A couple of weeks ago, I laid out an overview of Broad Street Hockey‘s zone entry project. I described some of the cool things we’ve found and called for readers to join the project, but I didn’t yet show any of the data to support my claims.
This article will be step one in that journey; we’ll delve into just how much of a team’s results are determined in the neutral zone.
As a reminder, zone entry tracking means we note each time a team sends the puck into the offensive zone (with the intent of generating offense; we exclude plays where they just dump it in and go for a line change). We note which player sent the puck in and how they did it — by dumping it, carrying it, etc. The result is that we can calculate how many shots, scoring chances, and goals are generated from each entry. Here’s what we find:
|Shots per entry||0.57||0.56||0.25||0.26|
|Goals per entry||0.039||0.035||0.014||0.017|
It appears that how a team gets the puck into the zone is as important as how often they do it. Maintaining possession of the puck at the blue line (carrying or passing the puck across the line) means a team will generate more than twice as much offense as playing dump and chase.
The common defense of dumping the puck in is that it is the sound defensive play, forcing the opponent to go the length of the ice. But while I need to update things with the full season of data, through 1/4 of the season there was no evidence that carrying the puck in was riskier than dumping it, so we will neglect that possibility for the purposes of this analysis.
We can then tease apart how a team’s play in each zone contributed to their shot differential — which, of course, ties closely to puck possession, zone time, scoring chances, and goals.
This graphic shows how we can roll together various statistics to calculate a performance score for each zone. For example, if a team gets an above-average number of shots per entry, they will have a positive offensive zone score. We can perform these calculations with a given player on the ice to see whether the team performed better in a given zone when he was on the ice.
|Forward||OZ score||NZ score||DZ score|
|Defenseman||OZ score||NZ score||DZ score|
Some things here make sense. It’s not hard to believe that Sean Couturier was the Flyers’ best forward in the defensive zone, or that Kimmo Timonen was their best defenseman in all three zones.
But at least as many of these results stand out as being surprising. Would you expect Daniel Briere and Jaromir Jagr to be just as inefficient in the offensive zone as Zac Rinaldo? Would you expect Nicklas Grossmann to be the second-best defenseman in the offensive zone but by far the worst in the defensive zone?
When numbers are wildly out of line with expectations, that can mean there’s an exciting discovery coming, that our intuition is wrong. It can also mean that the numbers just aren’t particularly meaningful, that they aren’t really measuring what we thought.
One of the most common ways to take a first glance at whether a metric is meaningful is called a split-half reliability test. We look at how well one half of the data (the odd-numbered games, for example) predicts what happened in the other half. If you can’t look at the results in the odd-numbered games and guess what happened in the even-numbered games, then whatever you’re looking at probably isn’t really measuring a true talent; it’s mostly random numbers generated by statistical noise.
The net result here: our surprising results in the offensive and defensive zones appear to be based on a not-particularly-reproducible metric. It may still be true that some players have skills that help the team get more shots per zone entry, but at the end of a year we can’t reliably tell which players those are — we know who did well in the offensive zone this year, but don’t have strong reason to believe they’ll do well in the offensive zone again next year.
The neutral zone is a different story. The split-half reliability there is 0.44, high enough that we can be 97% sure that this is a real correlation and not just random results. Given a half-season of neutral zone data, we can make a decent guess at what will happen in the other half-season.
Moreover, the neutral zone results look like they are not just statistically significant, but meaningful in practice as well. It’s pretty reasonable to see Jagr and Claude Giroux leading the forwards and Timonen and Matt Carle leading the defensemen.
So I’m ready to believe that neutral zone performance is a repeatable measure of a real talent that the players have, and that the offensive and defensive zone scores we’ve calculated might not be. But we still haven’t really answered the question posed in the title of this article: How important is neutral zone play?
It is still possible that neutral zone score (like hits or fighting majors) is reproducible but doesn’t have much impact on the outcome of the game. And it’s possible that offensive and defensive zone score (like shooting percentage or save percentage) has a huge impact on the outcome but fluctuates quite a bit.
We know that outshooting the opponent is important. And we know it is a reproducible skill, so it seems unlikely that it would be driven heavily by the irreproducible attack zone results. But we can confirm this in practice by looking at how strongly our components relate to the overall shot differential.
The neutral zone score alone explains twice as much of the spread in shot differential as the offensive zone score does, and ten times as much as the defensive zone score does. These factors alone do not completely determine a player’s shot differential; obviously how often a player is used for an offensive zone faceoff is a factor, as is the team’s performance immediately following the faceoff (compare Couturier getting 0.45 shots between the faceoff and a clear with Wayne Simmonds getting 0.31 shots).
Still, at least for this one season, at least for this one team, we can predict a player’s overall shot differential with reasonable accuracy (+/- 0.7%) without knowing how he did in the offensive zone, how he did in the defensive zone, where he got deployed, or how he did on faceoffs. This isn’t just because someone who’s good in the neutral zone is probably good at those other things too — the cross-correlations between these metrics were all below 0.15.
At least for this one team, neutral zone performance is the major driver of overall shot differential, which in turn drives results. What remains to be shown is whether this is a quirk of a small dataset or something that will prove to be true as we collect more zone entry data, but for now we will tentatively suggest that the neutral zone may be a much bigger factor in a team’s performance than had previously been supposed.