How Analytics Help an NHL Team the most – Preventing Stupid Mistakes

In the last few weeks, four prominent analytics-minded individuals have been hired by NHL teams.  First Sunny Mehta of the classic Irreverent Oilers Blog (and poker player) was hired to head New Jersey’s brand new analytics department.  Second, Eric Tulsky (@BSH_EricT of SB Nation’s OutNumbered blog, amongst others) announced he was hired by an anonymous NHL team.  Then Kyle Dubas, GM of the CHL’s Soo Greyhounds, who was known to be using analytics to try and succeed despite being a small CHL market team, was hired to be the assistant GM of the Toronto Maple Leafs.  Finally, just yesterday, Tyler Dellow (@mc79hockey) was hired by his favorite team, the Edmonton Oilers.  These very public hirings have given rise to people wondering how much the signings will improve the team.

However, while all the sexy talk about analytics revolves around these guys changing how teams play, the biggest way an analytics guy, or just analytics in general, can improve these teams is much much simpler:  By preventing teams from making stupid mistakes.

Phil Birnbaum wrote a post on this, referencing baseball, which applies very much to hockey here.  Analytics only helps a team outsmart other teams to the extent other teams don’t have people looking into the same things.  By contrast, Analytics will always help you avoid stupid mistakes that could set you back quite a bit.

Take a look at this past free agent market.  The Isles were looking for a top 4 D Man.  Let’s say they followed the analytics and came to believe Anton Stralman was their man, and offered him 20 Million over 5 years.  That would’ve probably been fair value for what Stralman offered – quite a bit due to his great possession play – yet it would’ve been more than what conventional analysis would’ve said Stralman was worth.

And here’s the thing:  THEY STILL WOULDNT HAVE WON THE BIDDING FOR STRALMAN.  Correctly following the analytics #s only helps when others aren’t doing the same thing – the Isles STILL can’t outbid richer teams who are on to the same targets, really.  In this case, being smart analytically wouldn’t have helped the Isles at all.

Now again, let’s look at another free agent D-Man: Brooks Orpik.  Orpik was considered a top 4 D Man in Pittsburgh by mainstream media.  He made Team USA for goodness sakes.  But the possession #s suggest Orpik is long past his prime and is no longer any good at all – rather he’s a freaking boat anchor.  By paying attention to the possession #s, the Isles would know not to bid much at all for Orpik, and thus the analytics serves a big benefit.  (You could argue the Isles couldn’t afford Orpik even if they thought he was good, but you can use any other bad D man, such as Derek Engelland instead).

This applies to more than free agency of course.  

Last year, there were no analytics publicly available that would suggest that Calvin de Haan was either ready for the NHL or that he’d be any good in it.  (It’s possible the team had such of course, but we didn’t, and there will be situations where the team doesn’t have enough data to make an analytics-based decision.)  Despite this, we DID have analytics available about the alternatives – Radek Martinek, Matt Carkner, and Brian Strait – all being bad options.  Analytics would’ve told you that, and would’ve possibly resulted in a call up of what turned out to be one of our better D.  Was there any guaranty that would be the case?  No.  But the unknown is more likely to be a better outcome than a known bad.

We’ll go into what are the known bads in an upcoming post – there are still a few on the Isles current roster.  Analytics won’t necessarily make the other guys any better, but avoiding the known bad options would already be a significant win.

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