The salary cap has been an incredibly important part of hockey ever since its arrival in the 2005-06 NHL season. It helped create parity between franchises rather than players repeatedly flocking to the wealthiest clubs. For fans and media, it’s another factor by which to evaluate players, creating folk heroes on league-minimum deals or villainizing former stars because they aren’t living up to their new contracts.
And for teams, it’s about squeezing every penny and getting the best out of your roster year-by-year. The teams that use it the best are usually the ones that are consistent contenders, and the teams who have their rosters littered with albatross contracts usually find themselves at the bottom of the league.
In the coming weeks, we’ll be taking a look at which teams do the best job with their salary cap. However, this isn’t just as simple as identifying who has the most cap space or which teams have all their star players locked up for cheap. There are several different factors to consider, some more complex than others.
Which is what this article is for. Before we dive into the rankings themselves, I figured I’d take the time to explain some of the aspects of this system, including what each category actually represents and the process behind creating some of them.
Without further ado, let’s begin.
Good Contract Percentage
‘Good contract percentage’ as a definition should be relatively easy to figure out without me telling you. Good contract percentage is a basic summary of how many of a teams’ contracts are good and how many are bad. I add the parameter that the contract has to have a cap hit of $1 million or higher, because once you get into the six-digit figures, it doesn’t really affect the cap whether the player is worth it or not. Besides, if the player is signed to that kind of deal and really good, there’s a category for that later on.
What does need to be explained with good contract percentage is the process, and what exactly constitutes a good or bad contract. It needs to be identified objectively.
To do that, I need a way to value both players and salaries on their own. To value players, I created a statistical profile of every player signed to a contract with NHL experience. And I mean every player. I collected data on forwards, defensemen, and goaltenders from all 32 NHL teams, from Patrick Kane to Dmitri Samorukov, who’s played all of 2:28 in the NHL.
For the skaters, I created this statistical profile using 10 different stats in three different categories.
- Even strength scoring: This basically just uses your standard scoring rate stats. I’ll be using goals per 60 minutes to evaluate goal scoring, primary assists per 60 minutes to evaluate playmaking, and points per 60 minutes to evaluate overall scoring creation, with all stats at even strength.
- Even strength play-driving: This one gets a little bit more complicated, as I’ll be using Evolving Hockey’s Regularized Adjusted Plus-Minus, or RAPM. For an in-depth breakdown on the stat, check out the twins’ glossary on it here. It gives the isolated impacts of players, so for me, it’s the best stat to use to evaluating play-driving. I’ll be using RAPM expected goals for per 60 minutes and RAPM corsi for per 60 minutes to evaluate offensive generation, and RAPM expected goals against per 60 minutes and RAPM corsi against per 60 minutes to evaluate defensive suppression, with all stats at even strength.
- Goals above replacement: If you haven’t heard of goals above replacement, or GAR, you’ve probably heard of wins above replacement, or WAR, through hockey, or even baseball. For those who haven’t, here is Evolving Hockey’s definition of it, since I will be using their GAR model as well. I will be using offensive GAR per 60 minutes to evaluate a player’s overall offensive impact, defensive GAR per 60 minutes to evaluate a player’s overall defensive impact, and penalty GAR per 60 minutes to evaluate their discipline and ability to draw penalties, with all stats at all strengths.
I then ranked every player by each statistic into different tiers and then aggregated them to get an overall tier. The top 32 at both forward and defense are put into the elite category, and after that, it differs by the two positions. For forwards, the 33rd to 96th ranked are considered first-liners, 97th to 192nd are second-liners, 193rd to 288th are third-liners, and 289th to 384th are fourth-liner, with everyone below that considered replacement level. For defensemen, the 33rd to 64th ranked are considered top pair defensemen, 65th to 128th are second pair, and 129th to 192nd are bottom pair, with everyone below that considered replacement level. The only exceptions are if there is a tie for the last spot in a tier, which in that case all players involved in the tie are placed in the higher of the two tiers.
For the goalies, I created a statistical profile using six different stats.
- Quality start percentage: I think this one sort of explains itself. It shows the percentage of starts a goalie makes in which he puts in a quality effort that should win your team a game so long as they play well in front of him.
- Even strength save percentage: I probably don’t need to explain save percentage to hockey fans, but this is basically that, except with only shots and saves made at even strength to get a better judgement of their play at a fair level of strength for both teams.
- Even Strength delta fenwick save percentage: This stat is based on fenwick save percentage, which is a goalie’s save percentage on goals, saved shots, and missed shots (so not including blocked shots). It takes their fenwick save percentage and subtracts it by their expected save percentage to give an idea as to how many more scoring chances they are stopping than expected based on how the team is playing in front of them.
- Even strength goals saved above expected: This one is the same concept as delta fenwick save percentage but more of a focus on goals than saves. It’s another way to evaluate how good a goalie is performing, even if the team in front of him is struggling.
- Goals above replacement: Same thing as the skaters’ stat.
- Games played: This one basically just acts as a neutralizer for goalies with small sample sizes. That said, goalies who can get into a lot of games demonstrate good durability to avoid getting hurt and can be trusted in starting roles.
Like with the skater evaluation, I ranked each goalie by each stat, and then aggregated them to get an overall tier. The top five are put into the elite category, sixth to 32nd are considered starters, 33rd to 64th are backups, and everyone else is considered replacement level.
Once I had all of the players ranked, I then used the same ranges for each positions for the cap hits. So, the 32 highest cap hits among forwards are elite-level salaries, and so forth. In doing so, it allows a very easy comparison between their performance and their cap hit.
These are the different tiers for each position.
Elite: $8,205,714 +
1st: $5.5M – $8,137,500
2nd: $3.15M – $5.45M
3rd: $1.25M – $3.125M
4th: $1M – $1.2M
Elite: $6.5M +
1st: $4.6M – $6.25M
2nd: $2.5M – $4.55M
3rd: $1M – 2.45M
Elite: $6.25M +
Starter: $3.4M – $6,166,666
Backup: $1M – $3,333,333
So, this is where the actual good contract percentage comes in, which is easy enough to explain now with the context of the process. If a player is paid more than he is worth (ie. a replacement level player making a salary of a second liner), he has a bad contract, and if a player is paid as much or less than he is worth, (ie. a bottom pair defender making bottom pair money, or an elite goalie making backup goalie money), he has a good contract. And then the percentage comes from however many good contracts a team has in comparison to how many contracts above $1 million its on its cap sheet.
Overall, this category helps evaluate how good teams are at signing players to market-value deals and avoiding those costly long-term deals for aging grinders that end up getting bought out or dealt along with a second-round pick in three years.
One final note: this system can be picky, and it will lead to a few instances that differ from normal opinions, some that I don’t always agree with either. Players like Sidney Crosby or Leon Draisaitl end up with “bad contracts” because some of their stats are bad enough to drag them from elite to first line forward even though they make elite player money. But 99 times out of 100, it works.
Okay, the other criteria don’t require such long-winded explanations, so if you’ve made it this far, the worst is over.
Quality Cheap Deals
Remember when I said that players below $1 million were in a different category? This is it. The general premise is to evaluate teams based on how good they are at finding cheap deals to players that are capable of playing at the NHL level to navigate around the salary cap.
So that means that not every player making under $1 million is included. A lot of them have never played, so they aren’t considered, and even then, they still have to be above the replacement level tier to make this category. The idea there is that contracts under $1 million should be for replacement level players, so it’s not a “cheap deal” if they’re making that money, and anyone above that is considered on a steal of a contract. Naturally, teams are rewarded in particular for getting big production out of their prospects on entry-level deals.
Teams are then ranked based on how many of quality cheap deals they have in their system.
Contracts with No-Trade/No-Move Clauses
Anyone with a fair amount of hockey and salary cap knowledge should know what no-trade and no-move clauses are, so I won’t waste any time there. Any player with one is considered, no matter how much they make. I then rank teams based on how few they have, with the idea being that teams with fewer clauses like this have more flexibility in moving pieces around to free up space, as well as not having their hands tied with forced protection in expansion drafts.
I’ve always used this category when using it in the past, but I think the recent debacle with the Evgenii Dadonov trade from the Vegas Golden Knights to the Anaheim Ducks is a good example of why having fewer players with clauses helps you navigate your cap.
Dead Cap Space
This part looks at the money on each team’s cap space that isn’t going to players on their NHL roster. This includes buyout cap hits, retained salary in trades, cap recapture penalties, termination penalties (looking at you, Los Angeles), and contracts buried in the minors.
This idea here should be pretty self-explanatory. You want as much cap space as possible to build your roster, so any cap space lost to players not on the roster is a waste.
Quality of their Core
When you think of a core player, you think of the players that you want to build your team around for the long term. However, a core player in this scenario is only half of that.
With this category, I look at players on each team locked up to contracts with a term of four years or longer, and evaluate them based on the quality of the players in this category. For this, I also use the statistical profile of these players, and average it, ranking teams from best to worst.
The idea here is to evaluate teams based on how they identify top-end players and lock them up long term, and as such, only sign depth players to shorter deals.
Cap Space to Skill Differential
And finally, the most important part of looking at a team’s cap space efficiency is by looking at how much cap space they actually have. However, it’s not quite as simple as ranking them based on cap space.
Instead, I take where they rank from one to 32 in the league in cap space, and compare it to where they rank one to 32 in the league based on their roster’s quality, acting under the assumption that the best team should have the least cap space, and the worst team should have the most. With this, it gives more leniency to competitive teams up against the cap, because that’s where they should be to be as competitive as possible.
To rank teams, I once again turned to each player’s stat profiles. I picked 12 forwards, six defensemen, and two goalies from each team based on the highest average ice time, and established that as their “main roster,” and then evaluated the team based on those players.
To get the final ranking of all 32 teams, it’s extremely simple. I take their rankings in the six categories and aggregate them to get an average ranking position, and then order the teams based on that.
It gives us a good picture as to which teams do the best overall in the different facets of managing the salary cap and which teams do the worst. Doing poorly in one category won’t completely destroy your ranking, just like doing well in one won’t save it either.
And that’s it. Starting next week, the true rankings begin, and we’ll work our way through all 32 teams to figure out which one is the best of the best.
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