Sep 212013
 

In a series of recent posts at mc79hockey.com, Tyler Dellow discussed a new concept (to me anyway) that he called ‘open play’ hockey. In a post on “The Theory of the Application of Corsi%” he wrote:

I have my own calculation that I do of what I call an open play Corsi%. I wipe out the faceoff effects based on some math that I’ve done as to how long they persist and look just at what happened during the time in which there wasn’t a faceoff effect.

This sounds strangely similar to my zone start adjusted statistics where I eliminate the first 10 seconds after an offensive or defensive zone face off as I have found that beyond that the effect of the face off is largely dissipated. I was curious as to how in fact these were calculated and it seemed I wasn’t the only one.

As far as I can tell, the tweet went unanswered.

In a followup post “New Metrics I” the concept of open play hockey was mentioned again.

I’m calculating what I call an open play Corsi% – basically, I knock out the stuff after faceoffs and then the stuff I’m left with, theoretically, doesn’t have any faceoff effects. It’s just guys playing hockey.

In the comments I asked if he could define more precisely what “stuff after faceoffs” meant but the question went unanswered. Dellow has subsequently referenced open play hockey in his New Metrics 2 post and in a follow up post answering questions about these new metrics. What still hasn’t been explained though is how he actually determines “open play” hockey.

Doing a search on Dellow’s website for “open play” we find that this concept has been mentions a couple times previously. In a post titled Big Oilers Data IX: Neutral Zone Faceoff Wins we might get an answer to exactly what ‘open play’ actually is.

As those of you who have been reading this series as I’ve gone along will be aware, I’ve been kind of looking at things on the basis of eight different kinds of 5v5 shift: Open Play (no faceoff during shift), six types of shift with one faceoff (OZ+, OZ-, NZ+, NZ-, DZ+, DZ-) and multi-faceoff shifts. The cool thing with seven of those types of shift is that I can get a benchmark of a type by looking at how the Oilers opposition did in the same situation.

So, as best I can determine, open play is basically any shift that doesn’t have  a face off.

The next question I’d like to answer is, how different is ‘open play’ from my 10 second adjustment. This is an interesting question because I have had this debate with many people that suggest that my 10 second adjustment isn’t adequate and that zone start effects are far more significant than my 10 second adjustment suggests. I have even had debates with Tyler Dellow about this (See here, here and here) so I am really curious as to what impact open play hockey has on a players statistics. Unfortunately, I don’t have much ‘open play’ data to go with but in the posts that Dellow has discussed it he has mentioned a few players open play corsi% statistics so I will work with what I have. Here is a comparison of Dellow’s open play stats and my 10-second zone start adjusted stats.

Player Year OpenPlay Corsi% ZSAdj CF% OZ% DZ%
Fraser 2012-13 50.8% 50.4% 40.1 25.3
Fraser 2011-12 52.8% 53.2% 31.1 35.5
Fraser 2010-11 45.2% 42.2% 30.4 35.1
Fraser 2009-10 59.2% 57.7% 29.2 40.5
Fraser 2008-09 51.8% 52.6% 30.9 37
O’Sullivan 2011-12 44.3% 42.0% 35.7 26
O’Sullivan 2010-11 45.2% 45.6% 29.4 34
O’Sullivan 2009-10 43.9% 44.1% 31 32.2
O’Sullivan 2007-08 45.5% 46.5% 29.9 29.4
Eager 2012-13 34.4% 35.6% 40.5 32.8
Eager 2011-12 42.0% 43.0% 29.6 30.7
Eager 2009-10 54.4% 54.5% 18.3 39.1
Eager 2008-09 52.9% 53.9% 22.6 37.4

I have incldued OZ% and DZ% which is the percentage of face offs (including neutral zone face offs) that the player had in the offensive and defensive zone. These statistics along with ZSAdj CF% can be found on stats.hockeyanalysis.com.

If it isn’t obvious to you that there isn’t much difference between the two, let me make it more obvious by looking at this in graphical form.

OpenPlayvsZSAdjustedCorsiPct

That’s a pretty tight correlation and we are dealing with some player seasons that have had fairly significant zone start biases. Ben Eager had a very significant defensive zone start bias in both 2008-09 and 2009-10 but a sizable offensive zone bias in 2012-13. Colin Fraser had sizable defensive zone bias in 2009-10 but a sizable offensive zone bias in 2012-13. Patrick O’Sullivan had a heavy offensive zone bias in 2011-12. There is no compelling evidence here that ‘open play’ statistics are any more reliable or better than my 10-second zone start adjusted data. There is essentially no difference which reaffirms to me (yet again) that my 10-second adjustment is a perfectly reasonable method to adjust for zone starts which ultimately tells us that zone starts do not have a huge impact on a players statistics. Certainly not anywhere close to what many once believed, including Dellow himself. Any impact you see is more likely due to the quality of players one plays with if one gets a significant number of defensive zone starts.

Update: For Tyler Dellow’s response, or lack there of, read this.  Best I can tell is he doesn’t want to publicly say what open play is or how it shows zone starts affect players stats beyond my 10-second adjustment because I might interpret what he says as thinking I am right despite him clearly thinking the evidence proves me wrong. I guess rather than have me make a fool of myself by misinterpreting his results so I can believe I am right he is going to withhold the evidence from everyone. I feel so touched that Dellow would choose to save me from such embarrassment as misinterpreting results over letting everyone know the real effect of zone starts have on a players statistics and why ‘open play’ is what we should be using to negate the effect of zone starts. Truthfully though, I am willing to take the risk  of embarrassing myself if it furthers our knowledge of hockey statistics.

 

Related Articles:

Face offs and zone starts, is one more important than the other?

Tips for using Hockey Fancy Stats

 

 

Sep 142013
 

A while back I came up with a stat which at the time I called LT Index which is essentially the percentage of a players teams ice time when leading that the player is on the ice for divided by the percentage of a players teams ice time when trailing that the player is on the ice for (in 5v5 situations and only in games in which the player played). LT Index standing for Leading-Trailing Index. I have decided to rename this statistic to Usage Ratio since it gives us an indication of whether players are used more in defensive situations (i.e. leading and protecting a lead and thus a Usage Ratio above 1.00) or in offensive situations (i.e. when trailing and in need of a goal and thus a Usage Ratio less than 1.00). I think it does a pretty good job of identifying how a player is used.

I then compared players Usage Index to their 5v5 tied statistics using the theory that a player being used in a defensive role when leading/trailing is more likely to be used in a defensive role when the game is tied. This is also an out of sample comparison (which is always a nice thing to be able to do) since we are using leading/trailing situations to identify offensive vs defensive players and then comparing to 5v5 tied situations that in no way overlap the leading or trailing data.

Let’s start by looking at forwards using data over the last 3 seasons and including all forwards with >500 minutes of 5v5 tied ice time. The following charts compare Usage Ratio with 5v5 Tied CF%, CF60 and CA60.

UsageRatiovsCFPct

UsageRatiovsCF60

UsageRatiovsCA60

Usage Ratio is on the horizontal axis with more defensive players to the right and offensive players to the left.

Usage Ratio has some correlation with CF% but that correlation is solely due to it’s connection with generating shot attempts for and not for restricting shot attempts against. Players we identify as offensive players via the Usage Ratio statistic do in fact generate more shots but players we identify as defensive players do not suppress opposition shots any. In fact, Usage Ratio and 5v5 tied CA60 is as uncorrelated as you can possibly get. One may attempt to say this is because those defensive players are playing against offensive players (i.e. tough QoC) and that is why but if this were the case then those offensive players would be playing against defensive players (i.e. tough defensive QoC) and thus should see their shot attempts suppressed as well. We don’t observe that though. It just seems that players used as defensive players are no better at suppressing shot attempts against than offensive players but are, as expected, worse at generating shot attempts for.

Before we move on to defensemen let’s take a look at how Usage Ratio compares with shooting percentage and GF60.

UsageRatiovsShPct

 

UsageRatiovsGF60

As seen with CF60, Usage Ratio is correlated with both shooting percentage and GF60 and the correlation with GF60 is stronger than with CF60. Note that the sample size for 3 seasons (or 2 1/2 actually) of 5v5 tied data is about the same as the sample size for one season of 5v5 data (players in this study have between 500 and 1300 5v5 tied minutes which is roughly equivalent of how many 5v5 minutes forwards play over the course of one full season).

FYI, the dot up at the top with the GF60 above 5 is Sidney Crosby (yeah, he is in a league of his own offensively) and the dot to the far right (heavy defensive usage) is Adam Hall.

Now let’s take a look at defensemen.

UsageRatiovsCFPctDefensemen

UsageRatiovsCF60Defensemen

UsageRatiovsCA60Defensemen

There really isn’t much going on here and how a defenseman is used really does’t tell us much at all about their 5v5 stats (only marginal correlation to CF60). As with forwards, defensemen that we identify as being used in a defensive are not any better at reducing shots against than defensemen we identify as being used in an offensive manner.

To summarize the above, players who get more minutes when playing catch up are in fact better offensive players, particularly when looking at forwards but players who get more minutes when protecting a lead are not necessarily any better defensively. We do know that there are better defensive players (the range of CA60 among forwards is similar to the range of CF60 so if there is offensive talent there is likely defensive talent too), and yet coaches aren’t playing these defensive players when protecting a lead. Coaches in general just don’t know who their good defensive players are.

Still not sold on this? Well, let’s compare 5v5 defensive zone start percentage (percentage of face offs taken in the defensive zone) to CF60 and CA60 (for forwards) in 5v5 tied situations.

DefensiveFOPctvsCF60

Percentage of face offs in the defensive zone is on the horizontal axis and CF60 is on the vertical axis. This chart is telling us that the fewer defensive zone face offs a forward gets, and thus likely more offensive face offs, the more shot attempts for they produce. In short, players who get offensive zone starts get more shot attempts.

DefensiveFOPctvsCA60

The opposite is not true though. Players who get more defensive face offs don’t give up any more or less shots than their low defensive zone face off counterparts. This tells me that if there is any connection between zone starts and CF% it is solely due to the fact that players who get offensive zone starts are better offensive players and not because players who get defensive zone starts are better defensive players.

You might again be saying to yourself ‘the players who are getting the defensive zone starts they are playing against better offensive players so doesn’t make sense that their CA60 is inflated above their talent levels (which presumably is better than average defensively)?  This might be true, but if zone starts significantly impacted performance (as would be the case if that last statement were true), either directly or indirectly because zone starts are linked to QoC, then there should be more symmetry between the charts. There isn’t though. Let’s look at what these two charts tell us:

  1. The first chart tells us that players who get offensive zone starts generate more shot attempts.
  2. The second chart tells us that players who get defensive zone starts don’t give up more shots attempts against.

If zone starts were a major factor in results, those two statements don’t jive. How can one side of the ledger show an advantage and the other side of the ledger be neutral? The way those statements can work in conjunction with each other is if zone starts don’t significantly impact results which is what I believe (and have observed before).

But, if zone starts do not significantly impact results, then the results we see in the two charts above are driven by the players talent levels. Knowing that we once again can observe that coaches are doing a decent job of identifying offensive players to start in the offensive zone but are doing a poor job at identifying defensive players to play in the defensive zone.

All of this is to say, NHL coaches generally do a poor job at identifying their best defensive players so if you think that guy who is getting all those defensive zone starts (aka ‘tough minutes’) are more likely to be defensive wizards, think again. They may not be.

 

May 012013
 

I brought this issue up on twitter today because it got me thinking. Many hockey analytics dismiss face off winning % as a skill that has much value but many of the same people also claim that zone starts can have a significant impact on a players statistics. I haven’t really delved into the statistics to investigate this, but here is what I am wondering.  Consider the following two players:

Player 1: Team wins 50% of face offs when he is on the ice and he starts in the offensive zone 55% of the time.

Player 2: Team wins 55% of face offs when he is on the ice but he has neutral zone starts.

Given 1000 zone face offs the following will occur:

Player 1 Player 2
Win Faceoff in OZone 275 275
Lose Faceoff in Ozone 275 225
Win Faceoff in DZone 225 275
Lose Faceoff in Dzone 225 225

Both of these players will win the same number of offensive zone face offs and lose the same number of defensive zone face offs which are the situations that intuitively should have the greatest impacts on a players statistcs. So, if Player 1 is going to be more significantly impacted by his zone starts than player 2 is impacted by his face off win % losing face offs in the offensive zone must still have a significant positive impact on the players statistics and winning face offs in the defensive zone must must still have a significant negative impact on the players statistics. If this is not the case then being able to win face offs should be more or less equivalent in importance to zone starts (and this is without considering any benefit of winning neutral zone face offs).

Now, I realize that there is a greater variance in zone start deployment than face off winning percentage, but if a 55% face off percentage is roughly equal to a 55% offensive zone start deployment and a 55% face off win% has a relatively little impact on a players statistics then a 70% zone start deployment would have a relatively little impact on the players statistics times four which is still probably relatively little.

I hope to be able to investigate this further but on the surface it seems that if face off win% is of relatively little importance it is supporting of my claim that zone starts have relatively little impact on a players statistics.

 

Apr 192013
 

Tyler Dellow has a post at mc79hockey.com looking at zone starts and defensemen and if you read it the clear conclusion is that zone starts seem to matter quite a bit. In the third chart you can see that defensemen who get the most extreme defensive zone starts have an average corsi% of 44.7% while the average corsi% for defensemen with the most extreme offensive zone starts is 53.3%. This would seem to indicate that for defensemen zone starts can impact your corsi% anywhere from -5.3% to +3.3%. This is far more significant than I have estimated myself using a different methodology so I pondered that part of the reason for this is that when you start in the defensive zone you are playing with weaker quality of teammates than when you start in the offensive zone. My reasoning is that players that get used primarily in the defensive zone are often weak offensive players as if you are a good offensive player you will be given offensive opportunities. I wanted to explore this concept further and that is what I present to you here.

Unlike Tyler Dellow I used forwards in my analysis but it is unlikely that this will have a major impact in the analysis as forwards and defensemen are always on the ice together. One difference between my analysis and Tyler Dellow’s is I used data from stats.hockeyanalysis.com where as Tyler used stats from behindthenet.ca. Behindthenet.ca includes goalie pulled situations in their data and this has the potential to greatly emphasize the impact of zone starts. I feel it is important to eliminate this factor so I have it removed from the data. I also only used 2011-12 data but that shouldn’t have a major impact on the results.

So, my theory is that players who start in the defensive zone are weaker players overall. The challenge to this is that players who start with players that start frequently in the defensive zone likely start frequently in the defensive zone themselves and thus their stats are subject to zone start effects so if they have weak stats we don’t know whether they are due to the zone starts or because they are weak players. My solution was to look at the players zone start adjusted stats that I have on stats.hockeyanalysis.com. These stats ignore the first 10 seconds after a zone face off as it has been shown that the majority of the benefit/penalty of a zone face off has largely dissipated after 10 seconds. I understand that it may seem weird to use zone start adjusted data in a study that attempts to estimate the impact of zone starts but I don’t know what else to do.

I want to also point out that I will be using ZS adjusted FF% team mates when the team mates are not on the ice with the player and this may also mitigate the ZS impact on the teammates stats. My reasoning is, if a player has an extensive number of defensvie zone starts, it is quite possible that when his team mates are not playing with him their zone starts are more neutral or maybe even offensive zone biased. It if there ever was a way to get a non-zone start impacted FF% to use as a QoT metric this is probably the best we can do.

Ok, so what I did was compare a players 5v5 FF% (fenwick %) and zone start adjusted 5v5 TMFF% (zone start adjusted FF% of teammates when team mates are not playing with him) and came up with the following:

FFPct_vs_TMFFPct_by_ZS

As you can see, TMFF% does seem to vary across zone start profiles as I had hypothesized though to a lesser extent than the players zone start influenced FF% which is to be expected. So, if we subtract TMFF% from FF% we get the following chart:

FFPct-TMFFPct_by_ZS

This chart indicates that the zone start impact on forwards once adjusted for quality of teammates (as best we can) ranges from -2.5% to +2.15% which is significantly lower than the -5.3% to +3.3% estimate that Tyler Dellow came up with for defensemen without adjusting for quality of teammates and using goalie pulled situations included in the data. That said, this is still more significant than my own estimates when I compared 5v5 data to 5v5 data with the first 10 seconds after a zone start ignored. When I did that I calculated the impact on H. Sedin’s FF% due to his heavy offensive zone starts to be +1.4% to his FF% and considered this an upper bound. To investigate this further I plotted the average difference between 5v5 FF% and my 5v5 zone start adjusted FF% and I get the following:

FFPct-ZSAdjFFPct_by_ZS

The above is an estimate of the average impact of zone starts using my zone start adjustment methodology which ignores the first 10 seconds after a zone face off. This is significantly lower than either of the previous 2 estimates as we can see in this summary table:

Methodology ZS Impact Estimate
T. Dellow’s estimate for defensemen -5.3% to +3.3%
My TM Adjusted estimate for forwards -2.5% to +2.15%
My 10 second after Zone FO adjustment for forwards -0.5% to +0.41%

I am pretty sure none of what I have said above will put an end to the impact of zone starts on a players statistics debate but at the very least I hope it sheds some light on some of the issues involved. For me personally, I have the most confidence in my zone start adjustment method which removes the 10 seconds after a zone face off. My reasoning is studies have shown that the effect of a zone face off is largely eliminated within the first 10 seconds (see here or here) and also because it is the only methodology that compares a player to himself under similar playing conditions (i.e. same season, almost identical QoT, QoC and situation profiles) eliminating most of the opportunity for confounding factors to influence the results. If this is the case, the impact of zone starts on a players stats is fairly small to the point of being almost negligible for the majority of players.

 

Mar 142013
 

I often see people using zone starts and/or quality of competition as a way to justify any players unexpectedly poor or unexpectedly good play. Player X has a bad goal or corsi ratio because he plays all the tough minutes (i.e. the defensive zone starts and against the oppositions best lines). I am pretty certain that quality of competition is vastly over emphasized (everyone plays against everyone to some extent) and is vastly overshadowed by individual skill and quality of teammates, and I think zone starts do as well.

Eric Tulsky at NHL Numbers.com posted a good review of the research into the zone start effects on corsi statistics and I recommend people give that a read. I want to look into the issue a little further though. Most of the attempts to identify the impact of zone starts on a players stats have been inferred by looking at the league-wide correlations or by actual counting of how many shots are taken after a zone face off. Both of these have their faults. As Eric Tulsky pointed out, taking a correlation of every players corsi with their zone start stats doesn’t take into account that it is the top line players that usually get the offensive zone starts and thus this likely over estimates the impact as these players do take more shots regardless of their zone start. Eric Tulsky also took the time to count the number of fenwick events that occur between an offensive zone face off and the time the puck leaves the offensive zone and estimated that to be 0.31. This would imply that every extra offensive zone start a player takes is worth 0.31 fenwick events. Of course, this doesn’t take into account that the best offensive players in the league typical get more  offensive zone starts but it also doesn’t consider what happens after the puck leaves the zone. If the puck leaves the zone under the opposing teams control there is probably a negative fenwick effect for the next several seconds of play reducing the 0.31 number further.

I want to get beyond these issues by taking a look at how zone starts affect individual players. I have previously argued that after 10 seconds of an offensive/defensive zone face off the majority of the benefit (or penalty) of an offensive (or defensive zone) face off has worn off. I wanted to take it a bit further to be sure that there is no residual effect and chose to conduct this analysis using a 45 second cut off. So, any time within 45 seconds of an offensive or defensive zone face off with no other stoppages in play will be eliminated in my face off adjusted data. This should eliminate pretty much every second of every shift that started with an offensive or defensive zone face off leaving just the play that occurred after a neutral zone face off or on the fly changes. I am going to call this ice time F45 ice time and it will represent ice time that is not in any way affected by zone starts. With this in mind, I will take a look at the differences between straight 5v5 stats and the F45 stats and the differences will give me an indication of how significant zone starts impact a players stats.

To do this I will look at both corsi for and corsi against stats on a per 20 minutes of ice time basis. It should be noted that corsi rates are about 7.5% higher during the f45 play (goal rates are ~15% higher!) so I will reduce the f45 corsi rates by 7.5% to account for this and conduct a fair comparison (previous zone start studies may have been impacted by this as well). Now, let’s take a look at eight players (Manny Malhotra, Dave Bolland, Brian Boyle, Jay McClement, Tanner Glass, Brandon Sutter, Adam Hall, and Taylor Pyatt) with an excess of defensive zone starts.

OZ% DZ% OZ%-DZ% FF20 FA20 FF%
Malhotra 12.2 54.6 -42.4 -3.09% 1.09% -1.0%
Bolland 19.8 40.5 -20.7 8.94% -5.25% 3.5%
B. Boyle 21.0 40.2 -19.2 2.87% 8.74% 0.3%
McClement 24.8 41.9 -17.1 -0.31% 1.34% -0.4%
Glass 20.5 37.1 -16.6 4.39% -6.00% 2.6%
Sutter 23.1 36.6 -13.5 -2.67% 2.32% -1.2%
Hall 20.7 33.9 -13.2 -4.06% 4.59% -2.2%
Pyatt 24.0 36.4 -12.4 0.38% -0.25% 0.2%
Average 20.8 40.2 -19.4 0.81% 0.82% 0.23%

The FF20 and FA20 columns show the % change in from 5v5 play to F45 play and the FF% column shows the 5v5 FF% – F45 FF%. The averages are a straight average, not weighted for ice time or zone starts. For players that have a significant defensive zone bias we would expect their F45 play to exhibit an increase in FF20 and a decrease in FA20 resulting in an increase in FF%. In bold are the circumstances where this in fact did happen. As you can see, this isn’t the majority of the time. It is actually kind of surprising that these heavily defensive zone start biased players didn’t see a significant and systematic improvement in their fenwick rates.

Now, let’s take a look at eight players (Henrik Sedin, Patrick Kane, Maian Gaborik, Justin Abdelkader, Kyle Wellwood, Tomas Vanek, John Tavares, Jason Arnott) who had a heavy offensive zone start bias.

OZ% DZ% OZ%-DZ% FF20 FA20 FF%
H. Sedin 49.3 16.2 33.1 -3.72% 1.81% -1.4%
P. Kane 41.4 20.3 21.1 5.94% 4.66% 0.3%
Gaborik 39.0 22.8 16.2 0.60% 2.32% -0.4%
Abdelkader 37.5 26.0 11.5 3.93% 3.49% 0.1%
K. Wellwood 36.9 27.6 9.3 4.54% -2.32% 1.7%
Vanek 36.2 27.2 9.0 -3.39% 1.06% -1.1%
Tavares 35.8 27.2 8.6 -2.39% 1.83% -1.0%
Arnott 36.4 28.0 8.4 -3.41% 1.81% -1.3%
Average 39.1 24.4 14.7 0.26% 1.83% -0.39%

For offensive zone start biased players we would expect to see their FF20 decrease, FA20 increase and FF% decrease when we remove their zone start bias. This is mostly true for FA10 (only Wellwood deviated from expectations) but less true for FF20 and FF% and overall the adjustments were relatively minor. Henrik Sedin had the greatest negative impact to his FF% but it only took him from a 55.2% fenwick player to a 53.8% fenwick player which is still pretty good. This could very well be an upper bound on the benefit of excessive offensive zone starts.

Eric Tulsky also presented a paper at the recent Sloan Sports Analytics Conference in which he suggested that a successful zone entry via carrying the puck in is worth upwards of 0.60 fenwick and upwards of 0.28 fenwick on a dump in. As pointed out earlier, Eric Tulsky counted o.31 fenwick between an offensive zone face off and the puck clearing the zone so and if the other team is clearing the zone with control of the puck, it is certainly possible that they will generate almost as many shots on their subsequent counter-rush essentially negating much of the benefit of the offensive zone start. Without studying zone exits and how frequently zone exists result in successful zone entries into opposing teams end we won’t know for sure, but the data shown above indicates that this might be the case.

The next question that might be worth exploring is, if there is no significant benefit to starting your offensive players in the offensive zone, is there a penalty? For example, might it be better for the Canucks to start the Sedin’s solely in the defensive and neutral zones on the theory that their talent with the puck will allow them to more frequently carry the puck into the offensive zone which, as Eric Tulsky showed, more frequently results in shots and goals. I am not certain of that but might be worthy of further investigation.  I suspect again any benefit/penalty of any zone start deployment will largely be overshadowed by the players individual ability and the quality of their line mates. The ability to win puck battles, control the puck and move it up the ice is the real driver of stats, not usage of the player.

All of this is to say that coaching strategy (at least player usage strategy) is probably not a significant factor in the statistical performance of the players or the outcomes of games and I suspect, as I previously found, the majority of the benefit of an offensive zone start is those situations where you win a face off, take a shot resulting in a goal or the goalie catching it or covering it for another face off.  If the play goes beyond that individual talent (puck retrieval for example) takes over and the opposition will get an opportunity to counter attack. This is why, as I previously determined, eliminating the first 10 seconds after a face off is sufficient for eliminating the majority of the effects of a zone start and even then, the effects are probably not as significant as we think they should be.

 

Feb 112013
 

When I updated stats.hockeyanalysis.com this season I added new metrics for Quality of Teammates (QoT) and Quality of Competition (Q0C). The QoC metrics are essentially the average Hockey Analysis Rating (HARO for offense, HARD for defense and HART for overall) of the opponents that the player plays against. What is interesting about these ratings, as compared to those found elsewhere, is that I split the QoC rating up into offensive and defensive metrics. Thus, there is a QoC HARO rating for measuring the offensive quality of competition, a QoC HARD for measuring the defensive quality of competition, and a QoC HART for overall quality of compentition (basically the average of QoC HARO + QoC HARD). The resulting metrics give a result that is above 1.00 for above average competition and below 1.00 for below average competition and 1.00 would be average competition.

Let’s take a look at defensemen first and take a look at the defensemen who have the highest QoC HARO during 5v5close situations over the previous 2 seasons. This should identify the defensemen who have face the best offensive players and her are the top 15.

Player Name HARO QOC
GIRARDI, DAN 1.036
CHARA, ZDENO 1.036
GARRISON, JASON 1.035
MCDONAGH, RYAN 1.034
WEAVER, MIKE 1.033
GORGES, JOSH 1.031
ALZNER, KARL 1.029
GLEASON, TIM 1.026
SEABROOK, BRENT 1.025
BOYCHUK, JOHNNY 1.025
SUBBAN, P.K. 1.025
PHANEUF, DION 1.025
CARLSON, JOHN 1.022
HAMONIC, TRAVIS 1.021
LIDSTROM, NICKLAS 1.021

That’s actually a pretty decent representation of defensive defensemen though there is a bias towards the eastern conference in large part because the eastern conference has more offense (the top 4 teams in goals for last year were eastern conference teams while 9 of the 11 lowest scoring teams were from the western conference).

Now, lets take a look at the forwards with the toughest offensive competition.

Player Name HARO QOC
SUTTER, BRANDON 1.032
PERRON, DAVID 1.032
CALLAHAN, RYAN 1.031
FISHER, MIKE 1.03
SYKORA, PETR 1.029
BOLLAND, DAVE 1.028
ZAJAC, TRAVIS 1.028
ELIAS, PATRIK 1.028
BERGERON, PATRICE 1.027
HAGELIN, CARL 1.027
ZUBRUS, DAINIUS 1.027
PLEKANEC, TOMAS 1.027
WEISS, STEPHEN 1.026
RECCHI, MARK 1.026
ERAT, MARTIN 1.025

Not a lot of surprises there.  They are mostly third line defense first players (IMO Brandon Suter is the best defensive center in the NHL and this is just more evidence of why) or quality 2-way players though as you go further down the list you start to see more offensive players showing up like Alfredsson and Spezza which is probably evidence of a coach wanting to line match top line against top line instead of a checking line against top line.

Where things get interesting is looking at who is 300th on the list of forwards in HARO QoC. It’s none other than Manny Malhotra of massive defensive zone start bias fame. Malhotra’s HARO QoC is just 0.980 while the Canucks center who is assigned mostly offensive zone starts, Henrick Sedin, has a HARO QoC 0.994, which isn’t real difficult but is somewhat higher than Malhotra’s. So, despite all those defensive zone starts by Malhotra (presumably because he is considered a better defensive player), Henrik Sedin plays against tougher offensive opponents. How can this be? Despite Malhotra’s significant defensive zone start bias his five most frequent 5v5close opponent forwards over the previous 2 seasons are David Jones, Matt Stajan, Tim Jackman, Joran Eberle, Matt Cullen. Aside from Eberle those guys don’t really scare you much. It seems Malhotra was facing Edmonton’s top line but not Calgary’s, Minnesota’s or Colorado’s. Henrik Sedin’s top 5 opposition forwards are Dave Bolland, Dany Heatley, Curtis Glencross, Olli Jokinen and Jarome Iginla. Beyond that you have Backes, O’Reilly, Bickell, Thornton, Zetterberg, and Getzlaf. Despite the massive offensive zone start bias, it seems the majority of teams are still line matching power vs power with the Sedins. The conclusion is defensive zone starts does not immediately imply playing against quality offensive players. It can be argued that despite the defensive zone starts Manny Malhotra plays relatively easy minutes.

Using a rigid zone start system like the Vancouver Canucks do actually makes it easier for opposing teams to line match on the road as they know who you are likely to be putting on the ice depending on where the face off is. If the San Jose Sharks want to avoid a Thornton against Malhotra matchup, just don’t start Thornton in the offensive zone. Here are all the forwards with >750 5v5close minutes and at least 40% of the face offs they were on the ice for being in the defensive zone along with their HARO QoC.

Player Name HARO QOC
Manny Malhotra 0.980
Jerred Smithson 0.977
Max Lapierre 0.970
Adam Burish 0.982
Steve Ott 0.993
Jay McClement 0.983
Sammy Pahlsson 1.014
Brian Boyle 1.010
Dave Bolland 1.028
Kyle Brodziak 1.002
Matt Cullen 0.998
Paul Gaustad 0.993

Only 4 of the 12 heavy defensive zone start forwards faced opposition that was above average in terms of quality while the majority of them rank quite poorly.

It is also interesting to see who plays against the best defensive forwards.  One might assume it is elite offensive first line players but as we saw above, teams seemed to want to avoid matching up top offensive players against Manny Malhotra. So, let’s take a look.

Player Name HARD QOC
FRASER, COLIN 1.044
BOLL, JARED 1.043
MAYERS, JAMAL 1.037
JACKMAN, TIM 1.035
MACKENZIE, DEREK 1.032
ABDELKADER, JUSTIN 1.031
CLIFFORD, KYLE 1.031
EAGER, BEN 1.029
BELESKEY, MATT 1.028
MILLER, DREW 1.028
KOSTOPOULOS, TOM 1.027
MCLEOD, CODY 1.025
NICHOL, SCOTT 1.024
WINCHESTER, BRAD 1.023
PAILLE, DANIEL 1.021

Pretty much only tough guys and 3rd/4th liners on that list. Teams are deliberately using the above players in situations that avoid them facing top offensive players and as a result are facing other teams third and fourth lines and thus are facing more defensive type players.

The one conclusion we can draw from this analysis is that quality of competition is driven by line matching techniques more so than zone starts.

 

Feb 212012
 

There was a twitter conversation between Gabe Desjardins and David Staples last night in which Gabe suggested that Daniel Sedin’s heavy offensive zone start bias resulted in an additional 7-9 points that he would not have gotten if his zone starts were more evenly split between offensive and defensive zone.  When I saw this I immediately though that seemed like a really high number so I decided to take a look though the play by play sheets and see how many of Daniel Sedin’s even strength points came from a faceoff in the offensive zone.  Of all of Daniel Sedin’s points so far, here are the only ones that might at all be attributed to an offensive zone faceoff.

Date Opppnent Type Time After Faceoff
Oct. 15 Edmonton Assist 8 seconds
Oct. 20 Nashville Goal 11 seconds
Oct. 29 Washington Assist 19 seconds
Nov. 29 Columbus Goal 8 seconds
Dec. 6 Colorado Goal 24 seconds
Jan. 31 Chicago Goal 29 seconds
Feb. 18 Toronto Assist 40 seconds

Every other point that Daniel Sedin got was either on the PP, after a faceoff in another zone or after a line change during the play or after the opponent had possession of the puck.  Even the points above we don’t know if the opposition had control of the puck between the faceoff and the goal, especially for the plays 19 seconds or longer after the faceoff (a lot can happen in 19 seconds) and the goal vs Colorado was during 4 on 4 play as well.  But for the sake of argument, let’s say we can directly tie all 7 of those points to being a result of offensive zone face offs.  Also, for the sake of easy math, let’s assume his OZone% is 70% (it’s actually closer to 80%).  So, on 70% OZone starts he scored 7 goals.  If we reduce his Ozone% to 50% you’d naturally think you’d lose an equivalent portion of points so he’d end up with 5 points instead of 7.  Net result, Daniel Sedin’s offensive zone start bias has accounted for just 2 additional points so far this season.

What about previous seasons?  Well, over the previous 3 seasons Daniel Sedin was on the ice for 197 5v5 goals for.  If we ignore the 30 seconds following an offensive or defensive zone start (and 30 seconds is more than ample to account for zone starts) he was on the ice for 151 goals for.  That means we can fairly safely assume that offensive zone starts at best resulted in 46 goals for.

Now, over the past 3 seasons Daniel Sedin was on the ice for 1164 offensive zone face offs and 656 defensive zone face offs for an OZone% of about 64%.  Those 1164 offensive zone faceoffs accounted for at most 46 goals meaning approximately every 25 offensive zone starts resulted in a goal.  If Sedin had a 50% OZone% over the previous 3 seasons instead of his 64% he’d have been on the ice for about 910 offensive faceoffs, or about 254 fewer than he actually had.  Since every 25 offensive zone starts results in a goal those 254 extra offensive zone face offs he took resulted in approximately 10 extra goals being scored.  So, on average Daniel Sedin was on the ice for 3-4 extra goals per season because of his offensive zone faceoff bias, and that is being generous with the math.  That result is not far off this seasons observations above.

So, considering one of the best offensive players in the game with one of the most significant offensive zone biases in the game is only on the ice for at most an additional 4 goals a season as a result of their offensive zone bias, I think we can chaulk up the zone start effect as mostly insignificant.  The majority of players aren’t near as talented as D. Sedin and his linemates are and the majority of players end up having between 45% and 55% zone starts.  As a result, the majority of the players probably only see a zone bias affect their stats by at most one or two goals a season.  It’s pretty much not worth consideration.

Of course, a corsi based analysis would show a more significant difference because zone starts affect corsi more than goals.

 

Feb 012012
 

Over the past week or so I have talked about a simple and straight forward method for taking into account variations in zone starts.  The method is to simply ignore the 10 seconds following an offensive or defensive face off.  By adjusting for zone starts in this manner we can see a fairly significant impact on stats and today I’ll take a look at what gets impacted and how.

To do this I took a look at 3 year data using the 2008-09, 2009-10 and 2010-11 seasons.  Using 5v5 data for players with at least 1000 minutes of ice time I identified the 25 players who had the highest percentage of their face offs in the offensive zone and the 25 players who had the highest percentage of their face offs in the defensive zone.   I then compared their 5v5 zone start adjusted stats to their non-adjusted 5v5 stats.  The statististics I looked at are on-ice goals for percentage, on-ice fenwick for percentage, shooting percentage, opposition shooting percentage, goals for per 20 minutes, goals against per 20 minutes, fenwick for per 20 minutes and fenwick against per 20 minutes.  The changes are as follows:

Top 25 OZPct Top 25 DZPct
GF% -1.17% 2.58%
FF% -0.99% 2.32%
SH% 15.00% 12.40%
OppSh% 15.31% 11.86%
GF20 2.40% 7.00%
GA20 4.69% 2.12%
FF20 -8.28% -2.89%
FA20 -6.36% -6.93%

What is interesting is that there are relatively small differences in GF% and FF% but differences in shooting percentages are very large (note that 15% change is from, for example, 10% to 11.5%, not the actual difference in shooting percentages).  Goal and fenwick event rates are somewhere in the middle but while goal rates rise when we ignore the 10 seconds after an offensive/defensive zone  faceoff, fenwick rates drop.  This means that while a lot of shots are taken in the 10 seconds after the faceoff, very few of those shots end up as goals.  As I mentioned yesterday, the league-wide shooting 5v5 percentage in the 10 seconds after the faceoff is around 3% while it is almost 9% the rest of the time.

Let’s look at some specific examples.  Henrik Sedin gets a lot of offensive zone faceoffs and as a result 19.6% of his fenwick against events come within the 10 seconds after an offensive/defensive zone faceoff but only 8.0% of his on-ice goals do.  In real numbers, Henrik Sedin was on the ice for 2634 fenwick for events and 523 occurred within 10 seconds of an offensive/defensive zone faceoff.  He was also on the ice for 212 goals for while only 17 occurred within 10 seconds of an offensive/defensive zone faceoff.

Manny Malhotra is the opposite of Henrik Sedin and gets a lot of defensive zone faceoffs.  As a result, 17.3% of all his fenwick events against occur within the 10 seconds after an offensive/defensive zone faceoff, but only 4% of his on-ice goals against do.  In real numbers, Malhotra was on the ice for 1710 fenwick events against at 5v5 over the past 3 seasons, but 296 came within 10 seconds of an offensive/defensive zone face off.  He was also on the ice for 75 goals against, but only 3 came within 10 seconds of an offensive/defensive zone faceoff.

What does this all mean?  It means that if you are doing a corsi/fenwick/shot/shooting percentage based analysis accounting for zone starts is really important because it can have significant impacts on these stats (less so for ratios though).  The impact on goals is much less significant but probably not something we would want to ignore depending on the analysis.  May as well use the 10 second zone start adjusted data for all player analysis.

 

Jan 312012
 

Just wanted to let you know that I have finally updated stats.hockeyanalysis.com to include 2011-12 data though I have not yet included multi-year data that includes 2011-12.

I have also included in this updated zone start adjusted data which adjusts for zone starts by not considering the 10 seconds following an offensive/defensive zone faceoff.  I have included both 5v5 and 5v5 zone start adjusted data and the 5v5 close, 5v5 tied, 5v5 up 1, 5v5 up 2+, 5v5 down 1 and 5v5 down 2 data are zone start adjusted.  It doesn’t make any sense to zone start adjust PP and PK so the 5v4 and 4v5 data is not zone start adjusted.

As always, if you have any issues or questions with anything at stats.hockeyanalysis.com let me know.

As an interesting aside on zone starts, I have noticed that zone starts affect shots/fenwick/corsi somewhat significantly but do not affect goal data much.  I thought this was strange at first but then the explanation became clear when I looked at shooting percentages.

Situation SH%
All 5v5 7.91%
ZS Adjusted 5v5 8.89%
10 seconds after Ozone faceoff 3.04%

Shots within 10 seconds of a faceoff don’t go in nearly as frequently as shots at any other time.  The reason for this is probably that the majority of these shots likely come from the point after an offensive faceoff win.  Also, the goalie is perfectly set and ready for the shot and the defending team has their players in optimal defending positions and are usually fully rested.

So, what does this mean?  It means you can actually probably pretty much ignore zone starts if you are looking at goal data.  Zone starts have very little influence on the rate at which goals are scored.

 

Jan 232012
 

One of the biggest omissions in my player rankings is making adjustments for zone start differences.  We know that Manny Malhotra has a significant bias towards starting his shifts in the defensive zone and that his teammates Daniel and Henrik Sedin have a significant bias towards starting their shifts in the offensive zone.  The result is Malhotra will unfairly be penalized for giving up more shots and goals against simply because he starts more often in the defensive zone and the Sedins have a huge advantage in generating shots and goals because of how often they start their shifts in the offensive zone.  The question is, how much of an effect does it have and how do we adjust for it?

Over the past couple of weeks I have been pondering these questions and I thought of two potential solutions to the problem.  The first solution is to find some sort of adjustment factor based on zone start statistics.  I briefly pondered a few ideas but wondered if a uniform adjustment factor can be fairly applied to all players who have varying skills and talents.  I decided that I would take a look at my second idea first.

My second adjustment idea is really a simple idea and really isn’t an adjustment at all.  The idea is to just ignore any play that occurs during some stretch of time after an offensive/defensive zone face off.  After some length of time, any advantage (or disadvantage) one might get from starting in the offensive (or defensive) zone would be nullified.  Worst case scenario is we have to eliminate ~45 seconds after every offensive or defensive zone face off which would essentially nullify the whole shift.

So, with that in mind I took a look at 3 year (2008-09, 2009-10 and 2010-11) 5v5 statistics and did a comparison of four different lengths of time to ignore after an offensive/defensive zone faceoff – 0, 10, 20 and 30 seconds.  To evaluate what is going on I looked at each players fenwick for and against per 20 minutes and calculated the correlation between each time after faceoff adjustment.  Here is what I found:

FenF/20 FenA/20
5v5 vs F10 0.8639 0.8451
F10 vs F20 0.9882 0.9866
F20 vs F30 0.9870 0.9883
5v5 vs F20 0.8718 0.8368

5v5 is no zone start adjustment, F10 is ignoring 10 seconds after an offensive/defensive zone faceoff, f20 is ignoring 20 seconds after and f30 is ignoring 30 seconds after.  The numbers are r^2 for fenwick for per 20 minutes and fenwick against per 20 minutes.

As you can see, there is a somewhat sizeable difference between 5v5 and the F10 adjustment but there is very little difference between the F10 and F20 or F20 and F30 and there isn’t really any difference between 5v5 vs F10 and 5v5 vs F20.  All of this tells me that any advantage (or disadvantage) a player gains because of their zone stars occurs during the first 10 seconds after an offensive or defensive face off.  After that, only the players talent matters and there is no benefit to removing more data from our analysis.

Wanting to confirm this works for a single season of data I decide to take a look at Manny Malhotra and Henrik Sedin’s stats from last season.

Malhotra FenA/20 Sedin FenF/20
5v5 14.16 15.39
F10 12.49 13.31
F20 12.44 13.66
F30 12.24 13.71

This confirms what we witnessed with the correlations using 3 years of data.  By ignoring the first 10 seconds after an offensive/defensive zone faceoff we can eliminate any benefit/penalty a player may get because of his zone starts.  When I finally get around to updating my stats site I intend to include F10 data as well and I think this is a simple enough solution to abandon any attempts at any other zone start adjustment technique.