Zone Starts, Corsi, and the Percentages

Matthew Coller has an interesting article on Puck Prospectus about Shea Weber and his poor Relative Corsi. His conclusion was that Weber’s poor Relative Corsi is largely due to his playing time with Paul Gaustad in which he posted a very poor CF% along with having a very heavy defensive zone start. His conclusion was that Weber’s poor Corsi with Gaustad is in a significant way caused by the heavy defensive zone start bias. This is a case of correlation not causation as I outlined in the comment section of that article. I recommend you take the time to read both the article and my comments because they are worthwhile reads.

My issue with the article is that I don’t believe that zone starts dramatically impact a players overall statistics as I explained here. I just haven’t seen any convincing evidence that zone starts would change a players CF% much more than 1-2% and for most players considering zone starts in player evaluation is not important. The relationship that Coller observed is important though because there is a clear relationship between zone starts and CF%. The relationship isn’t causal though. What the zone starts signify is a style of play. Players with a heavy defensive zone start bias are likely asked by the coach to play a defense first game and in many cases generating offense is not an important issue. The result is often a relatively minor deviation in a players CA/60 but a major deviation in a players CF/60 from the overall team stats. Let’s look at Paul Gaustad as an example. Gaustad has a OZone% of just 12.2% which means he has over seven times as many defensive zone starts as offensive zone starts. Here are how his Corsi stats compare to Nashville’s overall stats in 5v5close situations this season.

CF60 CA60
Nashville 60.0 53.0
Gaustad 38.8 51.9

As you can see, despite a heavy defensive zone start bias when Gaustad is on the ice the Predators actually gives up slightly fewer shots attempts against than they do overall but it is pretty close. Offensively though, when Gaustad is on the ice there is significantly less offense generated. If zone starts are the explanation one would probably expect there to be more balance between more shot attempts against and fewer shot attempts for but this is not the case. The likely explanation is that when Gaustad is on the ice the team is largely focused on not giving up a goal rather than generating offense. I suspect they do this largely by not giving up the puck and maintaining puck possession when you get possession. When you take a shot you are actually giving up control of the puck. You may regain control but so might the other team. If you are focused on preventing goals the best way to do that is to not give up the puck.

Lets take a quick look at Filip Forsberg who has played with a heavy offensive zone start bias indicating he is probably used in more offensive situations.

CF60 CA60
Nashville 60.0 53.0
Forsberg 69.4 53.2

Forsberg’s CA/60 is actually very similar to the team average and not all that different from Gaustad’s (higher actually) but his CF/60 is almost 80% higher. Again, this is unlikely to be zone start influenced but rather some combination of talent and playing style.

So, it seems that Ozone% is likely an indication of style of play, or at least an indicator of the main objective of the players on the ice, and we have seen that this can have a major impact on shot attempt rates.  I want to take this discussion one step further by looking at whether players can influence shooting/save percentages based on their style of play. Since shooting/save percentages are highly variable over small sample sizes such as the number of shots for/against taken while a player is on the ice during a single season we need to find ways to work around the randomness associated with the percentages. One way to do this is to group players based on similar attributes and take a group average. One of my favourite hockey analytics articles was this one written by Tom Awad in which he grouped similar players based on ice time and in doing so he found that shooting better than your opponent is a major factor in what makes good players good. In this case I have grouped players based on their OZone% and then took a group average Sh%RelTM and Sv%RelTM during 5v5close situations.

Ozone% Sh% RelTM Sv% RelTM
<30% -0.92% 1.26%
30-35% -0.43% 0.59%
35-40% -0.38% 0.80%
40-45% -0.18% -0.03%
45-50% -0.07% -0.07%
50-55% 0.48% 0.10%
55-60% 0.50% -0.16%
60-65% 0.52% 0.36%
65+% 0.24% -1.07%

Graphically here is what we get.

ZoneStarts_vs_Percentages

As you can see, there is a fairly strong relationship between zone starts and Sh%RelTM and Sv%RelTM. Players with a heavy defensive zone start will generally have a positive impact on his teams save percentage and a negative impact on his teams shooting percentage. Conversely players with a heavier offensive zone start bias will generally have a positive impact on his teams shooting percentage and negative impact on his teams save percentage. Some of this is likely player talent but a significant portion of it is likely driven by style of play as we saw with Corsi. It is next to impossible to identify these relationships by looking at individual players statistics because of the small sample sizes but when we group similar players together the relationship becomes clear and is a relatively strong one.

For perspective, Paul Gaustad’s OZone% over past three seasons with Nashville is 21.2% while his Sh%RelTM is -1.4 and his Sv%RelTM is +1.9.

The major takeaways I hope people get from this article are the following:

  1. Zone starts really do not have a significant impact on a players statistics.
  2. Zone starts can be an indicator of a players style of play and style of play can have a major influence on a players statistics (see my Coaching/Corsi dilemma article for more evidence of how style of play impacts Corsi).
  3. Players are able to, through talent and/or playing style, influence save and shooting percentages.
  4. Finding trends in shooting/save percentages can be difficult due to small sample size issues but that does not mean they do not exist. Hockey is a complex sport to analyze but being creative in grouping similar players can allow you to pull out valuable information that you otherwise could not.