SUN Hockey Pool

Why hockey's trendy advanced stats are a numbers game

The Rangers' Ryan McDonagh celebrates with his teammates after scoring against Montreal in Game 2...

The Rangers' Ryan McDonagh celebrates with his teammates after scoring against Montreal in Game 2 on Monday. McDonagh's goal came off a scrambly play that cannot be easily measured with advanced stats. (Getty Images/AFP)

STEVE SIMMONS, QMI Agency

, Last Updated: 5:16 PM ET

On the night I first began to question advanced statistics in hockey, the stats man who sits a few seats down from me in the press box began regurgitating the game in numbers.

Mikhail Grabovski, he said, was the best Leaf that night. According to the numbers, Jay McClement was the worst.

About an hour earlier, when a colleague asked for advice on who to pick as his three stars for the next day’s newspaper, we both bypassed Grabovski, neither of us liking his rather singular game that night, and talked about the value of McClement, who had been particularly strong both defensively and killing penalties.

When I asked the stats man about the discrepancy between what we’d seen and what the numbers showed, he answered: “Sample size.”

That always seems to be the answer when the numbers don’t match what a discerning eye can see.

In Game 1 of last year’s playoff series between the Leafs and the Boston Bruins, Toronto was badly outplayed. Only one Leafs player seemed capable of competing at that level — James van Riemsdyk. So, curious after the game, I asked my stats friend who had the best numbers for Toronto.

It so happened van Riemsdyk had them, but his numbers were just a percentage point better than Phil Kessel, who I thought had a dreadful game. Again, I asked: “How can the numbers be reliable, when two players can have such varying games and end with similar statistics?”

“Sample size,” I was told.

So I began to wonder: If what I’m seeing tells me one thing and the statistics tell me another, and the answer for the discrepancy is seemingly sample size, then at what point do you start to question how much individual analytics matter in hockey? And how many samples belie what the game really is?

Statistics matter and, in many ways, define baseball, a sport that is somewhat stationary in nature: Pitcher versus batter. A set offence versus a set defence. A team game of individual accomplishment.

The apparently new stats, which aren’t that new, are also historically relevant in baseball. The all-time leaders in WAR — wins above replacement — are Babe Ruth and Cy Young. The all-time leaders in OPS, the batting stat du jour, are Ruth, Ted Williams and Lou Gehrig.

You see those names, you can’t argue back. The sport is built for and by statistics.

Hockey is not so easily determined. And, in a way, the stance to match it with other sports has polarized the game, divided old and new, divided zealot and traditionalist. It’s not like there isn’t something to be learned from the new statistics, especially in a team way: It’s just they are in no way game-defining in the manner the analytics community believes them to be.

“Is hockey hard?” Brendan Shanahan, the president of the Leafs once said, not talking stats. “I don’t know, you tell me. We need to have the strength and power of a football player, the stamina of a marathon runner, and the concentration of a brain surgeon. But we need to put all this together while moving at high speeds on a cold and slippery surface while five other guys use clubs to try and kill us. Oh yeah, did I mention that this whole time we’re standing on blades 1/8th of an inch thick?

“Is ice hockey hard? I don’t know, you tell me.”

Hockey is hard to statistically quantify. It doesn’t stop every seven seconds the way football does. It isn’t a pitch at a time, or a possession at a time, with offence and defence defined, the way basketball is.

The average player may play 17 minutes a game — in 23 spurts of 45 seconds each — and have the puck on his stick no more than 40 seconds in total. That means for 16 minutes and 20 seconds, they are playing without possession of the puck.

If you can’t play without the puck in the NHL, for the most part, you can’t play or won’t play. So how, numerically, do you measure a player when 95% or more of his 45-second spurts is spent without the puck?

So much of hockey is random battles, loose pucks, ebb and flow, offence turning to defence and defence turning to offence often within seconds of each other.

Go back to Game 6 of last June’s Stanley Cup final and see how a championship was won. The Bruins and Blackhawks are tied with just more than a minute to play. Boston wins the faceoff. Possession. The puck goes back to another Bruin. More possession. Then Andrew Ference tosses the puck up the boards to no one. Turnover.

The puck went from Niklas Hjalmarsson to Dave Bolland to another Hawk, was rimmed around behind the net, and ended up at the Chicago point. A slap shot through traffic was deflected, causing Bruins goaltender Tuukka Rask to go one way, the puck another. The puck hit the goal post and bounced down in the crease.

And from behind the net came Bolland, away from the play, knocking the puck into an empty net for the Cup winner.

Offence determines scoring in almost every sport: In hockey, a defensive error — some quantifiable, some not — a breakdown, leads to more scoring than offensive creation does.

There are more random or scrambly goals than just Bolland’s title winner. In a different way, though, it was not unlike the key goal Ryan McDonagh scored in Montreal on Monday night. McDonagh took a slap shot in the direction of Canadiens netminder Dustin Tokarski. It didn’t seem like a scoring chance. But the puck hit Alexei Emelin in the pants, deflected off him, hit the goal post and then deflected into the net.

These are game- and series-changing plays: They can’t be defined by any statistic. There is a mistake and a bounce and a battle and a deflection and another bounce and a goal. And in the words of Jim Hughson: “That’s hockey.”

The Maple Leafs were among the worst Corsi and Fenwick teams (the best known of the advanced statistics) in the NHL this season. When they collapsed, the stats mavens were almost gleeful. They knew it was coming. They called it. The Leafs were their convenient poster-boy for the changing way to interpret hockey. And an easy target.

The mavens weren’t quite so accurate in their analysis of the Colorado Avalanche who, like the Leafs, gave up too many shots against and didn’t have the puck enough. But all Colorado did was win and finish ahead of Chicago and St. Louis. Not all shots on goal matter. Not all possession is meaningful puck possession. Not all faceoffs won will result in possession. Not all faceoffs lost end up with bad results.

The Los Angeles Kings, even before Marian Gaborik, were among the best possession teams in the NHL and yet among the most challenged to score goals. At one point in the season, they scored 16 goals in a 10-game period and followed that up by scoring three goals over six games: That’s 19 goals in almost 20% of the season.

At that time, the team that had the puck the most scored the least.

Even now, after his difficult playoff run, there are statistical breakdowns that will tell you Sidney Crosby had a strong playoffs with the Pittsburgh Penguins. He did not. He scored once. He had the puck, but created little offence for himself or those he played with. His Corsi numbers led the NHL: But the best offensive player in the game has scored one goal in his past 17 playoff games.

The statistics indicate Crosby had a fine playoffs. Crosby, himself, would disagree with the numbers. The stats people will tell you the game must adjust to the statistics but, really, the stats need to adjust to the game.

The game hasn’t changed all that much, other than speed and length of shift. The voices of analytics haven’t invented a new game, only a new way to look at it.

There is a place for what they do — just not a defining one. The game, through these eyes, is too free-flow, too incidental and accidental, too promiscuous to be naturally or easily analyzed with math.

Steve Simmons has covered the NHL for more than 30 years and has coached various levels of hockey for more than 20 of those years.

 

WHAT THE STATS DON’T TELL YOU

Why hockey doesn’t lend itself to statistical analysis the way other sports do:

There is no statistic to accurately quantify neutral-zone play.

There is no statistic that tell you which wingers gets the puck off the boards and out of their zone and which wingers do not.

There is no statistic that defines vision and creativity: That is pure subjectivity.

There is no statistic that measures play without the puck, a most important factor in most games.

There is no statistic, outside of individual team stats, that measures scoring chances which, again, is a subjective stat.

There is no statistic that separates a good dump-in from a bad dump-in. There is a difference, just as there is for a good line change and a bad one.

There is no statistic that indicates individual ability to win loose puck battles, especially in close games or the last minutes of periods or late in shifts.

steve.simmons@sunmedia.ca

 

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