Shorthanded

Can We Accurately Predict Which PK Units Will Score Shorthanded?

arikparnass Theory Tags: , , , , Leave a comment
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Last week I went on Montreal radio and talked about how dangerous the Ottawa Senators’ penalty kill units are. Led by speedy forwards like Curtis Lazar, Jean-Gabriel Pageau and Mark Stone, and with help from puck moving genius Erik Karlsson, the team has feasted on opposing power plays this year to the tune of the highest GF/60 minutes shorthanded in the league since at least 2007-2008. When considering the team’s league worst GA/60 — mixed with a little bit of film — it becomes clear that the Senators yield chance against in exchange for opportunities for on the break. It may not have been intentional at first, but once the team started capitalizing on its rushes, it seems likely coach Dave Cameron gave his players the green light to go, to try and come out on top on aggregate. The result? While being last in GA/60 shorthanded, the Senators are third in GF%. The problem with GF% when it comes to special teams though is that volume matters more when the ice is tilted. Two goals for and Eight goals against isn’t the same as Four goals for and 16 goals against. So goal differential per 60 is a more accurate measure of success on special teams. The Sens are 30th in GD/60 shorthanded, so it’s hard to say the strategy has been that much of a positive for the team (unless, say they’re down a goal and shorthanded near the end of a game).

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Does aggressive play on the penalty kill pay off?

Matt Cane Theory Tags: , , , , 2 Comments
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Late last week, Arik Parnass pointed out a particular peculiarity about the Ottawa Senators’ penalty killing so far this year.

While the Sens may be an extreme example, their numbers tell the story of a constant struggle that teams are faced with when killing a penalty: do you focus solely on your own end and do whatever it takes to prevent a goal, or do you allow your forwards to take the play to your opponents, trying for a shorthanded goal and forcing them to defend in a situation where they may not be expecting it.

This risk-reward question is one that’s central to the value of hockey analytics. It’s very easy to make decisions based on personal experience which is so often dominated by memories of things that are out of the ordinary – a coach will likely remember watching his winger get caught deep trying for a shorthanded goal, while forgetting the 2-on-1 opportunity he generated earlier in the game. It’s just as easy, however, for a fan to complain that his favourite team won’t put out their best forwards to aim for a go-ahead shorthanded goal without any data to back up their argument. The challenge for analysts then is to dig through the available data to figure out what past experience has taught us about the overall net impact of playing for a goal on the penalty kill, so that we can make an informed judgement as to what the potential costs and benefits are.

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