An early look into some of the new numbers available

From Wikipedia Commons: A graph showing the minimum value of Pearson’s correlation coefficient that is significantly different from zero at the 0.05 level, for a given sample size.

There are two new and very exciting frontiers being explored by the hockey analytics blogosphere. There is the manual tracking of zonal statistics, such as zone entries and exits. This area of research was first pioneered by Eric Tulsky and Corey Sznajder. Then there is the splicing of Corsi into microstates, such as looking at shot attempt differentials momentarily after face off wins or loses in particular zones. The early workers on these numbers were Tyler Delow and Muneeb Alam.
(side note: it should not be a surprise that one of each group was recently picked up by a NHL team this summer)

I recently was able to get data from the non-NHL hires named above (and will enjoy their contact while I can until they are picked up too). Sznajder provided me with zone entry and exit data for just over 60% of the NHL. If you would like to check out his project and contribute, check this link. Alam sent over shot attempt events 10 seconds after a defensive zone face off, which was further separated into wins and losses.

I originally received this data for study of the Jets and noticed what appeared to the eye to be a relationship, and wished to delve in further.

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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:


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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