Category Archives: Walter Foddis

Edmonton Oilers, Fancy Stats, NHL, Walter Foddis

Did Fayne feign struggling this season?

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The Oilers partially based their acquisition of Mark Fayne on information gained from analytics. James Mirtle of the Globe and Mail referred to Fayne as an “analytics darling.” (Not a term I would use, although somewhat common in the analytics lingo.) He was one of several players the Oilers acquired based on advanced statistics, which included David Perron, Teddy Purcell, Benoit Pouliot, and possibly Rob Klinkhammer. Because Fayne was considered a top-4 defenseman, a lot was expected from him. After all, he did play top-pairing minutes with Andy Greene in New Jersey.

At the beginning of the 2014/15 season, then coach Dallas Eakins put Fayne up against the toughest competition, along with Martin Marincin. Fayne (and Marincin) did not flourish. He, along with the Oilers, crashed and burned by December. Even after Eakins was fired, and Todd Nelson was brought on as interim coach, Fayne never did recover. He continued to struggle mightily. “But that’s because he faced the toughest competition,” one may interject.  Another could argue, “He’s the best defender on one of the worst defense corps in the league.” A few many even believe that Fayne played well, or as well as can be expected on a bottom-dwelling team. Finally, an embittered fan might explain Fayne’s performance with a more succinct, yet familiar refrain: “Because Oilers.” All these explanations could have kernels of truth to them, or not. Does the data support any of these explanations? Going the way of the scientist, I wanted to investigate the data and see what I could conclude.

My approach was scientific in that I began my data quest with questions (i.e., hypotheses) and then attempted to figure out how, or if, the data answered these questions (i.e., supported or didn’t support my hypotheses).  My questions included: Did Fayne feign struggling this season? (Did he actually struggle or am I seeing things that are not there?) If his struggles were real, what was the influence of the toughness of competition and quality of teammates? Assuming that his under-performance cannot be blamed on his competition or teammates, was their anything in his time with New Jersey that could have been a red flag? Was there some bit of data that should have given management (and analytics-inclined fans) a sign as to whether he could handle top-pairing, or even 2nd pairing minutes?

Scoring Chance +/-

I’ll begin with the most direct evidence by borrowing data from David Staples of the Edmonton Journal, who diligently tracks scoring chances from every game. Specifically, he records whether players contributed to offensive scoring chances or made errors in scoring chances against. He then computes a plus-minus of scoring chances. (Originally, the metric was named after Roger Neilson, who created it.). A positive value shows a player contributed to more offensive scoring chances than to errors when defending against scoring chances, and a negative value shows the opposite. The complete Oilers 2014/15 season can be viewed here. Fayne’s scoring chance differential was -2.52 per 60 minutes, which was 2nd worst among the Oiler’s regular defense; slightly worse than Ference, whose differential was -2.44. Only counting his errors on scoring chances against, Fayne averaged 7.76 errors per 60 minutes. This was actually better than most Oilers, except for (former Oilers) Petry and Marincin. Hence, his limited offensive contributions to scoring chances was the main culprit in his negative scoring chance differential. (Spoiler alert: His limitations on offense are a pattern throughout my analysis.) In any case, I submit that this is the first piece of evidence that Fayne under-achieved.

Passing Metrics

A second piece of direct evidence is passing data collected by Ryan Stimson and his colleagues for his Passing Project. These passing and shot attempt metrics are measures of a player’s offensive contributions that go beyond what is collected by the NHL. The metrics in the graph below include:

  • CC% and CC/60 = Corsi Contribution (or Shot Attempt Contribution), which are individual shot attempts, primary passes leading to shot attempts, and secondary passes leading to shot attempts. These are given as a percentage (i.e., proportion of shot attempts a player is involved in when on the ice) and per sixty minutes. These metrics tell you how much offense goes through that player while on the ice and also how often they contribute.
  • Composite SAG and SG represent the total number of shot attempts and shots a player generated from both primary and secondary passes per sixty minutes. SAG/60 is solely for the player’s primary passing contributions.
  • Entry Assists represent the number of controlled entries a player assisted on. This is determined by the number of passes in transition (prior to entering the offensive zone) that was recorded for each player.
  • SC Contribution% and SCC/60 are identical CC% and CC/60, but represent only the scoring chances a player was involved in. Passing data for scoring chances was combined with War-on-Ice’s scoring chance (link to definition) data to arrive at a player’s total number of scoring chance contributions. SC SAG/60 represents the number of scoring chances set up from a player’s primary passes.

 

Here is the way I read this graph. The top one-third percentile (67%+) is top-pairing defenseman range, 33%-66% is 2nd pairing, and below 33% is bottom-pairing. Reading from left to right, Fayne’s offensive contributions involving shot attempts (first 4 columns), he sits around the 2nd-pairing range. He is also 2nd-pairing for entry assists (i.e., controlled passes into the offensive zone). That’s one thing I remembered from watching games. That he had a solid first-pass on defensive zone breakouts. Where he drops off rapidly is his contribution to quality scoring chances, especially as a set-up guy. He was 10th percentile in primary passes leading to scoring chances. He then again sits around the 2nd-pairing range for scoring chance contributions, which includes scoring chance shots, as well as primary and secondary passes.

Is this evidence that Fayne under-performed? Depends on what we were expecting. As a top-pair, he under-achieved. As a 2nd-pair, he performed as expected, except when it came to setting up quality scoring chances. Surprisingly, Sekera is not that strong in setting up scoring chances either. Based on this, Sekera paired with Fayne might not be a good idea. Unfortunately, Schultz appears to be the only defender particularly good at setting up quality scoring chances. Both Fayne and Schultz are right-handed, so it’s unlikely, although not impossible, to pair them.

Quality Shots: Scoring Chances & High-Danger Scoring Chances

Next, I look at indirect evidence, namely, group measures such as a team’s shot attempt and scoring chance metrics. Based on these measures, especially the relative metrics, we make plausible inferences or guesses about a player’s contributions. I begin with Scoring Chances (SC) and high-danger scoring chances (i.e., shots from the slot area) provided by war-on-ice. These metrics are especially useful because they account for shot quality; locations and shot types that greatly increase shooting efficiency. (Unless otherwise stated, I present these scoring chance measures per 60 minutes.) We know that about 20% of shots from the slot (High-Danger) go in. Suppressing shots from the slot, then, has much more impact compared to shots from the blue-line, which only have a 4% efficiency rate.

Starting with suppression of quality shots, and relative to 170 defenders who played at least 750 minutes, Fayne’s High-Danger Scoring Chances Against  (17.16) and Scoring Chances Against (28.39) ranked 144th and 132nd, respectively. How do these metrics compare to other Oilers’ defensemen? Fayne’s High-Danger SC Against is 3rd worst, ahead of  Martin Marincin (19.13) and Keith Aulie (21.29).  With respect to general Scoring Chances Against, Fayne’s rank is better, but nothing special. When Fayne is on the ice, the Oilers are no worse, or better, when it comes to suppressing quality shots. In other words, the team’s defense is average with Fayne involved.

But when comes to the Oilers generating scoring chances with Fayne on the ice, the lack of quality shot production is glaring. Compared to other regular defenders, he ranked 169th in High-Danger Scoring Chances generated (11.12) and 167th in Scoring Chances generated (28.39). I’m hesitant to say this, but the numbers are pretty clear: Fayne’s scoring chance generation metrics are among the worst in the league. This wouldn’t be so bad if his defense compensated for this lack offense, but it doesn’t.

I could look at other numbers, like shot attempts for and against, to support the idea that Fayne struggled, but I think what I showed so far is sufficient. With the Fayne on the ice, the team’s defense in suppressing quality shots is par for the course , which isn’t saying much. What’s worse is that the team’s ability to generate quality scoring chances is severely hindered. So how does this all translate into team scoring? Based on shots from all areas, war-on-ice provides a Net Goals metric for each player. With Fayne on the ice, the team’s expected Net Goals was -0.41 (per 60 min.), which when multiplied by his time-on-ice, is -7.41 net goals. Only Aulie was worse (-0.54 per 60 min.).

Quality of Competition

The Oilers were expecting a solid top-4 defender, which his SAT% metrics with New Jersey suggested, but his Oilers’ numbers presented so far don’t support this notion. One explanation could be that Fayne faced the toughest competition on the team, which he did. However, as I have shown in previous posts (e.g., Gryba vs. Marincin & Sekera vs. Hamilton), a player’s quality of competition can be controlled. Specifically, using David Johnson’s WOWY (With-or-Without-you) tables, a player’s shot-attempt differential (SAT%) can be measured against different levels of competition. Moreover, the player’s SAT% can be compared to other players across similar levels of competition. If Fayne’s SAT% took a big hit only against tough competition, then we could conclude that quality of competition was major factor for his struggles. Let’s see if that is the case.

In the graph below, I present Fayne’s SAT% at different levels of competition (i.e., quality of competition [QualComp] SAT% ranging from 56% to 46%), I also compare him to Ference, who many would consider bottom-pairing at best, and Schultz, who some consider bottom-pairing (like myself), but on a defensively weak team like the Oilers, he is passable as a second-pairing defenseman. Before the analysis, my expectation was that all three were dominated by the strongest competition (i.e., QualComp SAT% of 54% or greater), but would improve considerably as QualComp softened to 50%. Then as competition dropped to players with a negative SAT differential (QualComp SAT% < 50%), Fayne’s SAT% would become positive. Finally, I was hoping that Fayne’s break-even point–the level of competition that players hold their own with a neutral SAT differential (50%)–would also be at least 50%, if not more. Here is what I found (click on graph to enlarge).

 

The first thing I noted was that Fayne was more similar to Ference than to Schultz, which is to say, not that good. In fact, Schultz looks amazing in comparison, but I don’t want to get carried away. Even against weaker competition, Fayne barely kept his head above water. This is most clearly seen in his break-even quality of competition SAT% of 46.9%. Notable players at this level include Devant Smith-Pelly, Nate Prosser, and Dalton Prout. Smith-Pelly I’ve know because he sometimes played with Anaheim’s top line, but in reality, he’s a bottom-6 forward (ranking 310 out of 410 forwards with 1.12 points/60 min). Prosser is in a bottom-pairing role for Minnesota and Prout is Columbus’ 4th more used defenseman. Overall, then, Fayne is holding his own against the bottom-half of opposing teams. This is what I would expect from a marginal second-pairing defenseman, or a solid third-pairing defenseman.

Quality of Teammates

I was still questioning whether Fayne was really that bad. Could it be because he played more minutes, compared to Schultz, with weaker possession players? One combination is the Oilers shutdown line, mainly Boyd Gordon and Matt Hendricks, and the other is any line with Yakupov. (Unfortunately, Yakupov is the weakest possession forward among the Oilers’ regulars). Fayne played 188 minutes with Hendricks and Gordon, whereas Schultz played 120 minutes; so that’s a significant difference. But then he played 136 minutes with Yakupov and Roy, and Schultz played 174 minutes with them. Moreover, when these players were on the ice with Schultz, they had a better SAT% (50%) then with Fayne (46%). Does this mean Schultz is a better defenseman than Schultz? I’m not suggesting that, but as these metrics indicate, Schultz appears to do a better job at generating offense than Fayne, which helps compensate a little for his deficits on defense.

Who Fayne had as a defensive partner also impacted his SAT differential. With Nitikin, it was almost respectable: 49.6%. With Marincin, it was only 46.3%. (Fayne’s adjusted SAT% was 46.7%.) Seems that being paired with a rookie, especially against the toughest competition, did not help Fayne. A few players did help Fayne gain a positive SAT differential. The main possession driver was Eberle, who helped Fayne exceed 50% when he was paired with either Marincin or Nikitin. I could go on to describe other combinations, but fortunately Micah McCurdy has done this work graphically with what he calls WOWY diagrams. This one below, if you follow it carefully, shows how Fayne and his teammates’ SAT differentials changed depending on whether they were with him or without him. I’ll use Marincin (#85) as example to help you understand the diagram (click on the diagram to enlarge).

 

(You may want to increase your font size to for this part.) The bottom-right half, below the red line, is positive possession (good). Above the red line is negative possession (bad). Red-boxes indicate a teammate’s SAT differential without Fayne. If you look to the top-right and below the red line, you will see #85 boxed in red, which is Marincin (SAT% = 50.7%) without Fayne. Then if you follow the line from #85 to the left, you’ll cross the cut-off line (where SAT% = 50%), into negative possession (SAT% < 50%), you’ll see #85 in black. That’s Marincin and Fayne together with a 46.3% SAT differential. In short, Marincin appeared to be better without Fayne than with him, although that’s probably too simple a conclusion. If you look directly above this point (85 in black), you’ll see #85 in blue, which shows Fayne without Marincin. Fayne, then, whether he played with or without Marincin, his SAT differential changed very little. Still, there was small effect in defense with Marincin, namely, there were fewer shots against.

I’ve only explained the effect of one teammate. So as you can see, this elegant diagram packs a lot information into one visual image, which is very cool.

The main take-home message of this graph is how many teammates’ SAT% improved with Fayne (numbers in black). You can barely see it, but one player’s SAT% was positive and that was Pouliot (#67 in black, which is behind #67 in red). That’s it. If Fayne was driving possession, if players gained an advantage offensively or defensively with Fayne, we would see a pattern in which “red” numbers move from left to right toward “black” numbers. We don’t see that pattern, even for Pouliot.  Pouliot’s SAT% is unchanged playing with or without Fayne. What we can conclude from this pattern is that Fayne is not a possession driver. Indeed, several player are worse with him than without him, which we can see in all the red numbers moving from right to left (e.g., Eberle #14 & Hall #4), from positive to negative SAT differentials.

What did Oilers management or analytics followers not know?

I believe I’ve shown that Fayne struggled as a top-pairing, and even 2nd-pairing defenseman this season. As per Staples’ scoring chance data, Fayne had the 2nd lowest plus-minus scoring chance differentials among the defense. Based on Stimson’s passing project data, Fayne’s passing effectiveness puts him in the range of an average 2nd-pairing defenseman. Notably, though, he rarely sets up quality scoring chances through primary assists. From the perspective of the Oilers’ generating and suppressing quality shots, the team (a) allows more (compared to the team’s average) High-Danger chances with Fayne on the ice, and (b) generates fewer high quality shots. Overall, the team’s expected Net Goals with Fayne on the ice is about -7. Is this due to his toughness of competition? I don’t see evidence for that . What I noted is that the team’s SAT differential with Fayne on the ice was not that strong even against weaker competition. As to the reciprocal influence of teammates, I noted that Fayne tended to reduce a teammate’s SAT differential. Some teammates improved Fayne’s SAT%, like Eberle, but given the one-way relationship, it supports the notion that Fayne is not a possession driver.

How did a strong possession player, a top-pairing defenseman on a strong defensive team like New Jersey, become what almost looks to be a possession liability? What did management miss (if anything)? What did the casual analytics follower miss? I say “casual” because Ryan Stimson, the Devils’ analytics blogger for SB Nation, was not a fan of Fayne and he had analytical reasons for that.

First, there is a hint that Fayne’s SAT% in 2013/14 was inflated by certain teammates, specifically, Greene and Jagr, who was the Devils’ best possession forward that season. When Fayne was paired with defensemen other than Greene (time-on-ice = 248 min), his SAT% fell significantly by over 10% to 46%. When Fayne wasn’t with Jagr, his SAT% fell by 5% to 52%.

Second, Stimson had collected passing data from the Devils 2013/14 season. (That was before he expanded the project league-wide.) One statistic that he focused on was a player’s passing efficiency, in particular, what percentage of shot attempt passes lead to shots on goal. He referred to this metric as Shot Attempt Generation Efficiency (SAGE). I think the idea is that if a player’s whose passes lead to more shots on goal, the player presumably has better passing skills, which, seems to me, involves awareness of the developing play, mobility, puck control, and accurate passing. In Stimson’s analysis of the Devils’ defense, Fayne had the lowest SAGE, which was about 30%. As Stimson concludes:

“Mark Fayne generated the third most shot attempts [compared to Devils’ defenders], but was the least efficient by a considerable margin. I think the Devils will be just fine in replacing him in the lineup. Certainly the volume [of shot attempts generated] was solid, but if there’s no efficiency, it’s just empty possession and inflating Corsi totals.”

I think Stimson’s findings provide a reasonable explanation of why Fayne did little to help the Oilers’ offense. We see confirmation of his passing inefficiency in the 2014/15 passing metric data, which shows a mediocre rate (45th percentile) of scoring chance contributions and an abysmal rate (10th percentile) of primary passes that lead to scoring chances. This 2013/14 data was not available to the public until after Fayne was acquired by the Oilers. Hard to say if Oilers management would have considered it seriously, given that it’s not official NHL data. Nonetheless, I think this is evidence that helps explain Fayne’s offensive weakness, which when combined with evidence of his SAT% being heavily dependent on Greene and Jagr, helps explain to some degree his struggles with the Oilers.

Recommendations

Based on this analysis, I offer the following recommendations. (1) Fayne needs to be on the ice with a defenseman who is a puck-carrier and who is more efficient in their passing, especially in the offensive zone. Although I noted above that Sekera might not be a good idea, he does fit the bill of being a strong passer. And Fayne is better than Sekera in executing passes that allow controlled offensive zone entry. In other words, they do appear to compliment each other. As a 2nd pairing combination, Fayne with Klefbom could also work. Klefbom’s passing strength is in the offensive zone, which may help offset Fayne’s limited passing efficiency. (2) Against opposing top-6 forwards, Fayne should not be paired with weaker (i.e., bottom-pairing) defenders, especially rookies and prospects. (3) Reduced time-on-ice with weak possession players, otherwise these line combinations can be a recipe for puck possession disaster. Here I’m thinking of the Oilers’ shut-down line (Hendricks, Letestu, & Klinkhammer) and any line with Yakupov. I do realize Fayne was often put on the ice with the shutdown line, but their SAT% together–47.8%– is simply not that strong. Being realistic, though, the Oilers depth on defense is still lacking. There might not be a better choice than Fayne for this kind of deployment.

This analysis took me much longer than expected. Part of the reason is that I was surprised by some of my findings. I wanted to make sure I wasn’t missing something important, or concluding too much from too little. As always, I’m receptive to new evidence and lines of reasoning. If you believe that I have missed important information or reasoned wrongly, please comment below. Also, if you have any questions or confusion about what I presented, please ask away. My underlying approach in this blog is to make analytics as accessible as possible to hockey fans. Thanks for reading.

Edmonton Oilers, Fancy Stats, NHL, Trades, Walter Foddis

The Battle of Alberta via Analytics: Andrej Sekera vs. Dougie Hamilton

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Sekera squeezes Toews along the boards

The Calgary Flames made a huge splash in acquiring up-and-coming defensive stalwart, Dougie Hamilton, of the Boston Bruins, for 3 draft picks. The deal shocked everyone. Apparently, the Oilers also negotiated for Hamilton, but Boston wanted Darnell Nurse, as well as 3 draft picks (#16 overall, and two second round picks). It seems Edmonton balked at trading Nurse, which to many fans is understandable. Fans, media, and management have much optimism in Nurse’s potential, which was reinforced by his stellar season and deep OHL playoff run with the Sault St. Marie Greyhounds. Somehow, though, Calgary negotiated a deal for less and the rest is history. The Oilers were desperate for a top-pairing defenseman, but they also needed a #1 goaltender. Their major draft-day trade turned out to be exchanging draft picks for Cameron Talbot of the New York Rangers. I like this deal and have high hopes for Talbot. (Based on war-on-ice computations of adjusted and high-danger zone save percentage, I had Talbot ranked quite high on my list of potentially available goalies.)

Still, free agency was coming up on July 1st and some potential top-pairing defensemen were available. On opening day, the Oilers acquired free-agent defenseman, Andrej Sekera. I was excited! In my regular Oilers Facebook group, we had discussed who we wanted for our top pair and Sekera’s name always came up. Indeed, Sekera appeared to be in high demand by other teams. Last season, Sekera was traded from the Carolina Hurricanes to help Los Angeles into the playoffs. Unfortunately, the NHL point system as it is (i.e., overvaluing overtime wins), and with LA’s bad luck in overtime, they didn’t make the playoffs. To this point, Sekera looks like the Oilers’ answer to filling at least one spot for a top-2 defender.

There appears to be wide agreement in the hockey world that Hamilton is going to be a stud defenseman, if he is not already. Sekera has been around for a few more years and is considered a solid defenseman, at least a top-4. Important to note, though, that he and Justin Faulk were the top-pair for the Hurricanes. Most would also agree that Hamilton is the bigger name–and Calary signed him to a fatter contract–, but what would an analysis of their metrics tell us? How much better is Hamilton? Or are they closer in performance than many would assume?

Shot Attempt Metrics: Quality of Competition

Unpacking David Johnson’s WOWY (With-or-Without-you) tables, which, in part, shows how each player performs against specific opponents, I used the opponents’ shot-attempt differentials as quality of competition (QualComp) indices. Opponents with higher shot-attempt differentials (SAT%; also known as Corsi) were considered tougher competition. I assumed that how Sekera and Hamilton performed against weaker competition was not as important. Instead, I focused on tougher competition, specifically, opponents with an average shot-attempt differential of 50% or greater. I divided the tougher competition into 5 levels of average SAT%–56%, 54%, 52%, & 50%–with each level having a range of +/- 1% and a sample size of about 30 opponents. This method allowed me to compare each player’s shot-attempt differential versus similar levels of competition.

In addition, I computed a metric that I call Break-Even QualComp SAT%, or Break-Even SAT%. (I introduced the metric here.) The Break-Even SAT% indicates the level of competition in which a player can hold their own, that is, maintain a 50% shot-attempt differential. The higher the Break-Even SAT%, the tougher the competition the player can handle. I graphed this quality of competition comparison below.

(Click on graph to enlarge. Use your browser’s [back] button to return to the article.)

Regardless of the quality of competition, both Sekera and Hamilton demonstrated impressive shot-attempt differentials. This is clear in their Break-Even SAT differentials with Sekera at 54.8% and Hamilton at 55.9%. Opposition at this SAT% level are typically first-line players on strong possession teams such as Pavel Datsyuk (Detroit), Sidney Crosby (Pittsburgh), and John Tavares (New York Islanders).

In terms of which player outperformed the other, there doesn’t seem to be a clear “winner,” although the Break-Even SAT% suggested that Hamilton was slightly ahead. To gain another perspective, I computed their average SAT differentials versus all opponents with a SAT% greater than 52%. I chose 52% because teams with a SAT% of 52.5% or greater have a 90% chance of making the playoffs. In a loose way, then, the following graph shows how Sekera and Hamilton performed against playoff-caliber teams.

As this graph shows, their respective shot-attempt differentials are nearly identical (52.1 vs 51.8). To be sure I was making a fair comparison, I  computed the average competition SAT%, which was about 54% for each player. By separating offense (SAG/60; shot-attempt generation) and defense (SAS/60; shot-attempt suppression), we have a glimpse into how their playing styles differ. Although their differentials are similar—difference of 4 shot-attempts per 60 min–Sekera is a “higher event” defenseman than Hamilton. Compared to Hamilton, Sekera’s teammates not only generate more shot attempts with him on the ice, they also allow more shot attempts. At this level of analysis, it seems that Sekera and Hamilton are similar in their ability to handle tough competition. However, quality of competition is only one side of shot metrics. The other side is a player’s quality of teammates.

Shot Attempt Metrics: Quality of Teammates

Using the WOWY tables, there’s two measures we can examine on the influence of teammates in relation to a player. First, there is the player’s impact on his teammates: How much does a teammate’s shot-attempt differential change, and which direction (positive or negative) does it change, when paired with the player? Second, there is the player’s dependence on his teammates: How much does a player’s SAT% depend, positively or negatively, on his teammate’s influence? This can be a complicated relationship. For instance, just because Player A’s SAT% improves a ton with Player B doesn’t mean Player A’s SAT% is overly dependent on Player B. That’s because Player A may also be improving Player B’s SAT%. They are better together than apart. Isn’t this the ideal of close relationships we strive for in life? As is true in life, it is true in hockey.

In the tables below, I present both players’ SAT dependency and impact on teammates. I focused on key teammates, namely, the 1st and 2nd line centers and the player’s primary defensive partner. I treated the centers as a proxy for the first and second lines. I further divided the measures into shot-attempt generation (SAG; also known as Corsi-For) and shot-attempt suppression (SAS; a.k.a Corsi-Against). There are many numbers, which can be confusing. The important numbers in bold, which I have labelled as “totals.” In fact, the numbers in bold are averages of the 3 teammates. If you only wanted to know the one number that would give you the most information, “total” (i.e., average) SAT/60 +/- would be it.

We would expect that when playing with the team’s top centers, or their primary D-partner, that a defender’s shot metrics would improve. (All the shot attempt rates are presented in 60-minute units.) On average, Sekera’s shot-attempt (SAT) differential improved by 9.7 shot attempts with the Hurricanes, and by 6.52 with the Kings.

As an side, as a member of the Hurricanes, Sekera’s shot attempt suppression metric remained stable (SAS/60 +/- = -0.68). In contrast, with the Kings his SAT suppression metric improved (SAS/60 +/- = -4.45). The reason? One plausible answer is that these are teams with different systems. In particular, the Kings appear to be more focused on (and perhaps better at executing) defensive systems.

Mainly because of Bergeron’s influence, Hamilton’s SAT differential improved by 13.35 shot attempts, on average.  Does this mean Hamilton is more dependent than Sekera for his higher SAT differentials? Not necessarily. Because the relationship is complicated, we also need to assess the other side: The player’s impact on his teammates.

Sekera had a positive impact on his centers in Carolina and Los Angeles, as well as his D-partner, McNabb, for the Kings. However, his D-partner on the Hurricanes, Faulk, performed worse with Sekera. Faulk’s SAT differential dropped by 10.3 shot attempts (per 60 minutes) when on the ice with Sekera. In the previous table, we can see that Faulk returned the favor by knocking down Sekera’s SAT differential by 8.27 shot attempts. Seems like that relationship was not working out too well; a bit dysfunctional even. Overall, though, Sekera’s impact on his key Carolina teammates’ SAT differential was positive (+2.31), as was his impact on his key Los Angeles teammates (+9.11).

Hamilton also had a positive impact on each of his teammates, both in terms of shot attempt generation (SAG/60 +/- = +9.8) and suppression (SAS/60 +/- = -.6.96). In fact, his impact total (+16.76) exceeded his dependency total (+13.35) by just over 3.4 shot attempts per 60 minutes. Continuing with the relationship analogy, Hamilton gave more to his teammates than he received.

A few analytics writers have recently cautioned about comparing players from different teams using these relative metrics. They argue there are so many other things going on with the teams (e.g., systems, line-combinations) that a straight across comparison would not be meaningful. I tend to agree with this line of thinking. Nonetheless, I think it’s still useful to note the general patterns, specifically, that although each player is dependent on major teammates to boost his SAT differential, each had positive impacts on his teammates’s SAT differentials.

WOWY Diagrams

Another way to express these dependency and impact relationships is through a diagram. Fortunately for Twitter followers of hockey analytics blogger, Micah McCurdy, we’ve been treated to elegant diagrams of teammate shot-attempt WOWY metrics. McCurdy created two types, spider and WOWY diagrams. Spiders are explained here and WOWYs here. To be frank, if you really want to understand how to clearly interpret the diagrams, then I do strongly recommend reading the explanations.

What spider diagrams provide, which I did not in the above tables, is how SAT differentials change with more than 2 players together. To keep things simple, I’ll break down the diagram to its essential points. The main pattern you want to see in spider diagrams is teammate combinations moving toward the bottom-right, below the red line. This bottom-right half indicates a positive shot attempt differential (i.e., SAT% > 50%).

Combinations above the red line are negative shot-attempt differentials (i.e., SAT% < 50%). The combinations in blue are the defender’s average SAT differential and his SAT% with his primary defensive partners. To the right of the diagram, player numbers are listed along with their names and a percentage, which represents the proportional time-on-ice the player shared with each teammate.

Sekera’s Spider WOWY Diagram – 2014/15

(Click to enlarge. Use your browser’s “back” key to return to the article.)

Hamilton’s Spider WOWY Diagram – 2014/15

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Most of Sekera’s and Hamilton’s teammate combinations are below the red-line. Sekera’s combinations with the Staal brothers, Eric & Jordan, are particularly dominating. In fact, their shot-attempt differential was very good (approximately 61% SAT%) when both brothers were on the ice. Interesting to note that that the Faulk-Sekera pairing actually does quite well when playing with either Staal brother. There is a small cluster of combinations that move diagonally upward to the right (still below the red line). This would show little, if any, change in shot-attempt differential, but an increase in both shot attempt generation and suppression (i.e., more events). This fits with what we found earlier in my analysis, that Sekera appears to be a higher event defensemen compared to Hamilton.

For Hamilton, the general trend of player combinations is downward (more shot suppression) and toward the right (more shot generation). Any combination with Bergeron was exceptionally strong. By my estimation, the 5-man combination of Smith, Bergeron, Marchand, Chara, and Hamilton had an impressive shot-attempt differential of 65%.

Quality Shot Analysis

So far, I’ve looked at the most blunt measure of shot metrics, shot attempts, which include blocked shots, missed shots, and shots on goal. Shot attempt metrics are useful and informative, but limit any analysis to broad brushstrokes. This past season, the bloggers at war-on-ice stepped up their measurement game and created metrics to account for higher quality shots, specifically, scoring chances and high scoring chances, which are mainly scoring chances from the slot area.

At this level of analysis, a difference between the two players is emerging. When Hamilton was on the ice, the Bruins generated significantly more scoring chances (54.7%), especially high-scoring chances (57.3%), than their opposition. Moreover, Hamilton ranked 7th in the league among defenders in terms of high-scoring chance differential and 21st in general scoring chance differential. Finally, Hamilton also had strong scoring chance differentials relative to when he is on the ice to off the ice.

Sekera’s high-scoring chance differential was in the average range (50%), relative to the league. When he was on the ice, he and his teammates allowed as many high scoring chances as they generated. His scoring chance differential was a better (51.4%), ranking him 71st in the league among defenders.

High & Medium Probability Zones

Above, I observed differences between the players in shot attempts generated and suppressed. Although their respective SAT differentials were similar, Sekera was a higher event player than Hamilton. In particular, it appears Hamilton and his teammates were better at suppressing shot attempts, whereas Sekera and his teammates were better at generating shot attempts. This loosely suggests that Hamilton may be stronger defensively. To get a clearer picture of these offensive and defensive differences, I once again used data from war-on-ice using the “Player-Hextally” option. Here war-on-ice breaks down shot locations to 3 major zones: high (the slot), medium, and low probability zones. I focused on the high and medium-probability zones because these are the areas where the action happens. For instance, shooting percentages are almost 5 times higher in the slot than from the low-probability zone.

In the following table, I present a team’s shot rates relative to the league, for and against, when the player is on the ice,. As well, I include these shot rates relative to the league and to the team. The shot rates are then divided into 2 zones: the slot area (high-probability) and the medium-probability zone (i.e., immediate area around the slot). Green boxes indicate strong numbers, whereas red boxes indicate weak numbers.

Consistent with Hamilton’s high scoring chance differentials above, he and his teammates generated 28% more shots from the slot compared to the league average. Moreover, with Hamilton on the ice, the team produced 25% more shots from the slot compared to him off the ice. From the slot, Sekera and his teammates were less productive than Hamilton and company, but still 5% above league average. Shot rates generated in the medium-probability zone were more similar, with Sekera having a slight advantage. Hamilton and his teammates had a shot-rate of 15% above league average, whereas Sekera and his teammates were at 17% above league average. Both players, then, appear very good in helping generate shots from the slot and medium-probability zones.

What about their respective defensive metrics? One difference is very clear: Hamilton and his teammates were far superior in suppressing shots from the slot compared to Sekera and his teammates. In fact, Sekera and company were below average, that is, they allowed 14% more shots from the slot relative to the league average.  Indeed, with Sekera on the ice, his team allowed 12.3% more shots compared to him off the ice. Although this doesn’t look good for Sekera’s defense, we also see that he and his teammates are solid at suppressing shots within the medium-probability zone–6% below league average–, whereas Hamilton and his teammates are no different than league average.

Net Goals

Now that we’re seeing differences between them more clearly, how does this translate into expected goals? Again, war-on-ice provides this information by dividing expected goals into scoring zones and then applying conversion rates to the shots to compute Net Goals per 60 minutes. (The Average Conversion figures suggest that these tables use shot attempts, not shots on goal.) I then computed Total Net Goals using the player’s time-on-ice.

The simplicity of Net goals is that it condenses many of the shot metrics into one number. When Sekera was on the ice, his team was expected to score 4 goals more than their opposition. Hamilton and his teammates were expected to score 11 more goals than their opposition. Hamilton’s advantage over Sekera is two-fold: He not only seems to help generate more goals from the slot (3.09/60 vs. 2.01/60 for Sekera), but also appears to help suppress more shots from the slot (-0.85 vs. +2.49 for Sekera). So despite their similarities at the level of shot attempt metrics, including the ability to handle the league’s toughest competition, when I went deeper into the data to account for shot quality, Hamilton appeared to be the strong player, both offensively and defensively.

New Passing Metrics

To this point, I’ve indirectly shown how each player contributes to his team’s scoring. All the above metrics are group measures: They indicate how the team is doing while the player is on the ice. But what about directly measured contributions? Fortunately, Ryan Stimson and his small army of volunteers have painstakingly collected such data! It’s called the Passing Project. Recently, one of his colleagues, Spencer Mann (Twitter: @SpenceIce), created graphs of the project’s main metrics. The graphs provide a clear snap-shot of a player’s strengths and limitations in terms of their offensive contributions. The percentile scores tell us how a player ranks compared to other defensemen.  For example, a score in the 80th percentile means the defenseman is better than 80% of other defenders on this metric.

To explain the metrics, I’ll quote Stimson (bold and italics added):

“CC% [SAC%] and CC/60 [SAC/60] are for Corsi Contribution [Shot Attempt Contribution] (individual shot attempts, primary passes leading to shot attempts, and secondary passes leading to shot attempts) percentage and per sixty minutes. These tell you how much offense goes through that player while on the ice and also how often they contribute.

Composite SAG and SG represent the total number of shot attempts and shots a player generated from both primary and secondary passes per sixty minutes. SAG/60 is solely for the player’s primary passing contributions.

Entry Assists represent the number of controlled entries a player assisted on. This is determined by the number of passes in transition (prior to entering the offensive zone) we recorded for each player.

SC Contribution% and SCC/60 are the exact same thing as CC% and CC/60, but represent only the scoring chances a player was involved in. I combined our passing data for scoring chances with War-on-Ice’s scoring chance data to arrive at the total number of scoring chances a player contributed to. SC SAG/60 represents the number of scoring chances set up from a player’s primary passes.

Source: Ryan Stimson, In Lou We Trust (SB Nation), Twitter: @RK_Stimp

 

Source: Ryan Stimson, In Lou We Trust (SB Nation), Twitter: @RK_Stimp

Based on these graphs, the main differences between Sekera and Hamilton are their respective strengths in zone entry assists and generating scoring chances. Hamilton was especially strong in his contribution to scoring chance ranking almost at 90th percentile compared to other defensemen. This is consistent with the indirect measures I presented earlier (i.e., Scoring Chance% & High-Scoring Chance%).  Sekera was not as strong as Hamilton, but still above-average ranking at about 60th percentile. I was disappointed to see that Sekera appeared to be weak (about 25th percentile) in setting up scoring chances from primary passes.

But when it came to assists that lead to controlled offensive zone entries, Sekera excelled with a ranking over 95th percentile. In contrast, Hamilton was surprisingly quite below average (30th percentile).  Reading from different sources, and from the few Los Angeles games I watched, Sekera’s passing stood out. His overall passing metrics easily support that observation. I’m excited about seeing Sekera’s passes lighten the load for the Oilers’ puck-lugging forwards, like Taylor Hall.

Summary

Hamilton and Sekera have both handled the league’s toughest competition and held their own. Indeed, their shot-attempt differentials are almost identical against this competition. A main difference that began to emerge was that Sekera was a higher event defender than Hamilton. That is, more shot attempts happened both ways when Sekera was on the ice. When examining each player’s impact and dependency on teammates, they both depended on their top 2 centers to bolster their SAT differentials, but at they also reciprocated by positively impacts on their centers’ SAT differentials. In addition, Hamilton had the benefit of playing with Chara, although Chara’s SAT% also improved with Hamilton. Sekera’s defensive pairing in Carolina, Faulk, was not a particularly good pairing, although they did well when on the ice with either of the Staal brothers. Despite limited time, Sekera appeared to work well with his defensive partner in Los Angeles, McNabb, as well as his centers, Kopitar and Carter.

Looking deeper into the data, in particular, by comparing scoring chances, as well as quality shots based on location, Hamilton came out on top both offensively and defensively. In terms offense, he was much stronger than Sekera in generating scoring chances, whether through primary passes leading to shots or taking shots himself. Hamilton also appeared to be better than Sekera in suppressing shots from the slot. Sekera’s main advantage over Hamilton, and most other defensemen in the league, was his ability to execute passes that lead to controlled zone entries. Sekera appeared to be especially strong in contributing to shot attempts and shots on goal, but the shots tended to be of poorer quality than those of Hamilton. This data suggests that Sekera is not a strong playmaker, yet it also suggests  that his passing will help the Oilers in their transition to offense, which is an area of weakness for them with their current blueline. (This link takes you to Sunil Agnihotri’s post on what the Passing Project data reveals about the Oilers’ defense.)

When Sekera was asked why he chose the Oilers, he hit on all the right notes. “I looked at the roster and I saw the team they had, the coach and the management,” Sekera said. “When I saw what kind of players they had there, it made my choice very easy. They have a lot of skill, a lot of speed and a lot of smart players. They have a good coach, a good GM and a good goalie. It was a good place for me to play with my style of hockey, so that’s why I chose Edmonton” (Source: NHL.com). Skill, speed, and intelligence is what Sekera wanted to match his style (and a 6th year on his contract didn’t hurt either).

I am excited about the upcoming season. McDavid alone may be worth the price of admission some nights. But to see a more complete team, one with a very competent defenseman like Sekera at the helm, gives me even more hope about the Oilers future. The time is coming soon for the rebuild to have a playoff-worthy structure and I see Sekera as a key piece.

Thanks for reading. There was a lot of information packed into this post, but I hope I made it understandable. Some of this information I’m presenting for the first time, so it’s entirely possible I’ve made errors or been unclear. If you have any comments or questions, I’d like to hear from you.

Walter Foddis
Twitter: @waltlaw69

Edmonton Oilers, Fancy Stats, NHL, Trades, Walter Foddis

Analytics of a Trade: Gryba vs. Marincin

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On McDavid Day, after drafting Connor McDavid first overall, the Oilers traded several of their draft picks. The specifics are a little messy, but one transaction involved trading away defenseman, Martin Marincin, to Toronto, which translated into acquiring defenseman, Eric Gryba, from Ottawa.

Quality of Competition Analysis

Not knowing a thing about Gryba, I decided to do analysis of the trade. First, though, some background on the players. Gryba was drafted in 2006 in the 3rd round. He is 27 years old and has played 3 seasons in the NHL. At 27, he is in his prime, and because of that, I don’t expect to see much improvement in his play, although it’s not impossible. Marincin was drafted in 2010 in the 2nd round. He is 23 years old and has played 2 seasons in the NHL. Marincin is not yet in his prime, but will hit them next year as an NHL player’s prime years tend to be from 24 to 29 years of age. In theory, at least, Marincin has a higher ceiling of improvement than Gryba.

For my analysis, I used even-strength (5v5) data from the 2014/15 season, unless otherwise stated. Based on David Johnson’s WOWY (With-or-Without-You) tables, I computed some “new” metrics I believed might be useful in comparing Gryba with Marincin. I often read the argument that runs something like, “But he plays tougher competition, which accounts for his lower Corsi.” This may be true, but it is an assumption that can be tested. Why not compare players at similar levels of competition? Using the WOWY opponent tables, that is what I did.

Introducing the “Break-Even Quality of Competition SAT%”

One of the first metrics I computed I refer to as the Break-Even Quality of Competition Shot-Attempt Differential. That’s a mouthful. The shortened form is Break-Even QualComp SAT%, or even shorter, Break-Even SAT%. (I use SAT% instead of the term, Corsi.) Using the opponent’s shot-attempt differential, this metric identifies the average level of competition in which a player achieves a neutral shot-attempt differential (i.e., SAT% = 50%). Put another way, it indicates the toughness of competition in which a player begins to hold their own. The sample size of this metric is about 50 opponents with a range of +/-2%. For example, if a player’s Break-Even QualComp SAT% is 52%, the range is from 50% to 54%.

Marincin’s Break-Even SAT% was 50.9%, which includes such opponents as Chicago’s defensemen, Mikal Rozsival and Brent Seabrook, along with forwards Milan Lucic (formerly of Boston; now of Los Angeles) and Alex Steen of St. Louis. This looks to be some decent competition that a top-4, or even top-pairing defenseman would be expected to handle. In contrast, Gryba’s Break-Even SAT% was 47%–4% less than that of Marincin–, which is substantial. Competition with a 47% shot-attempt differential includes defensemen Jack Johnson and David Savard (Columbus), as well as forwards Dominic Moore (New York Rangers) and Trevor Smith (Toronto). Gryba’s break-even quality of competition originated from very weak possession teams (e.g., Columbus & Toronto), or from moderately strong possession teams (e.g., New York Rangers), but who were lower on the depth chart.

On the graph below, I expand on this quality of competition (QualComp) comparison by showing each player’s shot-attempt differential at similar levels of competition. Starting from the left, the graph shows the players’ SAT% from the toughest competition (56% SAT%; e.g., Joe Thornton of San Jose, Anton Stralman of Tampa Bay) to opponents with a 46% shot attempt differential (e.g., top-line players on weak possession teams & bottom-6 players on stronger possession teams). At almost every level of competition, except for opposition with a 54% shot-attempt differential, Marincin’s SAT% exceeded Gryba’s differential. Also noteworthy, against the toughest levels of competition, both defensemen were thoroughly dominated. Against elite competition (QualComp SAT% = 56%), Gryba’s shot-attempt differential fell below 40%! This is Andrew Ference territory. (I’m not exaggerating. Against the toughest competition, Ference’s SAT% was indeed below 40%.)

High & Medium Probability Shots: Defense

Noting this discrepant performance that seems to show the Oilers gave up more than what they gained, I was disappointed. I carried on with the analysis, though. Shot attempt metrics are a useful, but very blunt measure. Fortunately, war-on-ice.com now provides detailed shot metric data for each player. Specifically, shots on goal are broken down by their location and translated into 3 primary scoring zones, which the war-on-ice authors identify as low, medium, and high-probability zones. For my analysis, I treat the medium and high-probability zones as the most informative. The high-probability zone is the slot area. From the slot, teams score at an average rate of about 20%. The medium-probability zone is the immediate area surrounding the slot in which teams score at an average rate of 8%.

With Marincin on the ice, the Oilers allowed 26% more high-probability shots relative to the league. (That’s pretty bad, if you hadn’t guessed.) When Gryba was on the ice, the Senators allowed 6% fewer shots relative to the league. However, the Senators were much better defensively than the Oilers. Relative to the league average, Ottawa allowed 4.2% fewer shots, whereas Edmonton allowed 23% more shots from the slot. Noting this huge difference of 27.2%, controlling for each team’s defense metrics was needed. This can be done by examining each player’s shots-against metrics in relation to their teams.

Relative to his team, Marincin (and his on-ice teammates) allowed 2% more shots from the slot, whereas Gryba and his teammates allowed 4.3% more shots from the slot. That is, both teams were worse at suppressing high-probability shots with Ottawa affected slightly more than Edmonton. However, Gryba’s impact did not result in the Senators suppression of high-probability shots to fall below league average. Marincin’s impact on the Oilers’ defense, though, made a bad defensive situation slightly worse.

In the medium-probability zone, Marincin and his on-ice company allowed 2% more shots than the team average. In comparison, Gryba and his teammates allowed 10.7% fewer shots relative to the team. Defending in this area, Gryba and crew seemed to be doing a better job, or did they? One could argue that there are fewer shots in medium-probability zone because more shots were allowed from the slot! If that’s true, what’s Marincin’s excuse? Edmonton’s defense was worse in both the medium and high-probability zones when he was on the ice.

All that being said, given that shots from the slot are 2.5 times more dangerous than medium-probability shots, I assigned high-probability shots more weight. To summarize, both Marincin and Gryba seemed to weaken their respective team’s suppression of very high quality shots.

Shot Attempt Metrics: Player’s Impact on Teammates

In addition to the quality of competition, a player’s shot-attempt differential depends heavily on the quality of his teammates. David Johnson’s site provides an overall shot-attempt differential metric, including for & against shot attempts, relative to teammates. This measure can suggest if a player drives possession. Maricin’s SAT% relative to his teammates was +1.1%, which is mostly derived from his influence in helping the team generate more shot attempts (+2.38 shot-attempts for/60 minutes). In contrast, Gryba’s SAT% relative to his teammates was -4.0%, which is mostly derived from his apparent inability to help the Senators generate more shot attempts (-7.2 shot-attempts for/60 minutes).

These values were useful as it seemed to show Marincin was significantly better than Gryba in helping his team generate offense. Still, I wanted to look at the underlying numbers of these metrics. so I unpacked the players’ WOWY teammate tables. I focused on teammates with whom the players shared at least 10% of their ice-time. Specifically, I examined how much Marincin and Gryba affected their teammates’ shot-attempt differentials.

Overall, Marincin’s impact on his teammates was, in a word, neutral. His teammates’ SAT% dropped slightly by -0.17% (-0.10 shot-attempts/60). Breaking it down by individual teammates, the players he appeared to help the most were Gordon (+4.2%), Nugent-Hopkins (+2.4%), and Yakupov (+3.7%). The teammates he negatively impacted were Hendricks (-4.5%) and Klinkhammer (-8.5%). All his other teammates’ SAT% changed very little. Gryba, on average, appeared to make his teammates’ SAT% worse, which is consistent with his SAT% relative-to-teammates metric.) In particular, Gryba lowered his teammates’ SAT% by 3.7%. Again, Gryba’s negative impact mostly involves lowering his teammates’ ability to generate offense with 7.1 fewer shot-attempts generated per 60 minutes.

On the defensive side of things, there wasn’t much of a difference. Both Marincin and Gryba appeared to be weaker compared to their teammates in suppressing shots, especially high quality shots. But in examining their impact on their teammates’ shot attempt differentials, a difference seems to be emerging. Marincin’s offense appears to be more generative than that of Gryba.

High & Medium Probability Shots: Offense

Examining shot generation in more depth, I focused on their contributions to high and medium-probability scoring chances. Here we find a massive difference in shots from the slot. With Marincin on the ice, he and his teammates are ability to generate 12% more high-probability shots, relative to the league, and 7.7%% more shots relative to the Oilers’ average. In contrast, with Gryba on the ice, he and his teammates generated 12.5% fewer high-probability shots, relative to the league, and 16.7% fewer shots relative to the Senators’ average. In other words, Marincin’s Oilers generated 24.5% more shots from the slot than Gryba’s Senators. What of medium-probability shots? Marincin and his teammates generated 7.1% fewer shots than league average, and Gryba and his teammates generated 10.6% shots below league average. In other words, similar to high-probability shots, Marincin’s Oilers generated more medium-probability shots than Gryba’s Senators.

Net Expected Goals

This shot quality information indicates that Marincin and his teammates generated not only more shot attempts, but substantially more quality shots than Gryba and his teammates. To make this more understandable–“All this talk about shots, but what about actual goals?”–these shot metrics can be converted into expected goals. Fortunately, using shot rates–for and against–in all three scoring zones, war-on-ice computed net expected goals for each player.

With Marincin on the ice, the Oilers’ net expected goals was +0.07 goals/60 minutes. Applying this to Marincin’s time-on-ice, this converts to +.78 goals over the season. Regrettably, then, whatever gains Marincin made in helping his line-mates generate more quality shots, much of this gain is canceled out by his limitations in suppressing quality shots.

With Gryba on the ice, the Senators’ net expected goals was -0.38 goals/60 minutes. Applying this to his time-on-ice, this converts to 6.07 goals against over the season. Gryba’s apparent weaknesses in both shot suppression and generation, especially the latter, ultimately result in 6 more expected goals.allowed than scored.

Shot Attempt Metrics: Impact of Primary Teammates

Returning to the shot-attempt differentials, I was curious to know what impact key teammates had Marincin’s and Gryba’s SAT%. Above, I noted their impact on their teammates’ SAT%, but what of the reciprocal impact of their teammates on their own SAT%? In particular, I wanted to know the influence of their primary defensive partner and first-line center, who is presumably their strongest possession center.

Primary Defensive Partner

Marincin’s primary defensive partner was Mark Fayne with whom he shared 50% of his ice-time. (Marincin also shared about 11% of his time with Schultz and 9% with Nikitin.) With Fayne, his SAT% dropped from 51.3% to 47.1%. When paired, they did play tougher competition. But as I noted above, WOWY tables allow us to compare players across similar levels of competition. I will be presenting a full analysis of all the Oilers in the next coming weeks, but what I can say here is that Fayne’s SAT% was less than Marincin’s SAT% at almost every level of competition. Also, Fayne’s lowered the SAT% of his most frequent teammates by -2.61%. What is one reasonable conclusion using this information? It appears that Fayne dragged down Marincin’s shot-attempt differential, which is not what we were expecting from the veteran. I’ll leave Fayne’s struggles for a future post.

When Gryba was with his partner, Mark Borowiecki, Gryba’s SAT% fell from 48.3% to 45.3% (-3%). Similar to Marincin, then, Gryba was worse off with his defensive partner than without, thus did not depend on his partner to carry him. Borowiecki’s SAT% also fell by about 3%. (48.2% to 45.3%). So either defenseman was not doing his partner any favours.

First-Line Center

When Marincin shared ice-time with Nugent-Hopkins–the Oilers #1 center–Marincin’s SAT% improved from 47.6% to 52.1% (+4.5%). Nugent-Hopkins’s 51.1% SAT% with Marincin was also an improvement from 49.7% (+2.4%). Thus, both players benefited from each other and improved to a respectable shot-attempt differential.

When Gryba was on the ice with Mika Zibanejad, the Senators #! center, his SAT% fell by almost 4% (47.7% to 43.8%). Thus, he was worse off with Zibanejad, as was Zibanejad, whose SAT% fell by a whopping 7.5% (51.3% to 43.8%). This is consistent with the overall pattern noted above: Gryba tends to drag down a a teammate’s shot-attempt differential, mostly in reduced shot generation. There is one center, though, who improved Gryba’s SAT% and that is Curtis Lazar. Lazar helped improved Gryba’s SAT% by 4.2%, but Zybra did not reciprocate: Lazar’s SAT% dropped by 1.3% when sharing time with Gryba. Overall, this pattern suggests that Gryba is not only not a possession driver, but that he drags down his teammates’ shot-attempt differentials.

Direct Contributions to Shot Attempts and Scoring Chances

Last season, Ryan Stimson and a host volunteers collected data for what Stimson refers to as the Passing Project. Some teams had their complete seasons analyzed (i.e., tracking events through video reviews of games), but most teams only had partial data. The Oilers had data from about a dozen games analyzed. A few of the metrics generated from this data are relevant here. One metric is referred to as a player’s Corsi Contribution (CC%). Relative to all shot attempts while he was on the ice, this metric counts what proportion originated from a player’s shot attempts, as well as his primary and secondary passes that lead to shot attempts. To account for direct contributions to quality shots (i.e., scoring chances), there is Scoring Contribution (SC%), which includes scoring chances (as defined by war-on-ice) and primary passes that lead to scoring chances. Finally, there is Shot Attempt Generation Efficiency (SAGE%). This metric computes what percentage of primary passes lead to shots on goals as a proportion unblocked and blocked shots. The SAGE metric answers the question: How often does a player’s primary passes toward shots on goals?

Below I compare the players on each metric and in parantheses, I note their league-wide rank among defensemen.

[table id=9 /]

Except for the SAGE metric, they appear to be quite similar to each other in their ability to generate shot attempts and scoring chances. The sample size is on the small end, especially for Marincin, (1/6th of his time-on-ice), which limits making any strong conclusions, but the metrics are informative.

Shot Metric Summary

Marincin has outperformed Gryba in various shot metrics. First, at almost every quality of competition level, Marincin’s shot-attempt differential exceeded that of Gryba. Second, when examining high quality shots–for and against–with Marincin on the ice, the Oilers net expected goals was about even. With Gryba, the Senators were expected to have 6 more goals against. Third, Marincin’s specific shot metric advantage over Gryba appears to be in helping his team generate more quality shots, which has some confirmation (limited by small sample size) from the Passing Project data. However, important to note that Gryba’s scoring chance contribution is equivalent to that of Marincin. Fourth, when Marincin shares ice-time with Nugent-Hopkins, both he and Nugent-Hopkins improve their shot-attempt differentials. When Zybra is on the ice with Zibanejad, both his and Zibanejad’s shot-attempt differentials drop considerably.

If most of these metrics point to Marincin as being the better defenseman, why did the Oilers trade him? To be honest, I don’t know. I would imagine a rationale would refer to Gryba’s greater physicality and that he is somehow a better stay-at-home defenseman. But if his physicality is not resulting in better shot metrics, offensively or defensively, than why does his physicality matter?

Point Production

Despite all this, there is one measurable area in which Gryba seems to have an advantage over Marincin and that is in actual point production. Although they seem fairly similar in their direction contributions to scoring chances, Gryba’s points/60 minutes of 0.69 (11 points; 6 primary assists & 5 secondary assists) exceeded Marincin’s production of 0.36 points/60 (4 points; 1 goal, 2 primary assists, 1 secondary assists). Perhaps Gryba produced more because Ottawa’s (5v5) shooting percentage exceeded Edmonton’s by 1% (8% vs. 7%) and generated more scoring chances (Scoring Chances/60 = 26.6) than Edmonton (SC60 = 24.6).

Proportionally, Gryba also contributed more to the team’s offense than Marincin. Gryba was involved in 32% of the team’s goals when on the ice. This metric is consistent across his first 3 seasons. Marincin, in comparison, was involved in 23.5% of his team’s goals when on the ice. Gryba’s and Marincin’s individual shot metrics were equivalent. Gryba’s shot rate was 3.77 shots/60 and Marincin’s was 3.14 shots/60.

Concluding Comments

I’m not a fan of this deal. I’m not sure of what it accomplished. Neither defenseman is particularly strong defensively, but as my analysis suggests, Marincin seemed to show more potential. Against almost every level of competition, he outperformed Gryba. Marincin’s Break-Even SAT% of 50.1% suggests he has an ability to hold his own against some reasonably strong competition, whereas held his own against much weaker competition. Gryba tended to negatively impact his teammates’ shot-attempt differentials. Although Marincin didn’t help his teammates’ SAT%, on average, neither did he worsen their SAT%. However, when Marincin played along side Edmonton’s top-line players, like Nugent-Hopkins, the improvement in shot-attempt differential was mutual. Breaking down the offensive side of Marincin’s shot metrics, his strength appeared to be his ability to directly contribute to scoring chances, especially in terms of his efficient passing. Gryba also shows some strength in creating scoring chances and moreover, out-produced Marincin. But his production advantage over Marincin is most likely due to the better offensive team around him. In fact, based on shot quality data (medium & high-probability shots), when Gryba was on the ice, Ottawa was expected to have 6 more goals against. Marincin, and his on-ice teammates, were expected to essentially break even in net goals.

If there is any fans of Gryba reading this article, I’d like to hear from you. What qualities does he bring to Oilers that are a measurable advantage over Marincin? To be honest, I’m not big on intangibles (e.g., grit, physicality), that is, unless a person can show how these qualities translate into measurable events that lead a team to score more goals than their opposition. I also welcome any other questions and feedback, especially on the new metrics and analysis. I hope I made the new metrics understandable to you.

Thanks for your patience  in reading this until the end. As I learn more about these metrics, I hope to make my future anyalyses on Oiler trades briefer.