Data May Not Drive Play, But It Should Drive Decisions

It’s not easy creating a data-driven decision-making culture in any organization, let alone one as bound in tradition and lore as the NHL, where hockey men are imbued with mythical powers of observation and judgement just by virtue of having played the game. And yet, the NHL is clearly moving in that direction. It may be at a glacier-like pace, but I suppose that makes sense, what with the ice and all. Despite some early stumbles, it’s probably safe to say that it is only a matter of time before data-driven decisions are the norm rather than the exception. Whether that happens while we still have glaciers is another matter.

But even when there is a managerial will and top-down direction to move toward a data-driven culture, it is often difficult to introduce data analysis into the existing decision-making process of an organization. It’s not just deep structural changes that are necessary, but also staff will need a robust change management process. It’s hard enough to get people to accept change, but a new culture requires that they go beyond acceptance and embrace it as a new way of doing things. This is a difficult process in any organization. However, it is made more difficult in the hockey world where many in positions of authority are in those roles precisely because they “played the game” and understand the traditional way of doing things.

But what if you could start from scratch and build something from the ground up? 

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10 Rules Of Thumb For Hockey Analysts

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  1. The point of hockey is to create goal differential. The point of hockey analysis is to find ways to improve it.

  2. Shot differentials today is goal differentials tomorrow.

  3. 100-10-1. 100 minutes of your time to create data, 10 minutes of the coach’s time to digest the data, 1 minute of the player’s time to absorb the data.

  4. Optimise workload, reduce uncertainty.

  5. If your findings are either always or never surprising, then review your methodology. 80/20 (in favor of confirming existing beliefs) is a good place to start.

  6. Your priority is to help the coach get a better night’s sleep and to help players maximise their experience.

  7. Know your place, but stand your ground.

  8. We are all on the same team.

  9. Who you compete against influences your results; who you work with dictates your destiny.

  10. The job is only done when you’ve trained someone to make you expendable.

 

Jack Han is the Video & Analytics Coordinator for the McGill Martlet Hockey team (not his full-time job). He also writes occasionally about the NHL for Habs Eyes on the Prize. You can find him on Twitter or on the ice at McConnell Arena.

Practical Concerns: The Analyst’s Plight

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(Image Wikipedia Commons)

Recently, the statistical analyst of an NHL team was let go in the aftermath of an underwhelming regular season and a puzzling decision involving one of the team’s most productive and iconic players. Rights and wrongs aside, the episode illustrated an uncomfortable fact: the analyst’s job is perhaps the most fragile one of all.

Imagine the tightrope walker, balancing him/herself atop a fine metal wire between two buildings. The job is a difficult one on the best of days, requiring a lifetime of practice and undivided focus. Randomness is not the tightrope walker’s friend. A gust of wind, a slight mis-step or even a meeting with an errand low-flying pigeon could yield deadly consequences.

While the physical stakes are different, an analyst’s career prospects (and personal well-being) are similarly affected by things out of his or her control. While job security in any field is dependent on market conditions, things are especially dire for the technical worker responsible for uncovering Truths, but ranked too low in the corporate hierarchy to effect real change.

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Genie Bouchard, Expectations & What Players Need To Understand About Stats

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This post is not really about Genie Bouchard, or even tennis in general, but let’s start with her.

On Thursday, Bouchard, the top-ranked Canadian player on the WTA Tour, lost 6-4, 6-4 to No. 8 seed Timea Bacsinszky at Roland Garros. The loss could be interpreted as another setback for Bouchard, who ranked as high as No. 5 in the world in October 2014. Since then, she has slumped, going 12-18 in 2015 after winning 39 and 43 matches in the previous two years. What’s gone wrong?

Possibly, the answer is nothing at all.

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Book Review: Caveman Logic & “The Only Rule Is It Has To Work”

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In my experience, using analytics to influence coaching decisions is a profoundly weird and incredibly interesting exercise, which is why I was very excited to read a book called The Only Rule Is It Has To Work, a newly released book written by Ben Lindbergh and Sam Miller, two Sabermetricians who took over a pro baseball team for a season.

Being a fast reader, I blasted through the pages in about two days. I’m happy to say that got a lot out of this book. If you’re here, you probably would too.

While I don’t know or care much about baseball, Ben and Sam are my kindred spirits. There are not many people who have had the opportunity to use analytics to directly impact how a sports team is run on a day-to-day basis. As I found myself leafing through the pages, I saw a lot of my own hockey experiences in the authors’ words.

Whether it was gaining the trust of the coaching staff, overcoming teething IT issues, or occasionally falling prey to heuristics and losing “objectivity,” I identified a great deal with Ben and Sam’s trials and tribulations. So much so, that I began tweeting at Ben before I even finished the book.

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Practical Concerns: “The Blind Side”, Intangibles and My Off-Season Plan At McGill

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(Photo credit: Derek Drummond)

At VANHAC, I was asked by a few people about how we use analytics in our program. Every season is different, and to gain a full appreciation of my intentions this summer, it’s worth digging into the central thesis of a football book.

What Really Drives Results?

“[Quarterback Joe Montana, wide receiver Jerry Rice and running back Roger Craig] are stars. They accumulated the important statistics: yards, touchdowns, receptions, completions. [Left tackle Steve] Wallace is not considered a producer. He has no statistics.” – The Blind Side: Evolution of a Game (Michael Lewis, 2006)

While Michael Lewis’ Moneyball did much to improve the popular understanding of analytics in sports, I happen to think that The Blind Side can help bridge the gap between traditionalist and numbers-driven analysts just as much as Moneyball did.

If you peel away the diverse storylines in The Blind Side, this is the central question behind Lewis’ book: What does a good left tackle do for his quarter-back (and by extension, their team)? And how much is that worth?

Very valuable, as it turned out. Unless an NFL team wanted your multi-million dollar quarterback seriously maimed by an opposing pass-rusher, it had better hire a left tackle with the size, speed and sense to keep up. The problem is, if this player does his job well, nothing happens that can directly be attributed to him – he has no statistics.

But conceptually, his impact on the game is not all that hard to identify. A good left tackle provides a safe, productive (and dare I say, fun) work environment for his teammates. By paying attention to the process of football, you can probably come up with a few good ways to account for that.

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(credit: Derek Drummond)

Building A Bridge

When you arrive to this conclusion about football players, it becomes a lot easier to see why the idea of “being a good teammate” and “having intangibles” matters to people working in hockey. I’ve alluded to this elsewhere, but there are really two aspects to creating that good working environment for other people – one can’t be expressed in numbers conveniently, but I reckon the other already can be. Both matter a great deal to the end result, and to how people feel in the process to getting there.

I didn’t have time to really dig into this during my talk at VANHAC, but this is probably the most important realization I’ve had in two years working for the McGill Martlets hockey program.

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The Shift: Breaking Down The L.A. Kings’ Secrets To Success

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By virtue of their 5vs5 shot differential, the Los Angeles Kings are the best team in hockey. As of Saturday night, the Kings are rolling along at 56.1% Corsi – #1 in the NHL by a long shot. In fact, the 3% gap between the Kings and the No. 2 Anaheim Ducks is the same as the one between the Ducks and the No. 15 Philadelphia Flyers.

So why are the King so good?

The simple answer is that they have good players executing a sound game plan developed by a good coaching staff. But how exactly does this manifest itself?

On March 26th, the Kings were beating up on the Edmonton Oilers in the middle of the second period when, in the span of 45 seconds, they put together – in my mind – a perfect, representative shift of everything that makes them a superior hockey team.

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Rebounds, Extended Zone Time, and the Quest For More Offense

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Long has it been argued that sustained zone time is a reliable way to not only prevent your opponents from scoring but as a way to produce offense of your own. The argument that is often made, or at least the one that’s often heard, is that the longer you are in the offensive zone the more likely it is that the defense will become fatigued and make a mistake that leaves someone open for a prime scoring opportunity. 

So let’s test that theory by asking a more data driven question; does sustained zone time lead to an increase in shooting percentage?

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What determines coach salaries? A look at NHL bench bosses

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This summer, the drama surrounding Mike Babcock drew my attention to the salaries of coaches in general. What factors play into how much money a coach earns? Babcock is known as a coach who’s won at every level. Are Stanley Cup wins a factor in what a coach gets paid? Maybe playoff wins? Regular season won-loss records? Something else?

Babcock’s contract – a mammoth 8 year long pact worth $50 million to coach the Leafs – brought the subject of coaching salaries to the forefront. At $6.25 million per season, Babcock earns more than double the annual pay of any other NHL coach with a publicly known wage.

For the Leafs, spending huge amounts of cash on team personnel makes sense – there’s no cap on coach salaries so that Leafs can wield their monetary advantage to sign the best bench boss available. For Babcock, it’s difficult to fault the long-time Red Wings coach for taking the big pay day. Beyond enriching himself (which he really, really did), Babcock has been very open about his desire to push coach salaries forward by setting a new standard. He probably didn’t imagine he’d earn more than Joel Quenneville and Darryl Sutter combined or that his term would extend three years past any other NHL coach. But, as perhaps the game’s best coach, the Leafs were willing to pay whatever was needed to pry Babcock out of Detroit.

But what types of thinking go into deciding how much a team is willing to pay its coach? Did Babcock earn the money because of his vast experience? Or maybe his excellent regular season record over a decade in Detroit? What factors correlate with coach salaries?

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Practical Concerns: My analytics pot roast

Credit: Stuart West

Credit: Stuart West

Despite spending a lot of time at the rink watching hockey, most of my talents lie outside of the game. One of my favorite things in the world to do is to cook. And my favorite thing to make is pot roast – a big portion of the cheapest cut of meat from the butcher shop, cooked on low heat for seven hours in bottom-shelf red wine with some onions, carrots and a secret spice mix.

Making good food is a nifty ability to have on its own, but having more or less grown up in the kitchen, I can also appreciate how the process behind cooking has practical applications in sports. Ingredients, technique and (just as importantly) timing is everything when you’re cooking, and those three things matter just as much when you are trying to improve a hockey team.

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