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We built the most average team in hockey

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Photo credit:Richard A. Whittaker/Icon Sportswire
Jon Steitzer
4 years ago
The summer boredom is soon coming to an end and with that, the opportunity to drop in non-newsy posts about hockey is coming to an end as well. That probably serves you the readers well, but we’re not quite there yet. Instead I give you an idea stolen from 538…
Interesting, right?
Well, it’s a bit easier to do that with baseball than it is to do with hockey, but screw it, I’m going to try anyways using 6 equally weighted measures for forwards, 6 equally weighted measures for defensemen, and ditto for the goaltenders. The end result, you guessed it, a completely average team.
The Forward Criteria
This isn’t an attempt a truly profound analytical model. This is just some fun to have in late August, so that being said the categories are time on ice, points, expected goals for %, corsi for %, penalty minutes, and hits. I can sense a lot of you cringing and others shrugging in acceptance.
Here’s where the title of the post becomes quite misleading. I’m not looking at averages, I’m looking at percentiles for each of these categories and then averaging them out. Annoyed yet? Sure you are. In reality only a select few of you are actually reading the methodology part, so I can put whatever I want in here. Banana fart sundae. See? No one is even going to acknowledge that.
Here are your forwards…
PlayerPosAvgDif
Mario KempeR0.5000
Trevor LewisC0.5000
Mikko KoivuC0.5000
Kyle TurrisC0.5020.002
Luke GlendeningC0.5020.002
Brandon DubinskyC0.498-0.002
Dale WeiseR0.498-0.002
Alex GalchenyukC0.497-0.003
Lukas RadilC0.497-0.003
J.T. BrownR0.495-0.005
Michael RafflL0.495-0.005
Dominik KahunC0.495-0.005
Jason PominvilleR0.5070.007
This is the most Minnesota Wild team that ever Minnesota Wilded. Not just because of the number of current or former Wild players on it, but the fact that is absolutely a perpetual bubble team group. It’s entirely possible that Turris and Galchenyuk are better than average, but using just 2018-19 numbers, this is how it shakes out.
The Defense Criteria
Well, it would be boring if I did it exactly the same as the forwards, so we’ll switch it up and go with ice time, points, hits, penalties, blocked shots, and corsi against. I’m writing this before seeing the results, so I can tell you, I’m excited to see what this produces.
PlayerAvgDif
Igor Ozhiganov0.5010.001
Radim Simek0.499-0.001
Josh Brown0.5020.002
Jordan Oesterle0.5030.003
Sami Vatanen0.5100.010
Ben Harpur0.490-0.010
Cam Fowler0.489-0.011
Jonathan Ericsson0.483-0.017
There’s the Leafs content you expect to see on Leafs blog. While Ozhiganov may have moved on, using my hastily thrown together criteria he was the most average defenseman in the NHL last season. I’m not sure what witchcraft I cast to lump Vatanen and Fowler into this mix, but there will be no second guessing of this, and we will all accept that Ben Harpur is marginally better than Cam Fowler. After all, hits, PIMs, and blocked shots, dude.
The Goaltender Criteria
While I think it is important to acknowledge their voodoo, and the fact that pretty much every goaltender has the same stats, I’ll proceed with the charade that is already producing what is a completely nonsensical lineup, that does in fact seem capable of being the 16th best team in the league.
For goaltenders we’re looking again at ice time, then we’ve got a whole lot of variations of save percentage. You’re getting shots against, save percentage, expected save percentage, high danger save percentage, and goals saved above average. Folk’s… this might actually tell us who the statistically most average goaltenders are.
There winners are…
GoaltenderAvgDif
Curtis McElhinney0.498-0.002
Anthony Stolarz0.5080.008
C’mon… deep down you knew it was going to be McElhinney, right? I guess the rub here is that we’re looking at all goaltenders that played in the league last season without a minimum ice time attached to them, and it was bound to be a couple of backups. I’m going to adjust it for goaltenders with over 500 minutes so we have something more applicable.
GoaltenderAvgDif
Philipp Grubauer0.497-0.003
Mackenzie Blackwood0.492-0.008
Mikko Koskinen0.5100.010
Honestly, I think I’m just as comfortable with the McElhinney and Stolarz results as with this group. Anyways, let’s look at the a potential roster
LWCRW
GalchenyukTurrisPominville
RafflKoivuKempe
DubinskyLewisBrown
KahunGlendeningWeise
Radil
LDRDG
FowlerVatanenGrubauer
OesterleEricssonKoskinen
HarpurOzhiganovWedgewood
BrownSimek
Yeah, there have been worse teams put together, but you should in no way be excited about any of this. It’s probably interesting that there’s no shortage of right shot defensemen on this team. It’s also likely this team can kill penalties. This team can make the playoffs, but you probably hope they don’t because who the hell would want to watch this group play?

Isn’t this a Leafs blog?

Fair enough. Here’s the breakdown of how the Leafs fared using my criteria which was used to find the most average player, not identify the best players. (remember hits, blocked shots, and PIMs)
Forwards
PlayerTeamPositionOverall
John TavaresTORC0.810
Zach HymanTORL0.803
Kasperi KapanenTORR0.737
Mitchell MarnerTORR0.705
Kenny AgostinoN.JL0.650
Alexander KerfootCOLC0.635
Auston MatthewsTORC0.633
William NylanderTORR0.595
Pontus AbergMINL0.490
Jason SpezzaDALC0.448
Frederik GauthierTORC0.435
Trevor MooreTORL0.338
Nic PetanTORC0.293
It’s time to catch Kenny Agostino fever. WOOOOOOOOOOOOOO!!!!
I’m going to state right now that I think Auston Matthews is better than him.
Defense
PlayerTeamOverall
Jake MuzzinTOR0.804
Tyson BarrieCOL0.714
Cody CeciOTT0.649
Travis DermottTOR0.642
Morgan RiellyTOR0.629
Jake GardinerTOR0.585
Ben HarpurOTT0.490
Kevin GravelEDM0.360
Martin MarincinTOR0.342
Justin HollTOR0.259
I found a way to create decent looking numbers for Cody Ceci, I’ll take my Nobel Prize in Math now please.
Also at this point it still feels like it’s worth include Jake Gardiner on this list while the Leafs move mountains to bring him back.
Goaltenders
Freddie Andersen had the 5th highest score in the league at .781, and neither Hutchinson or Neuvirth played enough to make the cut.

So…

None of this really means anything. This wasn’t really about finding the true middle of the pack based on true predictors of talent or what helps a team win. This was six categories on three different position groups and averaging them out into seeing what average players look like. While there’s some value in considering all these categories in establishing a single number to judge all players by, I’m going out on a limb here to say that there should probably be heavier weighting attached to points over penalty minutes.

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