David Johnson

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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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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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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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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Jun 032015

Over the last several days I have tweeted several times (here, here and here) about my Sv%RelTM statistic which can be found on Puckalytics.com which generated some interest from my followers as well as some skeptics.


The issue I have with that statement and others like it is that it uses a simple statistical model, applies it to all players, and then draws conclusions about all players based on the results without actually really understanding what the model is telling us or understanding all the inherent problems with measuring players ability to impact shot quality against.

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Is Hockey Analytics altering outcomes yet?

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Apr 262015

Hockey analytics is well behind analytics in other sports, particularly baseball, but we are now several years into what I will call modern (or current) hockey analytics which has largely focused on possession statistics such as Corsi and Fenwick. Last summer we even saw a number of teams publicly adopt analytics by picking up some prominent people from the public domain. Toronto, Edmonton, Carolina, Florida, and New Jersey to name a few. Results for those teams have clearly been mixed thus far but the greater question is whether hockey analytics, and possession analytics in particular, has had a greater impact on the game than just those few teams. I hope to answer some of those questions today.

One of the reasons why possession statistics such as Corsi became so popular is that it has shown that good possession teams often do well and it has also been identified as an undervalued skill as Eric Tulsky wrote about a couple of years ago. Contracts and salaries were generally given by teams to reward skills such as shooting percentage more than possession skills and thus possession skills were an undervalued talent. Teams could tap into this undervalued talent by getting good possession players at a fraction of the cost of good shooting percentage players. I warned that focusing too much on possession statistics is potentially harmful in the long run as it could result in players altering their playing style at the expense of what really matters, out scoring the opposition. I have shown that there is likely at least a loose inverse relationship between Corsi and shooting percentage implying that boosting one Corsi often has the negative consequences of reducing ones shooting percentage. I did this by looking at the impacts of coaching changes on Corsi and Shooting percentage and looking at the relationship between team CF% and Sh% when extreme outliers are removed.

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Is 4v4 overtime hockey a crap shoot we can or should ignore?

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Apr 132015

Since the Los Angeles Kings have been eliminated from the playoffs there has been a lot of discussion about why a team with such a good possession game failed to make the playoffs. This included my article from yesterday which generated a fair amount of discussion as well. A lot of the discussion can be summarized by the following tweet by Sunil Agnihotri referencing a comment by Walter Foddis.

The last paragraph is the one that interests me most.

“The substantive reason for LA not making the playoffs is the OT system, which does not reflect team strength. Statistically, OT outcomes have been shown to be a crap shoot. LA was unlucky in OT”

The fact that LA went 1-7 during overtime play does in fact mean that they were unlucky during OT play. They are a better team than that for sure (every team is expected to do better than that). OT results over the course of a single season are extremely random and thus one could consider them a crap shoot. The challenge I have is just because something is highly variable does that mean it is meaningless in our evaluation? Being unlucky in over time does not mean you are unlucky overall.

I’d hazard a guess that outcomes of the first 5 minutes of the second period for games that are played on a Thursday are highly random too. If a team missed the playoffs and had a terrible goal differential during the first 5 minutes of the second period in games that are played on a Thursday can we chalk up missing the playoffs to bad luck during the first 5 minutes of the second period in Thursday games? No, of course not. We don’t get to pick and choose what good luck or what bad luck we can blame results on. Just because we are more aware of bad luck that happens in overtime games doesn’t mean it is more important bad luck worthy of attributing blame to.

The reality of the situation is that unless you can be certain that the Kings OT bad luck is not offset by good luck during the remainder of the game you can’t blame the Kings missing the playoffs on their OT record.  I haven’t seen the complete luck analysis of the Kings season done to claim the Kings were unlucky during regulation and OT play as a whole so I am pretty reluctant to blame the Kings playoff miss on their OT record just yet.

The interesting question for me is whether 4v4 play is indicative of overall talent because if 4v4 hockey requires a completely different skill set then one could conclude that overtime play isn’t representative of true hockey talent. To answer this question I took the correlation between each teams 5v5close GF% over the past 8 seasons (to get large sample sizes though it would reduce the spread in talent) and compared it to their 4v4close GF% over the past 8 seasons (I used close since most 4v4 ice time is in OT and thus in close situations). Here are the results.


And the same for CF%.


Those correlations are good enough for me to consider that 5v5 skills are fairly transferable to 4v4 play and vice versa. Over small samples strange things happen, but to suggest that 4v4 play isn’t indicative of hockey skill and that is why one should ignore OT results is not valid either.

An interesting observation is that the slope on the CF% chart is almost exactly 1.0. The slope on the GF% chart is significantly higher than 1.0 which might indicate that 4v4 play is actually a better indicator of talent than 5v5 play (if you are good at 5v5 play you should be even better at 4v4 play). That said, if I force the intercept to zero the slop drops to 0.9958 or almost exactly even (and r^2 drops to 0.3123 with zero intercept) so maybe 5v5 and 4v4 are on par with each other. Regardless, this should at least alleviate Steve Burtch’s concern that poorer teams are more likely to score first during 4v4 play than during 5v5 play. I don’t believe that to be the case.

Now when we talk about shoot out record I think that it is safe to assume that the shoot out is a lot further from being representative of actual hockey talent than 4v4 play. There is probably not enough shoot out data to actually be able to do a similar analysis with any degree of confidence but I doubt there is much disagreement that the shoot out is a long way from being representative of real hockey.


Apr 122015

The other day I posted the following twitter comment after the Flames defeated the Kings to gain a playoff position while simultaneously eliminating the reigning Stanley Cup Champion Los Angeles Kings from the playoffs.

I posted this comment for two reasons. First because I think if you are being honest about evaluating possession analytics you have to consider the failures on an equal ground as the successes. I am certain that if the Kings defeated the Flames and ultimately made the playoffs over the Flames there would have been people that would use it as evidence that possession analytics is good at predicting future results. That would be a fair thing to do but you have to consider the failures too and possession analytics failed twice here, first with the Flames making the playoffs and second with the Kings missing. So, I made this comment because analytically it is the correct thing to do and I felt it needed to be said.

The other reason I made this comment was to see how people would react and to see whether people would react with fairness as explained above or in a defensive manner defending possession analytics and dismissing the Flames/Kings outcome as largely luck. For the most part the reaction was more subdued that I had thought but there were some jumping in defense of possession analytics including the following tweet from @67sound.

If you are relying on the LOS ANGELES KINGS to minimize the importance of possession metrics I don’t even know where to begin.

This is an over reaction because I didn’t actually try to minimize the importance of possession, I was just pointing out where it failed. If you follow me I use possession metrics all the time, I just think that there is too much consideration for when possession metrics succeed in predicting outcomes and too little consideration of when it fails and when other metrics succeed. I have talked about this before on a few occasions where people want to point out how well possession metrics are at predicting outcomes but not actually comparing the success rates against other predicting methodologies. In many instances possession statistics do a great job at predicting outcomes, but often goal based metrics actually do slightly better.

The follow up discussion to my tweet soon started to rationalize why the possession stats failed in predicting the Los Angeles Kings missing the playoffs.

Scott Cullen of TSN.ca wrote the following in his Statistically Speaking column about the Kings.

For starters, the Kings were 2-8 in shootouts and 1-7 in overtime games. Given the randomness involved in shootout results, that’s basically coming out on the wrong end of coin flips. 3-15 in overtime and shootout games, after going 12-8 the year before, is enough in tightly-contested standings, to come up short. Records in one-goal games tend to be unsustainable, but there’s enough of them in hockey that they make a huge difference in the standings.

Most of these are fair comments. The shootout record in almost completely random and not actually representative of how good they are at playing hockey (though I disagree with overtime records not being useful in evaluating how good the Kings are at playing hockey). With a bit better fortune the Kings likely would have made the playoffs and probably should have. The thing is though we all need to be careful not to use “luck” as a tool in confirmation bias as luck can be used to explain everything. Flames made the playoffs, write it off as good luck and move on without blinking an eye. They will regress next year, just watch. Kings missed the playoffs, write it off as bad luck and move on without blinking an eye. They will be better next year, just watch. A thorough review needs to be conducted, not just quickly write off anything that goes counter to our beliefs/predictions as luck.

The Kings missed the playoffs this year with 95 points. The previous four seasons they have had 100, 101 (prorated over 82 games), 95, and 98 points. So, on average the LA Kings have been a ~98 point team over the past 5 seasons. If they went 5-5 instead of 2-8 in shootouts that is exactly where they would have finished. For the most part this Kings team is what they have mostly been and what we probably should have expected. That is a good, but not elite, regular season team. Over these past 5 seasons they have finished 18th, 10th, 7th, 13th and 12th place overall. That actually compares somewhat poorly to the cross-town Anaheim Ducks who have finished 3rd, 2nd, 3rd, 25th, and 9th over the past 5 seasons. The Kings score adjusted Fenwick % over that time is 55.3% compared to the Ducks 50.3% and yet four of the five seasons the Ducks finished ahead of the Kings in the regular season. The reason for this is the Ducks have a 9.19 5v5close shooting percentage over the past four seasons compared to the Kings 6.69%. That difference is not luck. It’s a persistent repeatable skill that possession analytics doesn’t capture. Barring major off season roster moves no one should be predicting the Kings to end the regular season ahead of the Ducks next season. I suspect some will though just as was done for this season when using possession analytics to predict regular season point totals (Kings were predicted to get 107 points, Ducks 91).

So the Kings have been a pretty good but not a dominant regular season team. They have won the Stanley Cup twice during this period and have been a dominant possession team which has given us the perception that they are an elite team. Is it possible that we have generally over rated them because of their possession and post season success?  Maybe. Are they really a great team or just a good one that got hot when it mattered a couple times? It’s a question worth asking I think but if you just chalk up missing the playoffs this season to luck it is probably one you won’t be asking.

While we are on the subject of teams that were predicted to regress this season one such team is the Colorado Avalanche. A lot of people are tossing them out as an example of where possession statistics successfully predicted their failures this season. A major reason for predicting this regression was due to regression in their shooting and save percentages as Travis Yost of TSN.ca wrote prior to the season.

Using that regression for forecasting purposes, expect Colorado to shoot around 7.89 per cent for next year at evens and stop around 92.47 per cent of the shots.

Those are 5v5 shooting and save percentages Yost is talking about. In actual fact Colorado’s shooting hasn’t regressed this year as it is more or less identical to last seasons 5v5 shooting percentage (8.75% this season vs 8.80% last season). Save percentage has regressed almost what Yost predicted (92.52%) so he was right there (the role luck played in this is unknown though) but a major (and maybe the primary) reason for the Avalanche’s failures this season is they are playing a substantially worse possession game than last season. Colorado’s 5v5close CF% dropped from 47.4% last season to 42.9% this season which is a massive drop and likely the major reason for their failures this season. That drop can largely be attributed to letting two of their best CF% players leave in the off season – Paul Stastny and PA Parenteau and replacing them with poorer possession players in Iginla and especially Briere. Coaching may be a factor too. So some of the Avalanche’s failures this season can be attributed to a regression in save percentage but a significant part of it is due to poor off-season roster decisions.

Once again, we need to be careful with the “I told you they would regress” and leave it at that if the majority of their regression is due to factors you didn’t predict (to be fair Yost did mention that the Avalanche’s possession might drop a bit due to roster changes as well but it wasn’t the crux of his argument). It is quite possible, if not highly likely, the Avalanche is in fact a well above average shooting percentage team and we shouldn’t expect it to regress next season just as we shouldn’t expect the Ducks to either.

I need to reiterate here that it isn’t that I don’t believe that possession is an important aspect of the game. It is. It is why the Kings are good despite terrible shooting talent. It is why the Leafs are bad despite good shooting talent. What I really want to see and why I always point out where possession failed is because I want to ensure is that everyone evaluates possession fairly in the context of the complete game. I often hear things like “no one ever said possession was everything” and yet I frequently hear claims made without any mention of factors other than possession metrics. The Kings being a perfect example. Everyone assumed they were a great team that, barring massive bad luck, would make the playoffs and when they didn’t make the playoffs they started throwing out all the evidence of that bad luck. Truth is it was perfectly reasonable to predict that with even a little bit of bad luck the Kings could miss the playoffs though I don’t recall anyone really suggesting that (correct me if I am wrong though). It is also fair to suggest that if Colorado made smarter off season roster moves they could have been a playoff team again and not regress nearly to the extent they did but the discussion about the Avalanche revolved around bad possession, high PDO, they were lucky and will regress a lot. I want to see a better balance in hockey analytics as I think too much of hockey analytics is dominated by possession analytics. That is why I write tweets like the one about the Kings and Flames. There needs to be more balance.

So, my final words of advice is if you don’t believe that possession is everything (which apparently none of you do) you ought to be doing more than just conducting possession analytics. If you can honestly say you are doing that I congratulate you. If you can’t, well, what you do next is up to you.