The Faker’s Guide to Advanced Stats in the NHL
So apparently it’s time for the hockey world to fight about advanced stats again.
The most recent outbreak of hostilities was focused (as these things have a disturbing tendency to be) on the Toronto Maple Leafs. First it was forward Joffrey Lupul tweeting about Corsi. Next up was assistant coach Greg Cronin sharing his views on possession stats. If you missed out on those particular skirmishes, don’t worry. Another one should be along any minute now.
If you’re an old-school type who doesn’t like advanced stats, you’re … well, you’re not reading this, because you instinctively slammed your face into your browser’s “close” button as soon as you saw the headline. And if you’re already an advanced stats proponent, you don’t have time to read this because you’re already busy fighting a civil war over what the various stats should even be called.
So that leaves the fans who haven’t chosen a side yet. And in many cases, those who still find themselves on neutral ground might be there because they don’t actually understand what the whole debate is about in the first place.
If you’re interested in learning more about advanced statistics but aren’t sure where to start, there are plenty of good resources available. Eyes on the Prize is running an ongoing summer school feature that will walk through some of the basic concepts. Broad Street Hockey has a basic glossary of terms, and Behind the Net has a more detailed one. Arctic Ice did a whole “Understanding Advanced Stats” section a few years back that has a ton of good stuff. And those are just a few examples. If you’re willing to roll up your sleeves a bit, you can educate yourself fairly quickly.
But what if you’re not interesting in learning more? What if you just want to seem like you did? What if you’d rather know just enough to fake your way through a conversation about advanced stats, while doing as little actual thinking as possible?
If so, I may be able to help you out. Let’s see if we can make our way through this together, with my handy 12-step program for faking your way through the world of advanced hockey statistics.
Step 1: Don’t be afraid
First things first: Hockey’s “advanced” stats aren’t actually all that advanced, at least as far as the math goes. Are you comfortable with tricky concepts like addition and subtraction? Awesome, you’re halfway there. If you also understand what a percentage is and/or can divide by 60, you’re going to be golden.
And beyond the math, the concepts themselves aren’t all that complicated, either. If you can get past the new terminology, most of them will make intuitive sense to anyone who’s ever played shinny.
Practical tip: You can do this. Stay with me.
Step 2: Have realistic expectations
You may be aware of the sabermetrics movement in baseball, and how it’s slowly but surely been revolutionizing our understanding of the game. Hockey hasn’t really come close to that yet, for two key reasons. One is that baseball’s advanced stats work had a several-decade head start. The other is that baseball lends itself to much cleaner data, and has so much of it to draw from. By contrast, much of the data that advanced hockey stats rely on has been tracked for only a few years. We can go back and calculate Babe Ruth’s career WAR, but we don’t know Wayne Gretzky’s Corsi and never will.
The advanced stats movement in hockey is still in its infancy. It’s making progress, but it hasn’t really revolutionized all that much yet. For the most part, its proponents are well aware of that, and you should be too.
Practical tip: If you’re looking to learn more about a sport that you love, you’re going to find this worthwhile. If you’re setting the success bar at “life-changing breakthroughs,” prepare to be disappointed.
Step 3: Don’t say “moneyball”
Just don’t. Yes, you read the book. (OK, fine, you saw the movie and heard about the book.) Yes, it was about advanced stats. But don’t bring it up. You’re just going to have to trust me on this one.
Practical tip: Saying “moneyball” in a discussion about hockey stats is like saying “totally radical” while working as an undercover narc at a high school.
Step 4: If you remember only one thing, make it this word: “possession”
Hockey is a game of scoring goals and preventing the other team from scoring them. But goals are relatively rare events in hockey, so relying purely on goal-based stats to predict future performance is dicey.
If we can’t rely on goals, though, we need something that’s more common and acts as a reasonably good stand-in. For many of hockey’s advanced stats, that’s puck possession. After all, you can’t score if you don’t have the puck, and you can’t be scored on if you do. (Unless you are Chris Phillips.) And since one team or the other has the puck for the majority of every hockey game, we’ve got far more to work with than if we relied just on goals.
Again, this way of thinking is not especially advanced. You’re basically asking “Which team controls the puck?,” which is something fans have understood was important for decades. Possession was one of the intermission stats in NHLPA Hockey 93, and that game kept like only four stats.
So using possession seems relatively intuitive. That’s the good news. The bad news is that there isn’t really a stat that directly measures puck possession. So we look for a decent proxy. That’s where shots come in, which we’ll get to in Step 5.
Practical tip: During any discussion of a specific player, casually say, “Yeah, but those possession numbers …” and then trail off knowingly.
Step 5: Corsi and Fenwick
Corsi and Fenwick are two of the terms you’ll hear most often as you’re faking your way through the world of advanced stats. They measure essentially the same thing: how many shots are directed at each net at even strength.
Note that that’s “shots directed at the net,” not just plain old shots. That’s because we’re including traditional shots (i.e., goals and saves) as well as shots that miss the net. Corsi also includes blocked shots, while Fenwick doesn’t. (Yes, there’s a reason for that. No, you will never need to actually know what it is.)
You can express Corsi and Fenwick as a plus/minus or a percentage, and apply them to either a team or an individual player. If a team is taking more shots than it’s allowing, that means it’s (very probably) possessing the puck more than its opponents, which means that (all else being equal) you’d expect it to score more goals than the other team.
This is where you’ve probably started mumbling about shot quality, since Corsi and Fenwick and any variations seem to assume that all shots are created equal. That’s obviously not true, since a breakaway or a goalmouth tap-in are more likely to result in a goal than an unscreened shot from the point. But as it turns out, there’s little evidence that teams or players can get consistently outshot while still getting better chances. And while there have been efforts to track scoring chances as opposed to just shot attempts, the results usually just wind up mapping pretty well to each other. So over the long term, we can largely ignore shot quality — not because it doesn’t exist, but because it tends to even out over time.
Corsi and Fenwick turn out to be great predictors of future success. Learn to love them.
Practical tip: As a side note, Corsi and Fenwick are both named after people. They’re not acronyms. This doesn’t sound important, but it is. If you ever write “CORSI,” someone will immediately point at you and yell “Fraud!” At this point, they’ll quite possibly stab you.
Step 6: A word about takeaways and giveaways
These are relatively new NHL stats that have only become prominent in the last decade or so, but if possession is so crucial, you’d probably assume that takeaways and giveaways are especially meaningful. But as it turns out, having a lot of takeaways is often bad, and having a lot of giveaways is often good.
That sounds crazy, but stay with me. If your team has the puck, it can’t record a takeaway. So players/teams that have great puck possession numbers often do rather poorly in terms of takeaways, and vice versa. The same logic applies in reverse to giveaways — you need to possess the puck before you can give it away.
(This also applies to hits, another stat that fans would assume is a positive but can actually be a symptom of a team/player with lousy underlying possession numbers.)
That doesn’t mean that a hit or a takeaway is a bad thing. Obviously, if the other team already has the puck, then taking it away is a good thing. It’s just that if you spend the whole season racking up big numbers in these sorts of categories, there’s a good chance it’s at least partly because the other team always has the puck. And that’s part of the reason why these particular stats don’t correlate well to wins.
One more thing: Takeaways, giveaways, and hits all fall into the category of “real-time” stats, which are incredibly subjective and unreliable, and prone to huge home/away swings that can be attributed to rink bias. Most people disregard them.
Practical tip: Anytime someone mentions a real-time stat, scoff and ask if they’re referring to road splits only.
Step 7: Death to plus/minus
OK, so you kind of understand basic possession stats. At this point, you may have found yourself saying “Wait, Corsi and Fenwick are basically the same idea as that old-fashioned plus/minus stat that I already know and love, only with shot attempts instead of goals.” And you would be right! You may then have continued, “That must mean advanced stats guys love plus/minus!” And you would be more wrong than you have ever been about anything in your entire life.
Remember when we said that goals were relatively rare events and purely goal-based stats could be unreliable? Plus/minus is a purely goal-based stat. And it’s one that penalizes players with defensive roles and/or bad linemates (more on that in a second).
Plus/minus is basically hockey’s answer to baseball’s RBI: a once-cherished statistic that, upon reflection, is actually more or less useless. Ignore it.
Practical tip: If you mention plus/minus, advanced stats folks will actually hiss at you.
Step 8: Context is king
Possession is the building block. Once you can fake an understanding of that, you’re halfway home. But you can also expect to encounter a whole lot of additional stats that sound much more complicated. They are, but in a way that makes enough sense that they shouldn’t trip you up.
Opponents of advanced stats sometimes try to dismiss the numbers by talking about context — you have to watch the game, man! But the advanced stats crowd is one step ahead of them, and has already developed plenty of stats that at least try to factor context into the equation.
Don’t teams take more shots when they’re trailing and fewer when they’re protecting a lead? Yes, which is why we have Fenwick Close. What if a good player is just stuck on a crappy team? Let’s check his Relative Corsi. What if he’s stuck with bad linemates? Let’s look at Quality of Teammates. Does he always line up against the best players? Quality of Competition. But his coach uses him as more of a defensive type of … Zone Starts! You get the idea.
Practical tip: These stats come up less often, so if you’re a faker you can probably get away without really understanding any of them. Just don’t try to shout “context!” at somebody who’s reached a conclusion you don’t like, because they probably have a stat in their back pocket that’s going to make you look bad.
Step 9: Goaltenders: Save percentage good, wins bad
Wins are a bad way to measure a goalie’s worth in hockey for the same reason that they’re a bad way to measure any individual player’s worth in any sport (i.e., they’re just too team-dependent). Goals-against average is better, but still flawed because a goalie can’t control how many shots he has to face. We’d rather look at save percentage, because it doesn’t punish a great goalie who’s stuck on a team that’s giving up a ton of shots every night.
So save percentage is good. Even-strength save percentage is even better, because it eliminates the quality of special teams (which the goalie can’t really control) from the equation. But if that number isn’t handy, you can usually just go with save percentage anytime the subject of goaltending comes up.
Practical tip: Don’t say “wins.” God help you if say “wins.”
Step 10: Understand the role of random chance
Random chance is a nice way of saying “luck,” and that’s good because luck is a dirty word in sports. If we say a team was lucky in a win, it sounds like we’re saying they didn’t deserve it. And as fans, it’s more satisfying if we can tell ourselves that every result was the right one, and that wins and losses are earned by skill and effort alone.
But the truth is that, like any sport, luck plays a huge role in hockey. Some stats can offer insight into its impact. PDO is a simple but crucial stat that takes a player/team’s on-ice save percentage and shooting percentage and adds them together, with anything significantly above (or below) 1.000 being a red flag of especially good (or bad) luck. Luck isn’t sustainable, so keep an eye on teams and players who’ve had an especially lucky or unlucky stretch. They’re great candidates to bounce back or crash back to earth.
There are other ways to try to measure luck, but they all lead us back to the same place: A lot of what happens in this sport is just plain random.
Practical tip: Not everything needs a narrative. Embrace randomness.
Step 11: When things go bad, your safe word is “sample size”
If you want to create a catchy narrative, you can do it based on a very small amount of data (or, if you’re really good, no data at all). But if you want to be confident that you’re drawing conclusions that are actually useful, you’re going to need to base them on enough data to filter out the flukes and the noise. That’s where sample size comes in.
One game isn’t a big enough sample size. Neither is one playoff series. In many cases, even a full season isn’t enough. This ends up coming up a lot in advanced stat discussions, and can make it tricky to draw firm conclusions.
Practical tip: If you’re trying to fake your way through an advanced stats conversation and feel like it’s getting away from you, just furrow your brow and say, “Well, that’s a good point, but of course you have to be careful of sample-size issues.” Everyone will nod solemnly and look at the ground, at which point you can make a break for it.
Step 12: Be prepared for counterintuitive conclusions
If you’ve made it this far, you probably want to know what exactly these stats guys have been learning about the NHL. And you’re in luck, because they’ll tell you. (Good lord, will they tell you.) But there’s one final caveat — you’re going to hear some things that don’t seem to make any sense.
That is to say, you’re going to be told things that are deeply counterintuitive to what you’ve come to believe during your life as a hockey fan. That’s a good thing — if after all this work advanced stats only confirmed what everyone already knew, there’d be no point — but it can still be pretty jarring.
Here’s an example: Does a good defenseman help his goaltender record a better save percentage? Of course! A great defensive defenseman pounces on rebounds and clears the front of the net and doesn’t let his opponents get great scoring chances. He makes his goaltender better. Anyone who watches hockey knows this.
Except … well, this. The numbers don’t play nice with common sense.
Once you go down the advanced stats rabbit hole, this stuff happens all the time. Sometimes it’s big things and sometimes it’s little things, but it’s not rare and it takes some getting used to.
Practical tip: If, after all of this, you hear somebody say something that seems to go against your hockey fan common sense and (this is the key) they back it up with numbers, which are you going to trust? Can you at least accept that it’s possible, just possible, that the numbers are right?
If you can’t, then you’re not going to be able to fake this after all. This whole thing has been a waste of your time. Sorry about that.
But at least you can probably get a job with the Toronto Maple Leafs.