Hockey Coaching Analytics Feature

ANALYTICS

CHEMISTRY

CAN A METRIC SAVE THE OILERS? HOW ONE OF THE NHL’S WORST TEAMS CROWDSOURCED ITS FANS TO DISCOVER A TRUE MEASURE OF QUALITY COACHING.

BY PETER KEATING

THE COACHING RUNDOWN We analyzed the résumés of the 122 current head coaches/managers in the four major sports and found that the NHL has the most experienced men in charge.

Twelve hockey coaches have 10-plus years at the helm, nearly double the average of the other leagues.

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QUALITY CONTROL

ANALYTICS

When devising the Coaching Activity Index, Oilers fan Michael Parkatti discovered a strong correlation between coaches who actively manage their rosters (i.e., how willing they are to give their players distinct roles) and how often their teams win. What better metric, then, to use as a baseline for our ranking of all NHL coaches. Given Joel Quenneville’s position atop the list for the second consecutive season, Chicago should prep for another deep playoff run. —PETER KEATING

CHEMISTRY

ONCE UPON A TIME, SABERMETRICS WAS EASY. Really. In the first few decades of sports statistical analysis, researchers almost exclusively studied baseball, a game of individual performances that add up to a team result.

And the simplicity made for straightforward conclusions: If Carlos Beltran is a better player than Vernon Wells, we can reliably project the Yankees will be better playing Beltran, regardless of whether Jacoby Ellsbury or Brett Gardner mans centerfield. One plus one equals something pretty close to two.

Now that analytics has moved into more dynamic sports like basketball, hockey and soccer, researchers have to reckon with games in which teammates can affect each other’s play far more than they do in baseball. Just because the Bruins’ David Krejci has more assists than fellow center Patrice Bergeron doesn’t necessarily make him a better fit to play with winger Brad Marchand, Boston’s leading goal scorer.

In fluid sports, one plus one might equal two or four, or minus-three.

And here’s the thing: It’s in the interaction

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EDMONTON’S EIGHT-YEAR SLIDE It’s been a slippery slope ever since the Oilers lost the 2006 Stanley Cup Finals to the Hurricanes.

Coaches the Oilers have employed since 2006-07.

Craig MacTavish, the team’s current GM, is the only one to last more than two seasons.

Edmonton is 66 games below .500 since 2006-07, the worst mark in the NHL by 36 games.

Oilers’ playoff appearances over the past seven completed seasons, the NHL’s longest active drought.

effects among players that we find the true impact of coaching. To win in continuous sports, coaches must deploy athletes in real time who fit together and augment one another’s skills. They must be masters of chemistry. And statheads are now figuring out how to grade them on their lab work.

The best example so far comes from Edmonton, which has more reason to be an analytics hotbed than you might realize. The Big E is a provincial capital and college town, overflowing with government scientists, academics and students. It has an entrepreneurial, high-tech private sector, with an abundance of applied-math intellectual talent.

And it’s home to nearly a million insanely avid but long-suffering fans dedicated to its only big league franchise, an Oilers team that has plunged from being one of the NHL’s great champions to enduring a sixth straight losing season. If you could cross Boston, San Jose and the North Pole, you’d get Edmonton.

During the 2004-05 lockout, the Oilers began working with Darkhorse Analytics, a local consulting firm, to mine its data vaults.

But it wasn’t until 2011 that the club set up its own analytics working group, including local data specialists, professors and bloggers to help management assess players. The following spring, the Oilers co-sponsored Edmonton’s first “hackathon” competition; the city and local businesses gave out sets of big data to residents, who competed to find new solutions for problems ranging from preserving forests to maintaining airline fleets.

And in December 2012, during last season’s NHL lockout, the team spun off its own Hackathon 2.0, inviting fans to “conduct a predictive analysis of your choice on some dimension of potential value to the Oilers.”

It was an inspired idea: A major league franchise was actually willing to crowdsource suggestions for helping its operations. “Here’s your chance, armchair GMs,” said Dan Haight, president of Darkhorse Analytics. “You can actually participate.” And participate they did.

“We thought if 100 people registered, that

9. DALLAS EAKINS EDMONTON OILERS CAI: 115.6

22. PAUL MAURICE* WINNIPEG JETS CAI: 83.7

10. MICHEL THERRIEN MONTREAL CANADIENS CAI: 115.4

23. BRUCE BOUDREAU ANAHEIM DUCKS CAI: 78.6

11. JOHN TORTORELLA VANCOUVER CANUCKS CAI: 114.3

24. PAUL MacLEAN OTTAWA SENATORS CAI: 78.1

12. PATRICK ROY COLORADO AVALANCHE CAI: 111.4

25. TED NOLAN* BUFFALO SABRES CAI: 76.5

13. KEN HITCHCOCK ST. LOUIS BLUES CAI: 110.2

26. DARRYL SUTTER LOS ANGELES KINGS CAI: 73.2

14. JACK CAPUANO NEW YORK ISLANDERS CAI: 103.6

27. PETER HORACHEK* FLORIDA PANTHERS CAI: 71.0

2. CLAUDE JULIEN BOSTON BRUINS CAI: 134.9

15. MIKE BABCOCK DETROIT RED WINGS CAI: 99.4

28. JON COOPER TAMPA BAY LIGHTNING CAI: 69.8

3. RANDY CARLYLE TORONTO MAPLE LEAFS CAI: 128.7

16. TODD McLELLAN SAN JOSE SHARKS CAI: 97.8

29. DAVE TIPPETT PHOENIX COYOTES CAI: 69.6

4. ALAIN VIGNEAULT NEW YORK RANGERS CAI: 126.4

17. BOB HARTLEY CALGARY FLAMES CAI: 92.9

5. CRAIG BERUBE* PHILADELPHIA FLYERS CAI: 125.9

18. TODD RICHARDS COLUMBUS BLUE JACKETS CAI: 90.5

6. MIKE YEO MINNESOTA WILD CAI: 123.6

19. BARRY TROTZ NASHVILLE PREDATORS CAI: 88.9

7. DAN BYLSMA PITTSBURGH PENGUINS CAI: 119.9

20. ADAM OATES WASHINGTON CAPITALS CAI: 87.3

8. KIRK MULLER CAROLINA HURRICANES CAI: 118.2

21. PETER DeBOER NEW JERSEY DEVILS CAI: 84.8

1. JOEL QUENNEVILLE CHICAGO BLACKHAWKS CAI: 157.9

30. LINDY RUFF DALLAS STARS CAI: 51.9

*MIDSEASON REPLACEMENT; CAI NUMBERS COVER WHOLE SEASON. ALL DATA THROUGH JAN. 28.

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By the time first-year GM Craig MacTavish hired head coach Dallas Eakins in June 2013, they had a shiny new metric at their disposal.

The winning entry of Hackathon 2.0 was about coaching and chemistry, and it came from Michael Parkatti, a manager of economic research and analysis for the government of Alberta and a diehard Oilers fan, whose research looked at how teams use their forwards. Parkatti found that the more coaches strategize about using their rosters— choose certain players for particular lines and start certain skaters in specific zones— the more their teams win. “Some people say coaching doesn’t have any impact in hockey, but I found there’s a correlation between how active coaches are and how well teams do,” Parkatti says.

Says MacTavish about analytics: “A different perspective can lead you to ask questions you wouldn’t be asking if you’re just watching games. Why is a guy who we think is a very good player losing matchups? The numbers could point to a lot of explanations about chemistry, or the players he’s facing.”

But how do you comprehensively measure something as abstract as coaching activity, especially given the chaos of line shifts?

Parkatti’s work is a tale of how data can discern what the eye cannot. It starts with a few basic building blocks. The most fundamental: Corsi Number, which is the difference between how many shots are directed at the opposing goal and how many are directed at a player’s own goal while he is on the ice. (Pioneering hockey analytics blogger Vic Ferrari, also an Oilers fan, began popularizing this stat, named after Sabres goaltending coach Jim Corsi, in 2007.)

Corsi Number counts missed and blocked shots as well as shots on goal, so it tracks a larger sample size of scoring chances than the traditional plus/minus rating. And at the

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BARTOLO COLON’S

WEIGHT ISN’T A CAUSE FOR CONCERN BUT FOR CELEBRATION HIGHEST AVERAGE WAR PER SEASON

BY PITCHERS AGE 38 OR OLDER AND IN THE TOP QUINTILE OF BMI 1.0

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3 GAYLORD PERRY

5 BARTOLO COLON*

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4 TED LYONS

5.0

3.7 3.4 3.0 *Colon has 9.1 post-age-38 WAR. He’s projected to accrue 5.6 remaining WAR. WAR figures according to FanGraphs.

Last season Colon shocked the baseball world.

At age 40, and spuriously listed at 265 pounds, the A’s starter dropped 18 W’s, his most since 2005; a 2.65 ERA, his lowest since ’02; and a 141 ERA+, the second best of his 16-year career. It was the fifth straight year in his late-career comeback in which he’s posted an ERA+ over 100.

But maybe the baseball world shouldn’t have been so shocked. After all, history shows that the chubbier a pitcher is as he ages, the more slowly his skills decline. Of the top 20 pitchers over age 37 in WAR, 10 were in the top quintile of Body Mass Index—guys like David Wells, Cy Young, Roger Clemens, Kenny Rogers and Gaylord Perry. Similarly, if you look at all pitchers who were active past their 35th birthday, those in the top quintile of BMI saw their ERAs increase 5% more slowly and innings totals decline 8% more slowly than their peers.

As for Colon in 2014, our projection model sees the Mets’ new $20 million man going 10–8 with a cool 3.60 ERA at age 41. And there will be nothing shocking about it.

—DAN SZYMBORSKI, ESPN INSIDER

team level, it’s the best predictor of winning percentage of any hockey metric.

To isolate the impact of individual athletes, analysts use Relative Corsi, which is the difference between a team’s Corsi Number when a player is on the ice and when he’s off it. For instance, Bergeron’s Relative Corsi at the Olympic break was 22.4, meaning Boston was taking 22.4 more net shot attempts per 60 minutes when he was on the ice, the best margin of any player in the NHL who had played at least 50 games, according to BehindTheNet.ca.

To make his metric coaching-centric, Parkatti then studied the strength of each forward’s line, along with the strength of the line he was matched against. Finally, Parkatti used an advanced stat called Offensive Zone Start Percentage to find where on the ice the forward usually played.

Parkatti’s key insight was to recognize that on some teams, these three metrics vary widely among players, while on other clubs, they don’t differ much at all. He attributed this difference in variability to coaching activity. If a coach is meticulous about strategy, Parkatti reasoned, some of his players will have a high rating on these scales while others will score low, as members of the team maintain very different linemates, competition and offensive tasks. On the other hand, if a coach lets his talent skate with or against any old players, or start shifts willy-nilly, their context stats will all look alike.

Looking more closely at the stats chosen by Parkatti shows he’s really on to something. For example, if we graph Quality of Competition with Offensive Zone Start Percentage for the Blackhawks, we find their players clumped into distinct and extreme clusters. Jonathan Toews, Marian Hossa and Patrick Sharp all start in the offensive zone on more than 60% of shifts and play against Chicago’s strongest competition.

Clearly, Joel Quenneville sees these players as his club’s strongest offensive line. Kris Versteeg and Andrew Shaw begin their respective shifts in the offensive zone even more frequently but against below-average competition;

“A DIFFERENT PERSPECTIVE CAN LEAD TO QUESTIONS YOU WOULDN’T ASK IF YOU WERE JUST WATCHING GAMES.”

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Quenneville recognizes they can shoot but is shielding them from top opponents. Brandon Bollig, Marcus Kruger and Ben Smith face above-average competition while starting fewer than 25% of their shifts in the offensive zone. This is the Blackhawks’ fourth line, and Quenneville is giving them the dirty work of grinding down tough opponents.

Now look at the same data for the Dallas Stars under first-year coach Lindy Ruff—it’s a formless blob. Valeri Nichushkin, Alex Chiasson and Ryan Garbutt usually play different positions on different lines, but their stats don’t reflect divergent roles; there’s just not much disparity in the quality of their opponents or where they start their shifts. “If the numbers are close together,” Parkatti says, “a coach isn’t really thinking strategically about who’s playing with or against whom, as though it will all come out in the wash at the end.”

Parkatti merged his metrics into one scale he calls the Coaching Activity Index (CAI). Last year the Stanley Cup–winning Blackhawks, under Quenneville, ranked first; Washington, under then-first-year coach Adam Oates, ranked last. The Capitals were good (tied for ninth in points), but overall Parkatti found a strong correlation (0.59) between

the CAI and team standings.

When The Mag estimated the CAI for this season, we found a significant linkage too. (See “Quality Control” on page 81.) Edmonton is one of the few outliers in its own metric.

Despite the Oilers’ 20–33–7 record at the Olympic break, their coach, Eakins, ranked ninth in the CAI. Part of the reason: The club’s young roster—seven of its 13 forwards are under age 26—was overwhelmed by a travelheavy, injury-plagued start to the season. The Oilers won just four of their first 21 games, and after that Eakins adapted his strategies to his personnel. The team has since steadied (16–18–5). “We have a philosophy in mind, and we’re going to persist,” says MacTavish. “We’ve had some setbacks, but ultimately our strategy is going to pay off.”

So yes, there’s more to strategy than in-game decisions, and Parkatti’s findings cover only forwards. But his metrics reveal important truths: Effective coaching in hockey requires defining key roles, finding players who can fill those roles and keeping them there. Chemistry, then, is the synergy that comes from every player on the ice knowing precisely where he’s supposed to be and what he’s supposed to do.

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