When I started writing this week’s article, I had initially planned to try and give some insight into the pressures of goaltending and how a massive contract can impact a guy. I kind of veered left, though, as I looked for a way to quantify not just a goalie’s performance, but his overall value to the team.
It may seem pretty straightforward, but to fully understand it, I felt that some simple metrics could be combined to give a better total insight.
Using www.capgeek.com, I pulled the cap hits for the goalies with the top 25 cap hits for the 2012-2013 season, but listed their cap hits from the previous season. Since I view cap hit as a real measure of a player’s perceived worth to a team, this was an obvious place to start, as it gave me a ranking based on perceived market value. By using their upcoming cap hit, I get an idea of their most recent market valuation, while showing the actual cap hit from the previous season shows who may have been providing more value to his respective team.
From here, the plan was to simply create a metric that should correlate to overall value by looking at three separate aspects: (1) Save percentage [Sv%], (2) relative shot count intensity relative to the other top 25ers [RelSCI] and (3) percentage of games played out of 82 [GameCtAdj]. Basically, Sv% tells me how effective they were at a base level, RelSCI tells me how “hard” their workload was and GameCtAdj should tell me how much of a workhorse they were.
Since I made up (as far as I know, at least) two of the three metrics, I’ll take a second to explain in greater detail.
Save percentage is straightforward. I won’t waste time on that. Relative shot count intensity, again shown as RelSCI, is a pretty simple metric. What I’ve done is calculated average shots per game played for all of the top 25 and then normalized these values to the average of all of these. See the table below for more detail:
As an example, Henrik Lundqvist, who we’ll come back to later, had an average shots per game played of 28.3 according to my numbers. RelSCI, shown on the right, shows a 1.00 for him, which means his average shots per game played (28.3) was almost identical to the total average shots per game played (28.2). Workload-wise, this provides a simplistic argument for how much effort a guy has to put forth in an average game. There are some wrinkles to this approach, though.
First of all, not all shots are created equal.
Martin Brodeur, in his prime, may have seen a relatively low shot count, but with how the Devils played, the ones that did get through were of slightly higher quality. Secondly, shot count can actually reward lower-quality rebound control. I’ve seen the argument posited before that a guy like Luongo actually has an artificially inflated shot count, as his primary focus is not necessarily on trapping the puck, but more on blocking it. Following this train of thought, it could be argued that he creates more rebounds and shot opportunities. Finally, it doesn’t explicitly capture the strength of the team in front of a guy. Or at least, I believe, it may appear that way at first glance.
When the RelSCI for a goalie like Brodeur or Bryzgalov is looked at, it’s notably lower than 1.00 relative. Meanwhile, a guy like Cam Ward has a very high RelSCI. This would actually seem to make sense if analyzed from a team strength perspective, as the Flyers and Devils very likely had a stronger defensive corps than the Canes did.
So that’s RelSCI. It’s not perfect, but it does at least simplistically capture a goalie’s workload. The second stat created is a lot more straightforward, and that is GameCtAdj. Assuming an opportunity to play all 82 games (i.e. no lockout or unplayable games), it’s nothing more than the percentage of games a goalie actually played, seen below:
This number alone can actually speak volumes about the value of certain goalies, such as Pekka Rinne, who played or appeared in an astounding 89% of his team’s games. Conversely, Rick DiPietro, who is in this list again because of cap hit and not because I think he’s anywhere near the top 25 in the NHL, played an overwhelming 10 percent of the Islanders’ games.
The next step was to calculate all three values for each goalie (Sv%, RelSCI and GameCtAdj) and then multiply them together to get an overall market “value.” Since they can all be represented as decimal values around one, the resulting value isn’t too hard to understand. A guy who saves a lot of shots, sees a lot of shots and plays a lot of games will ultimately have a higher market value. While not groundbreaking, there is some insight to be had here. Take a look at how this system compares with the cap hit ranking for each guy:
What really jumped out to me in looking at this was that the guys who my system would rank two through six are not overwhelmingly appreciated or lauded goaltenders. While Ward and Kipper get a decent amount of press, they’re largely overlooked due to their teams’ performance or their market. Adding in the 2011-12 cap hit really drives this point home. To make it a bit more interesting, I took the total cap hit from last season (not salaries) and proceeded to redistribute it based on this new ranking (Sv% x RelSCI x GameCtAdj).
The bargain goaltenders from last year, some of whom that are now no longer bargains (Rinne), really start to jump out at this point. The column all the way on the right (click the image to see in better detail) shows the difference in a particular goalie’s cap hit versus the adjusted salary cap hit calculated using the new ranking (green means value, red means closer to DiPietro territory):
What was really interesting to me, getting back to Lundqvist, was how he started out ranked right where his cap hit placed him based on save percentage. Great for Rangers fans, right? Not necessarily. Factor in the RelSCI and Sv%, though, and you see he drops to the 11th spot. What does this say? Am I really arguing that he’s not a top goalie in the league?
No, I’m not. What I’m saying is that Lundqvist is not a value goaltender. You pay for what you get from him. Also, he has a good backup, because the Rags finished first without his having to play in a quarter of the games. Beyond this, his RelSCI reveals that he sees fewer shots than his counterparts. Again, it could very strongly be argued that this should not count as a negative, but I think it can be taken at face value here. Ultimately, using a purely quantitative approach with a linear redistribution of cap hit, you actually get less than what you pay for relative to other goalies out there. That is, of course, ignoring the premium you have to pay for top talent.
Looking at Pavelec, the chart above highlights why Winnipeg gave him the contract they did at the end of the year. His cap hit from last year was low ($1.15M), but he provided much more value than that to his team. Importantly, it also emphasizes that Rick DiPietro’s contract is an outright joke at this point.
From a Flyers perspective, it reflects that Bryz was not an absolute bust, but hit the cap much harder than the value he provided to the team. Obviously, this does not indicate his future performance will be like this.
All things considered, I think a lot can be taken from the above data. I could spend many more hours analyzing it, but I think the last column really does sum it up. A lot of guys who provided their teams with exceptional value last year have been rewarded with new contracts (Rinne, Price, Schneider, Pavelec, Dubnyk), and the ones who didn’t were apparent to most NHL fans.
My next step with this will be to track on a historic basis (at least back to 2008), as it seems that the more underpaid a goalie is, the higher his value-based overpayment becomes when he finally does cash out.
As always, I’m interested in your thoughts on this week’s Crashing the Crease, so feel free to comment or reach me on Twitter (@HeyItsBrenno).