Carolina Hurricanes

Analytics: Depth no magic formula for winning in NHL

Arguably the greatest contribution of hockey’s analytics revolution is its ability to subject long-standing articles of faith to cold hard logic.

One example of this is the cult of depth. Whether you’re listening to young bloggers or old-time commentators, eventually you’ll hear someone talk about how in today’s NHL, teams have to be deep to win.

Pundits of all stripes routinely laud the Blackhawks and Kings for their remarkable depth and abuse the Penguins for imagining some hanger-on wingers and a bunch of spare parts are enough as long as they have Sidney Crosby and Evgeni Malkin.

The argument’s the same: Deep teams win, shallow ones don’t.

No doubt everyone would love to have lots of great players.

But in the real world, teams have to make trade-offs. For example, they need to ask whether two top-50 scorers are as good as one guy in the top five? Or how much they value an elite shutdown defenseman like San Jose’s Marc-Edouard Vlasic vs. a one-dimensional forward like Phil Kessel of the Leafs.

Despite the conventional wisdom (or maybe because of it), nobody has analyzed whether more depth means more winning. In fact, most commentators don’t even bother to define what they mean by “depth.”

One way of thinking about whether or not a team is deep is to look at ice time. Presumably, coaches with deeper rosters are willing to allocate ice time more evenly, and the best players get more minutes.

There are two problems with this. First, some players log minutes because they’re just that good, while others play because their teammates are just that bad.

Take a guess (no cheating) who leads all NHL forwards in average time on ice (21 minutes, 15 seconds). Penguins sniper Sidney Crosby? Ducks forward Ryan Getzlaf? Washington’s Alex Ovechkin?

Nope. It’s Ryan Nugent-Hopkins of the Edmonton Oilers.

That’s right – fans who used to watch a one-two punch of Wayne Gretzky and Mark Messier now can cheer for Nugent-Hopkins and whichever third- or fourth- line center the Oilers allow to play the second.

The fact that the guy logging the most minutes in the league sits 93rd among forwards in total points says more about how truly bad the Oilers are than how good he is.

But looking at overall ice time can be misleading in other ways.

A team that’s playing from behind is more likely to gamble; one that regularly trounces opponents will roll four lines late in the game.

If we’re talking about depth, the most important one is 5-on-5 close play, meaning the game is within one goal during the first two periods or tied in the third. After all, if a coach is willing to throw guys out in close games, that suggests he trusts them.

Looking at ice time in those situations can yield new insights. For example, you might conclude the Rangers’ “go-to guy” is Derek Stepan, who leads the team’s forwards with an average of 18:09 per game. In fact, during 5-on-5 close play, Stepan is seventh, averaging 7:29 per game.

But once you start looking at all of a team’s forwards, you’re very quickly into a jumble of indistinguishable numbers. For example, if Carolina’s Eric Staal (first on the team) logs 9:17 vs. Zach Boychuk’s 6:19 (12th), is that a big difference or a little one?

To figure that out, we looked at a common statistical measure called standard deviation, which measures the spread in ice time among each team’s top 12 forwards. All things being equal, we’d expect deeper teams to have a lower standard deviation (less of a spread in ice time between their top 12).

There were some surprises for sure.

For example, the Blackhawks rely heavily on their top guys when the game is close. When measured by the standard deviation of their top 12 forwards’ ice time, Chicago ranks 21st in the league, a fact that suggests coach Joel Quenneville doesn’t think as highly of players who aren’t named Patrick Kane, Jonathan Toews, Patrick Sharp or Marian Hossa as others might.

More important, when we looked at the correlation between standard deviation and points percentage, it was positive, meaning teams that were relatively “thinner” did better. The correlation was admittedly small (0.06), meaning the relationship wasn’t strong, but it does mean being “top heavy” doesn’t necessarily hurt if your top guys really are that good.

Not only is “depth” hard to define, it may not be a necessary ingredient to win either.

Copyright 2015 by Ian Cooper, The Department of Hockey Analytics

Distributed by Torstar Syndication Services

The Department of Hockey Analytics employs advanced statistical methods and innovative approaches to better understand the game of hockey. Its three founders are Ian Cooper, a lawyer and, former player agent; Dr. Phil Curry, a professor of economics at the University of Waterloo; and IJay Palansky, a litigator at the law firm of Armstrong Teasdale, former professional poker player.

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