The Most Boring Possession in Hockey: Exploring What Happens after NZ Faceoff Wins

Something in hockey has been bugging me for years. Technically a lot of things about hockey bug me, but let’s not get sidetracked right off the bat. The irritating aspect of hockey I want to focus on today are neutral zone faceoff wins. They rarely ever lead to anything interesting.

In fact, if you compare possessions that start with a NZ faceoff win to all other possible possession types, they consistently lead to the fewest shots and expected goals per possession. Using InStat Data for the 2018/19 NL season, it becomes clear that even the territorial advantage compared to faceoff wins in the defensive zone is practically non-existent (each point represents a team’s season performance):

Is Winning Faceoffs Actually Bad?

If we accept that NZ faceoff wins are difficult to make work offensively, what are they good for? The current value of neutral zone faceoff wins lies not in the offense you may be able to generate off of them, but the lack of offense your opponent can muster following your possession.
If you look not just at the current possession but at the following possession as well, NZ faceoff wins actually look pretty good defensively. In the following chart, I’ve plotted the xG per 100 possessions of the initial possession as well as the xG of the following possession if possession of the puck switches teams and the xG of the following possession if the team initially in possession regains possession after that (another FO win, puck recovery, etc.). NZ Faceoff Wins are split by location and labelled on the right, other possessions are labelled on the left.

If we add up the xG for and against of the following possession (as an xG differential), we see that while still negative – in hockey it is simply more likely the opponent regains possessions and brings it up ice than that a team chains possessions without interruption – faceoff wins at center ice and at your own blue line are actually among the least damaging defensively on the following possession.

If we now sum up those two, we arrive at the average combined xG value of a possession type and the possession following it. Center ice faceoff wins average 0.16 xG per 100 Possessions, faceoff wins at your own blue line are worth 0.00 xG per 100 possessions and faceoff wins at the opponents blue line are worth -0.02 xG per 100 possessions.
From that, one could argue that if your target was to maximize your expected goal difference over the next two possessions and you find yourself in a faceoff at the offensive blue line, it’d be prudent to lose that faceoff, since on average, a win is a net negative for you while a loss would be neutral for the opponent. The same could be argued for the defensive blue line, a win would be no net gain for you while a loss is a net negative for your opponent.

Though if I’m being honest, the numbers are way too close for me to seriously argue this and team effects (as you can see in the first chart, teams can be 0.3 xGp100 better or worse than other teams in these possessions) likely change the math here, but I think it illustrates how little positive effect these faceoffs actually have.

What to do about it

The obvious answer to the question I have yet to ask is: Dump ins. Yes, dump ins are the main reason for this lack of offense. In National League (Swiss League for the Non-Swiss) hockey…

  • Non-faceoff possessions starting in the defensive zone average about 17 dump-ins per 100 possessions
  • Non-faceoff possessions starting in the neutral zone average about 28 dump-ins per 100 possessions
  • Possessions following faceoff wins at a team’s defensive blue line average about 35 dump ins per 100 possessions
  • Possessions following faceoff wins at center ice average about 50 dump ins per 100 possessions
  • Possessions following faceoff wins at a team’s offensive blue line average about 65 dump ins per 100 possessions

Obviously there are reasons for this. A faceoff is a set piece of play that allows the defensive team to quickly find its defensive structure. And a win at the opponent’s blue line is especially susceptible to dump ins since the forwards have no reason to back off with the blue line compressing the opponent’s passing options.

Note: I might post something about faceoffs at the defensive blue line at some point since they offer much more variety in approaches, but I’m going to focus on faceoffs at the offensive blue line here.

Now, simply because possessions following a NZ FOW at the opp. blue line have the highest frequency of dump ins doesn’t mean that that’s the correct strategy to use. If we split these possessions by whether or not they feature a dump in, the split looks as follows:

Possessions with a dump in (per 100 poss): 5.5 Shots, 0.17 xG
Possessions without a dump in (per 100 poss): 32 Shots, 0.68 xG

Keep in mind, possessions without dump ins also contain possessions where there was no entry at all. So just the fact that these dump ins are so expected and therefore so easy to defend makes that much of a difference. But what plays are there to improve upon the tried and true dump and chase?

While there are certainly some odd plays out there …

… the most widely used and reproducible example usually looks something like this:

A D-D pass to the weak side defenceman who tries to enter the zone. If successful, this play usually involves either some manner of inattention by the opponent or casual interference by the inside winger.

The more fun version gives both the defenceman in possession of the puck (who to pass to) and the inside winger (who to cover) an option to choose between the winger and the defenceman. In the next example, the Lugano (black) winger is quite aggressive expecting the D-D pass which opens up the winger inside:

Here, you can clearly see the Davos (yellow) winger prepared for this play waiting for the defenceman to make up his mind on who to pass to:

And there are even some clever twists on it, with the winger passing the puck outside (even though it’s well defended here) :

While these can work and they are most certainly more useful than dump ins, their ubiquity makes them quite easy to snuff out. Popular tactics literature offers these two additional ideas:

From Ryan Walter’s and Mike Johnston’s “Hockey Plays and Strategies”:

I saw this used a bit a couple of years ago and it pops up every now and then. I’ve seen it work a little bit, but mostly as a way to get the winger into the OZ with more pace for a more intense forecheck rather than a controlled entry.

And from Ryan Stimson’s “Tape to Space: Redefining Modern Hockey Tactics”:

I haven’t seen this out in the wild so I can’t say much about it other than that it’d be nice to see it tried out, the multiple picks and wide stance to free up space certainly seem like an appealing option off a draw.

In general, I think NZ faceoff could use some more creativity like Ryan’s. Conservatively, the difference between the best and the worst teams is currently worth 1.2 goals over an NLA season (without evaluating the knock on effects that controlled entries have on following possessions). A creative coaching staff could probably stretch that to be worth quite a bit more. And practicing a few set plays shouldn’t be asking too much, especially not in a league that mostly plays on weekends with the entire week to practice.

Exit Types Don’t Affect Entry Quality (Much)

Last time, we saw that a team exiting its defensive zone with possession is much more likely to enter their offensive zone. Do the advantages end there, or do possession exits also improve the quality of zone entrances? Perhaps leaving the defensive zone with possession makes it easier to keep possession as they enter the offensive zone, and that leads to more shots per entry. Maybe pass-outs create space for more passes in the offensive zone, which improves shot quality.

It turns out that there is not much of a difference in entry quality by exit type; exiting with possession makes it more likely to gain the offensive zone, but the advantages quickly dissipate. That said, there are some interesting variations in how those zone entries play out. The differences are small enough that they could be random chance, but it’s worth taking stock of what we know with the data we have.

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Evaluating Nordic Drafting – A Potential Market Inefficiency

Over the last decade, teams have taken significant steps to improve their NHL entry draft approach. To do this, a number of teams have bolstered their analytics staff to identify the current “gaps” in prospect scouting. Whether it’s the Detroit Red Wings being the first team to dive head first into drafting Russian players, and then later Swedish players, or the Tampa Bay Lightning prioritizing small, skilled forwards, teams are looking for any available edge. More recently, the Pittsburgh Penguins have put a premium on overage players, as Namita Nandakumar found that overage players make the NHL faster. What’s the next big market inefficiency?

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Projecting NHL Skater Contracts for the 2019 Offseason

We recently released the final version of our contract projections for the 2019 NHL free agent class (they can be found here). Our initial projections went up in mid-April, and even though it’s only been a few weeks, we’ve had numerous questions about how the model was designed, how it works, what it means, etc. I thought we might be able to answer all the questions about it on twitter, but alas it was just a dream. A quick recap: this is our third year doing contract projections for the NHL offseason. While the model/projections this year may seem quite complicated, our first version was very simple: a few catch-all stats and a linear regression model to predict salary cap percentage (cap hit / salary cap). We use cap percentage to keep salaries on the same level as the salary cap changes. Over the last few years, we’ve developed a few new methods, and this year we took quite a bit of inspiration from the method Matt Cane used for his 2018 NHL offseason salary projections.

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Statement from Hockey-Graphs about Jason Baik

On Wednesday Night, Hockey-Graphs became aware that one of our contributors, Jason “jsonbaik” Baik, had been convicted of Sexual Assault in Allegheny County, Pennsylvania (Pittsburgh). To be utterly clear, Hockey-Graphs condemns these actions absolutely. Upon becoming aware of this horrible news, we have terminated our relationship with Mr. Baik and all contributions from Mr. Baik have been removed from this site.

We here at Hockey-Graphs wish to express our support for those who have been victims of Sexual Assault, Rape, or related crimes. As such, we encourage our readers to support organizations dedicated to help support victims of such heinous acts. If you can, please consider a donation to National Organizations like the Rape, Abuse & Incest National Network (RAINN) or local organizations such as the Pittsburgh Action Against Rape (PAAR) and the Women’s Center and Shelter of Greater Pittsburgh.


Hockey-Graphs Editorial.

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Visualizing Goaltender Statistics Through Beeswarm Plots

A picture is worth a thousand words. Yes, it’s a cliché, but when it comes to visualizing data, an individual can tell a story via the choices they make when presenting their data. One of the most common visualizations is a plot showcasing the frequency and distribution of an event. Data like this are often presented in a histogram or box-and-whisker-plot. However, a limitation of both of these types of plots is that neither shows the individual where each data point falls. On the other hand, a beeswarm plot allows the user to see where each individual point falls across a range. A random jitter effect is applied to maintain a minimum distance between each point to minimize overlap.

Inspired by the wonderful graphs from Namita Nandakumar and Emmanuel Perry, I thought I would attempt to visualize how goaltenders have fared in goals saved above average over the course of their careers.

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NL Ice Data: A Swiss Hockey Analytics Website

In the last 10 years, I have been impressed by the development of the hockey analytics community in North America as well as the tools made available to the public in the hope of increasing the general hockey knowledge.

Unfortunately, in Switzerland, the Swiss Ice Hockey Federation (SIHF) does not provide the same level of information as there is in North America and keeps part of its proprietary data for itself. As such, fans and journalists, except on very rare occasions, don’t have access to the same kind of in-depth researches/analyses as there are in the NHL or some other European leagues. Plus/minus is still THE hockey statistic for some journalists or analysts.

The first part of my project with the Hockey-Graphs Mentorship program was to create a platform entirely dedicated to Swiss hockey statistics, called NL Ice Data, the main goal was to exploit as much as possible the available data and to give fans access to additional statistics the SIHF doesn’t necessarily provide:

  • GF/GA: for players, RelGF%, GF/60, …;
  • time on ice deployment and evolution;
  • linemates information;
  • aggregated shot tracker maps per player, goalie and team;
  • and many others.

Current features include the same core of statistics for players, goalkeepers and teams: statistics, fouls, shootouts and shot tracker maps. Easy to use, the website provides interactive tables and charts so that fans can engage more with data. Additional features, charts and metrics will be added along the project.  

By slowly integrating further metrics and concepts after the website’s launch (xG or Game Score for example), the modest goal is to build overall knowledge amongst fans. A secondary goal was to have a platform ready to publish more *advanced* statistics (including at the player level) as soon as the League publishes more of its proprietary data.

How Much Do NHL Players Really Make? Part 2: Taxes

Although published NHL salaries may seem exorbitant at times, players’ annual income is subject to a number of withholdings that limit their take-home pay. As we explained in Part 1 of this series, players lose some of their earnings to escrow – a reconciliation process arising out the Collective Bargaining Agreement between the league and the NHL Players’ Association. Another expense that reduces a player’s earnings is something that all workers in the United States and Canada are subject to: taxes.

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