A Review of the New Jersey Devils Zone Exits: 30 Games In

Zone Exits Season-to-date:

This is a look at New Jersey Devils skaters zone exits data from Game 1 until this point in the season. You’ll see seven new columns from the individual game reports. These are for Games Played (B), Successes Per Games Played (R), Attempts Per Games Played (S), Defensive Zone Turnovers per Games Played (T), Goals For % (U), Difference between GF% and PE% (V), and Quality of Competition (W). These are added to illustrate which players have been most consistent and most productive. All other columns are the same.

Also, user “Shakey” posted in the comments on the 20-game summary asking about correlation between PE% and Goals Per Game. For this summary, I decided to look at PE%, CF%, and Goals For % and see if there was any relationship between them. Read on to find out what I discovered.

Terms You’ll See

OS%: Overall Zone Exit Success Percentage

PE%: Possession Exit %

TofTm% QoC: Average Time on Ice of 5-on-5 opponents

CF%: Corsi For %

GF%: Goals For %

All Quality of Competition, GF% and CF% figures were pulled from ExtraSkater. First up, the defensemen. Let’s get to it.

Defensemen:

With 8 games under his belt, Jon Merrill leads the blue line in PE% at an astounding 65.4%. I can’t expect it to stay that high, but it’s very encouraging for a rookie to be doing that well in maintaining possession. Most notably, 25 of his 52 attempts are successful passes, so he’s doing quite well at finding the correct outlet for a breakout. Merrill averages the fewest attempts per game and that would remain consistent with his higher success rate. It is still only 8 games, so we’ll see how he does at the 40 game mark.

The other rookie, Eric Gelinas, is tied with Andy Greene for 2nd on the team at 46.8 PE%, but both are far behind Merrill. Gelinas’ main problem has been the turnovers: he averages nearly 1.5 a game and while that’s down from his previous mark of 1.82, it remains 4th highest of the defensemen. Granted, he’s a rookie and he has a lot of potential, I’d like to see him not take as many risks. Gelinas and Merrill face the weakest competition of any defensemen, but they’re at least performing quite well minus the aforementioned turnovers. Gelinas did improve in both OS and PE% from his first 11 games, while facing slightly stronger competition.

Adam Larsson had a slight dip from the previous summary to this one. His OS% dropped 3.5% and his PE% dropped almost 2%. His turnovers rose from 1.06 to 1.25. At the same time, his QoC dropped ever so slightly. He only played 3 games since the last summary, so it’s not a significant change as we all saw how much better he was playing with Gelinas. Come back soon, Larsson.

Andy Greene’s OS% (1.4%) and PE% (3.7%) both rose, and his turnovers decreased, so it was a positive 10 games for Greene. He continues to face the toughest competition, though it’s gone down slightly. Greene has both the highest success and attempts per game. It’d be great if he could continue to climb the PE% ladder and we’d have another defenseman over 50% to possibly start matching up with some of the other teams.

Marek Zidlicky’s OS and PE% both went down by 1% and 1.8% respectively. His turnovers remain a problem as they went up by .12/game. Zid’s competition went down slightly, but he needs to cut it out with the turnovers. He’s so carefree with the puck at times that I wonder if the team would be better off with Merrill and Volchenkov/Salvador as the 3rd pairing once all are healthy.

Peter Harrold. Turnover Machine. His previous rate was 1.93 for 15 games; in only 5 games, he managed to increase that to 2.45/game. That is ridiculous. His OS% is the lowest on the team and his PE% is 2nd lowest, ahead of only the mighty puck-mover A-Train. Looking at these stats, I can confidently say that Peter Harrold is the last defensemen we’d want attempting to exit the zone. Simply put: he’s the worst on the blue line at doing so.

Anton Volchenkov. Never much for maintaining possession, Volchenkov makes up for it by being conservative and having the 2nd lowest turnover rate of .86/game. It’s actually come down .02 from the last summary. As a 3rd pairing defensemen, he’s quite serviceable, especially considering the PK time Deboer gives him. Volchenkov does face weaker competition, but he can still be effective in this role.

Mark Fayne has seen his QoC rise since being paired with Andy Greene. Not much, but it’s evident those two are out there against tougher competition more often than not. Similar to Volchenkov, Fayne isn’t much for maintaining possession on the season, but if you look closer, his PE% has increased almost 8% in 10 games, which means he’d have to have been around the 45 – 47% rate in this last 10 game stretch, which would be good enough for Andy Greene territory. His OS% increased as well by 3%. His turnovers dropped minimally, but it was good to see the sizable improvement for Fayne.

No change for Salvador as he’s still under lock and key somewhere.

Forwards:

Mattias Tedenby only played in 3 more games since the last summary, bringing his total to 12, but he found ways to significantly improve his percentages in those 3 games. His OS% improved to 83%, whereas his PE% improved 11 points to 34%. It’s still not good, but progress is progress. Tedenby still has one of the higher turnover rates among forwards at .75/game, so there’s still work to be done with him.

Fellow Swede Jacob Josefson played in 7 games of the last 10 and improved as well. His 53.1 PE% is 4th highest among forwards. His OZ% was already excellent and he still found a way to improve it 1 point to 89.8%. Josefson has the 2nd lowest turnover rate among forwards (behind only Ryan Carter) and his CF% is 50.6%. Why is he not playing again? Granted, he has faced the weakest competition of all forwards, but it’s not by much. He maintains possession almost twice as much as Stephen Gionta. 4th line center material? I think so.

Dainius Zubrus continues to excel at his zone exits, with 92.6 OS% and 55.8 PE%, all while doing it at top QoC. His line mates Travis Zajac (50 PE%) and Jaromir Jagr (60.7 PE%) also continue to do well. Zajac faces slightly better QoC, which perhaps accounts for him being out on the ice to help close out games more often than Zubrus and Jagr? Just a guess, but that difference has to come from somewhere. Zajac does turn the puck over more frequently than Jagr and Zubrus, a little less than twice as much actually. Overall, these 3 are very good.

Andrei Loktionov continues to regress a bit as he’s come down from 70 PE% at 10 games, to 63.3 PE% at 20 games, and now he’s at 59.6 PE%. It’s still 2nd best among forwards, but I hope it stabilizes at this point. His OS% dropped 0.3%, so that remains high. Loki also is in the top half of the forwards in terms of turnovers with only 1 every 3 games. His QoC dropped overall in the 9 games he’s played since the last summary, most likely due to line shuffling.

Patrik Elias has played in all 10 games since the last summary and is getting back to the Patty we know. His PE% rose to 51.1% and his OS% rose to 92.4%. Elias plays against some of the tougher competition on a nightly basis and holds his own. Nothing surprising here. Adam Henrique continues to surprise, however, and not in a great way. After starting the season strong, he’s settled into that 46.1 PE% (down 0.3% from the last summary) range and commits a turnover slightly more than every other game. He’s not facing the hardest QoC and his OS% is the same as the previous summary. Here’s hoping Henrique can pick it up again soon.

Michael Ryder has improved slightly in all phases and remains about the average for the forwards as a whole. Steve Bernier dropped a little over 3% in his possession exits, but his OS% improved about 2%. His turnovers have come down while playing against similar competition according to ExtraSkater. When the team’s healthier, Bernier’s an asset to have in the bottom six.

Someone who may be lucky to stay in the bottom six, Damien Brunner, improved slightly to 45.4 PE% while decreasing his turnovers slightly. He’s playing against similar competition as Bernier, however, and not doing as well, so he’ll need to start scoring ASAP if he’s not going to excel in this department. Newcomer Reid Boucher only played 2 games in the first 30, so he’ll be more heavily-scrutinized in the next summary.

Stephen Gionta only played 3 games since the previous summary, and I’m afraid he’s gotten worse. His PE% is a miserable 28.2%. That’s only higher than Tim Sestito and he’s only played 3 games. Yes, Cam Janssen has a higher PE%, granted it’s on about a third of the attempts as Gionta. Still though, Gionta can’t be among the 12 best forwards on the Devils anymore. His line mate, Ryan Carter, improved dramatically, jumping 10.5 points to a 41.6 PE%. Still below average for the team, but a significant jump. He’s still above 90% for his OS%, which is solid. Get healthy and come back, Carter.

Ryane Clowe. No change. Supposedly we may see him again in the near future. Let’s hope he’s finally healthy for his sake. Tim Sestito’s played in 3 games, so his stats are there, but it’s much too small a sample size to say anything more.

Zone Exit Comparison:

I’m going to continue to look at the same six teams and compare them with the Devils as the season progresses. Sometimes, you’ll see the totals increase quite a bit, others times not as much. This is simply a result of the other volunteers getting to it when they can, so it won’t always be uniform, but it’s all we’ve got to compare.

Using the database I’ve been submitting my totals to (ZEN), I was able to pull reports for the Los Angeles Kings, Dallas Stars, St. Louis Blues, Tampa Bay Lightning, Philadelphia Flyers, and Pittsburgh Penguins. I’ve cleaned the data a bit and added the players’ positions to more easily compare defense and forwards. Why these teams? Well, not every team has as diligent a volunteer as these teams, so my choices were limited. Even still, I feel it’s a good mix of East and West, as well as teams that are higher and lower in the standings.

You’ll notice varying amounts of attempts for some players and the reason for that is that not all volunteers have tracked the same number of games as each other. Still, these 5 teams had the highest totals and for that I thank the people that have taken the time to stick with this project. Keeping that in mind, 6 different people (myself included) are tracking these and each person may interpret something a bit differently than the other.

Also, the way the reports pull, each exit attempt is not recorded by type, but rather by success, failure, and possession, so it’s not as detailed, but it still offers a solid idea of how well players and teams are at exiting the zone. I’ve only included players on other teams that played a minimum of 10 games. They are sorted by position and in descending order of total number of exit attempts.

So, let’s see if there’s been any improvement this time around.

Zone Exits Devils vs Flyers

You’ll see that the Devils defensemen do a slightly better job at OS%, but slightly less at PE%. In the end, probably a wash. Both teams have their better players (Streit and Meszaros here) and their rejects (Grossman). Same story with the forwards: slightly better OS% and slightly lower PE%. Again, a virtual wash with the Flyers.

Zone Exits Devils vs Penguins

Pittsburgh, unfortunately, is simply much better than the Devils at zone exits. The closest the Devils come to matching the Pens is the PE% for defensemen, where the Pens have a 2% edge, but their OS% is so much higher. The forwards excel much better than the Devils in both categories, but that’s to be expected when you look at the rosters. Although it was interesting to see that James Neal’s PE% is the 3rd lowest on their team, maybe if tried kneeing the puck accidentally out of the zone it’d be a better play for him, one he knows how to make with repeatability.

Zone Exits Devils vs Lightning

The Lightning defense is worse than the Devils on PE%, but have the same OS%. Similar to the Devils, they have one defenseman above 50 PE% (Matt Carle) and the rest at varying degrees of success. The forwards are still higher than their Devils counterparts. These totals haven’t been updated since the last summary, so I wonder if the volunteer bailed. It’s still a decent sample size to compare a poor defense group to a good forward group.

Zone Exits Devils vs Kings

Similar to the Lightning, these totals remained the same. This just in: The Kings are good.

Zone Exits Devils vs Blues:

The Blues defensemen came down slightly, but are still incredibly successful when compared to these other teams. Also, Alex Steen is having a great year everywhere on the ice apparently. His PE% is 79.5%!! That’s the highest I’ve seen. In fact, the Blues have 3 forwards in the 70%tile or higher: Steen, Patrik Berglund, and Vladimir Tarasenko. We can only sit here in envy.

Zone Exits Devils vs Stars:

The Stars totals only went slightly (1 or 2 games it looks like), so some people have been slacking. Most of these are still the same.

I wish there was more to compare to, but we’re at the mercy of others for this data.

Positional Averages

We can still take a team’s average zone exits with possession and compare to each other in different and new ways. Above, you’ll see the positional averages for OS% and PE%. You’ll see some slight changes even to some of the teams that didn’t have anything new. The reason for this is I went back and took a true percentage of their successes/attempts rather than an average of the players. It’s more accurate this way.

So, not much has changed. The Devils increased slightly in each category, but are still far behind many other teams. They have the lowest forward PE% and their defensemen are only ahead of the Lightning. Maybe there will be more totals the next time around.

PE% versus CF% and GF%

Now to the new and, what I think, really cool stuff. Corsi has long been one of the key stats to predicting and explaining performance: the better teams posses more of the puck and so they will score more. To see if PE% had any merit as a predictive type of stat, I compared it to both CF% and GF%, as well as comparing CF% to GF% to see how close the numbers were. An “advanced stat triple threat match” if you will.

I’ll begin individually and then go to the position groups and finally a team comparison. One thing that quickly stands out is that Anton Volchenkov has the highest GF% of all Devils defensemen (Of course, Tim Sestito’s is 100%, so…yea, it’s not the end all-be all individual stat). If you look at each player’s PE%, CF%, and GF%, you may not see anything particularly interesting right away. Neither did I at first: it seemed most of it was in line with what we’d expect from most of these players. So, I wanted to continue what I started last time and look at the differences between the percentages.

Zidlicky has a 51.8 CF% and a 44.9 PE%, which results in a difference of 6.9%. In fact, going down the defensemen, Zid’s difference between the two is the smallest, so maybe that’s not the best correlation to use. So, maybe there’s a better correlation between CF% and GF%? Bryce Salvador actually had the lowest difference at 3.8% (53.8 CF% and 50 GF%), which is much better than 6.9% in the previous comparison. In fact, there were three others (Zid, Volchenkov, and Fayne) that if you looked at Corsi For Percentage as a predictor of Goals For, they were all better than the Zidlicky example involving PE % and CF%.

The problem was in the positional average. At the end of the day, the average difference between CF% and PE% was 10.3% and the average difference between CF% and GF% was 9.9%, so neither correlated that well to the other. That leaves us with PE% and GF%. Yahtzee.

In the previous two comparisons, 3.8% was the closest I could get in terms of a difference between the two percentages, with this third comparison, Greene, Zidlicky, Harrold, and Gelinas are all within 2%. In fact, when you take the positional average for both stats, the difference is only 0.4%. Not enough? Let’s look at the forwards.

The forwards had closer percentages than the defensemen, but the PE% and GF% difference won out again at 0.6%. Obviously the GF% will fall for Janssen and Sestito, but it should rise for others, perhaps not as extreme, but we’ll see how it balances out next summary.

I’ve tried to take these three stats and compare them in a way to see how they relate. Based on this data, if the Devils increase their PE%, and not necessarily their CF%, they should score more goals.

I know what you’re thinking, “it’s just one team.” Right. So, let’s look at the other six teams I’ve included in this article and see how it all shakes out.

What we find here is that CF% is a better indicator of GF% for the Kings, Penguins, and Lightning. PE% is a better indicator of GF% for the Blues, Devils, Stars, and Flyers. Slight edge to PE%. But, if we look at the average difference between each group of stats (PE% vs GF%; CF% vs GF%) you see that PE% only had a 2.6% average difference, whereas CF% had a 3.7% difference. So, based on this data, PE% had smaller swings in difference and is more closely tied to GF% than CF%.

Now, it’s certainly too soon to say PE% is the next “fancy stat” in terms of predicting performance, but it certainly piqued my curiosity once I tabulated all of this data. I’ll be checking and rechecking at the next summary to see how it shakes out then. What are your thoughts on this? What could be done to best improve my analysis?

And please offer your opinions, suggestion, criticisms, statements, analysis, etc. Collectively, I’m sure this could be a great forum for debate. What would you like to see next time? Are there any stats you’d like to see brought into this discussion?