FENWICK'D
This time I’ve added in the effect of faceoffs, because for every additional own zone draw you take you expect to lose 0.6 on your Fenwick number.
The simple truthiness of what we are measuring here, which is shots at net, less the blocked ones, with a bit of context (faceoff zones) added in. We’re not mining through reams of NHL.com stats, we are just trying to measure what we see is important, that being having the puck in the right end of the rink and getting it on net. I’ll add in ‘shifts that end in an own zone draw’, and the opposite, for next time that I do this (get your tall glasses of vinegar ready now, prospect fans!) ;) One step at a time though, for the few that are into this sort of thing.
Anyhow, this is how it shakes out. If you run these sensibly corrected Fenwick numbers from the table below against the H2H icetimes of the linemates ... you're back where you started with at the raw numbers, which is exactly as it should be if the world is round.
CORSI CORRECTED FOR STARTING SHIFT
Corsi corrected for Starting Shift Location
As a general rule, a player’s Corsi number is a reasonably good indicator of his ability to drive territorial play at even strength.
Having said that, it’s easier for some players to accrue a good corsi number than others.
For example, a player that plays on a good team, shares the ice with good linemates, and plays against weak competition is greatly advantaged over a player that plays on a poor team, shares the ice with poor linemates, and plays tough minutes.
Another factor that influences Corsi is starting zone location at even strength. That is, a player that starts his shifts more frequently in the offensive zone will, on average, have a better Corsi number than a player that starts his shifts more frequently in the defensive zone.
The purpose of this post is to attempt to correct for this.
As reported by Vic Ferrari in this post, each extra starting Offensive Zone Faceoff a player takes at even strength is worth approximately 0.6 Fenwick, where Fenwick is equivalent to [SHOTS FOR + MISSED SHOTS FOR] – [SHOTS AGAINST + MISSED SHOTS AGAINST].
Of course, the correction factor for Corsi will necessarily be larger due to the inclusion of blocked shots.
A brief analysis indicates that, at the level of individual players, the ratio of Fenwick to Corsi is approximately 0.80.
Thus, in adjusting each player’s corsi to reflect starting zone location, I applied the following formula.{CORSI + [(STARTING D-ZONE SHIFTS – STARTING O-ZONE SHIFTS)* 0.8]}However, in order to give a more accurate representation of each player’s abilities, I thought it necessary to control for ice-time as well.Not having the EV ice-time handy, I merely used each player’s starting EV shift total as a proxy for EV ice-time.
Adjusted Corsi=
{CORSI + [(STARTING D-ZONE SHIFTS – STARTING O-ZONE SHIFTS)* 0.8]}*1000
[D-ZONE STARTING FACEOFFS+O-ZONE STARTING FACEOFFS+NEUTRAL ZONE STARTING FACEOFFS]
Essentially, it’s corsi adjusted for zone location, divided by total starting EV shifts, multiplied by 1000.
BALANCED CORSI
I’ll describe a new balanced Corsi metric that compares a player’s Corsi score to that of peers with similar usage.
It’s much harder for a player who’s starting in the defensive zone all the time and playing against tough competition to have a positive Corsi score. So in the spirit of the balanced zone shift analysis, let’s look at what happens if we group players by their offensive zone start percentage and look at whether they control shots better than their similarly-used peers.
Basically what I did for each player; I took the 100 closest comparisons in usage (50 above and 50 below) and averaged their results to see what I should expect from someone with that role. As you’d imagine, we expect better results from people put in easier spots
From here, we can compare people’s performance to that of their peers.
Relative Corsi is a measure of how much a player elevates his team – you compare how the team does with him on the ice to how they do with him off the ice, and give him credit for the difference. In theory, this should dramatically reduce the impact of his teammate’s skill.
If relative Corsi is a measure of how much a player elevates his team, it stands to reason that it would be harder to dramatically elevate an elite team. Ex. It is much easier for a +1% player to elevate a 45% team to a 46% team, but expecting him to raise a 55% team to a 56% is unreasonable.
ADJUSTING FOR ZONE STARTS
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
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.
EXPECTED SF & SA FOR DIFFERENT ZONE STARTS
A player’s Zone Start% has a pretty good correlation with their Corsi Number at 5v5, with a sizable chunk of the variance due to player skill and luck. It also has a good correlation with shots-for/against differential at 5v5.
It’s much harder for a player who’s starting in the defensive zone all the time and playing against tough competition to have a positive Corsi score.
The most widely-used approach assumes that each offensive zone start is worth an extra 0.8 shots on the player’s Corsi shot differential (which is like +/- but using shots rather than goals, and correlates strongly to puck possession and offensive zone time).
Using that approach, an average player who started in the offensive zone as often as Daniel Sedin did last year would have been expected to see his team get 56.1% of the shots, while an average player put in Manny Malhotra’s skates would see his team get just 39.2% of the shots.
With differences that large, we can understand why it would be important to correct for a player’s zone starts.
ZONE START ADJUSTED CORSI
how zone starts might affect their on-ice shot differential.
The Beginnings
The first look at quantifying the impact of attack zone faceoffs came from Vic Ferrari looking at how many goals follow within a minute of a 5v5 faceoff.
About 2/3 of the goals were scored by the team that took the draw in the offensive end
This strongly supported the notion that a player’s usage could have a significant impact on his results and opened the door to trying to quantify how large that impact could be.
In his next post on the topic, Vic reported that each defensive zone draw decreased a player’s Fenwick shot differential by about 0.6 shots
Likens reasoned that since blocked shots represent about a quarter of the Corsi events, the 0.6 figure calculated for Fenwick (which excludes blocked shots) should probably swell to 0.8 for Corsi (which includes blocked shots), and he published a league-wide adjusted Corsi chart.
It has always bugged me that we don’t really know where the 0.6 figure came from; Vic just asserted it without any explanation. My guess was that he used the same methodology as in his previous post looking at goal rates, counting how many shots were taken between the faceoff and the player leaving the ice and averaging across each player-faceoff, but we don’t really know.
Alternative Methods
Although the Ferrari/Likens approach is by far the most widespread, a number of other investigators have gotten involved.
Dirk Hoag used a regression of season totals, looking to see how many more shots went to the players who often started in the offensive zone. He found that each additional offensive zone start correlated with an extra 1.1 Corsi shots. I could imagine that this approach might overstate things slightly by assuming more causation than actually exists – since more teams deploy their top-line in the offensive zone than in the defensive zone, the skill of those top-line players will make offensive zone starts look more beneficial than they really are. So this estimate might be a bit high, but serves as a reasonable upper bound.
The same issue plagues my own look at comparing players to similarly-deployed peers. In that approach, I looked at the Corsi or Corsi Rel of players with similar offensive zone starts as an empirical baseline for what we should expect of someone in that role. The result is similar in many ways to the direct correction, but like the regression approach it cannot distinguish correlation from causation
JaredL observed that teams get about 40 shot attempts per 60 minutes between an offensive zone faceoff and the first line change, and the advantage almost completely dissipates by the time a single player leaves the ice. He used this to make corrections for how a player was used, with less influence from usage than was observed in the other approaches. A reasonable approximation to his corrections had a player’s Corsi/60 going up by about 0.18 shots for every percentage increase in offensive zone starts.
This approach reduces the concern about player usage considerably.
There could still be some skew if the best players take a lot of offensive zone faceoffs, since they would tend to outplay the lesser opponents who take the defensive zone faceoff, and some of their superior skill would be lumped into the assessment of how valuable an offensive zone faceoff is. However, the skew would be confined to the fraction of a shot extra that they generate in that brief period, whereas using their whole-game statistics as Dirk and I did results in their superior play throughout the game all being factored into the importance of the faceoff
Plugging through some arithmetic leads us to the conclusion that each extra offensive zone faceoff was worth about 0.25 shot attempts
These numbers are much less than the figure of 0.8 shot attempts that grew from Vic’s assessment. I had guessed that Vic’s method was similar to Jared’s, but the disparity in their results leaves me uncertain what Vic actually did. Let’s look at some other methods for assessing the impact.
David Johnson showed that the impact on Fenwick can be almost completely negated if you throw out the first ten seconds after an attack zone faceoff.
This correction factor against the number of extra offensive zone starts the player had gives a slope of 0.35 shot attempts per faceoff,
Yet another approach comes from our zone entry data. I have not published data on faceoffs since a midseason review, but one of the things zone entry data lets us do is count how many shots come between the faceoff and the first time the defense clears the puck.
So an offensive zone faceoff increases your Fenwick For by about 0.31-0.32 shots, which means it probably increases your Corsi For by about 0.4 shots.
Where Do We Go From Here
Overall, no two estimates are in direct agreement, but the analyses that are known to derive from looking directly at the outcomes immediately following a faceoff converge in the range of 0.25 to 0.4 Corsi shots per faceoff – one-third to one-half of the figure in widespread use.
It is very likely that we have been overestimating the importance of faceoffs; they still represent a significant correction on shot differential, but perhaps not as large as has been previously assumed.
USAGE ADJUSTED POSSESSION RATES
Players who improve their team’s share of on-ice Corsi events or “drive play” are rationally the ones who pre-eminently increase a team’s chances for success.
Unfortunately, raw Corsi% on its own doesn’t necessarily identify a player’s true ability to drive puck possession. Other factors like the quality of a skater’s linemates, the quality of his competition and zone start percentages have a measurable impact on raw possession rates. We often describe these factors together as player usage or deployment.
In order to isolate skill we need to strip away the effects of usage.
There are biases that exist between these factors and possession rates that would otherwise skew the coefficients determined via regression analysis.
Some of these biases include:
· Better possession players tend to receive more offensive zone starts (inflates the weighting of zone starts in the regression analysis)
· Better possession players tend to face tougher opposition (this deflates and even reverses the perceived relationship between quality of competition and possession rates)
· Better possession players tend to play with better linemates (inflates the weighting of teammate metrics in the regression analysis)
Notice the significantly increased importance of quality of competition, slightly decreased coefficient for quality of team-mates and the decreased weighting of zone starts using my method. This is the result of removing the biases identified earlier.
Also notice how an incremental change in TMCF60 is much more significant than a change in OppCA60 when predicting observable CF60. However, when predicting CA60, the coefficient for OppCF60 supersedes that of TMCA60. What this is likely showing is that players have more control over shot generation than shot suppression
Opposition metrics even out very quickly as sample size increases whereas team-mate metrics maintain a much higher level of variance.
Usage Adjusted Corsi%. Essentially the Corsi% a player would be expected to achieve in a vacuum.
OPEN CORSI
(Dec 2014)
Open Corsi is intend to look at the possession of hockey teams when they are no longer under the influence of a faceoff win or loss in each of the varying zones.
A number of techniques were used, all of which suggested that play had entered “open corsi” around the same time.
The 37 seconds mark appears to be when the trendline has reached 50%.
For an Offensive Zone Win this is 25 seconds. League average corsi for this amount of time is 83%. As the reciprocal holds true, the effects of a defensive zone loss suggest a league average corsi of 17% for 25 seconds.
This suggests the effects of the faceoff wipe out after 17 seconds and the league average is 56%.
The reciprocal is true for Defensive Zone Wins at 44% for 17 seconds.
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