The Rangers Are Terrible on the Road

All data below provided by Corsica.Hockey

Flaws in the analysis below:
- There is insight missing. These context/input metrics are not enough, and the output metrics are also not enough.
- Other items of interest could be things like Penalties Drawn/Taken home vs away and the following special teams success. Hank/Georgiev splits, and their GSAA home/away. I mean, a million other things. This barely scratches the surface
- Building off above, only 5v5
- I steered away from using relGF% and individual points due to sample size, but if there’s a player who is torching points at home and silent on the road, could be worthwhile digging in there, as well.
- Many more!

At the time I sat down to draw up this blog, the Rangers have played 28 games on the season under David Quinn. Their record during home games, a more than respectable 10-4-1. On the road, however, they fall to 3-8-2. Among NHL teams, this is the most egregious difference between home and away record.

Uncertain as I am that there is a clear way to figure out why this is happening by just using data, I wanted to take a look anyway. We’ll start with context metrics like TOI%, Zone Start Rate, Quality of Competition and Teammates, and then finally output metrics like relCF% and relxGF% - and a brief analysis of what I think each visualization tells us.

First, Time on Ice Percentage.

 dashed line is where Home TOI% == Away TOI%

dashed line is where Home TOI% == Away TOI%

Really nothing to dive into here. Quinn is giving basically equal amounts of TOI during both home and away games. There are a couple of players who find themselves ‘off the line’. Chytil seems to play more at home than on the road and Andersson vice versa, but this could very easily just be sample size oddities.

Next, Zone Starts. Hopefully begin to see some changes here where the difference between first change and last change may come into play.

haZSR.png

And differences we see. Names that stand out for more offense on the road than at home: Andersson, DeAngelo, Howden, Vesey, Smith. For the opposite, more defensive zone starts at home than on the road: Zibaenjad, Staal, Pionk, McLeod, Buchnevich. These are names we’ll have to keep in mind when we start looking out more performance related metrics, but, we continue with context.

Quality of Competition. My anticipation is that we see more discrepancies here than we do with Quality of Teammates. Quinn can control his five-man units much more easily home and away than he can who his players line up against. Both metrics of quality we’ll be using are based on Corsica.Hockey’s xG model.

haComp.png

One thing I love that this visualization shows, that admittedly I forgot it would. Look how bunched together a lot of these names are. The differences in Quality of Competition are much, much lower than we expect them to be. It’s not as easy as Staal and Pionk always out there against the other team’s top opposition.

At the same time, these bunched together data points are going to limit the oddities that we do see in differences between home and away, which should’ve been anticipated by me, but alas, wrong again. Of course we have some major outliers here like Andersson, Claesson, Vesey, Zucc, Buchnevich, and McLeod.

Otherwise, I’m not entirely certain if there’s anything we can discern from this besides continuing to realize that the differences in QoC are small.

Now the same, but with teammates.

haTeam.png

This, as anticipated, is very similar to the zone start rate visualization. This is something that Quinn has full control over for both home and road games. It doesn’t matter what the opposition coach does because Quinn controls what five man units go onto the ice.

It should be highlighted though that the bunching of players here is far more spread out than the bunching on the same Competition based viz.

Biggest item that stands out to me here is Pionk, who both on the road and at home gets fed the easiest usage in terms of Quality of Teammates, which will be important to keep in mind in the now coming output visualizations.

Relative shot attempt percentage. Keeping in mind that this metric tells us the difference between an individuals shot attempt percentage and the shot attempt percentage of the team when that individual is not on the ice. This same methodology is used in the second visualization, Relative expected goals percentage.

haCF.png

Neal Pionk: Best teammates, bad output. Gotta love it.

So this is where we have to start using our input visualizations with our output visualizations. What do we remember from those first few charts? TOI and Teammates show no major differences between home and away, totally controlled by Quinn. Quality of competition, while showing differences, is too bunched together to glean anything from. Zone start rate, however, did show some items of interest. Recall:

And differences we see. Names that stand out for more offense on the road than at home: Andersson, DeAngelo, Howden, Vesey, Smith. For the opposite, more defensive zone starts at home than on the road: Zibaenjad, Staal, Pionk, McLeod, Buchnevich. These are names we’ll have to keep in mind when we start looking out more performance related metrics, but, we continue with context.

Andersson: Bad both on the home and on the road, worse at home.
DeAngelo: Positive in both venues, better at home.
Howden: Bad in both, basically the same
Vesey: Positive on the road and negative at home
Smith: Good, even.

Zibanejad: Good both, better on the road
Staal: Horrid on the road and a positive player at home
Pionk: I mean, come on
McLeod: Bad hockey players are bad at hockey
Buchnevich: Great at home, bad on the road.

Quick idea, maybe limit Pionk’s minutes on the road? Let’s see what relative xGF% says…

haxgf.png

Just want to take a random moment and point out that Kreider is absolutely amazing at hockey. Moving on to the same exercise as above:

Andersson: Better on the road than at home.
DeAngelo: Negative in both venues, worse on the road.
Howden: Negative in both venues, worse at home
Vesey: Almost even on the road and negative at home
Smith: Both negative, but about even.

Zibanejad: Great on the road and bad at home - interesting.
Staal: Positive both, about the same, this has been a good exercise for Staal.
Pionk: Better numbers than on relCF, but still a negative player in both venues.
McLeod: Bad hockey players are bad at hockey
Buchnevich: Negative in both venues, about even.

In summary, i t’s probably worthwhile to create a quick data table that looks at TOI% and relCF% and relXGF%.

First, away, where there is clearly something wrong with this team:

awaySummary.PNG

Pionk and Staal get caved on the road. I’m not sure if it’s as easy as just saying: well, play them less! But, it is something to keep an eye on during road games.

homeSummary.PNG

The Staal-Pionk imapct is somewhat mitigated on home ice where they’re, still bad, but not a complete waste of ice. Hayes is really, really good. Imagine what he could do with proper QoT? But that seems to be changing tonight as he suits up at the wing with Kreider and Zibanejad. I’m interested to see how that goes.

Ultimately, there wasn’t a ton of insight here to get some information. The home/road splits for the Rangers were equally as terrible under AV last season, 21 wins at home and only 13 at home. This, a major difference from the year before, with 21 home wins and 27 road wins.

Perhaps it’s just randomness at work and small sample sizes. Ultimately, besides overplaying Staal and Pionk on the road, it is seemingly difficult to explain via context the differences in success.

Jimmy Vesey and how to spend too much time dissecting a bridge deal

It was announced today that the Rangers have re-signed Jimmy Vesey to a two-year bridge deal with an AAV of $2.275m. Any normal person will look at that and say: "Fine." and that's about it. I'm not normal.

I've never been totally convinced that Jimmy Vesey would amount to anything more than a third-liner in the NHL. More often than not, players who spend four-years in the NCAA and enter the NHL at age 23 don't fare well in the pro-game. I expected more of the same from Vesey. To his credit, Vesey has actually outperformed my expectations from him. If you would've told me that after two years, Vesey would have 33 NHL goals, well, I'd be pretty surprised.

Now, not to discredit Vesey's goal scoring ability, which he's shown, but the rest of his game is, utilizing shot attempt metrics and xG... bad for a player who gets the ice time that he gets.

via Corsica.Hockey, Jimmy Vesey has skated 1929 minutes aggregated over the past two seasons. There have been 144 forwards over the past two years to skate in 1900 minutes. These are Vesey's peers in terms of time on ice. This is how Vesey ranks among these skaters in some advanced metrics, 5v5:

RelTCF% 136th
RelTxGF% 139th
RelTGF% 141st

If you're more into actual production:

Points per 60: 132nd
Goals per 60: 57th
Assists per 60: 144th

Which begs the question, well, where is Vesey's on-ice utility that earned him these 1900 minutes across two seasons, and a bridge contract that saw him get a raise? I'm not seeing it.

My hypothesis is that the best part of Vesey's game is his play within 10ft of the opposition net. Luckily for this project, I have scraped the 2017-2018 season from the NHL, and we can take a look.

Last year, Jimmy Vesey had 187 individual unblocked shot attempts. Of these 187, 32 were within 10 feet of the opposition net. I was pretty shocked by this number, expecting it to be way higher. Now, to be fair, 10 feet is an arbitrary number that I used. I'm actually not totally certain what the 'optimal' distance to use is for a net-front performance analysis. Any tips? I'm sticking with 10. But we carry on.

If we set the cutoff to a minimum of 20 attempts from 10 ft or less, we get a sample size of 182 players (this is now all situations). Using this sample, Vesey ranks T-59th in total unblocked attempts, T-33rd in goals scored, and 34th in unblocked shot attempt %. Ultimately, not terrible. 

For fun, here are the top players in terms of attempts, goals, and FSh% last year:

t10 attempts.PNG
t10 goals.PNG
t10 fsh%.PNG

From here, I was actually kind of shocked that only 8 of Vesey's 17 goals last year were from 10ft or less. My memory is bad, maybe that is not as surprising to some of you. So I wondered where the rest of his goals came from. They were: 

128 (ENG), 13, 12, 12, 21, 11, 21, 150 (ENG), 17.

Again, showing the arbitrary cut of 10ft might not have been generous enough. Because if we open that to 15, we get 12 of Vesey's 17 goals from that distance.

In terms of Vesey's overall unblocked attempts and goals, we can use a density chart to visualize:

vesey-fenwicks-1718.png

Doesn't really help without context, so let's compare to someone a lot of people consider to be a 'floater', Pavel Buchnevich.

Buchnevic Fenwicks.png

These charts are very obviously different, again leading us to believe that Vesey does do a job close to the net. For context, Buchnevich also had 32 unblocked attempts from ten feet or less but only scored on 6 of them.

The conclusion here is basically that Vesey is for lack of a nicer term, a bad player who gets the minutes that he does. His perceived utility is that he's often in front of the opposing teams net, which seems to be true via the density plot, and is a good, but not great or elite finisher of these attempts. However, good enough to see if he can sustain this type of playstyle, and maybe get luckier and finish more of the attempts that he gets from there. Probably exactly why Vesey was worthy of a bridge-deal.

And thus fully concludes spending way too much time on something that probably didn't need to happen.

 

Neal Pionk: What Do We Have Here?

On the surface, one of the few bright spots of this forgettable Rangers season was Neal Pionk. Pionk, who will be 23 at the start of next season, was an undrafted NCAA prospect out of UMD. After starting the season with the Wolf Pack, Pionk was called up to the Rangers on February 8th, and almost immediately became one of AV's go to guys. Over his 28 games, Pionk would average 22:23 of ice time a night, highest on the team, and would play in all situations.

The games on MSG and NBC Sports were often filled with Pionk praise. And maybe he deserved it, but also, maybe he kind of didn't.

A major part of the "eye-test" is always going to be: "who is on the ice a lot?" And that makes sense. If you're watching the game, you're going to remember the players who play a lot, good or bad.

Metrics below via Corsica.Hockey

Let's look past the eye-test for a minute, and see how Pionk matched up against the rest of the league's d-men in some key metrics, 5v5, with a minimum of 300 minutes time on ice (n=238).

 The bold metrics signify that negative is good

The bold metrics signify that negative is good

Using team relative metrics allows us to ignore the noise of playing AV hockey that Pionk had to deal with, since the relative metrics allow us context in to how he performed relative to his team.

What we see is that Pionk had some really, really bad on-ice performance where he was bailed out by timely goaltending and shooting. We see this because his on-ice shot attempt xG numbers rank very poorly. Interestingly enough, it's the offensive side of the game where Pionk really struggled. With Pionk on the ice, the Rangers generated 11.93 less shot attempts and 0.81 less expected goals per 60 minutes. His shot attempt suppression numbers (relCA60) left something to be desired, but were not totally terrible.

We need to take into account however that Pionk was essentially thrust directly into a top-pairing role halfway through his first professional season. Context is important, but it can't be used as a crutch for poor performance. We can't say things like: "Pionk isn't bad, look at the situations he had to deal with?" Instead, we should view this as: Perhaps Pionk was not suited for the role that he was put into at this point. The difference here being that usage isn't an excuse, it is context. If Pionk wasn't performing well in his usage, his usage should be changed.

The Rangers, however, need to figure out how much of Pionk's poor on-ice performance was usage based, and how much Pionk's poor on-ice performance was, well, poor play. I'm uncertain if there is an effective way to do that, but it won't stop me from trying.

We could utilize a tool like Steve Burtch's dCorsi to understand a baseline impact of usage and other factors on a players shot attempt numbers.

I'm going to go in a slightly different direction, and use the similarity scores from the old MILLER model to see if we can find similar players to Neal Pionk, and evaluate how their careers progressed.

Note: I don't have a good write-up on MILLER, surpirsingly, but it operates similar to 538s CARMELO model for basketball. Similar being a very, very generous word for MILLER. I'm working towards touching this up. Very long-term project for me, though.

It should also be pointed out that this is strictly for fun. I'm putting no real-world weight onto this. Just running the 'model' and having some fun with the outcomes.

Essentially, what the similarity boils down to is just finding comparable players. Looking at items like age, usage, and on-ice performance. The 'model' uses these metrics, and their weights within the 'model':

Individual:
P160 - 3
iCF60 - 3
ixGF60 - 3

Relative:
relTCF60 - 2
relTCA60 - 2
relTxGF60 - 3
relTxGA60 - 3
relxFSh - 1
relxFSv - 1

Usage/Context:
TOI/GP - 1
relZSR - 1
xGFQoT - 1
xGFQoC - 1

Admittedly, there is no statistical significance to any of these weights. It is totally subjective, totally malleable. And really, I'm posting them in the hopes that someone tells me I'm an idiot, and the weights are applied wrong, so we can get a stronger sense of similarity.

Data was obtained via Corsica.Hockey and Hockey-Reference

pionk1.PNG

Running the 'model' for Neal Pionk, we get only 16 d-men seasons for ages 21-23 with a positive similarity score.

Cody Ceci being the #1 similarity comparison here really does not paint a great picture for Pionk. It should be noted that a 29 really isn't that strong, so the model had a tough time coming away with a strong-similarity for any player for Pionk, which I think makes some sense given the sample size of Pionk information that we have.

I should also again caution that this is straight up Gorilla Math, Grama ROUNDERS style, and everything about it is up for debate. That's the only way this will get better.

But, we soldier on.

If we take a look at how the age-progression of these players went in terms of, what I believe are significant metrics to evaluating defensive play, relTxGA60 and relTCA60, it follows this path:

Flaws here:

The following plots don't weight the players with higher similarity scores to Pionk more than the guys with lower ones. It's all even when it should not be. In the iterations of MILLER where I had been projecting forward goals and point totals, weights were applied for strength of similarity. Future iterations should have this weighting processed into the results, but for now, these do not.

Thus, the following visulizations assume that everyone with a positive similarity score to Pionk had the same weight. That's wrong. But, here we go.

pionk_reltca.png
pionk_relTxga.png

It should be noted again that in this particular metric, a negative number is good. We can see that the few players that Pionk was similar to had a it of trouble getting into those negative numbers throughout their careers, as Pionk's trendline, by using these players, eeks positive on the relTxGA60 side and can't seem to get negative on the relTCA60 side. Further noted that the players we do have here, for data into their 30s, really struggled. But that's a long way away, and things do change.

I'd be remiss if I didn't once again call out that none of this is gospel. That we only have 28 games of data on Neal Pionk. That in these 28 games, he was playing Alain Vigneault hockey. That in these 28 games, he was mostly seeing #1 pairing matchups. I don't think I can stress this enough.

The hope is that next season, with a new coach, that they come in and recognize that maybe Pionk isn't suited to being a first-pairing D in the NHL just yet. And hopefully, the ease of his usage provides an uptick in his on-ice impacts. The strongest bet in the world is that when we run this model again for Pionk next off-season, we see a much larger and more generous sampling of similar players.

We hope.

The Rangers `Sophisticated` Stats Package

In a recent interview with Newsday, Rangers coach Alain Vigneault dropped this quote:

 Newsday: Ryan McDonagh returning to form for Rangers | 12/20/2017

Newsday: Ryan McDonagh returning to form for Rangers | 12/20/2017

Now, in AV's defense here, when the team you coach is 30th in the league in shot attempts against per 60 minutes of 5v5 play, well, you can't really go on record saying that you care about it or coach it because, well, you're terrible, and that wouldn't be a good look for you. 

But this isn't really a good look for the Rangers, either. The studies are essentially conclusive at this point. Shot attempts, Corsi, is the foundation of the analytics movement. CF% is a better predictor for future team success than scoring chances, some xG models, and actual goals. These are all things we know, and the debate on these should be behind us at this point. Then, you see a quote like this from a respected coach in the NHL, and we get set back.

Let's see if we can figure out what the Rangers do value in their `sophisticated` stats package, and let's see if we can do it using only the data available on Corsica.Hockey, publicly available data. Obviously we won't know for sure, but let's see what we can infer.

The picture we'll be looking at is 5v5 data, unadjusted, team ranks for this season. We already know that the team is 31st in CA/60, but in some other telling metrics, they rank:

26th in CF/60
30th in CF%
7th in GF/60
21st in GA/60
15th in GF%
2nd in xGF/60
31st in xGA/60
20th in xGF%
1st in xFSh%
30th in xFSv%

Above, I've bolded the highlights. 

What can we infer? The Rangers don't care about quantity, they care about quality. But, the damage they're doing to themselves on defense with the quality against they allow is partly cancelling out the good things they're doing on offense to generate the quality shots. Can one exist without the other?

I think this is AV hockey in a nutshell. Sacrificing defense for offense. The man-to-man scheme that the Rangers employ in the defensive zone allows for quick breakouts, rush opportunities, odd-man rushes. But, it leaves Lundqvist and Pavelec out to dry, and if they aren't making the saves, and the Rangers aren't finishing their chances, the score gets ugly.

Luckily for the Rangers, their goaltending duo has been carrying the load and masking a lot of the defensive deficincies we see with our eyes and on the stat-sheet.

Sean Tierney, ChartingHockey, has a very handy-viz based off of Manny's K metric that displays the Rangers thought process quite well:

 The visualization displays what is discussed above. The Rangers strength lies in the quality they can generate for themselves. The Rangers weakness lies in the quality against they allow. The system?

The visualization displays what is discussed above. The Rangers strength lies in the quality they can generate for themselves. The Rangers weakness lies in the quality against they allow. The system?

 

Further, we need to debate sustainability. What's more repeatable, quantity or quality? If you're coaching to quality, as the Rangers clearly are, are you playing with fire?

To isolate what teams are generating in terms of quantity versus quality, we'll be exploring split CF/60 (a measure of quantity generated) and split xFSh% (an isolated quality metric based off of Manny Elk's expected goals model). I'll also be using a split-half of last season's games in order to get more data.

First half: through Jan 10th

split_cf60.png
split_xfsh.png

On the visualizations above, the blue line represents the line of best fit via a linear model, the gray line is a simple x=y line.

Basically, the visualizations show us what we know, what we've known since the beginning of the movement. Shot attempts have a very strong repeatability. It's a skill. We don't see the same strength in shot quality. 

Flaws in the above: The sample size is way, way too small. To make a stronger point, we should analyze and chart these splits for each season going back to 0708. I didn't do that. I should've done that.

Thus, if you're coaching to quality rather than quantity (or a strong mix of both, which is pretty clearly the best option), then you're playing with fire. This `sophisticated` stat package that the Rangers are boasting essentially seems to boil down to what the Rangers thought they were getting when they originally hired John Tortorella, safe is death.

Now, 35 year-old Lundqvist is still producing as one of the best goalies in the league (8th in the league in goals saved above expected per 60 among starters), and the system and stats-package that the Rangers and AV are utilizing is exploiting that fact. And maybe they should be, but there is a serious lack of quantity, and we see that relying on quality isn't as sustainable.

Further, noting again how reproducible quantity is, we see the Rangers being nearly dead last in the league in terms of allowing attempts against. Unless the system changes, well, there's no end in sight for this.

We can debate endlessly the types of players that we think the Rangers are missing, specifically in my opinion, a RHD to pair with McDonagh that is an undervalued shot suppressor (think Connor Carrick in Toronto, Josh Manson in Anaheim before people woke up, etc...), and perhaps maybe a winger in the mold of Benoit Pouliot circa 1314 to complement our centers and make sure the game is going in the right direction. But if the system isn't going to change, or if we're going to bring these types of players into the fold just to find Carrick in the pressbox instead of Steve Kampfer, then what's the point?

The `sophisticated` package and system are played out. We see it every single year. The Rangers launch themselves into the playoffs via a ridiculous PDO, and then the game tightens up, the refs swallow their whistles, there's less space on the ice, there is less opportunity for rush attempts for. The game clogs. And what do we see in the playoffs? Lundqvist left out to dry, because that's the system, and the offense disappears.

Rinse and repeat, but `sophisticated`.

The Rangers Should Not Bet on JT Miller

Two blogs in one month brought to you by: Me needing to kill vacation time at work so I don't lose the hours.

All stats for this blog provided by Corsica.Hockey unless otherwise stated.

Chances are if you got into a discussion about the New York Rangers and JT Miller, you'd hear a lot of praise. On the surface, the 24 year old C/W has 24 points, good for 2nd on the team. He's been utilized across the forward lines at all positions by the coaching staff. He's trusted, and they like him. Maybe they should. But what happens when you dig deeper into those numbers?

For starters, his goal scoring has become a concern. Despite two goals in the last two games (one an empty netter, but at the end of the year, a goal is a goal, and no one is being picky about them in contract negotiations (cc: Michael Grabner's agent)), Miller is still struggling. Of his six goals this year, only 2 have come during 5v5 play (12th on the team), for a 5v5 goals per 60 rate of 0.27 (last among TOI qualified forwards on the team). The reason for JT's goal scoring downfall is no mystery, he's not shooting nearly enough. At an individual shot attempts per 60 of 9.07, the only forwards on the team who attempt less shots than JT are Desharnais, Nieves, Carey, and Fast.

If we dig slightly further into JT's point total (back to all situations), we discover that half of his 18 assists are secondary. The only issue with this is that secondary assists are less repeatable than primary assists. They are given out less often, and are far more subjective on the scoresheet. A point is a point, but a primary point is a bit more important.

Another item to take a look at is how JT's career is pacing thus far:

Miller has been in the NHL full-time for this season and the two prior, so if we take a look at these three years, we can see how Miller is trending in some important metrics:

 (5v5 only to ignore noise of differentiating PP time)

(5v5 only to ignore noise of differentiating PP time)

Now. It is important to keep in mind that JT's shooting percentage has plummeted this year, and GMs are extremely susceptible to trading low on players when their Sh% has a dip year (see: Stepan, Derek or Smith, Reilly).

The million-dollar question becomes, what is JT's actual shooting %? Well, we can use Manny Elk's expected goals model to see where JT should be. JT's expected total sits just under 4 goals at the moment. The thing is, even taking into account his low shooting percentage, his expected total is also drastically beneath where it should be, and again, this falls back to the fact that JT Miller is not shooting the puck enough during 5v5 play.

His iCF60 dipped from 15-16 into 16-17, but he kept up his shooting percentage, so the goals still came. This year, thus far, JT's shot attempt numbers have dipped again, and the shooting percentage has not been sustained.

One more point on this is, was JT Miller ever actually a 14 or 13% shooter? Rephrasing: Is JT Miller's shot good enough to be a 14% shooter? Going back once again to Corsica's expected model, In 16-17, Miller outshot his expected rate by nearly 2%, in 15-16, that number was 4.5% Again, the million-dollar question: Do you bet on JT Miller reaching or exceeding his expected total again? How good is his shot?

JTM_prog.png

With this viz, coupled with the data tale above, we can see that in terms of expected goals, JT is essentially the same player this year than he has been in year's prior (slight dip), but, he's not converting. Is this because he's shooting less? It could be. Is it bad luck? It could be. That's the bet. Where do you fall?

There are things that JT Miller does well on the ice, obviously. His versatility is important. Up and down the lineup, and across the line. JT is also a good, if not great, passer, and is adept at carrying the puck into the zone.

But, he's not scoring goals anymore. And, he's never been a true diver of shot attempts. Securing a now barley positive relative shot attempt percentage this year after a -4% last year. JT is also failing at suppressing shot attempts against, operating at a +4.3 after a+6.62 relCA60 last year (the amount of shot attempts per 60 that Miller is on the ice for relative to the team when he is off the ice). 

There was a sneaking suspicion last year that it was Kevin Hayes bringing down JT's shot-attempt based metrics, but Hayes has rejunivated the strong shot suppression game we saw from him in 15-16, operating at -3.04 this year. It should be noted that JT, while allowing shot attempts against, is also still driving shot attempts for, with a +4.22 relCF60 slightly. JT is slightly outpacing his weak suppression with strong generation.

What does this all mean and why is it important? Well, the Rangers are coming up on what is going to be a very important off-season in terms of the direction they want to take the team. Nash's $7.8m AAV is going to come off the books, and Jeff Gorton finds himself with four restricted free-agents to re-sign. JT Miller, Kevin Hayes, Jimmy Vesey, and Brady Skjei will all be in need of a new contract. Vesey can't really demand more than $2m AAV, so that's an easy one if the Rangers decide to make it easy. The big decisions will come at the table with Miller, Hayes, and Skjei.

Brady Skjei should be bet on long-term. Kevin Hayes, if he continues his strong play from this season, should also get a long-term deal.

But what do you do with Miller? JT Miller who has 6 goals in 33 games this season, 6 in 45 dating back through last year's playoff run, and 12 in 87 if you dial that back to last year's all-star break. This is a goal-scoring slump that is flying under the radar as JT continues to rack up A1s and A2s.

Do you bet long-term on that banking on JT rekindling whatever it was he had before that had him outshooting his expected totals at the rate which he was? Coupled with the decline in his shooting rate?

I don't think I would.