Tag Archives: G Money

Edmonton Oilers, Fancy Stats, G Money, NHL

Assessing the Western Conference Playoff Landscape by @Oilersnerdalert

Who’s the favourite? We know the Oilers are going to play San Jose first, but in the big picture, who should the Oilers want to play most/least? Enquiring minds want to know!

There is no magical way to ‘know’ what’s going to happen in the various series, but we can look at how the season has shaped up and make some decent guesses about who to favour.  I’m going to walk through a few different methods and we’ll see what we see.

Don’t Use Standings

First, one thing that I’ll start with is to stay you can generally throw the regular season standings (or points) out the window.  There is a fairly weak relationship between points and post-season success (though it may surprise you to learn that the PresCup winner, far from being ‘cursed’, actually tends to do quite well in the post-season).

Head to Head Score Adjusted Corsi

Our first data stop will look at how the various West teams fared head to head in 5v5 score adjusted Corsi. Since the refs tend to put away their whistles in the post season, the importance of 5v5 is magnified, so this gives us a sense of how the ‘playoff’ part of their games went.

http://i.imgur.com/diHK3SO.png – source @OilersNerdAlert

From an Oilers perspective, MIN and ANA look like good matchups, while the others, especially CGY, CHI, and S.J look like tougher matches. But wait! Didn’t we go undefeated against CGY?? Not to mention STL? How is this possible?

Actually, this is the weakness of any statistical look – small sample size. In the case of CGY vs EDM, the Oilers blew out the Flames early in several games. For example, the first game of the season, the gameflow looks like this:

http://i.imgur.com/rbbg7Cn.png – source @OilersNerdAlert

The Oilers basically ran up the score early, dominated for most of the game, and then went into a defensive shell – which allowed the Flames to run up the fancystat counters, even as their loss was already written. Same story in the last game of the season, where the Oilers ran up a 5-0 edge and then coasted.

Now you might ask, isn’t score adjustment supposed to take care of that? Well – yes, sort of. It takes care of that at a statistical level. The score adjustment is done based on league wide averages.  So it works really well when you get at least 10 to 20 games of data, enough where the averages start to apply in a meaningful way.

But a score adjustment at a game level, or even at a season series of 4 or 5 games, while it will almost always push the fancystats in the right direction, won’t necessarily be reflective of that game or games. Especially if there were blowouts, as there were twice in this series.

So I wouldn’t take these numbers too seriously. That’s why I put this look first – it’s actually not that reliable IMO. It’s more for interest.

And as we’ll see later, if the Oilers face the Flames, take the Oilers all the way!

Head to Head Records

I don’t actually know if anyone has tested to see if head to head records have any predictive power for the post-season (my gut says probably not), BUT I sure do like this!

https://twitter.com/humantorch/status/851498850430976000

Goal Differential

I mentioned earlier that points are not that great a way to assess post-season chances. A much better a predictor is goal differential. (Read the detailed analysis here: https://www.stats.com/insights/nhl/debunking-myth-playoff-vs-regular-season-hockey/)

When we look at the teams sorted by goal differential, it gets pretty interesting:

http://i.imgur.com/KPObkSA.png – source NHL.com

Now Edmonton is starting to look more like a powerhouse than a weak sister, yes?  If Talbot Talbots and McDavid McDavids, the Oilers can beat anyone.

And of course, the weak sister in the West is in fact … Da Flames.

By the way, you might be wondering – isn’t this basically PLUS MINUS, and isn’t PLUS MINUS the pariah of the fancystats world?

Indeed, it is – at the player level. That’s for two reasons – the assignation of plus minus at the player level is extremely noisy, and because goals are such rare events, it takes multiple seasons to generate enough player sample size to overcome that noise – and by that time, your player has usually changed (situation, or even age!)

We don’t have as much noise, or as much of a sample size problem at the team level though, which is why goal differential works pretty well.

Score and Venue Adjusted [Corsi, Fenwick, Expected Goals]

Now let’s get on to some actual fancystats. I’m using Corsi, Fenwick, and corsica’s xGF.  Corsi has historically been something of a gold standard for predicting the future.  What’s interesting though is there’s an argument to be made that this relationship may be weakening as more teams pay attention to shots/possession and the resulting ‘market efficiency in action’ takes away some of the advantage historically measured by shot metrics.

We’re going to take the full seasons 5v5 measures and rank teams that way. So we’ll roll all three together to get a sense of where the teams fit:

http://i.imgur.com/OZPNHdY.png – source corsica.hockey

Ooh, that’s a bit ugly, isn’t it?  The Oilers are much weaker by this measure – ranking 7th, 2nd, and 6th out of 8.  So why such a big difference from goal differential?

Well, the easiest way to way outperform (or underperform) your underlying shot metrics is through the quality of goalering (you can also do it through special teams but I’d say that’s ‘harder’ in some sense).  So I think this really reflects the fact that Cam Talbot this season has been incredible – arguably a Top 4 or Top 5 goalie league-wide.

If the Oilers are to have success in the post-season, he’s going to have to continue his strong play. No surprise there.

San Jose looks a lot like the powerhouse that made it to the Cup Finals last year. Not going to be an easy series!

I guess the saving grace is that the Flames are still weak at 5, 8, and 8.

Tweaking the Fancystats

There’s an interesting tweak we can make to these numbers to increase their assessment/prediction capability. One of the things we know is that shot metrics in-season have their peak predictivity around 20 to 25 games, after which there is a slow decline in predictivity. Some of that is due to increasing randomness as games predicted declines.

But I think a significant part of it is also that teams change over the course of the season. Key players get hurt (or come back from injury).  Sometimes coaches change.  Teams get into a groove or fall out of one.

So we have this balance to find – we want the maximum amount of data possible, but if we use data that’s too old, it isn’t actually reflective of the team right now.

As it turns out, using the last 25 games gives adequate data volume and yet doesn’t get overloaded with games from early in the season that aren’t really indicative of a team now, producing a fairly high level of predictivity. (see for example Micah Blake McCurdy’s work on his Oscar prediction model).

So let’s look at two things – how a team did over the last 25 games of the season, and also the trend of that data, as a bit of a projection as to the direction of the teams level of play.  (Note: out of laziness, I’ve taken data for the Oilers from Feb 15th, which equates to 25 games. Other teams may be a bit more or less – ha ha, too bad for them! More seriously, it shouldn’t change the results much, if at all)

Let’s take a look.

Here’s the West teams from best to worst in SACF% over the last 25 games:

http://i.imgur.com/7ocNV8l.png – source corsica.hockey

Oooh.  Still sucks to be the Oilers on that basis though, doesn’t it?  But we also know that those 25 games started with a fairly poor stretch for the Oilers, but they’ve been coming on strong of late. And the opposite is true for the Flames. So let’s look at the trend over those games too.

http://i.imgur.com/2HcmCVp.png – source corsica.hockey (chart by @OilersNerdAlert)

Hmm, that’s encouraging, right? Despite the rather soft numbers the Oilers put up in the second half, in fact (as the eyes would suggest), the Oilers appear to be improving in a big way as they head towards the post-season. Yeah!

Cowtown on the other hand – again, as the eyes would suggest – are sliding back to Earth after the unsustainable hot streak that pulled them into the playoffs.

San Jose has solid numbers, but is neither hot nor cold.

I’ll leave you to mull over the rest of the trends.

Putting it All Together

We’ve taken a few different looks at how teams did in the regular season to get a sense of how they might fare in the playoffs.  Now, has this work given you the definitive guide to who’s going to win the West?

Ha, of course not! Statistics give you a sense of which way the probabilities lean, they are most certainly not fait accompli.

Rather, what we’ve got is some sturdy data to suggest which teams are leaning positive and which are leaning negative.

You still have to understand context though. Statistically, Anaheim is looking pretty good – but if Lindholm, Vatanen, and Fowler are out or not 100%, that’s a huge hit. Ditto San Hoser and Jumbo Joe. (In fact, one of the defining characteristics of Cup champions is that they are good and healthy when they hit the playoffs, and are still mostly healthy, or at least healthier than their opponents, by the time they get to the finals).

The Oilers meanwhile actually look pretty good, my friend!  Probably not to win it all, but I’d say we’ll be a tough out even for a legit contender.

And with McDavid and Dadbot on our side, anything can happen.

Bring it on!

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Beer League Hero, Edmonton Oilers, Fancy Stats, G Money, NHL

G-Money on Dealing Nugent-Hopkins

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***Yesterday we posted an article speaking to the disappointing seasons that Ryan Nugent-Hopkins and Jesse Puljujarvi were having and I asked the boys for their thoughts on what we should do with the Nuge. You can read that article HERE.

I did the same on Twitter with this poll and I’m not surprised with the results. It’s not like my followers are wrong 🙂

It’s funny, each time I’ve decided to post an article detailing how unimpressed I am with a specific player, they decide to go ahead in the following game and put up some points… Maybe I should do it more often.

Now, as I mentioned in the article yesterday, G-Money’s (@oilersnerdalert) excerpt was lifted from a longer reply to my question and now I’d like to share G-Money’s full reply with you. Bon Appetit! – BLH***

Q: What’s wrong with Ryan Nugent-Hopkins, and Should the Oilers Trade Him?

A: a. Nothing other than a run of bad luck, and b. God no!

On point a, one of my lenses for looking at a player is “WoodMoney“, the matchup-based quality of competition methodology that @Woodguy55 and I put together. Here’s a look at how much time Nuge is spending facing the various levels of competition, and how he is doing so far this year as compared to the other two main centres (I’ve included both Corsi and DangerFen for the Elite tier, but only CF% vs the others so as not to turn an intimidating table of numbers into an overwhelming one ):

Nuge

% TOI vs Elite – 41%
CF% –  45.9%
DFF%  – 44.9%
% TOI vs Middle – 39%
CF% – 53.9%
% TOI vs Gritensity – 20%
CF% – 59.7%

McDavid 

% TOI vs Elite – 32%
CF% – 53.4%
DFF% – 56.0%
% TOI vs Middle – 46%
CF% – 55.1%
% TOI vs Gritensity – 22%
CF% – 56.8%

Draisaitl 

% TOI vs Elite – 27%
CF% – 49.5%
DFF% – 48.0%
% TOI vs Middle – 49%
CF% – 50.0%
% TOI vs Gritensity – 24%
CF% – 55.0%

Conclusions

On point a:

1. McDavid is stupid good. He destroys everyone.

2. Nuge is being used by TMc as the shutdown power vs power centre this year. Not McDavid. Not Draisaitl. Nuge is the guy spending 41% of his time against the best players in the NHL. That’s creating a ton of clear air for McDavid and Draisaitl. If you’re comparing things like points, you better take that into account. Nuge’s points are being sacrificed to give the other two a chance to score more.

3. When Nuge is up against those great players, it’s true he’s struggling to keep his head above water.  Moreso than in years past.  And he’s not the only one. My suggestion: give him Eberle and Pouliot on an ongoing basis. Let those two (who are both struggling) right their ships. Nuge’s ship will get fixed right along with them.

4. When Nuge is not against those great players, against pretty much every one else, he runs roughshod.  The Nuge is Yuuuuuuuge!

5. So there is nothing wrong with Nuge, except:

On point b:

Nuge is shooting at 5.1% this season. He has a career average of 11.2% prior to this season. So he’s shooting at less than half of his career average.

He’ll find his groove again, guaranteed.

Every player’s sh% varies wildly above and below their long-term average. And it’s more or less random (if a player could control it, they’d always shoot above their average, which would raise their average, which means they’d shoot at random above and below that average, which…)

That’s just how it goes. Sh% controls you, you don’t control sh%.

Now as for trading Nuge … well, my thought process is always that whether it makes sense to trade a player is based entirely on the return.  Anyone is on the block if what you’re getting back is good enough.

But you know what would be stupid though? Trading a player at what would in effect be the maximum possible discount because of one of those sh% lows.

***With G-Money’s balanced analysis and down-to-Earth reasoning, it’s hard, for me at least, to want to move Nuge ASAP because I’m curious as hell as to what the Oilers might look like if they have all three of McDavid, Draisaitl, and RNH humming along on the offense.

What do you think? Let us know in the comments below!***

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Beer League Hero, Edmonton Oilers, G Money, NHL

Rumblings From the Beer League Heroes Batcave

OILERS V. MAPLE LEAFS… #YIKES

So I had to watch the game after work yesterday and I’ll be honest, it wasn’t as entertaining as I thought it would be. I can watch the Oilers play and be content with a good effort if they lose. But last night, something was definitely out of place. The door was ajar if you will.

How the Oilers can get that many powerplay chances and not come away with one goal is beyond me. How they Oilers coaching staff can continue to use the same tactics on said powerplays is also leaving me baffled.

  • Where’s Puljujarvi and this shot of his we are continually being teased with?
  • Why isn’t anybody in the media doing their job and asking McLellan during the scrums about Jesse’s missing shot? They’re happy to bitch and moan about in online but I haven’t heard one MSM member question Todd McLellan about this.
    • Then today we see Eberle taken off the 1st PP unit and then replaced by…. Mark Letestu! Because he wasn’t commenting about playing too many minutes last year at the beginning of this season when he was scoring to start the year. Makes perfect sense!
  • Why does Andrej Sekera get any time on the PP?
    • His hatred for shinpads is getting old.
  • The other teams have the Oilers figured out and since the PP runs through 97, they just cover the cross-ice lanes and clog everything up like by bathroom sink. Thus, leaving McDavid to do it himself of move the puck to the point which still doesn’t help because the shooting lane is full of players.

Another thing I didn’t notice right away while watching the game but after, when I was reading G Money’s post-gamer over at his site, is the shooting charts 5×5. Check them out.

What the f*ck are the Oilers doing out there if they aren’t shooting the puck? We know that McLellan likes shot volume… So turn it up to 11 for Christ’s sake!! Why are they doing their best to get the opposition’s goalie a shutout every time a losing streak comes along?

Hopefully, they can get something together versus Patrik Laine and the Jets tomorrow night.

The new lines that were being run at practice are as follows:

Jesus, Mary and Joseph! How Pouliot stays in the lineup is mind-boggling, isn’t it? Not even the fancies have his back this time around. So to that, I say he’s must have some interested parties and the Oilers are showcasing him.

Taking out Kassian and Hendricks will bring the intimidation level down a bit but if they’re not performing to McLellan’s standards, then perhaps it’s time for a little break. In Hendricks case, he’s looking his age these days. I love the guy but let’s get that conditioning back up to par and then give it another go. As for Kassian, he’s looked a tad off for the last few games.

The team lacked bite against Toronto and adding in Pitlick and Slepyshev could bring back a bit of that edge and it most definitely will bring more speed.

Why hasn’t Anton Slepyshev been able to hold down a spot on this team? He’s been hitting, shooting, and creating chances for the majority of the games he’s played in. More so that say, Zach Kassian or most recently, Pat Maroon. This is a player that was once described as one of the best in the KHL before coming over. He’s a Russian that has accepted there’s a certain path that one has to follow when attempting to break into the NHL and that must mean something. For the most part, I wanna say the kids that come over from Russia expect to be slotted into their NHL team’s starting lineup right away but not with Slepyshev. He’s been a good soldier so far and I think he should be rewarded with an extended run.

Mark Fayne has been recalled because Eric Gryba was hurt at practice. This falls under the category of “Who gives a shit?” because I’d be more than surprised if he made it into the lineup. Then again Matt Benning has been having a tough go of it as of late… Maybe Battlin’ Matt Benning gets a rest one of these days and Fayne draws in.

Talbot needs another break here and Gustavsson made some sweet saves in the third period last night. I’d like to see Gus get a start in the next couple of games.

OILERS GETTING A VISIT FROM A SPECIAL SOMEONE?

Gawd I hope that G Money can shed some light on this play from the Oilers game yesterday when he returns from the game on Saturday 🙂

@Oilersnerdalert is G Money, if you’ve been following the Oilers online through our site, Lowetide’s comments section, Twitter, and his own page; you’re very aware of who G Money is.

The BLH family has known about this for a while now but we were asked to keep it on the down low. But today is a new day and our old boy Zachary Laing has let the cat out of the bag with a little slap and tickle…

G Money just released a tweetstorm explaining the invite as well. So congrats to him and let’s hope for all the best!

 

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Beer League Hero, Edmonton Oilers, NHL

What In Sam’s Hell is Going On?

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Well, the wheels are falling off of the bus and we’re veering into the ditch. Edmonton is 4-5-1 in their last ten games now but have the games really been that bad?

After every game I head over to G Money’s website and read his post-gamers because he recaps the game in a common tongue even though he’s using some mad stats to show you how the Oilers were successful or not. You can check that site out HERE.

I want to quote what he said in his latest game review because I think what he says is something as fans we lose touch with when the Oilers are winning AND most of all when they are losing. And honestly, if any fan in this generation knew a thing or two about losing, wouldn’t it be Oilers fans?

You know, one of the things I try to do here and on Twitter is provide a level, objective assessment of every game. (Sometimes I fail because I am first and foremost a fan.)

One of the things you have to try and do to make that happen is, believe it or not, ignore the score. The score in any one game is affected by all kinds of weird and wacky things. In the short term, it can and does diverge – sometimes widely – from the underlying process. All the time.

Over the long term, though, the score and the scores do tend to align strongly with the underlying process.

It’s the process that matters in the big picture. That’s what we’re trying to measure with all these stupid fancystats.

When it comes to process, the Oilers had unsustainably good sh% & chances vs shots conversion (that is to say, they were converting their poor shot rates to dangerous chances at an unsustainably high pace) early in the season. (And I went on record and said so).

Right now, as painful as this streak of losses is (and losses tend to be more painful than wins), the situation is just the opposite. The Oilers are having unsustainably bad sh% & chances vs shots conversion (that is to say, they are converting excellent shot rates to dangerous chances at an unsustainably low pace) lately. They’re missing wide open nets, multiple times a game.

Just as the good streak inevitably ended, so too will this bad streak, and probably soon. (I’m going on record in saying so).

The Oilers were nowhere near as good as the early winning streak said we were.

The Oiler are nowhere near as bad as this recent losing streak says we are.

Seriously. We’ve been through 10 years now where the underlying numbers have confirmed that this team was complete and utter sh*t.

This year, we are not sh*t! At long last! The underlying process now – even with the woeful effort in the third period in this game, and it really was abysmal – is noticeably better than it has been in the past. It is what will drive the longer term results.

If you check out G’s site and read the review of the Ducks game from yesterday, you HAVE to check out the shot charts… It’s unreal! The Oilers should’ve won that game but they couldn’t capitalize no their chances and they couldn’t stop  Anaheim from scoring on theirs… Riz-diculous I say!

IMAGINE IF WE HAD…

The Hall-ogists are coming out now in full force now that the 2ndary scoring has dried up a bit. So, I wanted to take a look at how Hall, Lucic, Demers, and Larsson are doing this year via stats.hockeyanalysis.com and compare them.

My findings (5×5, 50 minutes minimum) are thus:

Name TOI Pts/60 Shots/60 CF% CF60 RelTM  CA60  RelTM
Lucic 246 .97 5.11 53 3.19 -1.27
Hall 220 1.64 7.6 51.3 5.78 2.38
Demers 244 .74 3.19 54.5 6.88 -2.15
Larsson 301 .40 3.38 51.4 2.37 1.88

So by all accounts and no surprises, Hall is dominating Lucic on almost every level here. That is Taylor Hall’s M.O. and his job really but Milan Lucic wasn’t a 1st overall pick either. Hall absolutely should be putting up massive offensive numbers and the fact that he still isn’t has to be a helluva lot more thought provoking than how he compares to Lucic’s production so far.

Sure, they make the same amount of money but they’ve also accrued the same amount of goals this year (5). Maybe we should compare Pat Maroon’s production to Hall’s because that’s who is playing in Taylor’s spot right now.

When you think about it, who should really be in a position to make the playoffs more at this point, Edmonton or New Jersey?

As for Demers vs. Larsson, do we need to take into account that they are very different styles of dman or should we leave that out? In any event, Demers takes the cake in stats like CF%, CA60 RelTM, and Pts/60. Whereas Larrson has the edge in TOI, Shots/60, and CA60 RelTM. I really want to call that one a draw.

As G Money says above, the boys are in an unsustainable funk but are bound to beat it. I don’t subscribe to the idea that if the Oilers had Hall and Demers instead of Lucic and Larsson that they’d be winning these games. It’s possible but I’ve seen too many years of Hall dropping off of the face of the earth when the team needed him most to believe that he’d shed that skin and things would be different.

THE SKID

How can the Oilers get off this path of mediocrity?

  • Give Cam Talbot a bloody rest!!
    • The guy just had twins and he’s on pace to play 77 games. Grant Fuhr he is not!!
  • Stop with the McBlender.
    • It’s telling me that the coaching staff has no idea what to do with their roster and if the head of the ship has no discerning clue as to the direction his ship should be going, that’s a yellow flag.
    • If Toddy Mac wants to double shift McDavid, do it. But be consistent with the lines.

I was really enjoying watching Tyler Pitlick up with McDavid and we do know that Jordan Eberle has had success with Nugent-Hopkins in the past… Draisaitl-Lucic-Kassian was looking sexy too!

I feel for Anton Slepyshev. He’s been super impressive to my eye this year and he can’t catch a break. I’ve loved his intensity and ability to get that puck on the net. When does he get a chance with 97? I know he took some shifts with 93 and didn’t look completely out of place. So when the team is losing consistently like this, what’s the hurt in trying Ol’ Slepy on the 1st line? We did it with “Lance” and that worked out OK.

Now I’m not saying that Pitlick, Slepyshev, and Kassian should spend an extended amount of time in the “top 6” but they add a fresh and youthful element to it from time to time. Jesse Puljujarvi has been getting better and better but, to me, Slepyshev, Pitlick, and Kassian have given me more to think about whilst watching them.

What do you think? What should the Oilers do? Let us know in the comments below!

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Even if you’re not an Edmonton Oilers fan, we’ve got a 16-bit Superstars design for nearly every major NHL player. From Matthews to Malkin, Crosby to Subban, Weber, Kane, and Ovechkin are there as well. We’ve even got some classic players like Lindros, Ryan Smyth, and Wendel Clark. If you want one done up custom, we can do that too! Just contact us at beerleagueheroes@hotmail.com or on Twitter @beerleagueheroe.

Lastly, anything you purchase goes towards keeping our little pirate ship afloat, so we appreciate anything you might pick up! Thank you! – BLH

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Edmonton Oilers, Fancy Stats, G Money, NHL

Hockey Desperately Needs a Better Competition Metric (Part 2 of 2)

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EDMONTON, AB – OCTOBER 25: Connor McDavid #97 of the Edmonton Oilers battles for the puck against Drew Doughty #8 of the Los Angeles Kings on October 25, 2015 at Rexall Place in Edmonton, Alberta, Canada. (Photo by Andy Devlin/NHLI via Getty Images)

This article is part 2 of 2.

In part 1, I noted that using shot metrics for evaluating individual players is heavily influenced by teammates, coaches usage (zone starts), and competition*.

I believe we have decent tools for understanding the effect of teammates and zone starts – but I believe this is not at all true for competition metrics (dubbed QoC, or Quality of Competition).

And the reality is that understanding competition is critical to using shot metrics for player evaluation. If current QoC measures are not good, this means QoC is a huge weakness in the use of shot metrics for player evaluation.

I believe this is the case.

Let’s see if I can make a convincing case for you!

*Truthfully, there are quite a few other contextual factors, like team, and score state. These shot metrics have been around for a decade plus, and they’ve been studied (and are now often adjusted) heavily. Some of the effects that have been identified can be quite subtle and counterintuitive. From the point of view of assessing *a* player on *a* team, it doesn’t hurt us to focus on these three factors.

It Just Doesn’t Matter – You’re Kidding, Right?

If you bring up Quality of Competition with many fancystats people, they’ll often look at you and flat out tell you that “quality of competition doesn’t matter.”

This response will surprise many – and frankly, it should.

We know competition matters.

We know that a player is going to have a way harder time facing Sidney Crosby than facing Tanner Glass.

We know that coaches gameplan to face Taylor Hall, not his roommate Luke Gazdic (so long, lads). And they gameplan primarily with player matchups.

Are our eyes and the coaches that far out to lunch?

Yes, say the fancystats. Because, they say, when you calculate quality of competition, you just don’t see that much difference in the level of competition faced by different players. Therefore, so conventional wisdom dictates, it doesn’t matter.

The Numbers Suggest Matchups Matter

I don’t have to rely on just the eye test to contradict this line of thought – the numbers do the work too. For example, here are the head to head matchup numbers (I trot these out as a textbook example of coaching matchups) for the three Montreal defense pairs against Edmonton from the game on February 7th, 2016:

vs

Hall

McDavid
Subban-Markov

~ 3 mins

~ 10 mins
Petry-Emelin

~ 8 mins

~ 5 mins
Gilbert-Barberio

~ 40 seconds

~ 14 seconds

Does that look like “Quality of Competition” doesn’t matter? It sure mattered for both Hall and McDavid, not to mention all three Montreal defense pairs. Fifteen minutes vs 14 seconds is not a coincidence. That was gameplanned.

So how do we reconcile this?

Let’s dig in and see why maybe conventional wisdom is just plain wrong – maybe the problem is not with the quality of competition but the way in which we measure it.

It Would Hit You Like Peter Gabriel’s Sledgehammer

I’ll start by showing you an extremely valuable tool for assessing players in the context of zone starts and QoC, which is Rob Vollman’s Player Usage Charts, often called sledgehammer charts.

This chart is for Oiler defensemen in 2015-2016:

This shows three of the four things we’ve talked about previously:

  • The bubble colour (blue good) shows the shot metrics balance of good/bad for that individual
  • The farther to the right the bubble, the more faceoffs a player was on the ice for in the offensive zone – favourable zone starts or coaches usage in other words
  • The higher the bubble, the tougher the Quality of Competition

Notice something about the QoC though. See how it has such a narrow range? The weakest guy on there is Clendening at -0.6. The toughest is Klefbom at a shade over 1.0.

If you’re not familiar with “CorsiRel” (I’ll explain later), take my word for it: that’s not a very meaningful range. If you told me Player A has a CorsiRel of 1.0, and another has a CorsiRel of 0.0, I wouldn’t ascribe a lot of value to that difference. Yet that range easily encompasses 8 of the 11 defenders on the chart.

So no wonder the fancystatters say QoC doesn’t matter. The entire range we see, for a full season for an entire defensive corps worst to last, is a very small difference. Clendening basically faced barely weaker competition than did Klefbom.

Or did he?  That doesn’t sound right, does it?  Yeah, the Oiler D was a tire fire and injuries played havoc – but Todd McLellan wasn’t sending Clendening out to face Joe Thornton if he could help it.

To figure out what might be wrong, let’s dig in to see how we come up with these numbers that show such a thin margin of difference.

Time Weighs On Me

The process for calculating a QoC metric starts by assigning every player in the league a value that reflects how tough they are as competition.

Then when we need the QoC level faced by a particular player:

  • we look at all the players he faced, multiply (weight) the amount of time spent against that player with the competition value of that player
  • we add it all up, and presto, you have a QoC measure for the given player

Assuming that the time on ice calculations are reasonably fixed by, you know, time on ice, it should be clear that the validity of this QoC metric is almost entirely dependent on the validity of the ‘competition value’ assigned to each player.

If that competition value isn’t good, then you have a GIGO (garbage in garbage out) situation, and your QoC metric isn’t going to work either.

There are three different data values that are commonly used for calculating a QoC metric, so let’s take a look at each one and see if it meets the test of validity.

Using Corsi for Qoc

Many fancystats people who feel that QoC doesn’t matter will point to this post by Eric Tulsky to justify their reasoning.

Tulsky (now employed by the Hurricanes) is very, very smart, and one of the pillars of the hockey fancystats movement. He’s as important and influential as Vic Ferarri (Tim Barnes), JLikens (Tore Purdy), Gabe Desjardins, and mc79hockey (Tyler Dellow). So when he speaks – we listen.

The money quote in his piece is this:

Everyone faces opponents with both good and bad shot differential, and the differences in time spent against various strength opponents by these metrics are minimal.

Yet all that said – I think Tulsky’s conclusions in that post on QoC are wrong. I would assert that the problem he encounters, and the reason he gets the poor results that he does, is that he uses a player’s raw Corsi (shot differential) as the sole ‘competition value’ measure.

All his metric does is tell you is how a player did against other players of varying good and bad shot differential. It actually does a poor job of telling you the quality of the players faced, which is the leap of faith being made. Yet the leap is unjustified, because players of much, much different ability can have the same raw Corsi score.

To test that, we can rank all the players last season by raw Corsi, and here’s a few of the problems we immediately see:

  • Patrice Cormier (played two games for WPG) is the toughest competition in the league
  • He’s joined in the Top 10 by E Rodrigues, Sgarbossa, J Welsh, Dowd, Poirier, Brown, Tangradi, Witkowski, and Forbort.
  • Mark Arcobello is in the top 20, approximately 25 spots ahead of Joe Thornton
  • Anze Kopitar just signed for $10MM/yr while everyone nodded their head in agreement – while Cody Hodgson might have to look for work in Europe, and this will garner the same reaction. Yet using raw Corsi as the measure, they are the same level of competition (57.5%)
  • Chris Kunitz is about 55th on the list – approximately 40 spots ahead of Sidney Crosby
  • Don’t feel bad, Sid – at least you’re miles ahead of Kessel, Jamie Benn, and Nikita Nikitin – who is himself several spots above Brent Burns and Alex Ovechkin.

*Note: all data sourced from the outstanding site corsica.hockey. Pull up the league’s players, sort them using the factors above for the 2015-2016 season, and you should be able to recreate everything I’m describing above.

I could go on, but you get the picture, right? The busts I’ve listed are not rare. They’re all over the place.

Now, why might we be seeing these really strange results?

  • Sample size!  Poor players play little, and that means their shot metrics can jump all over the place.  Play two minutes, have your line get two shots and give up one shot, and raw Corsi will anoint you one of the toughest players in the league. We can account for this when looking at the data, but computationally it can wreak havoc if unaccounted for.
  • Even with large sample sizes, you can get very minimal difference in shot differential between very different players because of coaches matching lines and playing “like vs like”. The best players tend to play against the best players and their Corsi is limited due to playing against the best. Similarly, mediocre players tend to play against mediocre players and their Corsi is inflated accordingly. It’s part of the problem we’re trying to solve!
  • For that same reason, raw Corsi tends to overinflate the value of 3rd pairing Dmen, because they so often are playing against stick-optional players who are Corsi black holes.
  • The raw Corsi number is heavily influenced by the quality of the team around a player.

Corsi is a highly valuable statistic, particularly as a counterpoint to more traditional measures like boxcars. But as a standalone measure for gauging the value of a player, it is deeply flawed. Any statistic that uses raw Corsi as its only measure of quality is going to fail. GIGO, remember?

Knowing what we know – is it a surprise that Tulsky got the results he got?

So we should go ahead and rule out using raw Corsi as a useful basis for QoC.

Using Relative Corsi for QoC

If you aren’t familiar with RelCorsi, it’s pretty simple: instead of using a raw number, for each player we just take the number ‘relative’ to the teams numbers.

For example, a player with a raw Corsi of 52 but on a team that is at 54 will get a -2, while a player with a raw Corsi of 48 will get a +2 if his team is at 46.

The idea here is good players on bad teams tend to get hammered on Corsi, while bad players on good teams tend to get a boost. So we cover that off by looking at how good a player is relative to their team.

Using RelCor as the basis for a QoC metric does in general appear to produce better results. When you look at a list of players using RelCor to sort them, the cream seems to be more likely to rise to the top.

Still, if you pull up a table of players sorted by RelCor (the Vollman sledgehammer I posted earlier uses this metric as its base for QoC), again you very quickly start to see the issues:

  • Our top 10 is once again a murderers row of Vitale, Sgarbossa, Corey Power Potter Play, Rodrigues, Brown, Tangradi, Poirier, Cormier, Welsh, and Strachan.
  • Of all the players with regular ice time, officially your toughest competition is Nino Niederreiter.  Nino?  No no!
  • Top defenders Karlsson and Hedman are right up there, but they are followed closely by R Pulock and D Pouliot, well ahead of say OEL and Doughty.
  • Poor Sid, he can’t even crack the Top 100 this time.

Again, if we try and deconstruct why we get these wonky results, it suggests two significant flaws:

  • Coach’s deployment. Who a player plays and when they play is a major driver of RelCor. You can see this once again with 3rd pairing D men, whose RelCor, like their raw Corsi, is often inflated.
  • The depth of the team. Good players on deep teams tend to have weaker RelCors than those on bad teams (the opposite of the raw Corsi effect). This is why Nicklas Backstrom (+1.97) and Sam Gagner (+1.95) can have very similar RelCor numbers while being vastly different to play against.

RelCor is a very valuable metric in the right context, but suffers terribly as a standalone metric for gauging the value of a player.

Like raw Corsi, despite its widespread use we should rule out relative Corsi as a useful standalone basis for QoC.

Using 5v5 TOI for QoC

This is probably the most widely used (and arguably best) tool for delineating QoC. This was also pioneered by the venerable Eric Tulsky.

When we sort a list of players using the aggregated TOI per game of their “average” opponent, we see the cream tend to rise to the top even moreso than with RelCor.

And analyzing the data under the hood used to generate this QoC, our top three “toughest competition” players are now Ryan Suter, Erik Karlsson, and Drew Doughty. Sounding good, right?

But like with the two Corsi measures, if you look at the ratings using this measure, you can still see problematic results all over, with clearly poor players ranked ahead of good players quite often. For example:

  • The top of the list is all defensemen.
  • Our best forward is Evander Kane, at #105. Next up are Patrick Kane (123rd), John Tavares (134th), and Taylor Hall (144th). All top notch players, but the ranking is problematic to say the least. Especially when you see Roman Polak at 124th.
  • Even among defensemen, is Subban really on par with Michael del Zotto? Is Jordan Oesterle the same as OEL? Is Kris Russel so much better than Giordano, Vlasic, and Muzzin?
  • Poor old Crosby is still not in the Top 100, although he finally is when you look at just forwards.
  • Nuge is finally living up to his potential, though, ahead of Duchene and Stamkos!

OK, I’ll stop there. You get my point. This isn’t the occasional cherry picked bust, you can see odd results like this all over.

Looking at the reasons for these busts, you see at least two clear reasons:

  • Poor defensemen generally get as much or more time on ice than do very good forwards. Putting all players regardless of position on the same TOI scale simply doesn’t work. (Just imagine if we included goaltenders in this list – even the worst goalies would of course skyrocket to the top of the list).
  • Depth of roster has a significant effect as well. Poor players on bad teams get lots of ice time – it’s a big part of what makes them bad teams after all. Coaches also have favourites or assign sideburns to players for reasons other than hockeying (e.g. Justin Schultz and the Oilers is arguably a good example of both weak depth of roster and coach’s favoritism).

So once again, we find ourselves concluding that the underlying measure to this QoC, TOI, tells you a lot about a player, but there are very real concerns in using it as a standalone measure.

Another problem shows up when we actually try to use this measure in the context of QoC: competition blending.

As a player moves up and down the roster (due to injuries or coaches preference) their QoC changes. At the end of the year we are left with one number to evaluate their QoC but if this roster shuttling has happened, that one number doesn’t represent who they actually played very well.

A good example of the blending problem is Mark Fayne during this past year.  When you look at his overall TOIQoC, he is either 1 or 2 on the Oilers, denoting that he had the toughest matchups.

His overall CF% was also 49.4%, so a reasonable conclusion was that “he held his own against the best”.  Turns out – it wasn’t really true.  He got shredded like coleslaw against the tough matchups.

Down the road, Woodguy (@Woodguy55) and I will show you why this is not really true, and that it is a failing of TOIC as a metric. It tells us how much TOI a player’s average opponent had, but it doesn’t tell us anything more.  We’re left to guess, with the information often pointing us in the wrong direction.

A Malfunction in the Metric

Let’s review what we’ve discussed and found so far:

  • QoC measures as currently used do not show a large differentiation in the competition faced by NHL players. This is often at odds with observed head to head matchups.
  • Even when they do show a difference, they give us no context on how to use that to adjust the varying shot metrics results that we see. Does an increase of 0.5 QoC make up for a 3% Corsi differential between players?  Remember from Part 1 that understanding the context of competition is critical to assessing the performance of the player.  Now we have a number – but it doesn’t really help.
  • The three metrics most commonly used as the basis for QoC are demonstrably poor when used as a standalone measure of ‘quality’ of player.
  • So it should be no surprise that assessments using these QoC measures produce results at odds with observation.
  • Do those odd results reflect reality on the ice, or a malfunction in the metric? Looking in depth at the underlying measures, the principle of GIGO suggests it may very well be the metric that is at fault.

Which leaves us … where?

We know competition is a critical contextual aspect of using shot metrics to evaluate players.

But our current QoC metrics appear to be built on a foundation of sand.

Hockey desperately needs a better competition metric.

Now lest this article seem like one long shrill complaint, or cry for help … it’s not. It’s setting the background for a QoC project that Woodguy and I have been working on for quite some time.

Hopefully we’ll convince you there is an answer to this problem, but it requires approaching QoC in an entirely different way.

Stay tuned!

P.S.

And the next time someone tells you “quality of competition doesn’t matter”, you tell them that “common QoC metrics are built on poor foundational metrics that cannot be used in isolation for measuring the quality of players. Ever hear of GIGO?”

Then drop the mic and walk.

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