Adjusting for Zone Starts: Zone Start Adjusted Corsi

In a previous article I discussed zone starts and introduced a new approach to analyzing the effect of zone starts - breaking up performance by each type of ice time: offensive zone the first shift after the faceoff, offensive zone faceoff after a change, after a neutral-zone start, defensive-zone start after an on-the-fly change and the first shift after a defensive-zone faceoff. In this article, I will introduce a metric that adjusts for zone starts and a simplified metric that provides a good rule-of-thumb for you to use when looking at BTN.

Zone Start Adjusted Corsi

The idea is simple: take a player's ice time and use the league average Corsi for each type of start to determine what an average player's Corsi would be with the same ice time. Subtracting that off will give you how much he is above, or below, what the average player would get with his ice time. To see how this works, let's look at the poster child for zone-start adjustment, Manny Malhotra. Here is a chart summarizing Malhotra's time in each start, along with his Corsi numbers:

Manny Malhotra	Time (mins)	Corsi / 60
Ozone, first shift	55.2	48.913
Ozone, after change	170.8	-7.376
Neutral Zone	340.3	-6.524
Dzone, after change	142.3	-13.073
Dzone, first shift	178.7	-31.234
All Time	887.2	-9.265

Here are the league averages for each type of ice time:

League Average	Corsi / 60
Ozone, first shift	40.147
Ozone, after change	2.818
Neutral Zone	0
Dzone, after change	-2.818
Dzone, first shift	-40.147

Weighting by Malhotra's ice time gives us -5.496, meaning that if someone performing at the league-average level was given Mr. Malhotra's ice time he would have a Corsi of -5.496. To get Manny's Zone Start Adjusted Corsi we subtract that off, in other words add 5.496, to get -3.769.

A Rule of Thumb: Simplified Zone Start Adjusted Corsi

That's all well and good, but it would be nice to have something a little more portable. Even with all the data, I'd like to be able to just pull up BTN and get an idea how to adjust for a guy's Ozone%. To get something simpler, I recorded the Ozone% according to BTN for all of the players with at least 600 minutes of even-strength-goalies-on ice time and ran a regression to get the average adjustment for a given ozone%. Here is a scatter plot of the 508 players. The numbers on the x-axis represent how far off from 50% Ozone%, the y-axis is the size of the adjustment or the negative of what the average player would get with the same ice time:



As you can see, a simplified formula will come very close to the more complicated version above which forces us to look at the individual data. Any differences are based on how much time a player spends in the relatively neutral situations where he is jumping on the ice after a faceoff at either end. The result of this is a simple formula. To adjust for zone starts, multiply how many percentage points the player's Ozone% is from 50% by 0.18 and add or subtract accordingly. In formula, with Ozone% out of 100:

Simplified Zone Start Adjusted Corsi = Corsi/60 - (Ozone% - 50)*0.18

Another way to think about it is to add or subtract 1.8 for every 10 percentage points. So if you gave a guy with even zone starts 60% Ozone starts then we'd expect his Corsi rate to go up 1.8. If you put him in more defensive spots with just a 30% Ozone% then his Corsi will drop about 3.6.

Results:

I don't want to clutter it with a 900-row table, so I'll make a table with the top 25 and another with a few players of interest with particularly high or low Ozone%. Here is a google spreadsheet with all the Zone Start Adjusted Corsi stats from 2010-2011.

Rank	Player	Team	Pos	Zone Start Adjusted Corsi	Corsi	Time On Ice
1	Kyle Wellwood	SJS	F	22.125	22.203	462.1
2	Torrey Mitchell	SJS	F	18.504	18.336	791.9
3	Joe Pavelski	SJS	F	17.304	15.939	1039
4	Ryane Clowe	SJS	F	16.571	16.715	1148.7
5	Alexandre Picard	MTL	D	16.538	17.308	634.4
6	Mason Raymond	VAN	F	16.515	17.695	922.3
7	Ryan Kesler	VAN	F	16.509	16.588	1135.7
8	Brian Rafalski	DET	D	15.305	16.083	1033.4
9	Nikolay Zherdev	PHI	F	15.029	14.418	653.4
10	Justin Williams	LAK	F	14.843	14.717	1043.7
11	Evgeni Malkin	PIT	F	14.773	15.509	607.4
12	Sean Bergenheim	TBL	F	14.652	13.625	916
13	Tim Jackman	CGY	F	14.645	16.275	726.3
14	Viktor Stalberg	CHI	F	14.217	16.208	799.6
15	Pavel Datsyuk	DET	F	14.039	13.3	848.1
16	Logan Couture	SJS	F	13.743	14.078	1133.7
17	Alexander Steen	STL	F	13.722	14.48	1081.5
18	Jason Demers	SJS	D	13.685	13.591	1169.9
19	Mikael Backlund	CGY	F	13.376	14.034	761
20	Patrik Elias	NJD	F	13.275	13.306	1082.2
21	Mark Letestu	PIT	F	13.262	14.613	759.6
22	Tomas Holmstrom	DET	F	12.91	13.418	840.7
23	Chris Higgins	VAN	F	12.183	11.156	790.6
24	Brian Gionta	MTL	F	12.08	12.151	1185.1
25	Tyler Kennedy	PIT	F	11.813	12.581	996.8

People of interest:

Player	Team	Pos	Zone Start Adjusted Corsi	Corsi	Time On Ice
Henrik Sedin	VAN	F	7.185	11.803	1235.3
Patrick Kane	CHI	F	10.52	13.738	1139.9
Marian Gaborik	NYR	F	-7.194	-4.829	882.1
J-P Dumont	NSH	F	2.476	4.62	662.4
Ville Leino	PHI	F	-3.973	-2.187	1097.6
Manny Malhotra	VAN	F	-3.769	-9.265	887.2
Blair Betts	PHI	F	-15.221	-18.412	501.9
Steve Ott	DAL	F	-4.526	-8.346	1020.9
Jerred Smithson	NSH	F	-6.98	-10.442	965.3
Dave Bolland	CHI	F	-1.198	-3.2	806.3

Please Leave Feedback!

As this is my first effort in coming up with a new statistic, I would love some feedback on this. Does the methodology make sense? Is the Ozone% adjustment of .18 per percentage point pretty close to what you've been doing? Any and all comments appreciated.

On Zone Starts

How valuable are offensive-zone faceoffs? How much should we adjust the Sedins' stats to take into account that they take three faceoffs in the offensive zone for every one at the other end? Are coaches using their guys effectively, or should they almost turn them into specialists like Vigneault does in Vancouver? Is it better for a coach to focus on zone starts, matchups or just roll lines to keep his guys fresh and the best playing the most?

These are just a few of the many, many questions that relate to zone starts that come up in hockey analysis. We'll put off dealing with most of those until later, this article will be more of a broad discussion and introduction to what I feel is a novel approach that we'll be using a lot.

The Data

I'm going to start with team stats. This is pretty strange because zone starts aren't a team issue. A team that has more offensive-zone starts than defensive has earned them; good zone starts aren't just handed down to the team by some suit. In contrast, a player only reaps what he sows, as it were, if he or a teammate ices the puck or possibly after a very short shift. Most faceoffs are taken with fresh players that were on the bench when the stoppage occurred. Despite that, using team stats is a good place to start because we can get a pretty good idea how the location of the most recent faceoff affects results.

The data come from a common source: NHL play-by-play and roster reports. In this case, I am using every game available for the 2010-2011 regular season. As usual, it's even strength with both goalies on the ice. Using the roster reports, which say when each player was on the ice during the game, I isolated these 5 situations:

Ozone, first shift - the most recent faceoff was in the team's offensive zone. All players that were on the ice for the faceoff remain on the ice.

Ozone, on-the-fly - the most recent faceoff was in the team's offensive zone. At least one player who was on the ice for that faceoff has left.

Neutral Zone - the most recent faceoff took place in the neutral zone.

Dzone, on-the-fly - the most recent faceoff was in the team's defensive zone. At least one player who was on the ice for that faceoff has left.

Dzone, first shift - the most recent faceoff was in the team's defensive zone. All players that were on the ice for the faceoff remain there.

To clarify, once any of the original faceoff guys have left the ice the rest of the time before the next stoppage is in the on-the-fly category. So the faceoff shift is only that first shift, even if later it so happens that the 10 skaters that were on the ice are out there together.

Note: I have separated out the team results to keep this post about the general concepts. If you want to see how good your team is in each situation, you can find that here.

Ice Time and Goals

Let's take a look at how much time was spent and how many goals were scored on average in each situation. This will give us a very rough idea how important faceoffs are in the offensive or defensive zone.

Situation	Ice Time	%	Goals	%
Ozone, first shift	452.5	11.4%	21.2	13.8%
Ozone, on-the-fly	765.1	19.3%	32.3	21.0%
Neutral	1538.9	38.7%	54	35.1%
Dzone, on-the-fly	765.1	19.3%	34.8	22.7%
Dzone, first shift	452.5	11.4%	11.4	7.4%
Total	3974.1	100%	153.6	100%

The first thing I noticed is that the first shift only accounts for about 37% of the ice time following a faceoff at either end. Part of that is that I'm being very strict defining the first shift; it would increase if I allowed for one guy to leave the ice, for example. In any case, you might wonder why we focus so much on who is on the ice for a faceoff when so much of a player's ice time after an ozone/dzone faceoff started with him jumping onto the ice.

The goals columns give you a pretty strong hint. Look at the second and fourth rows. Following a faceoff outside the neutral zone, once a change was made more goals were scored by the team that took the faceoff in their defensive zone! We'll later see that this is likely just due to random chance, but it seems clear that if you come on in an on-the-fly change it's more like a neutral-zone start than being on for a faceoff at either end.

Corsi

Let's look at the average team Corsi rate for each situation. I use the average team rate for each situation so we don't have the endogeneity effect I wrote poorly about a couple months ago. In other words, if we simply averaged out all the ice time we'd overestimate how important it is to be in the offensive zone because good teams tend to get more faceoffs in the offensive zone than bad teams so we'd be lumping in team quality with ozone-faceoff value.

Situation	Corsi/60
Ozone, first shift	39.957
Ozone, on-the-fly	2.603
Neutral	0.019
Dzone, on-the-fly	-2.784
Dzone, first shift	-40.103

Here you can see how much having a faceoff in the good zone helps your team out territorially. I'm sure that there is an effect didn't surprise you, but suspect how large it is might have. It's also interesting to note that this almost completely goes away once the first change happens. This shouldn't be a surprise either, teams don't change without the puck leaving the zone, but one might have expected more of a ripple effect. The most common way to change lines is to dump the puck and give the other team possession, albeit starting behind their own net. It does not appear that being able to breakout the Flying V gives you much of an edge.

This is a nice segue back to why we should care so much more about who is actually on for the faceoff and not just ice time afterward. Let's exaggerate and suppose that Henrik Sedin and Manny Malhotra spend the same amount of post-offensive-zone-faceoff time on the ice but Hank's is all first-shift time while Manny doesn't take a single offensive-zone faceoff. Sedin's ozone time will be extremely favorable, and Malhotra's only slightly so. In this extreme scenario we would need to adjust Sedin's stats a lot to take his type of ice time accurately into account. Being out there on the faceoff in front of their goalie is over 15 times as favorable as jumping on the ice after.

Conclusion: More To Come

When I thought of this method of separating out the ice time, I did a little fist pump. While I don't think this, or really any metric, will blow everything else away it seems like a good way to analyze zone starts and give us better insight into player value and coaching decisions. We will be using this and related methods a lot in the future, especially the coming weeks leading up to the season. Coming down the pipe is a new player metric to adjust for zone starts. We'll also do in-depth analysis on zone starts for the two teams that focus the most on them: the recently rivalrous Vancouver Canucks and Chicago Blackhawks. Are Vigneault and Quenneville outsmarting the rest of the league or is it a case of fancy coaching syndrome? If you ask nicely, we could probably do something similar for your favorite team, or even your favourite team.

Here is a link to the team data.