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.

On Luck, Skill and Sample Size in Shooting Percentage

The roles of skill and luck in shooting are an important and often misunderstood part of hockey analysis. This is particularly true when analytical and traditional fans get together. A typical discussion might go something like:

A: Steven Stamkos got 91 points with 45 goals at the age of 21. He's definitely getting 100 points next year, and could easily score 50 goals a season as he improves.
B: Yeah, but he was lucky to make 16.5% of his shots. That's not sustainable and his numbers will probably go down next year, even if he does actually improve.
A: So shooting is all luck? You clearly know nothing about hockey and should actually watch some games instead of just sitting at your computer all day coming up with fancy stats that don't mean anything.

Turns out both guys have a point. Stamkos should probably expect his stats to drop next season because, in addition to goal numbers trending downward for the league as a whole, he's probably not burying 16.5% of his shots. On the other hand, I person B probably should watch more hockey, it is a great game.

A lot of the confusion comes from incorrect either/or thinking. Scoring on a high percentage of your shots is a result of both luck and skill. I'm not just putting down the traditional fans here. Those of us in the analytical community, myself included, are prone to bad thinking as well. While most people ignore or underrate the importance of luck in hockey in general and shooting in particular, we tend to go too far the other way, chalking everything up to luck and ignoring the skill aspect.

Shooting for a high percentage is skill based. Two of our favorite writers, JLikens of objectivenhl fame and Gabe Desjardins from BTN and arcticicehockey have written several articles on the subject.

In case their articles are not convincing enough, let's consider two of the best players in the game: Henrik Sedin and Sidney Crosby. While not necessarily known as snipers, these players have all the skills that one might think lead to their team putting a high percentage of shots in the net. Both have elite vision, passing ability, hands, positioning and, in one case, telepathy. We would all expect their teams to have better shooting percentages when they are on the ice than when they are sitting on the bench or worse. The numbers bear this out. Here is a chart with their teams' performances at even strength with both goalies in net from the last four seasons combined. The stats are courtesy of noted Driving Play reader Vic Ferrari's timeonice scripts, which you can find information on how to use here.

Team	Goals	Shots On Goal	Shooting %
Penguins, Crosby on Ice	236	2160	10.9%
Penguins, Crosby off Ice	414	5347	7.7%
Canucks, Henrik on Ice	273	2624	10.4%
Canucks, Henrik off Ice	359	4709	7.6%

As you can see, the Pens with Crosby shot 3.2 percentage points higher than they did without him. While some of it may be variance, with the number of shots they took with him on, that's a difference of 69 goals or more than 17 goals per season due to better shooting. The Canucks shot 2.8 points higher with Henrik on the ice, a difference of over 73 goals, more than 18 per season, when you consider how many shots they took with him on. For the statistically minded, these shooting-percentage differences are very very very significant. To give you an idea, it varies field to field but the most common benchmark is for there to be less than a 5% chance of results this extreme, or more so, due to variance alone. That's a 1-in-20 chance. For Crosby, there is a 0.00045% chance, or less than 1 in 222,000. For Hank there is a 0.00235% chance of results that extreme due to randomness alone - less likely than 1 in 42,000. Again, 1 in 20 is the usual mark. The data confirm what anyone would guess from watching a few games - Henrik Sedin and Sidney Crosby help their teams shoot better. (Note: if you are a hater and/or think that it's the likes of Alex Burrows and Pascal Dupuis who are driving these results, feel free to be wrong. The point of this is to provide evidence of shooting skill and clearly someone has it when these two are on the ice.)

Let's now look at the role of luck on shooting percentage. To do this, I will run simulations comparing the results of a typical team that shoots well and one that does poorly. In this article on objectivenhl, which is worthy of being linked again, JLikens finds that the average team shoots at an 8.1% clip 5-on-5, with a standard deviation of 0.48%. Going by this, a team that is good at shooting, let's say 7th or 8th best in the league, would have a true 5-on-5 shooting percentage of something like 8.42%. On the other hand, a team that is bad at shooting, say 7th or 8th worst in the league, would be expected to score on about 7.78% of their shots.

Let's see how things shake out. Below is a chart giving the results of 10,000 simulations for various numbers of shots where team A has a true shooting percentage of 8.42% and team B shoots at 7.78%. The first two columns tell you the given time period and number of shots for each team. The next three columns tell you how often the team good at shooting outshot the bad (column 3), the bad team outshot the good (4) and how often they had an equal shooting percentage (5). The last two columns give what percent of the time someone looking at the data, and not knowing the underlying percentages, would get statistical signficance at the 5% level. Notice that in the last column, the statistical test would reveal that B is significantly better at shooting than A despite their shooting skill actually being over half a percentage point worse.

Time period	Number of Shots	A scores more	B scores more	Goals scored equal	A > B Significant	B > A significant
One Period	8	31.9%	28.7%	39.4%	1.3%	1.1%
One Game	24	42.6%	36%	21.4%	6%	4.5%
1/4 Season	500	62.3%	33.2%	4.5%	10.5%	2.2%
1/2 Season	1,000	68.6%	28.5%	2.8%	13.4%	1.4%
1 Season	2,000	76.4%	21.7%	1.9%	17.9%	0.8%
2 Seasons	4,000	85%	14%	1%	27.8%	0.2%
3 Seasons	6,000	90.1%	9.3%	0.6%	36.1%	0.1%
4 Seasons	8,000	93.6%	6%	0.4%	43.7%	0.1%
5 Seasons	10,000	95.6%	4.1%	0.3%	50.3%	0%

You probably didn't find the results surprising for that first row, representing a period of play. The most common outcome, happening about 40% of the time, is that the two teams remain tied, most often at 0. The team that shoots better due to getting higher-quality shots, hitting the corners better and so on is only slightly more likely to be the one that is ahead if you know that one of them is. Less than 32% of the time will the better team find themselves ahead after a period in which both get the league average 8 shots, whereas they'll be behind almost 29% of the time.

Lower on the chart it gets more troubling, especially for us bloggers. The most common sample point for analysis is half a season. Generally the best way to study the persistence of something is to split the season in half, typically first half vs second half or even-and-odd numbered games, and compare the two samples. This works well because teams should be the same or very similar. If you study something over multiple seasons you aren't getting the same teams every year due to player and coaching changes. In half a season, the team near the top in shooting skill has only about a 2 in 3 chance of outscoring the team near the bottom with the same number of shots. There is also little chance, roughly 13%, of finding that the better team is significantly better at shooting if you were looking at the data. Even over a whole season of shooting data, there is a 1 in 4 chance that the worse team will get better results. It isn't until we get several years worth of shooting results that it tilts heavily in favor of the better shooting team and that's not realistic because teams change so much each offseason and the simulations assumed the same percentage each season.

As you can see, luck plays a huge role for all reasonable sample sizes. This is the fundamental reason why shooting stats are better than goals. Luck is less of a factor for number of shots taken than number of shots made, so they are more reliable indicators of skill over samples of a season or less. If over a season there is a 1 in 4 chance that a good-shooting team is outshot by a bad-shooting team then it's tough to say that a team's results are due to skill and not just random luck.

In a future installment I will look at how persistence is affected by sample size.