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William Nylander’s slow start was expected in the sense that he had missed training camp and two months of the season after a delayed contract signing on December 1st. We’re a month down the road now.

As the weeks drag on, it’s time to take a look and see how Nylander’s performance charts out. I think a few things have become clearer about his 5v5 play, as shown in the SKATR viz below. I just updated SKATR today (January 1st) and this is something I most wanted to see and discuss with fresh data.

The bars are percentile ranks among forwards with 100+ minutes and are based on 5v5 per 60 minute stats. Each bar is labelled with the percentile (100=best) and ranges in colour from dark red (worst) to dark blue (best).

Keep in mind that the 2018–19 stats shown on the left are based on only 136 minutes at 5v5 in December, mostly playing with Nazem Kadri. The right chart covers his 2017–18 season. As he plays more minutes, the measures will become more stable (whether he improves or not). Stats are mainly from corsica; a glossary for the SKATR stats can be found here and the actual interactive SKATR viz is here if you want to explore.

Individual Stats:

The top 10 bars in SKATR highlight his individual performance.

As most know, he has struggled to put points on the board, and even adjusted for his reduced playing time he ranks in the bottom 3%, still without a goal and just two assists. This is a dramatic departure from his 93rd percentile ranking last season on points, although Matthews certainly helped last season. The celly’s have been rare.

Game Score is an overall measure and includes goal and assist stats, shots, on-ice shot share, face-offs, and penalties. Nylander is at the 38th percentile right now, compared to a very good 86th percentile last season.

It’s noteworthy that his early fatigue led to some glaring penalties and helps explain his miserable penalty +/- ranking: bottom 2% in the N.H.L.

Although the points are scarce, it’s also important to note that his production in terms of shots per 60 and shots from higher quality locations (expected goals per 60) are very good — 83rd percentile. At first I thought that maybe his lack of goals had to do with perimeter shooting but as you can see, he did get chances in some sweet locations.

These are just his unblocked shots at even-strength — green is shots on goal, purple is misses. If you compare to the red zone in my overall heat map for the league-to-date, you can see the red area where goals have been scored most frequently.)

I’ve included a new stat in SKATR, estimated shot assists which is based on manually collected passing data and modelling (see glossary for more on who and how). But to put it in simple terms, a large repository of manually collected passing data from a dedicated team of volunteers was used together with on-ice and individual data on shots and assists to build a model and extrapolate to an estimate of passes leading to shot attempts. These pre-shot passes are called “estimated shot assists” and are fairly predictive of actual shot assists.

What I neglected to add in my first draft of this article is something I should have shared — estimated shot assists are predictive of future assists and the sum of shot assists and shots (called shot contributions) is more predictive of future points than actual points. This link shows the importance of shot assists and shots as a leading indicator of future points.

What is striking is how this stat highlights Nylander’s estimated passing performance, as he sits in the top 5% despite being rusty. This also illustrates how passes alone need a finisher to become assists, just as expected goals need one. It appears that he is still helping set up chances, they just haven’t turned into assists very often in these early games. I’ve seen this happen many times before in small game samples.

Although watching a game will show what’s happening on these plays better, it’s usually pretty safe to assume that puck luck is involved, something I’ve seen turn around on a dime (as I predicted would happen with Marner). But this just confirms what I already knew, looking at the bulk of passing data available on Nylander, as presented in a viz by Ryan Stimson — details on the acronyms below can be found at his viz site. Nylander is elite at many shot and passing traits and the December stats are consistent.

To sum up, I would score William Nylander’s individual stats as a C for December. His point stats are a D but the underlying production is a B (or higher).

Context

Before I cover on-ice stats I am going to flip down to his usage context at the bottom of SKATR. Babcock tried Nylander on Auston’s line in the first game but that was a reach, he wasn’t ready.

Although we see him playing mostly with Kadri, what is striking is how lower in quality his competition* was in December as Babcock made efforts to not expose him. This is Babcock to a T, he is very aggressive when it comes to usage and line matches. It’s made easier by having the Tavares and Matthews lines draw top opposing lines to them like moths to a flame lol.

A full 94% of forwards faced stiffer competition on average this season than William. Last season, Nylander was at the 80th percentile as he mostly skated alongside Auston Matthews. It might be easier to see in the Leafs Report chart below where only the GOAT and Ennis faced weaker opposition.

*Quality of competition is based on corsica’s “TOI% QoC” which looks at opponent players’ share of ice time.

Also of note, when it came to face-off starts in the D zone and O zone, Nylander was started in the D zone 38% of the time which was in the bottom 10% for forwards. So his usage was sheltered in December.

On-Ice Stats

When I talk about underlying stats, this is usually what I’m referring to. While a count of goals in a small number of games can be fun, it’s usually dumb. It depends on whether that puck bounced on its edge, whether that goalie had a sleepless night, whether a post became a rubber magnet and a dozen other random things. It’s better to focus on numbers that have larger samples and aren’t based on a short-term goal streak or slump.

The advantage of on-ice stats is that they give us a picture of what is happening in the 200 foot game 5 on 5 and they come with larger samples so they are usually going to be more reliable.

Nylander’s on-ice shot share, or CF%, is excellent. He leads the Leafs in both CF% (54.7 ) and xGF% (57.3) for forwards with 100+ minutes. Compared to forwards across the league, he ranks at the 86th and 90th percentiles. What’s even more telling is the fact that his respective relative stats are at the 80th and 90th percentiles. In other words, the Leafs dominate two-way possession with Nylander on the ice and Nylander stands out from his teammates in a good way, achieving even better shot differentials relative to the teammates he plays with.

You’ll notice the red bars — like almost all Leafs, the high pace/high chance game they play tends to boost the “for” stats and show worse shot suppression (the “against” stats). The most important percentiles are the % stats when it comes to looking at Leafs players.

Bottom-line: William’s December was good when it came to his on-ice stats. I’d score him a B+, only because I have to discount a little for his context.

Note that average quality of competition across a sample of games has been shown to not be that big a factor in things like CF%, while quality of teammate is more consistent game to game and is generally more important. This article might have an overstated title but it helps explain.

Summing up

I have written and tweeted about Mitch Marner when he was in a slump and the underlying numbers supported my opinion then to just be patient. I’ve done the same with Kasperi Kapanen as well. I’ve also gone the other way when I identified Leo Komarov as being too high in the lineup. Because he had persistently poor underlying numbers.

In William Nylander’s case, I see parallels to the early season goal slumps Mitch Marner has gone through. So my advice is be patient and avoid the temptation to criticize his play. Keep an eye on his entire game and I think you’ll see how these numbers come about.

Note: the viz used in this article are at my Tableau Gallery.

Supporting data behind SKATR is from corsica.hockey, naturalstattrick.com, offsidereview.com, moneypuck.com, and puckpedia.com. The microstat data referenced is provided by @Shutdownline, Ryan Stimson et al, estimated shot assist model by Alan Wells (@loserpoints).