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.