Using Data to Inform Shorthanded Neutral Zone Decisions

The following is data is all at 4-on-5 with both goalies in their nets. A special thanks to Evolving Hockey for data and their scraper.

In March of 2019, Mike Pfeil coined the term “powerkill” at the Seattle Hockey Analytics Conference. It was much more of a small excerpt from his whole presentation, but it seemed to motivate Meghan Hall and Alison Lukan. In the coming months, Lukan would write about how the Columbus Blue Jackets utilized an aggressive approach in their penalty killing system, while Hall would present at RITSAC and OTTHAC before they finally came together to present at the Columbus Blue Jackets Hockey Analytics Conference in February.

Looking to continue researching this phenomenon, I set out to answer a few questions I had. In order to give shots some added context beyond what the NHL’s public data supplies, throughout the last few months, I tracked shot assists and where possessions leading to shots had started. As a side benefit, I was also able to filter out shots that didn’t appear to exist, were recorded incorrectly, or where the possession started at 4-on-4.

In 2016, Matt Cane developed a metric to approximate penalty kill aggressiveness by combining penalty kill controlled and failed entries for, and dividing them by the entries a penalty kill faces from their opponent. The theory behind that being that penalty kills that attempt to control more entries into the offensive zone are inherently more aggressive. Hall and Lukan also found that a penalty kill’s rate of controlled entries has a strong correlation to the rate at which they take shots.

Part of the reason these two stats have such a strong correlation is that the vast majority of shots require a zone entry. Not including rebound shots, 82% of 4v5 shots stemmed from possessions starting outside of the offensive zone over the course of the 2019-20 season.

zones

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How repeatable is performance in the Offensive, Defensive and Neutral Zones?

A few years back, Eric Tulsky (and others at Broad Street Hockey) pioneered the start of neutral zone tracking, or rather the tracking by individuals of every entry each team makes from the neutral zone into the offensive zone during a hockey game.  The idea of this tracking was simple:  Neutral Zone play is obviously important to winning a hockey game, but NHL-tracked statistics contain practically no way to measure neutral zone success overall.   Zone Entry tracking remedied that, by giving us both individual and on-ice measures of neutral zone performance.

An overall measure of neutral zone performance that we can find with zone entry tracking is called “Neutral Zone Fenwick.”  By using the average amount of Fenwick events resulting from each type of zone entry (Carry-in or Dump-in), we can create an estimate of what we’d expect a player’s Fenwick % to be with them on the ice based on the team’s neutral zone play with them on the ice.  In essence, this is a measure of a player’s neutral zone performance, helpfully done in a format that we’re already pretty familiar with – like normal Fenwick%, 50%=break even, above 50% = good, below = bad.

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What’s the deal with Andrew MacDonald: Why do the statistics suggest he’s terrible?

Did you really think I was going to miss the opportunity to post the AMac with chains gif again? You thought wrong.

Islander Defenseman Andrew MacDonald is one of the hot names being bounced around during the trade deadline.  On one hand, this makes sense: He’s making basically nothing on his current contract, he’s one of the time on ice leaders in the NHL this year and has handled top level competition for a few years now.

On the other hand, his conventional fancystats show a well…..massive decline:

AMacThreeYear

Yikes.  That 2013-2014 number is downright terrible, dropping MacDonald into the bottom tier of defensemen.  And no zone starts and certainly not competition (see this article for an analysis of AMac vs various levels of competition) does not account for this.  If you believed the fancystats, AMac isn’t just not a top tier DMan, but not even a 2nd or 3rd pairing guy who could help any team at all.  Yet teams seem to believe he’s worth a high pick?  So what’s going on?  Is the conventional thought completely wrong here?

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