Quantifying Gretzky’s “Office” in The Modern NHL

If you start learning about Wayne Gretzky’s iconic hockey career, you will eventually hear about something called “the office.” The area behind the net was dubbed “the office” because it was where Gretzky wreaked havoc on opposing defenders and preyed on helpless goalies. By taking the puck behind the net, Gretzky forced the opposition to pay attention to him while turning their backs to his four other teammates. His spectacular play in “the office” is one of the reasons he tallied so many assists throughout his career. While teams have gradually adapted their defensive systems and tactics to control this area of the ice, the advantages of playing the puck behind the net in the offensive zone still exist. Courtesy of Ryan Stimson and the Passing Project, we can now analyze data from the 2015-16 season to see which forwards succeed by playing below the icing line.

Quantifying play within this area may seem complicated at first, but we can make it easier for ourselves by simplifying things as much as possible. While Gretzky’s “Office” was only the section of ice behind the net, we’ll expand our version of this space to include the entire area below the icing line.  We can be certain that every shot assist from below the icing line has two characteristics:

  • A player located below the icing line makes a pass to a teammate.

  • The recipient of the pass shoots the puck.

These two characteristics can now be quantified using the data released in the article linked above. Analyzing the data will tell us how often players make passes from “the office” (and elsewhere below the icing line) that lead to shots. We will also be able to see which players receive these passes and take the subsequent shots most frequently. Although we probably won’t find anyone as dominant as we’d expect Gretzky to have been, it is fun to look at the data nonetheless.

Lets begin by determining which forwards are the best at being sources of danger when they have the puck below the icing line. The metric that quantifies this is called Behind-the-Net Shot Assists per 60 minutes, or BtNSA/60 for short. In other words, a Behind-the-Net Shot Assist is any pass that originated below the icing line and led to a shot. We are looking at a sample of 282 forwards who played at least 300 minutes at 5v5 during the 2015-16 regular season games that have been tracked. Here is the top 15:

Screenshot 2017-03-06 15.03.22.png

The first thing I noticed is how good Henrik Sedin is. His 3.201 BtNSA/60 dwarfs the black line which shows the average of 0.972 BtNSA/60. When he has the puck behind the net, he assists on shots more than three times as often as the average forward in this sample. That is very, very good.

Sidney Crosby also shows up here, as does Joe Thornton. Fresh off recording his 1000th career assist a few nights ago, Thornton is considered one of the best playmakers in the league. Their ability to create offence in this way is merely one reason why they are both elite players.

While a Behind-the-Net Shot Assist is awarded to a player who completes this type of  pass, the next step is to see which players were frequently on the receiving end of these passes. But first, a bit of clarification. Since not all passes lead to shots, the following distinction is crucial: behind-the-net passes that lead directly to shots are different than behind-the-net passes that do not. The latter is not a Behind-the-Net Shot Assist because a shot was not taken after the pass was completed. Because we want to know who is taking these shots most often, we should not assume that these shooters are also the most frequent recipients of behind-the-net passes. It is important to know what the data is telling us before we analyze it.

Here is the leaderboard using the same sample as before, but this time we are measuring forwards by how often they shot the puck after receiving a behind-the-net pass:

Screenshot 2017-03-07 13.05.57.png

Nino Niederreiter shows up first here, taking a shot following a behind-the-net pass more than once every 30 minutes (2.159 times every 60 minutes) at 5v5; the average is 0.830 iBtN Shots/60. Niederreiter’s result is interesting itself, but more importantly, I believe, is that the previous leaderboard revealed that he is also one of the best passers from below the icing line. If you look at the first leaderboard again, you will see that Niederreiter shows up third overall, with 2.491 Behind-the-Net Shot Assists every 60 minutes at 5v5. This suggests that Niederreiter might have a subtle talent that is overlooked by the traditional hockey statistics. Further analysis of Niederreiter’s play below the icing line should be conducted to determine how and why he is so successful in both of these categories.

While Henrik Sedin led the way in terms of Behind-the-Net Shot Assists, his twin brother, Daniel, was the 6th most frequent shooter following a pass from below the icing line. I found this to be quite fascinating because their chemistry is undoubted by traditionalists and the analytics crowd alike, and this is yet another lens through which we can view it. Henrik is responsible for digging the puck out from below the icing line and then passing it in front, where Daniel is waiting to receive the pass and to shoot the puck towards the net. Playing below the icing line is one of the ways the Sedin twins create their magic.

Altogether, we can graph each player’s results in both metrics to visualize where their results rank in both areas.

Screenshot 2017-03-08 12.01.06.png

The x-axis measures the first metric in this post: Behind-the-Net Shot Assists. Players who appear to the right of the vertical green box are the best passers from below the icing line. Along the y-axis, you will find the most frequent shooters following a behind-the-net pass. The forwards who appear in the second leaderboard shown in this post are the ones who appear towards the top of this graph. The boundaries of both green boxes represent one standard deviation above/below the mean (i.e. the gray line) for their respective metrics. In the top right, you can find the players who are most involved in passing from behind the net and shooting the puck following these passes.

Two linemates on the Washington Capitals – Niklas Backstrom and Alex Ovechkin – are another interesting case study here. They spend most of their 5v5 ice time together, yet they appear on opposite ends of the graph. We can see that Backstrom is found in the bottom right, meaning he records Behind-the-Net Shot Assists quite frequently but is rarely the shooter, while Ovechkin is found in the upper left, indicating that he is the shooter more often than the passer. This passes (no pun intended) the eye-test because everyone knows that Ovechkin loves to shoot and is very good at it, too. Like the Sedins, I’d presume that Backstrom and Ovechkin work together to generate offence from below the icing line. Perhaps this is one reason why Ovechkin is arguably the best goal scorer of this era.

If you would like to see how the players on your favourite team perform in these metrics, you can do so here.

The ability to generate offence from behind the net has clear strategic advantages for teams who seek to be as offensively dynamic as possible. While entering the offensive zone with control of the puck is the best way to generate shots, teams with players like Nino Niederreiter or the Sedin twins in their lineup will probably create more shots following a dump and chase than teams without them. Sure, dump-ins are inferior to controlled entries, but players who can generate offence from “the office” or elsewhere below the icing line minimize the gap between both options.

Although I don’t suggest that coaches should preach the dump and chase, I assume that this strategy might be less risky if conducted with players who excel at generating offence below the icing line. In the future, we will eventually be able to pair this data with zone entry data to determine if this impact actually exists. Assuming that the impact is noticeable, I highly doubt that it will turn out to be a preferable option to controlled entries. At best, it is most likely the best option for an inferior strategy.

Of course, all of this theory assumes that playing below the icing line is a skill that can be repeated year after year. In other words, is a player’s BtNSA/60 a repeatable skill? From  the post linked at the beginning of this article, we know that BtNSA/60 is one of two components within a metric called Danger Zone Shot Assists. Compared to G60 (i.e. current scoring rate), Danger Zone Shot Assists per 60 minutes is a metric that is not only more repeatable, but is also a better predictor of future scoring than a players current scoring rate itself. A players Danger Zone Shot Assist rate is calculated by adding his Behind-the-Net Shot Assist rate with his Royal Road Shot Assist rate (i.e. how often he completes passes which travel across the slot prior to a shot being taken). While BtNSA/60 itself might not be an improvement upon existing metrics, it is definitely a core component of a statistic that improves our ability to predict a players future scoring rate.

Predicting future scoring is very difficult, especially in an era where goals are scarce and defences thrive. The data from the Passing Project is one resource that can help us discover new ways to repeatedly create offence — a mission that is now more important than ever before. The data shows us how players like Niederreiter and Backstrom break down these modern defensive systems by posing offensive threats whenever they have the puck below the icing line. Passing from this area is an avenue for success in the low-scoring environment that is today’s NHL. But for Gretzky, it was just another day at “the office.”

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Hello Anchoring Bias, My Old Friend: How Anchors Impact Player Evaluation

Pierre McGuire once offered the following analysis of a top prospect at an NHL Entry Draft: “Toronto wins big here. This is huge for the Toronto Maple Leafs and their organization going forward. This is the start of their rebuild, and you couldn’t build it on better shoulders than [Player X’s].”

Without any context, it may seem obvious who McGuire is talking about here: Auston Matthews. Indeed, McGuire is certainly implying that whichever player he is discussing is bound to become the Leafs saviour someday. It should not surprise you to learn that this quote was said during the first round of an NHL Draft, rather than a later round. After all, describing a player in such a manner is normally reserved for generational talents; these kinds of players do not fall very far in the draft, if at all. When analysts describe players in this light, casual fans expect nothing less than greatness, especially in Toronto.

So would it surprise you if Pierre McGuire is describing a player not named Auston Matthews? Here is some additional information to consider: McGuire is talking about a player selected in the top 5, but not first overall. The mystery player is not Auston Matthews (1st in 2016), nor is it William Nylander (8th in 2014). Perhaps he is describing Mitch Marner — the Leafs 4th overall pick in 2015? While I don’t blame you for being convinced that Marner is the correct answer, this is still false.

The final clue is that Player X is a defenceman who was drafted 5th overall by Toronto. Unfortunately, Morgan Rielly (5th overall in 2012) is not the correct answer either. It should be clear by now that there is something wrong with McGuire’s analysis of the player. From the quote alone, though, it is implied that we should expect greatness from this player. But in hindsight, without even knowing the player yet, we know that this is not what the player turned out to be. Not at all.

As the Leafs management team walked up to the podium to draft Player X, McGuire continued his analysis by saying: “When you can get a player like Luke Schenn, you just go crazy as a scout. This is a franchise player.”

(If you don’t believe me, see for yourself by watching Schenn get drafted).

Yes, Player X is indeed Luke Schenn — the 5th overall pick at the 2008 NHL Entry Draft. It is fine if you are surprised; what you feel right now is only a tiny amount of the surprise felt by Leafs fans upon realizing that Luke Schenn is actually a much worse version of the player many analysts like McGuire proclaimed he would to become. In reality, he never lived up to the unrealistic expectations that were set for him. The praise bestowed upon Schenn was not only preached by McGuire, though. To the best of my memory, many other analysts were wrong about Luke Schenn at the time, so it is unfair to single out McGuire for this mistake. Neveretheless, many of us fans still find it difficult to view Schenn as the player he is, because in our minds, he will always be the superstar who never was.

Naturally, the next logical step is to determine where everyone went wrong with Schenn. How and why do our expectations for some players get so out of hand? In Luke Schenn’s case, and many others like it, his status as a top 5 pick in the NHL draft attached to him like a parasite. For most NHL players, however, their reputations are not adversely affected by their draft position to the same extent as Schenn’s. But that does not mean it has no effect whatsoever. In general, a players draft position is likely to be mentioned whenever they are traded, signed as a free agent, or even discussed in conversation. As fans, we use their draft position as a reference point to begin formulating our opinions of the player. This is especially the case for younger players with little to no NHL experience. Despite the fact that players cannot control where they are drafted or the amount of hype surrounding them, our first impressions of a player significantly influence our judgements of their performance.

If our opinions of players are completely rational, we would never use a players draft position as a reference point to judge them. But as hockey fans, we know this is not the case. In Daniel Kahneman’s book, Thinking Fast and Slow, he explains how we allow our first impressions to lead us astray:

“The phenomenon we were studying is so common and so important in the everyday world that you should know its name: it is an anchoring effect. It occurs when people consider a particular value for an unknown quantity before estimating that quantity. What happens is one of the most reliable and robust results of experimental psychology: the estimates stay close to the number that people considered—hence the image of an anchor… If you consider how much you should pay for a house, you will be influenced by the asking price. The same house will appear more valuable if its listing price is high than if it is low, even if you are determined to resist the influence of this number… Any number that you are asked to consider as a possible solution to an estimation problem will induce an anchoring effect.

By using a players draft position to evaluate their potential, we are surrendering ourselves to the anchoring effect. The value that we consider is the players draft position, while the outcome we want to estimate is the future performance of that player. This presents a problem because we already know a player has zero control over their draft position, yet we use that value to estimate their future performance.

Like many problems, they often teach us important lessons. The lesson we can learn from anchoring bias is that an individual piece of information has the power to influence all of our subsequent analysis. Luke Schenn’s draft position suggested that he would become a franchise cornerstone, and the hype surrounding him at the draft validated that expectation. To this day, many fans still blame him for not becoming that type of player. You probably understand that first impressions are easy to form and difficult, if not impossible, to eradicate. The main takeaway here is that our estimations are led astray when we fail to ignore anchors.

Judging a player using irrelevant information like his draft position is no more useful than trying to determine the cost of a house for sale by its listing price. We assume, and oftentimes rightly so, that the value we are told to consider is one that the experts believe is correct. When a house is for sale, it is logical to assume that the asking price is the true value of the house, but it is definitely possible that this number is totally wrong. This is why we need to conduct proper research before we decide on the correct price. The same is true for hockey players: after Schenn was drafted, for example, all subsequent evaluations of his potential were anchored by the expectation that accompanies a 5th overall pick rather than his true talent level. It was easy to get fooled by the anchor because all of the praise surrounding him justified our impulse to refer to his draft position. Both factors collaborated to induce unrealistic expectations for him.

As it turns out, his true talent is closer to being a bottom pairing defender than what you should expect from a 5th overall draft choice. He still provides some value, but he is nowhere near as valuable as a superstar. This does not mean that he belongs in the AHL, but we often speak of him as if the opposite is true. The anchoring effect therefore minimizes our ability to acknowledge Schenn’s value.

In order to ignore anchors, we must be able to identify them to begin with. This is simply another way of saying that if we want to evaluate players more accurately, we must separate the signal from the noise. A players context-neutral production is the signal, and his draft position is the noise. Mistaking the latter for the former is what convinces analysts like Pierre McGuire that Luke Schenn is destined for greatness. By teaching ourselves how to recognize and avoid anchors in our everyday lives, we can provide more objective analyses.

Another manifestation of the anchoring effect is hidden within this article itself. Before I revealed that Pierre McGuire was describing Luke Schenn, you were expecting Player X to be a superstar. There is no indication in the opening paragraph that Pierre McGuire was not describing Auston Matthews. Due to this, your expectations were anchored on the expectation that Player X was either Auston Matthews or a similar Leafs player. Once I revealed the true identity of Player X, your thoughts on both Luke Schenn and McGuire’s analysis were heavily influenced by that anchor. While the hidden anchor within this article is a quote rather than a number, it still induces the same effect. In this case, our expectations are based on a certain “value”  — Auston Matthews is much more valuable than Luke Schenn — and we are surprised when that expectation is not met. The lesson this teaches us is that although anchors are sometimes useful, they are also capable of misleading us.

Although anchors influence our estimations, it is possible to move our estimations away from the anchor to a certain extent. Many of you likely experienced an adjustment of some sort as I slowly revealed the identity of Player X. Adjusting away from an anchor is also accounted for in Kahneman’s description of the anchoring effect, but he adds that “the adjustment typically ends prematurely, because people stop when they are no longer certain that they should move farther.” As I revealed more information, you became increasingly uncomfortable having Auston Matthews as your answer. You gradually adjusted your guess away from Auston Matthews and towards Morgan Rielly. By the time I revealed the final clue — Player X is a defenceman who was drafted by Toronto 5th overall — you would not have been surprised if I had said that Player X was Morgan Rielly. Sure, Rielly doesn’t exactly fit McGuire’s description of Player X, but he is still a major part of the current Leafs core.

Regardless of how well you adjusted from Matthews to Rielly, however, you were almost certainly surprised when you realized the true identity of Player X. This happened because you were unable to entirely ignore the anchor. You were expecting a superstar, and I gave you a bottom pairing defenceman. In this way, you can now see how all subsequent analysis is influenced by your first impressions.

In general, this is also how many hockey fans evaluate NHL players: we are anchored on where they are drafted, and we adjust our opinions of them as their careers progress. This is especially true around the trade deadline  — a time when many potential trades are being discussed by fans and analysts alike. In particular, the Leafs are still searching for a number one defenceman. Those who don’t believe that the Leafs need one will point a finger towards Morgan Rielly and say “he was drafted 5th overall in 2012 and is currently playing on the top-pair — he is our number 1 defenceman!” While you know that Rielly is definitely better than Luke Schenn, you also might believe that Rielly has not shown that he can succeed on the top pair just yet. That does not mean that Rielly is bad, it simply means that his maximum potential might only be as a top 4 defenceman, not a top 2. Morgan Rielly still provides value, but you know not to justify your impulse to call him a superstar by referring to his status as a former 5th overall pick. Less knowledgeable fans will be influenced by the anchor, but you won’t. You know better by now.

Finding Elite Defence Pairings Using Z-Scores

The NHL Awards Show is meant to be an entertaining experience for fans, but it is impossible to satisfy everyone.  Last year, many people were outraged that Drew Doughty was named the NHL’s best defenceman after a season in which the numbers suggested that he was slightly worse than Jake Muzzin, his own defence partner. In a sense, this outcome represents a fundamental divide amongst hockey fans with respect to how we evaluate defencemen. Traditionalists argue that Doughty was the best defenceman in the league that season and therefore deserved to win the Norris trophy. Those who value objective statistics, however, believe that Doughty was probably not the best defenceman on his own team, let alone the best in the entire league. Despite these differences in opinion, one fact that both sides can agree on is that Doughty-Muzzin is an elite defence pairing.

For the L.A. Kings and their fans, the “Doughty versus Muzzin” argument matters less than the “Doughty AND Muzzin” argument; if there was an award for the NHL’s best defence pairing, Doughty and Muzzin would likely be in the conversation every year. While the Norris trophy attempts to identify the best defenceman in the league, I will now try to quantify the output of elite defence pairings. By the end of this post, you will have a better idea of how to spot an elite defence pairing by looking at their statistics.

We’ll define a defence pairing as two defensemen who have played together for at least 500 minutes at even-strength since the 2013-14 season. Since I downloaded the data from corsica.hockey during the All-Star break, this sample does not include data from games played after January 27, 2017. In order to figure out how elite defence pairings perform, the first thing we need to do is determine what separates an elite pairing from a good pairing.

Defining Elite – An Important Analogy:

The first step we need to take is to determine what separates an elite pairing from a good pairing. In order to explain my methodology, we’ll use an analogy from school.

Imagine that you are in school and your teacher is returning your test grades. The teacher gives you two numbers: your mark (84%) and the class average (80%). Immediately, you know that you are 4% above average. This is certainly good because it tells you that you did better than most of your classmates. What it doesn’t tell you is how much better you did. You know that you’re mark is somewhere amongst the students who scored above average on the test, but you do not know your specific location amongst them. Is your mark exceptional, or are you barely above average? If you want to answer this, you need to ask your teacher for one more piece of information: the standard deviation. Using this third number along with the two numbers from before allows you to calculate your z-score. If you are already familiar with z-scores, you can skip the next section.

Calculating and Interpreting Z-Scores:

Your z-score is the answer to the question: how many standard deviations above the class average is my mark on the test? I prefer using z-scores to interpret my test results whenever possible because they are easily to calculate and simple to interpret. In fact, you are already halfway there! You already know that your mark is 4% above average because you subtracted your mark from the average. If you divide that number by the standard deviation, you have successfully calculated your z-score.

Your z-score will be a positive number if your mark is above average or a negative number if it is below average. If your z-score is 1, your mark on the test is 1 standard deviation above the class average. A z-score of -1 means that your mark is 1 standard deviation below average. Assuming we’re dealing with a normal distribution of test marks, 68% of your classmates will have z-scores somewhere between -1 and 1. The remaining 32% are either below -1 or above 1. Since we’re determining whether your test score is exceptional or merely good, we need to know if your z-score is above 1 or somewhere between 0 and 1.

If the standard deviation is 5, your z-score is 0.8 (since 4/5 = 0.8) which tells you that you are 0.8 standard deviations above the mean. That is good, but not great. But if the standard deviation was actually 3, your z-score would be 1.33 (since 4/3 = 1.33) which means that your mark is 1.33 standard deviations above the mean — a very good score! Ultimately, z-scores help us distinguish between exceptional and unexceptional results.

How can z-scores help us define “elite”?

Similarly to how we can use z-scores to interpret a mark on a test, we can also use them to interpret the results of a certain defence pairing in a specific statistic. Since there are a variety of statistics that can be used to evaluate defence, we will look at each statistic one-by-one. In any given statistic, I will define elite as any result that translates to a z-score of 1 or greater. In general, this will translate to the results that fall into the 84th percentile or greater*. If we want to determine if Doughty-Muzzin are elite shot suppressors, for example, we need to know the z-score of their on-ice CA60 (shots against per 60 minutes). Their CA60 of 44.87 is 1.83 standard deviations better than the CA60 of an average defence pairing, so they are elite shot suppressors.

*Why the 84th percentile? Recall from the previous section that 32% of all results will have z-scores that are either less than -1 or greater than 1. If these two subsets of the sample account for 32% of the entire sample, then each individual subset accounts for 32/2 = 16% of the sample. Since we’re concerned with the best (i.e. elite) results, we’re looking at the results with the highest z-scores and highest percentiles. 100 is the highest possible percentile, so 100 – 16 = 84, hence the 84th percentile or greater.

We now have sufficient information to be able to answer the following question: what do the statistics of an elite defence pairing look like? In the chart below, you can see for yourself, with explanations to follow.

Screenshot 2017-02-08 16.12.21.png

The first column of this chart is titled “Average of Elite Pairings” because it is the average result of all pairings whose z-scores were at least 1. While the first column gives us a general idea of what should be considered an elite result in each statistic, the second column, “Cut-off,” is the worst result in the sample which was considered elite. In other words, the “Cut-off” is the worst result in the sample that translates to a z-score greater than or equal to 1. Aside from z-scores, the third column is another way to translate where a cut-off ranks amongst the sample population. For example, in order for a defence pairing to be considered elite at suppressing scoring chances, they need to have a SCA60 of 6.47 (or lower) which translates to the 83rd percentile (or better) and a z-score greater than or equal to 1.04. Altogether, the chart above is a useful reference for evaluating the performance of above-average defence pairings.

Applications:

We’ve seen how z-scores can be a useful tool for distinguishing between elite and above-average results. The most obvious way that we can use them to judge defence pairings is by comparing the performance to other pairings using z-scores. I have done this with Doughty-Muzzin in the chart below:

Screenshot 2017-02-08 17.19.33.png

When Doughty and Muzzin are on the ice together, they are elite shot suppressors and are above-average at preventing scoring chances and in expected goals. None of this is surprising.

Another way to incorporate z-scores into our evaluations is to make a leaderboard for pairings in a certain statistic using z-scores as a cut-off rather than arbitrarily doing a “top 10” or “top 15.” I made this viz so you can see all defence pairings who are elite in CA60. You can also use the z-CA60 filter to view all of the worst pairings in CA60 as well, by setting it to show everyone with a z-score of less than 1. (A few Leafs pairings will show up here, by the way). 

Concluding Thoughts:

Altogether, the days leading up to the NHL Awards are often filled with debates surrounding certain trophies. Last year, it was Drew Doughty at the centre of the traditionalist versus analytics debate. Despite the divergent opinions on this topic, his play with Muzzin is certainly deserving of more recognition. Although there are no NHL awards for elite defence pairings, you can use z-scores for the results of individual players as well. So regardless of which player(s) will be involved in the debates this year, you now have another tool in your repertoire to objectively separate elite performance from above-average performance. 

On Claude Julien

It would be disingenuous to call Claude Julien losing his job polarizing. For all intents and purposes, Bruins fans, players [Marchand], coaches from around the league [Babcock], journalists and bloggers have condemned the move. There is a vocal minority that have been calling for his head for the better part of a decade, and not without some justification. A devastating upset at the hands of Carolina; blowing a lead 3-0 series lead to Philadelphia, which was capped off by blowing a 3-0 lead in game 7; being beaten by two inferior teams in 2012 and 2014, the latter after winning the presidents trophy; and finally, missing the playoffs in two straight seasons are some of the unforgivable sins of Claude Julien. Of course, he also brought the Bruins to relevancy again for the better part of a decade, delivering some of the best regular season and playoff hockey the city had seen since the 1970s. A free agent wasteland, a perennial mediocre team, a burgeoning joke of a franchise was turned around by a combination of two massive signings [Savard, Chara] and, a little less than a year later, by the arrival of Julien. Most can look past his failures and see how successful he was in Boston. So could the front office, for a time.

The NHL is cutthroat. Even a single season delivering less than expected of you is enough to lose your job. By these standards, its not surprising Julien was let go; only disappointing. Even when results are out of the control of coaches, they receive the credit and the blame. If one thinks that the cup win and the other trip to the Stanley Cup Final were a product of the personnel handed to him, rather than his coaching, its only intellectually honest to recognize that you must say the same of his failures. A coach can only do so much, night to night. Julien provided a system for the players to work in that produced great shot differentials and, most years, a strong goal differential. He gave them a chance to win. This year, his backup goaltenders did not return the favour. For the last two months, neither did his starting goalies. Julien was also victim of one of the lowest shooting percentages in the league. Maybe some of that was on the system, or shooting talent, but nobody could reasonably expect them to be this bad. That’s not to say the roster wasn’t talented enough, either. Some of the biggest weapons Julien had were shooting worse than their career percentages; Krug, Bergeron, Marchand, Beleskey, and Backes are not a lack of talent, by any stretch of imagination.

We’re left with a question of who to blame. Julien, the players and the front office- because of their poor drafting and questionable at best personnel moves- all share some of that blame. But the main culprit in Julien’s fate was simply luck. That same luck was beginning to turn: the Bruins were scoring more goals at the very end of Julien’s tenure. Unfortunately, it perfectly coincided with a downturn in goaltending. It covered up the fact that their results were beginning to normalize. Even the goaltending results were semi-luck driven. Even if the backups were truly bad, they couldn’t possibly be as bad as their results suggested, much the same as the Bruins goal scoring. There is no room for luck in business. Owners demand answers that are more than the numbers will regress towards the mean, even if it is fundamentally true. In the business world, and even in the general sense, people are uncomfortable with attributing events to random variance. The news will attribute statistically insignificant swings in the stock market to unrelated events, people will convince themselves that the money they made was a result of anything other than the circumstances they were born into, and coaches will get fired because not enough pucks bounced their way.

Now Bruins fans are left wondering what happens next. If you believe some of the more dramatic personalities, firing Julien is the equivalent of Hector putting on the armor of Achilles: ultimately dooming himself and the city of Troy. Maybe there is no escaping fate now. Maybe the Bruins are destined to begin their slide into mediocrity, if they’re not already there, starting this very minute. Maybe, but probably not. The farm system is looking to be the strongest it has been in decades. And in the short term, maybe the luck turns for the new coach, Cassidy. A coach can’t do much more than provide his players with a chance to win every night, which Julien did. Firing him might not signal the end of the Bruins, but it does represent another misstep by the Bruins front office. That front office will soon realise the meaning of death by a thousand cuts. One thing Bruins fans can be sure of is that wherever Julien ends up, success is soon to follow.

 

 

 

 

 

NHL POWER Rankings: January

First of all, these POWER rankings have absolutely nothing to do with straight up wins, they are simply describing how well a team is playing, regardless if they end up with the win, or not.

This month I’m switching it up a bit because of complaints. The single month isn’t really long enough to determine a teams skill, or “POWER;” so I have instead set every team to a 25 GP mark to determine these rankings.

I have made more changes into how I calculate the rankings as well. I have removed adjusted Fenwick save percentage. Instead I used the adjusted Fenwick save percentage in the PDO (Shooting% + save%) to give the teams who have actual shooting talent be rewarded, and vice versa.

There are 6 major components that factor into these POWER rankings (all components adjusted for score, and venue (home/away).

1. Corsi for percentage, or shot attempt percentage (CF%). Weighted at 29%.

2. Expected goals percentage (xGF%). Weighted at 36%.

3. Scoring chances for percentage (SCF%). Weighted at 29%.

4. Special teams expected goals percentage: add power play expected goals for and penalty kill expected goals against, then divide power play expected goals by the total sum of expected goals (STxGF%). Weighted at 6 percent.

5. Penalty differential per 60 minutes (PD/60). Added onto total.

6. Adjusted PDO (Adj.PDO): This years last 25 GP shooting percentage, weighted at 70%. Added onto last years shooting percentage (to stabilize, help reduce effects of potential luck), weighted at 30%. Then you get the shooting percentage added onto the Adjusted save percentage (using Adj.FenSV%) to help account for the quality of the defense in front of the goalie. This is a multiplier on the total.

Before we jump into the rankings, I wanted to state the game-by-game predictive ability of this. So through a fairly small sample size of 179 games (since the time I started this since in the season), this ‘model’ has predicted 59.8% of the games. This doesn’t account for home/away, games played in x days prior to game, or anything like that, though. Maybe I could do that in the future.

Now, onto the POWER rankings. I will display their rank, team name and score. In brackets will be how many spots up or down a team has changed from the POWER rankings back in December. I will also list their top 2 forward point scorers and their top defensive point scorer in the month of January.

1. Minnesota Wild 56.9 (+2)

The Wild rank number one after January. They come into these rankings doing very well at 7-3-0 in their last 10 games. This is a team completely loaded with depth, once they get to the playoffs, they will be a force to be reckoned with. Devan Dubnyk had a worse month than usual, posting a 8-2-0 record with a .910 save percentage. Their top scorers were Mikael Granlund (15P in 14GP), Jason Zucker (13P in 14GP), and Ryan Suter (9P in 14GP).

2. Washington Capitals 56.0 (+6)

Evgeny Kuznetsov has awoken, and so have the Capitals. All of their top players are producing phenomenal numbers, with 4 players in their lineup posting over point per game since January 1st. Braden Holtby has probably cemented himself as the Vezina trophy winner this month, posting a 10-0-0 record with a .932 save percentage. Their top scorers were Evgeny Kuznetsov (22P in 16GP), Nicklas Backstrom (21P in 16GP), and Matt Niskanen (11P in 16GP).

3. Montreal Canadiens 55.4 (-2)

The team is on a bit of an unlucky cold streak recently. They have had injuries to key players such as Brendan Gallagher, and Alex Galchenyuk. Never the less, the Canadiens have still had a good month statistically. Carey Price put up a less than stellar month for the second month in a row, with a 5-6-1 record, and a .906 save percentage. Their top scorers were Alexander Radulov (13P in 15GP), Max Pacioretty (12P in 15GP), and Shea Weber (10P in 15GP).

4. Los Angeles Kings 54.3 (+1)

Currently riding high on a 4 game win-streak surrounding the all-star break. Goalie Peter Budaj continues to impress, posting Jonathan Quick-calibre numbers with a 8-4-0 record and a .927 save percentage. Their top scorers have been Jeff Carter (15P in 14GP), Anze Kopitar (15P in 13GP), and Drew Doughty (12P in 14GP).

5. Boston Bruins 54.1 (+5)

The team with debatably the best line in hockey, with Marchand – Bergeron – Pastrnak. The team as a whole had just a .500 month of February, but it seems as though lots of that blame can be placed on a certain somebody. Tuukka Rask posted a 6-6-1 record with a .874 save percentage, which is just not good enough. Their top scorers were Brad Marchand (23P in 15GP), David Pastrnak (15P in 15GP), and Torey Krug (10P in 15GP).

6. Anaheim Ducks 52.8 (+12)

A team that didn’t score very many goals in January, but also didn’t allow any. It worked just fine for them in the end. John Gibson posted a phenomenal 8-2-1 record with a .946 save percentage. Their top scorers were Ryan Kesler (9P in 14GP), Ryan Getzlaf (9P in 10GP), and Cam Fowler (5P in 14GP).

7. San Jose Sharks 52.7 (+2)

One of the only teams that I believe has been in the top 10 in these POWER rankings for every month so far. Brent Burns has emerged as a front-runner for the Norris Trophy; and Patrick Marleau has had a resurgence in goal-scoring, getting his first ever 4 goal game. Martin Jones posted a 8-3-1 record with a .917 record this past month. Their top scorers were Brent Burns (20P in 15GP), Patrick Marleau (12P in 15GP), and Joe Pavelski (12P in 15GP).

8. Carolina Hurricanes 52.5 (+3)

Cam Ward’s age is playing a factor in his play recently, I’m not quite sure he can handle starting nearly every single game for much longer. Carolina has one of the easier schedules in the league for the rest of the season, and thus still has a chance to make the playoffs. Cam Ward posted a 6-6-0 record with a .883 save percentage. Their top scorers were Jordan Staal (11P in 13GP), Sebastian Aho (10P in 13GP), and Justin Faulk (6P in 12GP).

9. Pittsburgh Penguins 52.3 (-3)

Justin Schultz has posted yet another point per game month, in the absence of Kris Letang. Sidney Crosby and Evgeny Malkin continue to lead the Penguins to greatness, but Conor Sheary is a big loss. Matt Murray posted a 5-3-0 record with a .911 save percentage. Their top scorers were Sidney Crosby (14P in 11GP), Conor Sheary (13P in 11GP), and Justin Schultz (11P in 11GP).

10. Dallas Stars 51.8 (-3)

They have some of the best 5v5 goaltending in the NHL, and they also have some of the worst special teams goaltending in the NHL. What those goalies are doing over there is a mystery. Kari Lehtonen ranks 2nd in 5v5 save percentage since December 1st, but in January he posted a 2-4-3 record with a .900 save percentage in all situations. Antti Niemi on the other hand, had a 3-2-0 record with a .862 save percentage. Their top scorers were John Klingberg (12P in 14GP), Patrick Eaves (11P in 14GP), and Jamie Benn (11P in 10GP).

11. Winnipeg Jets 51.4 (+1)

Ondrej Pavalec proves to be an offensive superstar, as the Winnipeg Jets have been scoring over 4 goals per game with him in the cage. Pavalec has posted a 4-2-0 record with a .901 save percentage. Counter-part Connor Hellebuyck put up a 4-1-0 record with a .903 save percentage. Their top scorers were Mark Scheifele (20P in 15GP), Nikolaj Ehlers (16P in 15GP), and Jacob Trouba (11p in 15GP).

12. Columbus Blue Jackets 51.0 (-10)

A big fall from the Blue Jackets in the POWER rankings, which shouldn’t be too concerning, since they’re almost guaranteed a playoff spot. They could use a rebound month from Sergei Bobrovsky, who’s been great thus far, but not in January. Bobrovsky had a 4-4-0 record with a .908 save percentage. Joonas Korpisalo posted a 3-1-0 record with a .893 save percentage. Their top scorers were Seth Jones (10P in 14GP), Alexander Wennberg (10P in 14GP), and Cam Atkinson (9P in 14GP).

13. Nashville Predators 50.8 (+1)

Recently placed centre Mike Ribero on waivers. Dealt with injuries to key players in Colin Wilson, PK Subban, and Roman Josi. Pekka Rinne posted a 7-3-1 record with a .939 save percentage. Their top scorers were Ryan Johansen (11P in 15GP), Filip Forsberg (10P in 15GP), and Ryan Ellis (8P in 15GP).

14. New York Rangers 50.3 (+6)

Henrik Lundqvist continues to oddly be the problem in the big apple this year. Lundqvist has posted a 6-5-0 record with a .890 save percentage in January, which is quite underwhelming. Their top scorers have been J.T Miller (12P in 12GP), Michael Grabner (10P in 12GP), and Brady Skjei (5P in 12GP).

15. Detroit Red Wings 50.2 (+1)

The Wings have been just around .500 in their last 10 games played, so just average. This is another team who has struggled with poor goal tending in this season. The Wings have some secret ability to basically always be getting on the power play, as they are at a +22 penalty differential this past 25 games, which is the highest in the NHL. Petr Mrazek put up a 1-4-2 record with a .888 save percentage, whereas Jared Coreau had a 3-1-3 record with a .898 save percentage. Their top scorers Thomas Vanek (11P in 13GP),  Anthony Mantha (10P in 14GP), and Mike Green (6P in 10GP).

16. New York Islanders 50.1 (+3)

The Islanders picked the right time to get hot. They are now right back in contention for the Stanley Cup Playoffs. Thomas Greiss had a fantastic month, having a 6-2-3 record with a .935 save percentage. Their top scorers were John Tavares (14P in 12GP), Nick Leddy (12P in 12GP), and Josh Bailey (8P in 12GP).

17. Toronto Maple Leafs 50.0 (-13)

The Leafs are still actually good, but they did have a down month statistically. Not to worry though, Leaf fans, they’re still in playoff contention. Frederik Andersen had his worst month since October, posting a 6-3-2 record, with a .896 save percentage. Their top scorers were Mitchell Marner (16P in 14GP), James van Riemsdyk (16P in 14GP), and Nikita Zaitsev (6P in 14GP).

18. Ottawa Senators 49.4 (+5)

Having 6 players with at least 10 points this month is clearly a positive, and it looks like Craig Anderson could be back soon. Mike Condon has been a “revelation” apparently, –posting a 7-4-2 record, with a .911 save percentage in January. Their top scorers were Mike Hoffman (12P in 12GP), Kyle Turris (12P in 13GP), and Erik Karlsson (12P in 13GP).

19. Philadelphia Flyers 49.0 (+5)

Their special teams performance, and penalty differential have been their strong points in these rankings. Oddly, Steve Mason has continued to struggle, posting a 2-4-2 record with a .883 save percentage. Michael Neuvirth on the other hand had a good month, putting up a 4-2-0 record with a .921 save percentage. Their top scorers were Brayden Schenn (12P in 14GP), Jakub Voracek (10P in 14GP), and Ivan Provorov (6P in 14GP).

20. Vancouver Canucks 48.9 (+6)

Surprisingly to almost everybody,  the Vancouver Canucks are actually competing for a playoff spot. At the time of writing this, they’re only 5 points out, with 3 games on hand on the Calgary Flames. Unfortunately for them though, they have one of the most difficult schedules to finish off the season. Ryan Miller has been fantastic, putting up a 5-3-2 record, with a .937 save percentage. Their top scorers were Henrik Sedin (8P in 13GP), Bo Horvat (7P in 13GP), and Troy Stecher (4P in 13GP).

21. Calgary Flames 48.9 (+1)

Matthew Tkachuk continues to prove that he too deserves some Calder Trophy discussion, unfortunately, this years Calder class is insanely good. Brian Elliott posted a 3-3-1 record with a .905 save percentage; whereas counterpart Chad Johnson had a 3-4-0 record with a .887 save percentage. Their top scorers were Sean Monahan (13P in 14GP), Mikael Backlund (10P in 14GP), and Dougie Hamilton (10P in 14GP).

22. Edmonton Oilers 48.8 (-9)

Another big drop in the POWER for another young team. It’s a trend this month for whatever reason. The good thing is that Edmonton won games, and is now very likely to make the playoffs (finally). Cam Talbot had a 8-4-1 record with a .923 save percentage. Their top scorers were (of course) Connor McDavid (16P in 15GP), Leon Draisaitl (14P in 15GP), and Andrej Sekera (8P in 15GP).

23. Tampa Bay Lightning 48.1 (-6)

The Bolts find themselves at the bottom of the Eastern conference, just as everyone predicted. They’re definitely feeling the loss of Steven Stamkos at the moment. If they want any chance at making the playoffs, I think they would need to acquire a legitimate top-4 defenseman. Ben Bishop has a 2-2-1 record with a .893 save percentage; whereas Andrei Vasilevsky had a 1-7-1 record with a .892 save percentage. Their top scorers were Tyler Johnson (11P in 14GP), Nikita Kucherov (10P in 14GP), and Victor Hedman (8P in 11GP).

24. Chicago Blackhawks 48.1 (+1)

This is the first month all season where Corey Crawford has struggled. At least they had their 3 main stars up front produce some offense. Crawford had a 6-5-0 record with a .894 save percentage. Their top scorers were Patrick Kane (14P in 14GP), Jonathan Toews (11P in 14GP), and Duncan Keith (10P in 14GP).

25. Buffalo Sabres 47.8 (-10)

Had yet another great month for Ryan O’Reilly, who should be in consideration for the Selke Trophy in my opinion. The goalies were also good. Robin Lehner had a 4-1-1 record with a .916 save percentage; and Anders Nilsson put up a 3-4-1 record with a .920 save percentage. Their top scorers were Ryan O’Reilly (12P in 13GP), Evander Kane (11P in 14GP), and Cody Franson (7P in 14GP).

26. Florida Panthers 46.2 (-5)

A monster month from James Reimer, and receiving Aleksander Barkov, and Jonathan Huberdeau back from IR could help them make the playoffs. They can definitely sneak in, only 3 points out with a game on hand. James Reimer had a 3-1-1 record with a .920 save percentage; whereas Roberto Luongo posted a 3-4-1 record with a .918 save percentage. Their top scorers were Vincent Trocheck (13P in 13GP), Jonathan Marchessault (8P in 13GP), and Keith Yandle (7P in 13GP).

27. Arizona Coyotes 45.8 (+2)

This is probably the first time Arizona has gotten out of the bottom 3 teams in POWER. They actually won a fair amount of games this month, going roughly .500. Mike Smith went 4-5-1, and had a .905 save percentage. Their top scorers were Radim Vrbata (11P in 13GP), Oliver Ekman-Larsson (8P in 13GP), and Brendan Perlini (7P in 13GP).

28. St. Louis Blues 45.4 (-)

Their underlying numbers are completely abysmal. But, they have went just around .500 this month, as has every team (almost). Jake Allen had a 2-4-0 record with a .864 save percentage; whereas Carter Hutton put up a 4-3-0 record with a .904 save percentage. Their top scorers were Alexander Steen (16P in 14GP), Paul Stastny (14P in 14GP), and Kevin Shattenkirk (11P in 14GP).

29. New Jersey Devils 45.3 (-2)

Not a very good team, but they had a month good enough to still have some playoff hopes. They sit 5 points out of a spot with 0 games on hand, so it’s still possible, just very unlikely. Cory Schneider had a 5-3-2 record with a .928 save percentage; whereas Keith Kinkaid posted a 2-2-0 record with a .925 save percentage. Their top scorers were Kyle Palmieri (10P in 14GP), Taylor Hall (9P in 14GP), and Steven Santini (5P in 14GP).

30. Colorado Avalanche 38.6 (-)

My god. I’m so sorry Avalanche fans. At least you get Nolan Patrick, but you probably picked the wrong year to tank. Calvin Pickard posted a 1-5-0 record with a .912 save percentage. Their top scorers were Nathan MacKinnon (8P in 12GP), Gabriel Landeskog (6P in 12GP), and Tyson Barrie (4P in 8GP).

Statistics via Corsica.Hockey and NHL.com

Projecting The NHL Scoring Race

Up until the All-Star break, the NHL scoring race hasn’t gone entirely as expected. As usual, perennial contenders Sidney Crosby, Evgeni Malkin, and Patrick Kane, (I think that we can already include current leader Connor McDavid in this sector) are near the top. However, Cam Atkinson has powered the Blue Jackets by transforming himself into a scoring machine, and Brad Marchand has risen from simply an agitator to a top scorer. What’s in store for the second half of the season?

I decided on a project to predict the scoring leaders for the end of the season. Why? Not only could this be helpful and relevant for fantasy hockey, but it’s also interesting to see who could be holding the Art Ross trophy come June. First, a player’s Projected Goals (ProG) and Projected Assists (ProA) were calculated, culminating in Projected Points (ProP), which is the addition of ProG and ProA. Before getting into the methodology and results, there are a few things that I want to establish: This is not a list of the best players in the NHL. A player with a high number of Projected Points could be benefiting from playing with good line mates, a lot of power play time, or simply puck luck.

             If you wish, you can skip the explanatory paragraphs (which are rather mathematical and complicated) and just go to the later tables with results.

Let’s get started with the Projected Goals method. The initial data needed was a players’ games played, games remaining (assuming they don’t get injured), average time-on-ice per game and current goals. All of this data was taken from hockey-reference. Next, a player’s individual Corsi (shot attempts) For/60 (iCF/60) and career individual Corsi shooting percentage were taken from Corsica.Hockey. I used their career Corsi shooting percentage as an assumption that the player would regress back to how they’ve done throughout their career. Additionally, a players’s individual Royal Road shots per 60 minutes (iRR/60) was taken, courtesy of the Passing Project, spearheaded by Ryan Stimson. These are shots preceded by a pass across the royal road – a line across the center of the ice – which causes lateral movement by the goaltender and has a higher percentage of going in than an average shot (approximately 2-3 times more likely to result in a goal).

Let’s use Phil Kessel of the Pittsburgh Penguins as an example to understand how the Projected Goals are calculated. Currently, Kessel has 15 goals, 34 games remaining and plays an average of 17.53 minutes a night. He has 17.63 iCF/60 and 2.38 iRR/60, so roughly 13.5% of his shots follow a pass across the royal road. The shooting percentage for a royal road shot is about 15.5%, so the estimated royal road goals is 15.5% times the total number of royal road shots. His total royal road shots is 13.5% of his estimated total shot attempts, which is his iCF/60 multiplied by his estimated remaining ice time/60. The estimated remaining ice time is his average time on ice per game times the number of games remaining. This culminated in 3.66 royal road goals. The rest of the goals were calculated by multiplying his career Corsi shooting percentage (which was 6.09%) by his remaining iCF (excluding the ones that were royal road goals). This came to 10.54 goals, which added to his projected royal road and current goals, came to 29.20 Projected Goals for the season.

The top 15 skaters for Projected Goals are listed below:

(Note: Players with an asterisk use royal road data from the 2015-16 season, as the sample size for this season was too small)

Player Name ProG
Sidney Crosby* 45.29
Auston Matthews 41.76
Alex Ovechkin 41.34
Patrik Laine 38.22
Vladimir Tarasenko 37.64
Jeff Carter 37.47
Cam Atkinson 36.90
Max Pacioretty 36.65
Brad Marchand 36.46
Evgeni Malkin 35.98
Nikita Kucherov 35.53
David Pastrnak 34.51
John Tavares 34.25
Wayne Simmonds 34.23
Nazem Kadri 33.31

The Projected Assists were similarly calculated. Here, the current assists were added to projected royal road assists and projected other assists in order to get a total. However, instead of iCF, a player’s on-ice (not individual) Corsi for per 60 was used. The on-ice Corsi for per 60 was averaged with the league average to account for potential regression during the rest of the season. Also, royal road shot assists were used instead of individual royal road shots, and a player’s total shot assists per 60 was also included. The total shot assists per 60 were divided by the on-ice Corsi for per 60 to get a percentage of total shot attempts that the player got an assist on. This was then multiplied by the current on-ice Corsi shooting percentage, or in the case of royal road assists, 15.5%.

Here are the leaders for Projected Assists:

Player Name ProA
Connor McDavid 60.97
Evgeni Malkin 56.47
Sidney Crosby* 53.77
Nicklas Backstrom 51.49
Patrick Kane 49.80
Ryan Getzlaf 48.66
Alex Wennberg* 46.27
Mikael Granlund 46.06
Phil Kessel 44.58
Mats Zuccarello 43.90
Victor Hedman 43.09
Evgeny Kuznetsov 43.09
Erik Karlsson 42.13
Artemi Panarin 41.55
Claude Giroux 40.16

Here are the top-50 leaders in Projected Points:

Player Name ProG ProA ProP
Sidney Crosby* 45.29 53.77 99.06
Evgeni Malkin 35.98 56.47 92.45
Connor McDavid 30.84 60.97 91.81
Patrick Kane 30.75 49.80 80.55
Nicklas Backstrom 23.14 51.49 74.63
Artemi Panarin 32.97 41.55 74.52
Phil Kessel 29.20 44.58 73.79
Vladimir Tarasenko 37.64 35.98 73.62
Nikita Kucherov 35.53 37.79 73.32
Alex Ovechkin 41.34 31.64 72.98
Brad Marchand 36.46 35.21 71.67
John Tavares 34.25 36.75 71.00
Brent Burns 31.44 39.07 70.51
Tyler Seguin 31.89 38.33 70.22
Mark Scheifele 32.26 37.41 69.68
Cam Atkinson 36.90 31.53 68.42
Jeff Carter 37.47 29.26 66.72
Leon Draisaitl 29.96 36.56 66.52
Mikael Granlund 20.19 46.06 66.25
Nikolaj Ehlers 26.19 39.51 65.70
Jakub Voracek 26.29 39.33 65.62
Auston Matthews 41.76 23.85 65.61
Blake Wheeler 25.96 39.57 65.53
Patrik Laine 38.22 25.64 63.85
Ryan Getzlaf 15.19 48.66 63.84
Eric Staal 25.18 38.25 63.43
Jamie Benn 27.56 35.51 63.07
Joe Pavelski 27.64 35.31 62.95
Max Pacioretty 36.65 26.03 62.68
Mats Zuccarello 17.92 43.90 61.81
Nick Foligno* 27.61 34.00 61.61
Claude Giroux 21.33 40.16 61.50
Ryan Kesler 28.75 32.03 60.78
Alexander Radulov 22.27 38.34 60.61
Evgeny Kuznetsov 17.40 43.09 60.49
Corey Perry 22.02 38.33 60.35
David Pastrnak 34.51 24.84 59.35
Nazem Kadri 33.31 25.98 59.29
Alex Wennberg* 12.96 46.27 59.23
Mitchell Marner 19.86 39.18 59.04
Vincent Trocheck 31.25 27.44 58.70
Nathan Mackinnon 23.40 35.26 58.66
James van Riemsdyk 28.49 30.02 58.51
Derek Stepan 22.13 36.02 58.15
Wayne Simmonds 34.23 23.77 57.99
Charlie Coyle 22.87 34.82 57.69
Mark Stone 27.05 29.42 56.47
Mike Hoffman 29.77 26.41 56.18
Erik Karlsson 13.70 42.13 55.83
Ryan Johansen 18.30 37.35 55.65

While the exact methodology and number may be imperfect (they tend to feel a little low to me), I think that this gives a reasonable idea of how a player will finish the season. Feel free to do your own analysis of the totals. It’s interesting to see the sheer dominance of Sidney Crosby.

All Data comes from Corsica.Hockey, Hockey-Reference or the Passing Project

Where Does Auston Matthews’ Offence Rank Amongst The NHL’s Best Scorers?

Unless you’ve been living under a rock since October or not following hockey, you’ve heard that Auston Matthews is having an incredible rookie season. Within the first 47 games of his career, the 19-year-old is now representing the Toronto Maple Leafs at the NHL All Star game in Los Angeles. This brief break in the NHL schedule provides us with an opportunity to take a step back from his game-to-game accomplishments, and take a look at how his production this season ranks amongst some of his fellow All Stars. To do this, I have picked three players who most people would consider to be some of the best, if not THE best, players in world. I’ll introduce each players Offensive Production Chart, and then I’ll bring in Matthews’ chart at the end. My goal is to determine if Matthews’ production thus far is comparable to the league’s top offensive threats. 

Connor McDavid:

We’ll start with the player who the Leafs could have drafted, if not for one stubborn lottery ball: Connor McDavid. Although the Leafs won the draft lottery that gave them Auston Matthews, it was incredibly difficult to lose the opportunity to see a lifelong Leafs fan save the franchise. Here’s how McDavid has produced at 5v5 in his first season as Oiler’s captain:

Screenshot 2017-01-27 14.07.00.png

The categories on the chart on pretty self-explanatory. After his rookie season was cut short due to injury, McDavid made up for lost time this year by collecting more points in all situations (59) than any other player in the league before the All Star break. In fact, his 42 assists alone would tie him with Joe Pavelski and Mikael Granlund for 20th overall in league scoring. His remarkable ability to generate goals for his teammates is also exemplified by his primary assist rate at 5v5 — he has earned primary assists more often than every forward with at least 400 TOI except Nino Niederreiter. His 5v5 production alone may not scream “best player in the NHL” right now, but all indicators suggest that he could be there very soon. 

Sidney Crosby:

If there is a human being who is better at hockey than Connor McDavid right now, his name is Sidney Crosby. Fresh off a Stanley Cup victory last June, the Penguins captain has gathered 4 less points than McDavid, albeit in 9 less games. Here is how Crosby has produced at 5v5 so far this season:

Screenshot 2017-01-27 14.16.25.png

Sidney Crosby is approaching the end of his prime, but he’s certainly not slowing down. The only “blemish” on his offensive production in 2016-17, if you could call it that, is the rate at which he shoots the puck at 5v5. His iCF60 of 14.58 ranks 59th out of the 293 forwards in this sample. However, that is still 1st line caliber when you realize that there are 90 top line forwards in the league (30 teams in the league and 3 forwards on each top line). Sidney Crosby is still the best player on the planet.

Alex Ovechkin:

Since bursting onto the scene in 2005-06 by scoring 52 goals and 106 points as a rookie, Alex Ovechkin has been lauded as the best goal scorer in the entire league. Eleven years later, here is how “The Great Eight” has produced so far this season:

Screenshot 2017-01-28 14.10.03.png

The least surprising components of this chart are his shooting and scoring rates; everyone knows he shoots very often and scores a lot, and his numbers prove it. The number that caught my eye is his primary assist rate. Not only is Ovechkin the NHL’s most dangerous scoring threat, but he is one of the best playmakers as well (this season, at least). Perhaps the Capitals captain deserves more praise than he currently receives.

Auston Matthews:

After analyzing the current production of the NHL’s three brightest stars, let’s turn our attention to a fourth. This is how Auston Matthews has produced on offence in his rookie season:

Screenshot 2017-01-27 10.19.05.png

When it comes to scoring goals, Auston Matthews doesn’t need the man-advantage to dominate the league. His scoring rate is second only to Michael Grabner’s 1.76 goals per 60 minutes, with Crosby’s 1.71 not too far behind. Matthews is first in expected scoring and scoring chances. The frequency at which he earns primary points, shots, and shots on goal are all in the top 8 or better. Not bad for a teenager.

The only “weakness” in his offensive game is his playmaking ability. While his primary assist rate is barely above average, that is expected for someone who plays with Zach Hyman and Connor Brown. He might not shine like McDavid does in this area, but his scoring capabilities more than make up for it.

Despite all of this, I believe that it is still too early to declare Auston Matthews as the best player in the world, strictly in terms of offensive production. He might not put up over 100 points as a rookie like Crosby and Ovechkin did over a decade ago, nor is he as flashy as McDavid. But if his current production is any indication, he could definitely be in that conversation for years to come.