Rather than goals, assists and shots, they’re examining an on-ice grid that shows who scored how often from where, and who produced most at different points of a game. That info is then used to project the output a team can expect from a certain player and help attach a dollar-value to him.
Welcome to the world of statistical analysis, which is no longer the domain of baseball alone. Slowly, similar thinking is creeping into hockey’s executive ranks as teams seek ways to improve decision-making in the salary-cap era.
“I think it has to,” Craig Button, pro scout for the Toronto Maple Leafs, said Thursday from Denver. “When you’re looking at a salary-cap system, you have to find value out there.”
Button isn’t the only believer.
Los Angeles Kings general manager Dean Lombardi and his staff are busy developing new concepts for evaluation, closely guarding their emerging methodology.
Lombardi declined to discuss the matter when reached Thursday but recently told the Los Angeles Daily News: “It requires almost a cultural change to get your staff thinking a certain way and that’s what we’re working toward.”
Last summer the Minnesota Wild hired former Boston Globe writer Chris Snow as their director of hockey operations, in part, to do some statistical analysis for the club. Snow, 25, also chose not to comment.
Given how fiercely teams tend to protect their secrets, it’s safe to assume there are others.
And then there are several websites devoted to the cause such as hockeyanalytics.com and puckerings.com plus the broader Society for International Hockey Research.
The development of new statistics is just beginning.
“It’s about knowledge and knowledge comes in different shapes and forms,” said Button. “Anybody who talks about being competitive, you’re going to look at opportunities at give yourself an edge.”
The quest to better leverage statistics is rooted in baseball’s Sabrmetrics movement, which seeks to support or disprove contentions about players and the game through quantitative rather than empirical evidence.
Author and current Boston Red Sox executive Bill James is considered the grandfather of statistical analysis, starting a revolution in baseball thought through his self-published Abstracts.
Originally shunned by the game’s establishment, statistical analysis was used in arbitration cases during the 1980s and gained further traction in the early 1990s with the Oakland Athletics.
Vitriolic debates on the merits of traditional scouting versus performance scouting (as quantitative analysis is often called) ensued, boiling over after the release of Michael Lewis’ “Moneyball” in 2003.
The controversial book examined how the Athletics bucked convention to subvert their economic shortcomings and win. It rubbed many the wrong way for implying that the traditional methods used by most teams were flawed.
Still, over time more and more teams have bought into aspects of the approach, melding elements of it with traditional methods of player evaluation.
Can it work in hockey?
Baseball is a sport made for numbers, one that is easily broken down into measurable segments. Hockey is a more fluid game, one that is more difficult to quantify beyond wins, losses, goals, assists, shots, plus-minus and saves.
“The numbers are a lot more opaque in hockey,” said Button. “Who’s keeping tracks of where shots are taken from? You can get the overall number, but where are they coming from? . . .
“Some statistics are out-and-out lies.”
Take, for example, Rob Brown’s 1988-89 season for the Pittsburgh Penguins, when he scored 49 goals with 66 assists. Taken on their own, those numbers would suggest a superstar, but upon closer examination, one would find that playing wingman to Mario Lemieux clearly has its benefits.
Brown never approached those numbers again.
During his time as GM of the Calgary Flames, one stat Button especially liked was player production per-minute.
“It’s one thing that doesn’t get enough attention in our sport,” he said. “Time on ice and production, I think that’s one of the trend lines you need to look at.
“Two of the players that really come to mind are (Vancouver forwards) Henrik and Daniel Sedin, when you start looking at their time on ice and what they produce, you could certainly trend that with more ice time they were going to produce more points. Their points-per-minutes played is phenomenal.”
A stat like that can be particularly helpful in projecting how a third-liner on one team might perform on the second line of another. It can also allow a team to project how many goals for and against to realistically expect from its roster in a given year.
That information is especially useful when figuring out how much any given player is worth. Before the salary cap, teams could afford to pay players based on their past accomplishments because there was no spending limit.
Now, teams must get value-per-dollar to succeed.
“People get paid based on past production, they don’t get paid based on future production,” said Button. “If you’re going to pay a player 75 per cent more, is his production going to be 75 per cent more?”
Inevitably, as statistical analysis becomes more influential among hockey minds, there will be conflict between new and old. Button believes the two approaches will eventually find a way to co-exist.
“I don’t care what business you’re always going to be in a spot where change becomes difficult,” said Button. “Yes, traditional methods sometime clash but I think the best way to answer people is with proof.
“If you’re too readily going to accept things, if someone shows you that this is the way it is and you just say, ‘OK,’ shame on you. Challenging something and to have it hold up in practicality, that’s what you want to do. People who are close-minded are always going to be close-minded.”