s more apt to score, while when he’s on a slump, be definition, he won’t score.

So, when Laine is slumping, I probably don’t care so about his xGF if his big opportunities typically result in him breaking a stick and missing the net.

Thanks. Just want to see how much care is but into providing this stat.

 

[winnipegger]

I guess the only question I have after that excellent explanation by garret....is this:

Is the hockey world seeing results from adopting these data whole heartedly into the decision making process? It seems a fairly robust analysis, and it would seem that those who discount it are at a serious disadvatage. Is anyone in the managerial side still discounting it? And for those utilizing it in full; can they hold up a piece of paper in a boardroom and say "here, look, since taking these data into account our team's results have improved." I am just thinking about the Carolina hurricanes and how they struggle to even make the damn playoffs despite their shot metrics.

 

[garret9]

I guess the only question I have after that excellent explanation by garret....is this:

Is the hockey world seeing results from adopting these data whole heartedly into the decision making process? It seems a fairly robust analysis, and it would seem that those who discount it are at a serious disadvatage. Is anyone in the managerial side still discounting it? And for those utilizing it in full; can they hold up a piece of paper in a boardroom and say "here, look, since taking these data into account our team's results have improved." I am just thinking about the Carolina hurricanes and how they struggle to even make the damn playoffs despite their shot metrics.

Hurricanes were poor and had a bit of a culture (edited) issue in that they were seemed as a loser franchise.
They’ve used shot differential drivers to improve their wins per dollar, but you still need superstars to win and generally they both cost a lot, are hard to acquire, and everyone, with or without analytics, knows who the Crosby’s and McDavids are.
In otherwords, analytics are probably much better for making a good team great than they are at making a bad team good.

*this is all a hypothesis by me and not proven on anything

 

[garret9]

Caveman Guff here. Just some quick questions on xGF, since I see that tossed out on post game threads.

First question: When people post that the Jets xGF is something like 2.87, can you verify that takes into account the player taking the shot?

For example, there is quite a big difference Lowry taking a shot versus Schiefele (shooting%). If it takes into account the player taking the shot.

Seecond question: Is there some blanket formula that says “we expect a player to score X% of the time on a breakaway” that is used for purposes of expected goals but doesn’t acknowledge that the particular player on a breakaway SUCKS at breakaways?

Third question: Not so much a question but a comment. If you’re trying to apply xGF to a player like Laine, it probably doesn’t work very well. If he’s on fire, he’s more apt to score, while when he’s on a slump, be definition, he won’t score.

So, when Laine is slumping, I probably don’t care so about his xGF if his big opportunities typically result in him breaking a stick and missing the net.

Thanks. Just want to see how much care is but into providing this stat.

I'll try to help:

Shot Metric Types
Corsi - shot attempts (goals, saves, misses, blocks)
Fenwick - unblocked shot attempts (goals, saves, misses)
Shots (on net) - as implied (goals, saves)
Goals - as implied (goals)
xGoals - expected goals (There are many different formulas but most are Fenwick adjusted for shot location and other factors. Note: reasoning why it's Fenwick adjusted and not Corsi adjusted is because the locational data in NHL pxp uses where the shot was blocked and not where the shot was taken)

Metric Forms
_F - events for team or for team with player on ice, ex: CF, xGF
_A - events against team or against team with player on ice, ex: CA, xGA
_D or _PM - differential or plus minus, ex: CD = CF - CA
_F% - events for team relative to all events, ex: CF% = CF / (CF + CA)
_/60 - events relative to an hour of ice time, ex: CF/60

Type of Adjustments
Score Adjustments - adjusts the volume of shots based on the score of the game and period
Venue Adjustments - adjusts for home team advantage
Arena Adjustments - adjusts for arena biases in how some arenas tend to count more conservatively/liberally or tend to push shots one way or another
RAPM - adjusts using ridge regression for players on the ice (QoC and QoT), zone starts, schedule (B2B or not), and the previously mentioned adjustments



Now to try and answer your questions directly...

1) Generally not. xGF is to account for shot quality not finishing talent. These are considered two separate skillsets and so they are typically separated. There was on xGF model that accounted for shooter talent but that model no longer exists.

2) xGoals use things like rush shots vs sustained pressure shots to adjust for shot quality. Again there is no adjustment for player shooting, but that is intentional. You can think of things like:
GF => xGF x luck x finishing talent
GA => xGF x luck x goaltending talent

3) xGF still matters for a player like Laine. Yes the average shot for Laine at the same location is worth more than the average player, but that doesn't mean that more shots or closer shots are not better for Laine to have. It's merely the shot finishing translator would be different. Ex: Laine has his worst career xGD/60 this season and his worst GD/60 this season (both when RAPM adjusted). That's not coincidental. That said, with Laine and Lemieux both having the worst xGD/60 (RAPM adjusted) on the Jets, I give Laine more leeway than a player like Lemieux with less finishing talent.

 

[Whileee]

I'll try to help:

Shot Metric Types
Corsi - shot attempts (goals, saves, misses, blocks)
Fenwick - unblocked shot attempts (goals, saves, misses)
Shots (on net) - as implied (goals, saves)
Goals - as implied (goals)
xGoals - expected goals (There are many different formulas but most are Fenwick adjusted for shot location and other factors. Note: reasoning why it's Fenwick adjusted and not Corsi adjusted is because the locational data in NHL pxp uses where the shot was blocked and not where the shot was taken)

Metric Forms
_F - events for team or for team with player on ice, ex: CF, xGF
_A - events against team or against team with player on ice, ex: CA, xGA
_D or _PM - differential or plus minus, ex: CD = CF - CA
_F% - events for team relative to all events, ex: CF% = CF / (CF + CA)
_/60 - events relative to an hour of ice time, ex: CF/60

Type of Adjustments
Score Adjustments - adjusts the volume of shots based on the score of the game and period
Venue Adjustments - adjusts for home team advantage
Arena Adjustments - adjusts for arena biases in how some arenas tend to count more conservatively/liberally or tend to push shots one way or another
RAPM - adjusts using ridge regression for players on the ice (QoC and QoT), zone starts, schedule (B2B or not), and the previously mentioned adjustments



Now to try and answer your questions directly...

1) Generally not. xGF is to account for shot quality not finishing talent. These are considered two separate skillsets and so they are typically separated. There was on xGF model that accounted for shooter talent but that model no longer exists.

2) xGoals use things like rush shots vs sustained pressure shots to adjust for shot quality. Again there is no adjustment for player shooting, but that is intentional. You can think of things like:
GF => xGF x luck x finishing talent
GA => xGF x luck x goaltending talent

3) xGF still matters for a player like Laine. Yes the average shot for Laine at the same location is worth more than the average player, but that doesn't mean that more shots or closer shots are not better for Laine to have. It's merely the shot finishing translator would be different. Ex: Laine has his worst career xGD/60 this season and his worst GD/60 this season (both when RAPM adjusted). That's not coincidental. That said, with Laine and Lemieux both having the worst xGD/60 (RAPM adjusted) on the Jets, I give Laine more leeway than a player like Lemieux with less finishing talent.

This post should be stickied for future reference. Thanks.

 

[ps241]

This post should be stickied for future reference. Thanks.

Yea I agree (not joking) it would be a great sticky reference.

 

[garret9]

Generally players take about 5-15% of the shots that they were on the ice for.
High volume shooters usually around 18%.
Gallagher is at 21% and is the leader this season.

 

[DRW204]

Is there any publicly available trackers or stats guys that have tracked the amount of odd man rushes teams get/give up 5v5? As a team and with specific players on ice?