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The art of hockey analytics

NHL teams are crunching numbers like never before

Welcome to the NHL of the 21st century — an analytics arms race where the 30 teams are not only waging war on the ice, but looking for every kind of advantage they can find off it by crunching numbers on their players into a variety of metrics

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Blake Wheeler is in full stride with the puck seemingly glued to his stick. And, in that precise moment in which he makes a sharp turn to the Los Angeles Kings goal, it only seems like defenceman Slava Voynov — vainly turning in pursuit — has suddenly morphed into one of those giant orange traffic pylons.

In the next instant Wheeler is in front of Kings netminder Jonathan Quick, before slashing across the crease and depositing the puck in the back of the net.

Watching the Winnipeg Jets gifted winger flash the best of all his skills — the world-class speed meets a deft scoring touch — is the kind of scene that often has fans sliding to the front of their seats at the MTS Centre or bolting up right from the recliner at home in anticipation.

And to paraphrase Don Cherry, it doesn’t take a "rocket surgeon" for any hockey fan to see that brilliance from the Jets’ leading scorer in 2013-14 with their own two eyes. The basic numbers have confirmed all that, telling us that Wheeler set career highs in both goals and assists this season.

But some of the common analytics now in use around the National Hockey League — the "fancy stats" as many refer to them — can tell us all that and more about Wheeler. He has a 5v5 Corsi rating of 49.9 per cent, a Fenwick of 50.3.

"Huh?" began Wheeler when asked about the Corsi or Fenwick ratings last week. "I don’t even know what that is. I’ve never even heard of it.

"Maybe one day when I’m a GM I’ll look into it."

Welcome to the NHL of the 21st century, where a player is no longer evaluated primarily on the basics such as goals and assists, his plus-minus rating or goals-against average. In fact, if all the numbers now available to general managers were displayed on the back of a player’s hockey card, they would roughly be the size of a highway billboard.

This is all part of the NHL’s data revolution — an analytics arms race where the 30 teams are not only waging war on the ice, but looking for every kind of advantage they can find off it by crunching numbers into a variety of metrics.

It’s all a bit cloak and dagger, too, as teams are hardly sharing their findings and best practices with one another. Case in point, Jets’ GM Kevin Cheveldayoff agreed to speak to the Free Press about hockey analytics, but politely refused to deal in specifics as it related to his squad.

"I do have strong beliefs on it," explained Cheveldayoff. "We do some things done that are maybe different than an average team, although I can’t speak for them. What we talk about is not so much following the trends but trying to get out ahead of them. Like anything, you’re always trying to be innovative whether it’s your on-ice training or off-ice training, conditioning, nutrition.

"But with the popularity of Moneyball in baseball I think nowadays everybody in sport is saying, ‘What else can we do?’"

"The problem we’re trying to solve is that there are rich teams and there are poor teams. Then there’s fifty feet of crap, and then there’s us. It’s an unfair game. And now we’ve been gutted. We’re like organ donors for the rich. Boston’s taken our kidneys, Yankees have taken our heart. And you guys just sit around talking the same old ‘good body’ nonsense like we’re selling jeans. Like we’re looking for Fabio.

"We’ve got to think differently. We are the last dog at the bowl. You see what happens to the runt of the litter? He dies."

— Scene from the movie Moneyball in which Brad Pitt, playing Oakland A’s GM Billy Beane, outlines his new approach to finding players to his old-school scouting staff

*  *  *

Using analytics for any kind of edge is hardly new phenomenon in sports, even if the buy-in from professional teams must have seemed glacier-like for early innovators such as Bill James.

An American baseball writer, James self-published his first book, The Bill James Baseball Abstract, in 1977.

Studying boxscores — legend has it while he was doing night shifts as a security guard at a pork and beans cannery in New Jersey — James offered up info that revealed, for example, which pitchers and catchers gave up the most stolen bases against.

Those findings — he called them "sabermetrics" in reference to the Society for American Baseball Research — were so well read they became the precursor for today’s sports analytics explosion.

James, FYI, is now a senior advisor on baseball operations for the Red Sox and in 2006 was named by Time as one of the 100 most influential people in the world.

What James did to change the thinking in baseball, the movie Moneyball — based on the 2003 book of the same title by Michael Lewis — ultimately opened even more eyes across the sporting landscape.

But changing the thinking of old-school baseball scouts, which was one of the dominant themes in the movie, just barely scratches the surface of what analytics are providing for pro sports organizations.

Not only does it help them track things like the buying habits of their customers, but on the field of play — Dallas Mavericks owner Mark Cuban, as an example, insists the use of advanced stats help them win the 2011 NBA championship by telling them which players were best suited in a matchup with the Miami Heat.

The Mavs, as an example, started guard J.J. Barea midway through the series — even though he was shooting 5-of-23 at the time — to utilize his speed against the Heat and a matchup with Miami guard Mike Bibby (two games later Bibby was on the bench). As well, Dallas switched back and forth between zone and man-to-man defences to take Miami out of any kind of offensive rhythm.

So instrumental was their use of advanced stats, Dallas had their director of basketball analytics Roland Beech on the bench with head coach Rick Carlisle and gave him the unofficial title of "first stat geek with a championship ring."

The growing sophistication of analytics might be best represented by the list of featured speakers at the 2014 MIT Sloan Sports Analytics Conference in Boston, the highly-prestigious gathering of sports’ forward thinkers that featured NBA commissioner Adam Silver, Indianapolis Colts’ quarterback Andrew Luck, San Francisco 49ers president and co-owner Gideon Yu, John Henry, the principal owner of the Fenway Sports Group, and author Malcolm Gladwell.

One of the hockey research papers at this year’s conference, as an example: Tilted ice: How certain National Hockey League Teams are Manipulating the League’s Point System. A description, from the Sloan website:

"We use generalized linear models to find (i) a significantly higher fraction of nonconference overtime games, when compared to conference ones, and (ii) a subset of teams which have most often modified their on-ice behavior, as shown through more non-conference overtime games and lower scoring rates in the third period of tied contests. The varying overtime frequencies and passive on-ice behavior appear to be unintended consequences of the league’s policies, which, under the league’s 2013 realignment, will encourage even grosser manipulation."

Now, what has made hockey a bit slow to the analytics party is the very nature of the game. Unlike baseball, football or basketball, the fluidity to the action makes it more difficult to track outcomes and interactions between players. That said, since the 2007-08 season the NHL has provided documented play-by-play descriptions of games that detail not only face-off wins and losses, but hits, and who is on the ice at any one particular moment.

That’s the set of data from which many metrics are built, others are using video to break down the game even further and evaluate players and prospects.

The Jets have their video coach Tony Borgford do a lot of their pre-scout work and provide a layer of analytics on a daily basis for offensive and defensive zone starts, giveaways, turnovers, scoring chances for and against and where they come from on the ice.

As for the rest of what they do, well, again, that’s as secret as Cheveldayoff’s PIN code.

"Over the course of a season it’s interesting to look at all these stats because they can show you trends," said Cheveldayoff. "But there’s a deeper level of statistical and rational analysis of what’s going on in the game that we can take into consideration as well.

"Believe me, lots of people — educated people — are offering their thoughts not just on the Xs and Os of the game and trying to find a different level of understanding. They have found ways to dive behind scenes of NHL packages and do things that are more creative.

Old Stats: Plus-Minus

Click to Expand

A player gets a ‘plus’ if he is on the ice when his team scores a goal; a ‘minus’ for a goal against.

Background: first used by the Montreal Canadiens dating back to the 1950s.

FYI: The NHL awarded a NHL Plus-Minus Award to the player with the highest plus-minus statistic during the regular season from 1982–83 to 2007–08.

The highest plus-minus total ever recorded in one season was Bobby Orr, at +124, in 1970-71.

Plus-minus is largely now ignored by the analytics community because it is purely a goals-based stat — and goals are relatively rare — that doesn’t reflect puck possession, quality of linemates or players with defensive responsibilities.

"There’s a company out there that takes a game and shows how fast a player is going up the ice and then tracks every other players’ speed (Sportvu in the NBA; PowerScout Hockey for the NHL). There’s lots of presentations we get on a regular basis, some very cutting-edge stuff, that are already being used in other sports and are now trying to adapt to the National Hockey League."

‘Scientia potentia est’ (Latin for ‘Knowledge is power’)

— English scientist and philosopher, Sir Francis Bacon (1561-1626)

Meet Eric Tulsky, a 38-year-old Philadelphia native who holds a physics and chemistry degree from Harvard, a PhD in chemistry from UC-Berkeley and now works for an energy storage firm in the Silicon Valley.

"I’d rather not name the company," he explains in a telephone interview from San Jose, "because the work we’re doing is really secretive and we are in stealth mode."

Tulsky’s research, according to his bio, "has helped enable unique nanotechnology solutions to problems in DNA sequencing, solar energy, displays, and energy storage."

Using analytics for any kind of edge is hardly new phenomenon in sports, even if the buy-in from professional teams must have seemed glacier-like for early innovators like Bill James.

And then there’s this: he’s a huge Philadelphia Flyers fan.

Roughly four years ago Tulsky began writing for Broad Street Hockey, a Flyers’ fan blog, after educating himself on some of the basic metrics being used by hockey analytics, like shot differential and puck possession.

Unearthing trends he found interesting, he began emailing NHL teams with his results and theories. He heard back from one team, the Nashville Predators, who asked him to crunch some numbers for them.

"Basically, they had seen some stuff on video they were interested in and they asked if there was any way I could use stats to validate what they thought was happening, that it wasn’t something that had happened a couple of times that they were overreacting to," explained Tulsky. "They had a few questions that were along those lines and it turns out most of the things they were talking about I could back up.

"I don’t know if I changed anything because it was mostly in line with what they had seen, but it gave them a little more confidence."

Over the last few years Tulsky has become one of the most-respected thinkers in hockey analytics and has spoken at the last two Sloan conferences, promoting his thoughts on topics like offensive-zone entries, while writing for NHLnumbers.com and starting TZ Quantitative Analytics Group with partner Derek Zona. His findings — that players who carry the puck across the blue line produce twice as much offence as those that dump and chase it — is the exactly kind of hard data all NHL GMs and head coaches crave.

"It’s fun when you go to a sports analytics conference and you are having a conversation and someone from a team casually throws out, ‘Oh yeah, like you said in that article about (New York Rangers’ goaltender Henrik) Lundqvist a couple of months ago...’" said Tulsky. "That’s pretty neat.

"But hockey is not an easy game to analyze. There are reasons it’s been the slowest to come along. And these guys are acknowledged as the world’s best in hockey decision making so I don’t blame them for being skeptical and slow to buy into a bunch of math that isn’t how they grew up with the game. I didn’t play the game at any level and certainly don’t see the game the same way they do. So I can understand their hesitation to jump right in with whatever I have to say.

"But they’re paying attention now and seeing what people have developed. There’s a handful of teams that are really invested, there’s another larger handful that are trying to pay attention to it but aren’t quite sure yet what they believe and what they don’t and are trying to get a handle on what makes sense.

"Then there are teams that still just don’t get it."

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