Escrow has been a dirty word among players for the better part of a decade now, and it’s understandable knowing that they see an extra 10%, 15%, or even 18% shaved off of their every hard earned paycheck. However, it’s a necessary evil for the revenue sharing agreement in cap system to work. As it is right now, the salary cap system is set up to overpay the players from the outset of every season. The salary cap ceiling is set to create a 57.5/42.5 revenue split between the players and league. Escrow exists to redistribute the revenue so that it is split 50/50 at the end of every year, as the NHL and NHLPA agreed upon in the 2013 CBA. So every cap team in this league is an extra little bit of escrow tagged onto the players. There are also a few other well-known causes of escrow, such as the salary cap escalator, LTIR, and cap-advantaged contracts, that are explained here.
There is, however, one unique cause of escrow spiking that I have heard little of. It is something that happened in the two years after the 2013 CBA was signed and that something is also being discussed as a likelihood in the next round of labor negotiations. That something is compliance buyouts. Compliance buyouts are get-out-of-jail-free cards for teams that don’t like one of their contracts. These buyouts still require the teams to pay the player a large percentage of his contracted salary, but there is no cap hit associated with the buyout (unlike regular buyouts). Continue reading “34.5 million reasons I’d be against compliance buyouts in the next CBA if I were a player”→
I looked at Lundqvist’s year-to-date stats back in late November and found that his play this year has been statistically weaker than every season since 2007-08 (which was the first season I could get the necessary data). I have added in the last five weeks of data and re-configured my graphs to make them a bit more reader friendly:
I looked at how Henrik Lundqvist’s play (a) has compared on a year-to-year basis and (b) how it tends to progress on a year-to-date basis. I used 5v5 Sv% and expected goals saved above average per 60 mins (xGSAA/60) as my two metrics. Both sets of data came from Corsica.
I chose 5v5 Sv% because it tends to eliminate a lot of team effects. All situations Sv% can be drastically affected by special teams based on the systems play that a team employs (as well as the efficacy at pulling it off). Overall, 5v5 Sv% tends to map decently year-to-year so it’s a good metric to look at as a measure of skill.
I chose xGSAA/60 because it helps address the issue of shot quality. xGSAA/60 basically looks at the types and distances of the shots taken and determines how many goals a hypothetical league average goalie would give up that night (ie, expected goals). By comparing this expected goals number against Lundqvist’s actual goals allowed number, we get an idea of how he performed against a league average baseline. The “per 60 mins” part helps to normalize for time in net. This stat is also only measuring 5v5 play.
I wanted to emphasize Lundqvist’s play this year against previous years as a composite so I chose colors that aren’t well differentiated for previous years (ie, 2007-16). However, if there is interest in having all of the years differentiated I can fulfill that request.
Lundqvist isn’t doing well this year. His 5v5 Sv% is among the lowest of his career for this point in the season and his xGSAA/60 through yesterday is the worst of his career. There is some teeth to the idea that he is not playing at the same level as before.
His xGSAA/60 this year is very close to 0, suggesting that he is overall playing at a league average level during 5v5 play.
The idea that he is a slow starter is only true for the last few years. Earlier in his career he had solid starts to the season. There’s also a noticeable dip in almost every year for December, which is likely him cooling off from what looks like to be a lot of hot Novembers (just not this year).
His two best years by both stats appear to be 2015-16 and 2012-13. One year he received no votes for the Vezina while the other year he finished 2nd in voting. The Vezina voters really aren’t good at their job.
Honestly, I think it’s very fair to say we are seeing a below average performance from Hank relative to what we expect from him. This year he is facing a pretty normal mix of low-, medium-, and high-danger shots. It’s not like last year where the very high average shot quality he faced gave him average traditional stats and amazing underlying stats. Given the fact he’s coming off such a stellar season, I have a lot of hope that he can bounce back and play at a really high level this year.
However, going by the eye test I am at least a bit worried. There definitely have been games where he has let in total stinkers. And not just one stinker in one game, but a few stinkers in a few games each. But he still has long stretches where he plays solidly and makes great saves. Nonetheless, I would say there is not anything in particular that I see as an explanation for his poor play. No apparent injury or major changes in style of play. As far as I can tell, he’s more or less just playing more sloppy than usual.
Something I might investigate in the future is his progressive win threshold % (WT%) and loss threshold % (LT%). WT% is basically a measure of how often a goalie “steals games” and LT% measures how often they let in so many goals that the team in front is very unlikely to overcome it. Both are based on per game expected goals and goals against numbers. And at least for last year, he was absolutely elite by those numbers. One of the best in the NHL for both stats.
In this post I will evaluate this trade from the perspective of a New York Rangers fan. I plan to evaluate the following aspects of this trade:
Statistics-based performance review
Statistics-based usage review
Brief “eye test” statements
Contract, cap, and asset considerations
1. Statistics-Based Performance Review
Unless noted otherwise, all of the charts to follow were constructed using 2014-16 data for 5v5 situations with zone-, score-, and venue adjustments where applicable. The data has been sourced from Corsica. I will frequently use stats that say “/60”, which means the stat is adjusted to “per 60 minutes of time on ice.” This addresses any difference in time on ice by these players.
Here are some basic player data just to set the stage:
J.T. Miller became the first of four arbitration-bound New York Rangers to ink a deal. On July 13th, he signed a 2-yr, $2.75m AAV contract, leaving him just one year shy of UFA status. Many have proclaimed this contract is a steal for the Rangers, but I think it is right on point. I also believe it will provide a benchmark for many of the other players lined up for arbitration right now. Thus, I have used Miller’s contract to develop a simple cap hit prediction model for those other players.
Above, in the featured image, you can see what I believe is an accurate set of data about teams with cap advantage currently on their books. All of the points represent the level of cap advantage currently accrued by the off season of any given year. For example, the Chicago Blackhawks have $31.6m in cap advantage on their books at the time of the 2016 off season. This number will be true until the first day of the season (Oct. 12), when the AAV and salary numbers stick ticking up for the 2016-17 season. I have tried graphing the correct day-to-day changes of the contracts, but it just comes out overly complicated. It’s only really important when either trades or contract terminations occur mid-season; that makes up only about 25% of events that affect cap advantage accrual to date.
If there is a problem (or praise) you’d like to share with me about this different service I am trying out, please contact me at @Chris_Beardy on Twitter, by comment below, or even on Reddit at /u/ChocolateAlmondFudge.
Yesterday I introduced Part 4 of my series on the Cap Advantage Recapture Penalty (CARP), detailing a way the League could help the Predators out of their Shea Weber cap advantage situation using available cap space in previous seasons. Today I will provide a more simple and perhaps more likely solution to the Predators’ possible future woes.
(Just a quick note, my first, second, and third posts on this topic can be found at the links provided. I highly suggest giving them a read if you need some background info on what the CARP is and how it works.)
The Collective Bargaining Agreement (CBA), which introduced the CARP has a rather straightforward passage that might be interpreted to help the Predators:
Section 50.5(d)(ii)(B)(2):Notwithstanding the provisions of Sections 50.5(d)(ii)(A) and (B), in the event that any such Long-Term Contract is Assigned during its term, each Club for which the Player plays under the terms of that Long-Term Contract shall be subject to being charged with any and all “Cap Advantage Recapture” amounts it receives pursuant to that Long-Term Contract, provided, however, that if a Club Traded a Long-Term Contract prior to the execution of this Agreement (including any binding Memorandum of Understanding) under which it gained a “cap advantage,” the “Cap Advantage Recapture” shall not apply to that Club for that Long-Term Contract. For purposes of clarity, the Club to whom such Long-Term Contract was Assigned after the execution of this Agreement (including any binding Memorandum of Understanding) shall be subject to the Cap Advantage Recapture (if any).
[emphasis in the original text]
To put it more simply: Contracts that could incur a CARP that were acquired by trade prior to the signing of the 2013 CBA will not accrue cap advantage against the recipient team.
One thing I’ve grown to appreciate is the ability of teams to find loopholes in the current Collective Bargaining Agreement. It’s fun to see how teams find a way to give themselves an upper hand over other teams (at least, until the other teams follow suit). Just now, while perusing the CBA, I came across a piece of text that could have easily been a loophole had the NHL not been more careful:
Section 50.5(h)(ii): A Club shall be permitted to have an Averaged Club Salary in excess of the Upper Limit resulting from Performance Bonuses […] provided that under no circumstances may a Club’s Averaged Club Salary so exceed the Upper Limit by an amount greater than the result of seven-and-one-half (7.5) percent multiplied by the Upper Limit (the “Performance Bonus Cushion”).
The key here is that the Performance Bonus Cushion is an allowed exceedance over the Upper Limit (more popularly known as the cap ceiling or salary cap). It does not actually change the Upper Limit. This is also the case for the Bona Fide Long-Term Injury/Illness Reserve, typically referred to as the LTIR. It is described in detail in Section 50.10(d) of the CBA:
Section 50.10(d): […] the [LTIR] replacement Player Salary and Bonuses of such additional Player(s) may increase the Club’s Averaged Club Salary to an amount up to and exceeding the Upper Limit, solely as, and to the extent and for the duration, set forth below […]
Just like the Performance Bonus Cushion, the LTIR allows teams to exceed the Upper Limit, but does not actually change the Upper Limit for that team. So why is this a big deal?
Well, let’s make up a scenario where a team can expect to have a significant amount of performance bonuses to pay out for a specific year. Let’s say that in the near future, bionic technology is mastered and Wayne Gretzky and Bobby Orr are both implanted with cyborg technology, allowing them to play at a level beyond their peaks during their dominant careers. They’re both 35+ and thus can qualify for performance bonuses of up to a maximum of $2.85m each. At $5.7m total in bonuses, you’re already at 7.8% of the 2016-17 Upper Limit of $73.0m. So what could you do? Well, if the LTIR actually changed your team’s Upper Limit then you could maneuver a player or two onto the LTIR to create $3.0m worth of cap relief. This would up your Upper Limit to $76.0m and increase your maximum allowed Performance Bonus Cushion up to exactly $5.7m.
More realistically, we see a team that has an Aaron Ekblad type of player on an Entry Level Contract (ELC) as well as a Jaromir Jagr level talent on a 35+ Standard Player Contract (SPC). And in addition, you have other lesser players on ELCs and 35+ SPCs that reach only a fraction of their potential Performance Bonuses. For example, Michal Roszival got $200k for reaching his games played threshold in 2015-16 with the Blackhawks while Teravainen made $850k for reaching various bonus milestones that same year. Panarin earned the maximum of $2.85m for the bonuses he earned. So the need for additional Performance Bonus Cushion wouldn’t be too far-fetched in my opinion.
Fortunately, this loophole is addressed with the nature of the CBA. These two mechanisms seem to have been carefully constructed to avoid this sort of shenanigans. (In addition, the Performance Bonus Cushion overage ends up rolling over to the next year’s salary cap so my hypothetical situation would have repercussions.) However, there are many incredibly smart individuals out there now working on ways to break the system for their own benefit and it will be very exciting to see how they do it.
For at least the last two years, the idea has been floated around that the New York Rangers play two different games: one when Lundqvist is in the net and one when he isn’t. That idea goes on to posit that the New York Rangers who show up when Lundqvist is off the ice is a much better team. Those Rangers create more offense knowing that they can’t just grind out low scoring, one goal games, and they play a tighter defensive game knowing that the King can’t just bail them out when they misstep. Overall, they play a better game of hockey without their safety net.
In this article I intend to begin looking into the notion that a team plays differently depending on who the goalie in net is. I will do so by examining something less directly tied to the goalie position. I will be looking at the GF60 and CF60 rates of three separate teams split based on whether their franchise goalie is in the net or not.
The Narrative Behind it All
This idea isn’t without some cursory evidence. The Rangers were without Lundqvist for a sizable stretch in the 2014-15 season. A puck to the neck caused Hank to miss all of the Rangers’ games between Feb. 4th and March 26th, which was a 26-game stretch. Cam Talbot went on to shoulder much of the weight as the interim starter and ultimately he put up a great 0.926 performance. However, an early story line was the unexpected goal support from the Rangers for Talbot. From Feb. 10th to Feb. 22nd, the Rangers logged 33 goals in 7 games. And while they came back to earth shortly thereafter, the goal support still seemed to be there more for their goaltenders who aren’t named Hank. Talbot and MacKenzie stood in net for 38 games where the Rangers put up 3.04 GF60. Lundqvist received the slightly lower goal support of 2.89 GF60 over the course of 46 appearances. That came out to an extra 5.2% goals for Talbot and MacKenzie, which is an extra goal every 7 or so games.
Less Narrative, More Data and Graphs
I began by pulling 5v5 TOI, GF, and CF data for the New York Rangers’ goalies’ individual seasons from 2010-16, excluding 2012-13 (because the sample size is small for the lockout year). The data for guys besides Lundqvist were merged into a composite for each year.
The differences between the totals over these five seasons ended up being much larger than I expected. The Rangers scored an extra 0.23 GF60 in 5v5 play and generated 2.4 CF60 extra in 5v5 play when Lundqvist was on the bench or otherwise not playing. And in fact, the GF60 and CF60 data on a year-by-year basis was higher for the backups in 4 of the 5 seasons. The 5-year TOI totals were 16,202 min with Lundqvist and 5,758 min without. On a year-to-year basis, the TOI ranged from 2,225 to 3,088 min with Lundqvist and 743 to 1,785 min without. So the samples are decent in size.
Now, I do think this is compelling evidence that for some reason the Rangers are generally performing slightly better without Lundqvist in net. However, it is not possible to discern a reason from this data. Perhaps the players actually are more motivated to generate offense because they do not have their safety net. Perhaps the team’s coaches have made measurable changes to their personnel choices, such as decisions that are meant to try and keep the puck in the attacking zone. Perhaps the goalies themselves have significant contributions to the production, such as through stick handling.
To try and learn more about this apparent phenomenon, I investigated whether this situation has arisen on other teams. I chose to look at Nashville with Pekka Rinne and Chicago with Corey Crawford. Nashville, I thought, would provide a close parallel to the Rangers as both teams has generally been centered on an elite workhouse goaltender with a below average offensive team to support him. Chicago was to provide a stark contrast where I felt that Crawford was only depended upon to consistently deliver acceptable results and was infrequently leaned on to steal games. The Blackhawks tend to depend more on their offensive capabilities than the Rangers or Predators do, meaning that goaltending need not be as prized in Chicago.
The results were quite contrary to my expectations. The Predators, who seemingly would need to emphasize offense more without their star goaltender in net instead have mostly faltered in Rinne’s absence. Chicago, on the other hand, ramped up their production when they didn’t have their starter in net. It seems that the Blackhawks seek to capitalize more on their scoring opportunities to help support their backups.
Below is another look at the data. These graphs give direct year-by-year comparisons of the data for all three franchises with and without their starting goalie:
The GF60 data seems really noteworthy. For all three teams, 4 of the 5 dots fall on one side of the line. In the case of the Rangers and Blackhawks, they fall above the line, where offense is up when the starter is out. The Predators see a dip in production on all but one year when Rinne is off the ice.
The CF60 data mirrors the trend for the Rangers and Predators. However, the Blackhawks see their CF60 drop in 3 of the 5 years without Crawford in the net. This is starkly different than what was seen in the GF60 data.
I intend to investigate this idea further with other teams that have had a consistent starter across the past six-year period (e.g., Pittsburgh, Los Angeles, Dallas, etc). I will avoid teams that have significantly switched their starters in that period to try and avoid adding yet another set of variables into this analysis. Even someone like Lehtonen will be pushing it as he has effectively gone from being the #1 goalie in Dallas to a 1A/1B goalie this past year.
I know the Rangers better than I do any other team in the league so they would be the best team for me to dig deeper on this matter. I have a suspicion that personnel choices by the coaches may be a significant driver of what was observed for the Rangers. However, failing that I can also look into “puck luck” in those games. It is possible that what we’re seeing is just an aberration by pure chance.
Ultimately I think something like this could be an important part of understanding how a major roster change could affect a team in indirect ways. There could be an argument made that moving Lundqvist for a slightly above goalie could be a better change than would be expected than just by the GA60 and salary cap impacts. There is evidence that that a slight GF60 bump could occur, which would in part mask a rise in GA60.
Thanks to the 2015-16 Diving/Embellishment List on Scouting the Refs and the easily accessible data on Corsica, I made a quick graph of how 5v5 player penalty rates break down depending on whether the player had either 0, 1, 2, or 3 diving/embellishment calls in 2015-16 or had been fined by the league for the same offenses.