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Determining NHL Player Value: Methodology

When trying to determine an NHL skater’s worth to a team, it’s frequently best to go back to the very basics. What is the team trying to achieve? It usually boils down to two things:

  1. Put up points
  2. Don’t let the other team get points

These are the main objectives of every person on the ice (aside from the goalie, who really only cares about No. 2 – unless they’re named Tom “All Time Goalie Points Leader” Barrasso).

After all, that’s what wins hockey games: having more goals than the other guys.

Now, we all know that some players are better at achieving those goals than others, and we can generally agree that the best players should make the most money. Or to rephrase – the more money you make, the better you’re expected to be at achieving those goals.

So how do we go about evaluating these two objectives per player? And how do we know what they should be expected to do?

First we need to level set on what the NHL Averages look like. To do this, I looked at the total cap spend from, and then pulled some other numbers, like Time On Ice (TOI), Fenwick Against (unblocked shots against), primary points (goals and primary assists), and days on roster from

By using a weighted average, I determined that the average NHL roster cost $67.987 million last year, and required the use of 28 players to make it through the season. This puts your average NHL salary at a $2.41 million annual average value (AAV).

This is both for forwards and defensemen. Why not separate the two? Because at this level it doesn’t matter. Forwards are expected to be defensively competent, and defenders are needed to put up points. The best forwards will still be better when compared to other forwards even after a mixed evaluation method is applied.

I also choose to use all situation stats (as opposed to even strength) for this methodology, as General Managers often pay players for their skills on special teams. Through other research I’ve done, I’ve found that generally two things are true for both forwards and defenders: the higher the salary, the greater the TOI%, the more points they have, and the more power play time they get.


Put Up Points

To evaluate the effectiveness of a skater offensively, I turned to a familiar metric–Primary Points per 60. Primary points are more predictable and repeatable than overall points, because secondary assists have been proven to be ‘incidental’ (read as: ‘lucky’). Using a rate evens the playing field, because guys who play a lot of minutes are likely to get more points, even if they’re not particularly effective with those minutes.

But again, how do we know who’s a good value?

The trick is to measure them against what you would expect to do for their salary. Since I already had the NHL averages, I was able to calculate the NHL Average Primary Points per $1 million in AAV – 6.226. Now that I had that, I was able to calculate an expected rate of Primary Points per 60 per $ (abbreviated as PP60$) and compare it to the actual rate a skater put up.

As you can see, both Skater A and Skater B exceeded expectations, but Skater B is the better value because they exceeded expectations even more.


Don’t Let the Other Team Get Points

This is the much harder objective to evaluate. We have many metrics that calculate possession as a percent, but more shots in a team’s favor don’t necessarily mean they’ve done a good job defending. Instead, I prefer to look at unblocked shots against (Fenwick Against, or FA), and again, I use it as a rate to measure the effectiveness of each skater.

But you can’t really pay someone per shot against, the way you can per point. After all, Shots Against are the opposite of what you want when defending, and Shots For don’t measure how well you’ve defended. Instead what we’re trying to look for is “Shots Prevented” which, frankly, is impossible to track.

So to approximate this theoretical number, I came up with what I call Inverted FA60 (abbreviated InvFA60). Basically, it’s a stand-in value that says “we know a team is likely to face no more than 100 potential shots (missed, on goal, and prevented) in 60 minutes. If FA60 measures missed shots and shots on goal, then the rest of the potential shots were prevented.”

So InvFA60 is just calculated simply: 100-FA60.

In all fairness, I could use any number as long as it’s around that upper bound of all potential shots – 80, 90, 120, but 100 keeps things simple. By using InvFA60, we now have a positive number to “reward” monetarily that is directly influenced by shots against, which we already measure.

Since we have the total TOI and total FA of the average NHL team, we can then calculate average FA60, and then the average InvFA60 for the NHL – 59.137. Because I already have the average NHL AAV ($2.41) I now have the average InvFA60 per $1 million AAV (InvFA60$) – 24.529.


This is where it gets tricky. Because Skater A’s salary is so low, despite having a higher FA60, they have a better InvFA60$ delta. And you might notice that Skater B’s Expected InvFA60$ is 100, not 122.645. Since I used 100 as the upper bound for actuals, so it has to also be the upper bound for expectations.

But there’s another aspect to this measure, too, and that’s TOI%. Defensemen typically find themselves with much higher TOI% and higher FA60 than forwards, even if they’re good at preventing shots. So to account for the extra workload of defenders (and “two way” forwards) we multiply that InvFA60$ Delta by TOI%.

And voila! Skater A is still a better value defensively because their AAV is significantly lower, but the difference between the two is much smaller.


Combining the Two

The last step is to combine offense and defense into a single skater value rank. I weighted this 50/50, but there are arguments to be made for the importance of one or the other.


In general, this kind of ranking gives preference to lower AAV contracts, Entry Level Contracts especially, as those aren’t given out by merit. However, as you can see from this example, it also can show you who is out performing their salary and who isn’t.

While Skater A & B are hypotheticals, Skater C is Nick Foligno and Skater D is Fedor Tyutin, of the Columbus Blue Jackets. One has great value, the other…not so much.

In the following series, I’ll be going division by division, evaluating teams on their value last year. You’ll pretty clearly be able to tell who was a boom and who was a bust for each team, and where certain teams are floundering in finding value.


Up Next: Finding Value in the Pacific Division

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