David Johnson

Sep 212015

This came up in a twitter conversation today and since I will be referencing both of these as part of my RIT Hockey Analytics Conference talk it might be a good idea to re-introduce them to anyone who are not familiar with them.

At their core, Rel and RelTM stats both attempt to account for the quality of teammates a player plays with. The problem with Corsi%, as Paul Bissonette points out, is that it is heavily driven by the players one plays with.

Ultimately, teams don’t draft based on Corsi or possession numbers. You draft a player because you’ve watched how they perform on the ice and then consider their potential to improve.

But somehow they’re starting to use this bullshit in contract negotiations. You have teams saying, “Oh, wow, look at this player in Chicago who had a 60 percent possession number.” Well, yeah, because he’s an average player playing with Toews and Kane. So all of the sudden a team signs him for $3 million a year even though he’s a $1.5 million a year player, and they’re shocked when his possession isn’t as good. Are you kidding me?

This is a flaw I think everyone agrees with which is why Rel and RelTM were developed. A player playing with all-star players will naturally put up (or at least have an opportunity to put up) far better numbers than players playing with mediocre 3rd and 4th line players. A player on the Blackhawks will have an opportunity to put up far better numbers than a player on the Sabres. The Blackhawks had a team Corsi% of 53.6% last year compared to the Sabres 37.5%. Rel and RelTM stats attempt to factor out the team aspect and isolate the individual contribution.

What is Rel?

Rel (or Relative) stats are calculated by taking the players “on-ice” stats (or what the team does when they player is on the ice) and comparing them to the players “off-ice” stats (or what the team does when they are not on the ice). So, CF% Rel is nothing more than on-ice CF% minus off-ice CF%. The resulting metric is an attempt to look at how the player is performing relative to the rest of the team.

The flaw with Rel stats is that it still doesn’t account for the player who plays with Jonathan Toews and Marian Hossa. The third guy on that line might not be a very good player but will look good because Toews and Hossa drive the play and boost his stats. Rel stats also fail when it comes to teams that are well balanced with no superstars but 3 very good lines where as a player on the first line of a very top heavy team will probably look better than they really should.

I’d say that Rel stats do a decent job of factoring out team-level system driven results but do not do a great job of isolating the players performance from his line mates.

What is RelTM?

RelTM (Relative to Team Mates) attempts to account for the 3rd guy on the Toews/Hossa line getting unfairly credited for playing with a pair of superstars. It does this by looking at how each of the players teammates perform with and apart from him and to what extent. So while Rel is a team level on-ice minus off-ice stat, RelTM is a player level with minus apart stat. Some people have referred to this as a “Combined WOWY” (combined with or without you) stat. It can be a little difficult to wrap your head around but it looks at Toews’ (and Hossa’s and every other teammate)  performance apart from the 3rd guy on the Toews/Hossa line and Toews’ performance with him and attempts to determine if Toews performed better or worse with him than apart from him. This gets done for every player and in essence RelTM tries to figure out how much on average he boosts his team mates statistics. The more team mates that have better statistics with him than apart from him the better.

While on some level this sounds like an improved statistic than Rel since it is directly looking at how much better the guys he actually plays with are with him than apart it isn’t perfect either. The most significant reason is probably that if you are the 3rd guy on a Toews/Hossa line it will be very difficult to boost the stats Toews/Hossa because they are so good. Another flaw is that often the result is that you end up just being compared to the next most important player at your position. When the regular first line RW isn’t playing with the regular first line C and LW it most likely will be the second line RW that is playing with them. So the RelTM stats to some extent tell you how much better/worse are you than the next best/worst player at your position on your team.


Which one of Rel and RelTM is better? I am a bit biased but I personally like how RelTM stats are calculated as I think looking at direct impact on the players teammates is a better route to go. With that said, there is some evidence that Rel is a little more persistent from year to year which may make it more useful. I’ll have more to say about this at the RIT Hockey Analytics conference but mostly I’ll show that both Rel and RelTM stats are almost certainly vastly superior to the raw stat counter part (i.e. CF% Rel and CF% RelTM are vastly superior to CF%). In different ways both attempt to and are relatively successful at factoring out quality of team and quality of teammates. Rel and RelTM stats are generally fairly highly correlated as well so there likely isn’t a huge difference between them. Furthermore, the more a team juggles its line up the better both of these stats, particularly the RelTM stat, will correctly isolate individual contribution and talent.

(if there is any confusion on these stats, feel free to ask questions in the comments. I’ll be happy to clarify and/or provide more details.)

You can find RelTM stats on my stats sites (stats.hockeyanalysis.com and puckalytics.com) and you can find Rel stats on many other stats sites (I plan on adding them to mine at some point as well).

Finally, if you have a chance to make it to Rochester, NY on October 10th consider signing up for and attending the RIT Hockey Analytics Conference. It looks like one of the best line ups of speakers these conferences have ever had with a wide variety of topics.


Aug 202015

Today the Carolina Hurricanes officially announced that Eric Tulsky has been hired in the position of “Hockey Analyst” with his role being defined as follows:

As Hockey Analyst, Tulsky will provide and analyze data to assist the hockey operations department and coaching staff.

The official announcement also mentioned that Tulsky had worked for the team last season on a part-time basis which we already knew. For me the interesting thing here is that we learn that Tulsky will be working with the coaching staff and thus I think it is safe to assume that he probably did some of that last season too. We do know that a significant portion of Tulsky’s analytics work has been related to zone entries and exits which really gets to the heart of coaching tactics.

We also know that Tyler Dellow worked closely with the coaching staff in Edmonton, especially Dallas Eakins (who was a major reason for Dellow’s hire in the first place). We also know that Kyle Dubas in Toronto is a big promoter of “puck possession” hockey and this likely influenced coaching tactics and playing style with the Leafs last season, especially after Randy Carlyle was fired.

I asked twitter earlier tonight whether any of the other analytics hires from last season had direct or indirect influence over coaching tactics and playing style (as opposed to just consulting in front office decisions) and as of yet no one has come up with a conclusive example. This leaves Carolina, Edmonton and Toronto as the best examples of where analytics may have had a significant impact in on-ice playing style and tactics which is where we might first see analytics having an impact (takes much longer to change roster than playing style).

Back in January I looked at the relationship between possession (Corsi%) and Shooting percentage (or Corsi Shooting% to be more precise) in 5v5close situations and found that there is an inverse relationship and I even estimated  the impact the Oilers improved Corsi Percentage (CF%) would have on their Corsi Shooting Percentage (CSh%). This is what I found:

So, their 5v5 CSh% has improved from 43.4% to 48.7%. If we plug that 5.3% improvement into the regression equation above we would expect that their CSh% would drop 0.65% where it actually dropped 0.89%. Edmonton dropped from 11th in CSh% last season to 27th this season.

We can now presume that the Leafs and the Hurricanes playing style may also have been influenced by “puck possession” analytics and we can see how it impacted their statistics as well as looking at full season stats for the Oilers. Here is what I find.

Edmonton CF% Sh% CSh%
2013-14 43.40% 8.25% 4.38%
2014-15 47.40% 7.16% 3.74%
Difference 4.00% -1.09% -0.63%
Expected Difference -0.49%
Carolina CF% Sh% CSh%
2013-14 49.20% 7.39% 3.78%
2014-15 51.70% 6.38% 3.36%
Difference 2.50% -1.01% -0.42%
Expected Difference -0.31%
Toronto CF% Sh% CSh%
2013-14 42.10% 8.24% 4.46%
2014-15 45.20% 7.52% 3.99%
Difference 3.10% -0.72% -0.47%
Expected Difference -0.38%

What is interesting is that each of these teams saw their puck possession stats increase (CF%) and their shooting percentage stats decrease (Sh% and CSh%). We also see that the expected change in CF% based on their change in CF% was fairly reasonably predicted by the negative relationship I found in January though in all three instances it underestimated the drop in CSh%.

Now, this is just three teams and it may just be random luck but it is quite an interesting observation and I have shown that there does seem to be an inverse relationship between CF% and Sh% when teams change coaches. Really, this all makes sense because if you take more risks offensively you might give up the puck more (hurting your possession stats) but you might also generate higher quality scoring chances if those risks pay off (boosting your shooting percentage). And of course the opposite is true if you play a more conservative offensive game.

Anyway, I found today’s announcement that Tulsky will be (and thus probably did last season) working with Carolina coaches an interesting one as it provided an additional test case.


Aug 102015

If you want to claim that a piece of data is random, then there must be no identifiable patterns within it for if there are, then the data is not random.

For example one can easily look at a long-term list of forwards sorted by on-ice shooting percentage and clearly see that it is not random. The top of the list is dominated by everyone we would identify as elite offensive forwards and the bottom of the list is dominated by 3rd and 4th liners. Even with just 2 years of data the list is fairly well sorted with a range/standard deviation not much greater than 8 years of data. There is meaning in the data.

This brings us to my latest stat of discussion, Sv%RelTM which I discussed in yesterday’s post where I showed with 8 years of data that players that post poor Sv%RelTM statistics are generally more offence oriented players while those that post a good Sv%RelTM are more likely to be defence oriented.

Of course, while long term, it is just one dataset and who knows, maybe it is just lucky it turned out that way. Plus we wouldn’t want to have to wait 8 years to draw any conclusions so I wanted to present some more data but by looking at 2 season datasets. In these charts I will look a the top 25 forwards in Sv%RelTM and the bottom 25 forwards in Sv%RelTM and compare the group average of the following stats:

  • GF60 RelTM
  • CF60 RelTM
  • %ofTeam DZFO / % of Team OZFO

You are probably well aware of the first two statistics which are offensive statistics relative to the players they are playing with (i.e. are they better offensive players or worse). The last stat gives us an indication of whether they started more shifts in the defensive zone than the offensive zone. The higher the number, the more defensive the players role is likely to be. So, here is what we get.




(Note: all data used is 5v5close data for forwards with at least 600 minutes of ice time in the 2 season datasets and 2000 minutes in the 8 season dataset)

What do we notice in these chats? Relative consistency. Every year the poor Sv%RelTM players are on average better offensive players and get a smaller percentage of the defensive zone starts (and presumably a larger percentage of offensive zone starts). You can’t get order from randomness so we can only conclude that Sv%RelTM is not a purely random stat.

How important is Sv%RelTM? Well, over 8 years the top 25 players had an average Sv%RelTM of +1.4 and the worst 25 players had an average Sv%RelTM of -1.3 (relative symmetry is nice too). If the average starting goalie posts a .915 Save % behind the top group they would have a .929 save percentage and behind the worst group it would be a .902 save percentage. Is that insignificant? While less than the variance in shooting percentage I wouldn’t say it is insignificant at all.

But what about the lack of year over year persistence?

The lack of year over year persistence in Sv%RelTM or similar statistics is the main reason why people doubt that players can in fact influence save percentage. No doubt this is a challenge to explain but my analysis above does clearly indicate that something is going on that isn’t random. How can we explain these two observations that seem to be telling us completely opposite things? Well, the only thing we can suggest is playing style is the most significant driver in Sv%RelTM stats and it is possible that very few players play the same style over multiple seasons. They may move up or down the lineup or change teams or a new coach gives them a different or more balanced role. Furthermore, there are actually very few pure offensive or pure defensive players in the league so we may only be talking the outliers that actually influence save percentage to a significant degree which means for many players maybe Sv%RelTM isn’t important to consider, but for those in the specialized roles it needs to be considered.

All of this tells us once again that role and playing style seem to be a significant factor in the statistics players put up. This isn’t the first time I have shown that playing style may dramatically influence a players statistics (see The Bozak-Corsi Dilemma and The Coaching-Corsi Dilemma). Playing style is a significantly under studied area of hockey analytics but might be far more important than we realize, especially for those with more specialized roles. We need to find better methodologies to identify, study, and account for the roles players play and the coaching style they are playing under.

To finish up, it is important to remember that order is not a result of randomness. A statistic may seem random in year over year correlations but if it exhibits order and structure in other areas, it isn’t random and there are other factors at play that make it look random (and we need to investigate and understand these factors to further our knowledge).


Aug 092015

Score effects are a well known and well understood observation in hockey analytics. Essentially what score effects tell us is teams play differently depending on the score and in turn the resulting statistics are altered because of it. To keep this simple, in general teams that are leading give up more shots, but a smaller percentage of them end up as goals (they also take fewer shots but a higher percentage of them end up as goals).

Let me reiterate the main point here. When a team has a lead they effectively give up more shots but those shots are, on average, of lower quality and thus a lower percentage of them end up being goals. This effectively means when playing with a lead teams play in such a way that they boost their goalies save percentage.

This brings me to Brandon Sutter. Whenever I suggest that Brandon Sutter has an ability to boost his goalies save percentage there is always a backlash from a portion of the hockey analytics community or from those that believe in and use hockey analytics.

So bottom line: if your justification for the Sutter signing is that he suppresses GA via awesome on-ice sv%, good luck. You’ll need it. –@67sound

There are even guys that are so adamantly against the idea that players can influence save percentage they have to completely twist what I am saying into a claim that I am not making just to prove their point that the whole idea that players can influence on-ice save percentage is just an insane concept.

The interesting thing is I am almost certain that the same people who dismiss the idea that a player can influence on-ice save percentage fully believe in the concept of score effects, including that teams generally have a higher save percentage when defending a lead.

So, my question is, if a team (and even individual players) can post higher on-ice save percentages when they are protecting a lead, why is it so unbelievable that an individual player can accomplish the same during all situational play. For example, Brandon Sutter.

Season Team Sutter GA60 Team GA60 Sutter-Team GA60 Sv%RelTM
2014-15 PIT 1.53 2.02 -0.49 1.8
2013-14 PIT 1.75 2.23 -0.48 2.4
2012-13 PIT 2.07 1.96 0.11 3.4
2011-12 CAR 1.65 2.43 -0.78 1.4
2010-11 CAR 1.84 2.48 -0.64 2.1
2009-10 CAR 2.18 2.48 -0.30 2.3

Now, a lot of players when they are on the ice outside of specialized situations (such as protecting a lead later in the game) don’t primarily focus on defense. For these players it is understandable that they don’t have a signfiicant impact on the oppositions shooting percentage. But some players, like Sutter, do and this is why he has consistently posted better on-ice save percentages than his teammates. There are in fact very few players that play this role almost exclusively over several seasons but those that do often exhibit the ability to boost their goalies save percentage when they are on the ice.

One thing I like to do to see if a statistic exhibits pure randomness is to look at the top players and the bottom players and see if there are any commonalities in the players within the two groups. If I can identify commonalities within the best or worst of a particular stat it tells me that that stat is probably not purely random.

So, what I did was take the top and bottom 15 players in Sv%RelTM over the past 6 seasons and compare them to their ZSO%Rel (from War on Ice). The theory is a higher ZSO%Rel would indicate they are given more offensive roles and a lower ZSO%Rel would indicate they played a lesser offensive role and thus presumably a more defensive role (ZSD%Rel would have been interesting to look at as well to explicitely identify defensive roles). Here are the results.

Rank Player Name Sv% RelTM ZSO%Rel
1 DAVID BACKES 1.9 -8.85
2 SHAWN HORCOFF 1.8 -2.7
3 BRANDON SUTTER 1.8 -11.6
4 KYLE TURRIS 1.7 0.83
5 BRANDON PRUST 1.7 -10.63
7 LOGAN COUTURE 1.6 1.62
6 DANIEL WINNIK 1.6 -6.03
8 MARIAN HOSSA 1.5 3.22
12 TRAVIS ZAJAC 1.4 2.21
9 DUSTIN BROWN 1.4 -0.03
11 NATHAN GERBE 1.4 -2.37
14 DEREK STEPAN 1.3 8.06
15 BENOIT POULIOT 1.3 7.27
13 STEVE OTT 1.3 -10.09
Top 15 Average 1.5 -2.57
183 JEFF SKINNER -1.1 8.92
184 ALEX TANGUAY -1.2 1.87
186 PATRICK KANE -1.2 18.54
187 JASON CHIMERA -1.2 -5.3
188 KYLE OKPOSO -1.2 6.38
189 MARTIN ERAT -1.2 -2.1
190 EVGENI MALKIN -1.3 11.96
191 TYLER BOZAK -1.4 2.47
192 MARCUS JOHANSSON -1.5 6.04
193 SAM GAGNER -1.5 8.06
194 EVANDER KANE -1.6 5.16
195 PATRICK SHARP -1.7 15.21
196 DERICK BRASSARD -1.8 10.59
197 PATRIK ELIAS -2.0 1.22
Bottom 15 Average -1.4 6.14

(The above are 5v5close stats for forwards with minimum 2500 minutes over last 6 years)

What we clearly find is that the players that have poor Sv%RelTM are generally offensive players. Only two of the 15 worst Sv%RelTM players (Chimera and Erat) had a negative ZSO%Rel indicating the majority of them played more offensive roles. Of the top Sv%RelTM players there are a number that have a positive ZSO%Rel but on average these players are getting fewer offensive assignments than the bottom 15 group. Only Stepan and Pouliot really stand out as having significantly more offensive zone starts.

Even just looking at the players names you would identify the majority of the top 15 as either solid 2-way players or more defensive specialists while the bottom 15 are more offensive oriented players.

Just as teams that are playing defensive hockey protecting a lead can boost their goalies save percentages so can players that are playing defensive hockey during play in 5v5close situations. Why this is such a surprise to people is beyond me.

Conversely, just as teams that are playing offensive hockey when trailing typically see their save percentages drop, players who are given primarily offensive assignments also see their on-ice save percentages lower than their less offensive oriented team mates.

In the end it really dumbfounds me that people can fully accept that score effects indicate that different playing styles can impact save percentage but can’t accept that players have an ability to impact save percentages if they are given primarily offensive or defensive roles.

This is not unlike the debates I have had with people about zone starts and face off winning percentage. There are many who have believed that zone starts had a significant impact on a players statistics while at the same time claiming face off win percentage had little to no value. This really makes zero sense to me. You simply can’t on the one hand claim that being on the ice for more (or fewer) offensive faceoffs than defensive faceoffs will have a significant impact on a players stats while on the other hand claim that winning those offensive or defensive faceoffs doesn’t really matter at all. It makes no logical sense to me and yet many leading members of the hockey analytics community believed that for years. Happily it is slowly changing and people are starting to realize zone starts don’t dramatically impact a players stats.

Similarly, there was a time where the hockey analytics community largely dismissed the notion of shot quality. Happily this is changing too and more and more people are accepting that shot quality exists and more people are researching shot quality and how to best quantify it, even if the results are still far from perfect.

It is time that we get past the old school hockey analytics belief that players can’t impact save percentage and start asking who does and how they do it.


An Expanded look at 5-year performance by NHL Centers

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Jul 112015

The other day I wrote an article looking at who the best two-way centers over the past 5 seasons and it generated a lot of interest and some requests to look at some more players. I am not going to go into too much analysis here but I have pulled together data for a number of additional centers which I hope will answer some of your questions. The majority of the players in these charts are new players but I have also included several players from the last post (Crosby, Datsyuk, Toews, Kopitar, Getzlaf, Sedin, and Bozak) to provide a comparison. Here is a list of the players you will find in these charts.


And now for the charts. Please check out my last post if you are unfamiliar with these statistics and what they mean. The last two charts are probably the most important in terms of overall value and contribution to the team.

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Jul 092015

Maybe the most important position in hockey is center, certainly more important than wing and probably more important than defense or goaltending. Even more important is having a real good two-way center capable of playing big minutes at both ends of the rink. Think about the recent Stanley Cup winners. In Chicago you have Jonathan Toews. In Los Angeles you have Anze Kopitar. In Boston you have Patrice Bergeron. Going further back you have Datsyuk in Detroit. These four guys are maybe the best two-way centermen in the league but I wanted to take a more analytical approach to answering that question.

Let me first come out and say that evaluating defensive play is something hockey analytics is still pretty poor at doing and I have said several times that the toughest question to answer in hockey analytics right now is how to separate the defender from the goalie. It is just a really difficult problem with the data we have access to today. It may not just be hockey analytics that struggles with answering this question. I have even questioned whether NHL coaches are capable of identifying their best defensive players. With that said, I am going to try and answer the question anyway.

In order to not miss any important players earlier in the week I asked my twitter followers to nominate two-way centers for which I should include in my analysis. I had lots of good responses, and even a few that I hadn’t thought of. A few people even suggested Sidney Crosby and Henrik Sedin both of whom I consider more offensive oriented players but I figured I’d throw them in the study as well. A few people even suggested Tyler Bozak which I can only assume was in jest but I am going to include him for fun, and as a test for my metrics. By testing metrics when evaluating individual players any metric must rate Crosby as the best offensive player in the NHL (no exceptions, he is by far the best) and must not rate Bozak very highly overall (because he just isn’t very good). Failure to meet these two standards indicated the metric in question is flawed. In selecting centers for this study I also wanted to select players with a long track record to make for easier, more reliable evaluation. For this reason I restricted myself to players with at least a 5-year track record and mostly looked at 5-year stats in my study.

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A look at free agent wingers Williams, Semin and Beleskey

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Jun 302015

I am sure this post will rattle some feathers in the Hockey Analytics community but hey, it won’t be the first time I have accomplished that.

I have been looking through the list of potential free agents looking for players that are possibly under valued, possibly over valued, or otherwise interesting for one reason or another. There has been a fair bit of discussion around the three players that are the focus of this post. Justin Williams has been a favourite of the hockey analytics community posting outstanding Corsi numbers year after year. Alexander Semin, who was bought out by the Carolina Hurricanes is one of those guys that seems to be hated by coaches, scouts, general managers, and “traditional hockey people” but analytics people look at his numbers and, last season aside, they look outstanding. Matt Beleskey is an unrestricted free agent that hockey analytics people want to warn teams about because he is coming off a career year with 22 goals driven largely by a high, and unsustainable, shooting percentage. The hockey analytics community are predicting he will be one of those guys teams will over pay for and regret the decision a year from now. So, I figured it would be worth while taking a deep look at these players because from my observations the deeper you look the more interesting things become and the story potentially changes.

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TOI% Correlations with Rel Stats

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Jun 282015

Yesterday I looked at what statistics TOI% correlates with which will give us an indication of how coaches distribute ice time to their players. It has occurred to me that TOI% is really a “Rel” statistic in the sense that TOI% gets handed out to players based on how the players compare to the rest of the team and not the rest of the league. So, in comparing TOI% to overall stats such as GF%, CF%, Sh% I am not really comparing apples to oranges. TOI% is a statistic relative to the players teammates while those other stats are relative to the league. In this post I plan on getting around this by looking at those other statistics relative to the players teammates where the Rel stats are calculated  by On Ice – Off Ice. Here is what we get.

TOI% vs R^2
GF60Rel 0.612
GF60 0.568
CF60Rel 0.547
Sh%Rel 0.484
CF%Rel 0.458
Sh% 0.453
GF%Rel 0.392
CF60 0.340
GF% 0.309587
CF% 0.157341
GA60Rel 0.132
GA60 0.104
Sv%Rel 0.095
Sv% 0.089
CA60 0.003
CA60Rel 0.002

In most cases the Rel stats have a higher correlation than the straight stats which makes perfect sense. A bad team still needs to give ice time to some not so good players and a great team will be limiting ice time to some relatively good players. When we compare players to their teammates and not the league as a whole we would expect the correlation with TOI% to get stronger and we do.

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What else does TOI% correlate with?

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Jun 272015

A few days ago I wrote a post looking at whether scoring chances and high danger scoring chances does a very good job at explaining variations in on-ice shooting percentages among NHL forwards. The short answer is that they do explain some of it (scoring chances better than high danger scoring chances) but is still a long way from being an ideal explanatory variable. We know that because I found that TOI% (the percentage of ice time the coach assigns to the player) had a far better correlation with shooting percentages.

In this post I want to take a look at how TOI% correlates with other metrics because it will tell us how coaches decide to dole out ice time. Using statistics from Puckalytics.com I come up with the following table of how well TOI% explains various metrics. To attempt to eliminate score effects I am using 8-year 5v5close data for all forwards with >2000 minutes of ice time.

TOI% vs R^2
GF60 0.568
Sh% 0.452
CSh% 0.441
CF60 0.340
GF% 0.310
CF% 0.157
CSv% 0.130
GA60 0.104
Sv% 0.089
CA60 0.003

It is clear coaches dole out ice time based on offense with a slight preference for shooting percentage over shot generation. Coaches don’t give the big minutes to the best defenders and especially not to those that focus on shot suppression.

It is interesting that coaches don’t seem to value defense as much as offense and especially shots against. It is probably an important reason why we find it so difficult to find trends and relationships in defensive statistics. It is a little odd because we know that some coaches clearly stress defense more than others and yet they don’t seem to be doling out ice time based on defensive results. This observation is likely the result of the belief that defense is a product of the system being employed by the team and less so the talent of the individual players where as offense is far more individual player talent driven.

If you can put the biscuit in the basket you will get a lot of ice time. For those who can’t, ice time is doled out more on who is most dedicated to playing the role they have been assigned in the system the coach has put in place.

What is interesting is if anything analytics is driving the bias towards offense even more. Guys like  Johnny Gaudreau (all 150lbs of him) and Tyler Johnson are all the rage right now. Watching my twitter feed I see analytically inclined Leaf fans getting excited for every small, skilled draft pick the Leafs make while ridiculing pick ups by other teams of big strong players like Los Angeles Kings with Milan Lucic.

Small, fast, skilled players that can move the puck up the ice and put the puck in the net and in while stay at home defensemen and defensive specialist forwards are getting pushed aside. I wonder how the table above will look 5 years from now.


Jun 242015

This past season War on Ice introduced two new shot quality metrics – Scoring Chances (SC) and High Danger Scoring Chances (HSC) which are defined here.  Stephen Burtch has previously evaluated this scoring chances with respect to their ability to predict future goal scoring and goal differentials and found them to be a better predictor than traditional possession statistics. As a strong believer in shot quality I am not surprised by this conclusion but with this post I want to take a closer look at really how well these metrics are at measuring shot quality.

The premise underlying this analysis is a simple one. The higher the percentage of overall shots (or shot attempts) that are scoring chances or high danger scoring chances the higher the likelihood the player will post a higher shooting percentage. So, I will evaluate the following “on-ice” relationships:

  • HSCF/CF vs CSh%
  • HSCF/SF vs Sh%
  • SCF/CF vs CSh%
  • SCF/SF vs Sh%

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