Proposed defensive zone face-off rule would have miniscule effect on scoring

jacketslightning

In its ongoing quest to increase goal scoring, the NHL/NHLPA Competition Committee has proposed a rule change to modify defensive zone face-offs so that the defensive zone player must always put his stick down first. This replaces the current format in which the visiting team player puts his stick down first. (Neutral zone face-offs would continue to follow the existing rule.)

It’s worth noting that over the past five seasons, the offensive zone team has only won face-offs at even-strength 48.4% of the time. Maybe the league has noticed and wants to bump that number up to 50% or higher, and thinks that a rise in scoring would follow.

But would this rule change actually cause a significant increase in scoring? I decided to do the math and the result was only a miniscule increase in goals. Here is my methodology:

  • I looked at every goal scored in the NHL from 2010-11 through 2014-15 and the outcome of the last face-off prior to that goal.
  • I calculated the league-wide rate at which goals were scored (i.e. goals per face-off) by either team after face-off wins and losses in each of the following situations: (1) even-strength offensive zone, (2) power-play offensive zone/shorthanded defensive zone, (3) shorthanded offensive zone/power-play defensive zone.
  • During these five seasons the home team won even-strength face-offs 51.6% of the time (or, a 1.6% advantage compared to a coin flip). While there are surely several factors contributing to this home-ice advantage, for the purpose of finding the maximum effect of the proposed rule change, I attributed the full 1.6% to the current rule forcing the visiting player to put his stick down first, and used this as an analog for the proposed rule.
  • I set up a model of scoring per game based on the number of face-offs per game in each of the above situations and the observed rate of scoring after each face-off, according to whether the offensive zone team won the face-off. I adjusted the offensive zone face-off win percentage to be 1.6% higher in each situation as a result of implementing the proposed rule change, and calculated the resulting change in scoring.
  • Here is all of this in a spreadsheet if you want to check my numbers or try plugging in some other number besides 1.6%. (Note that it won’t change the result much.)

So here is the change in scoring per game:

FO/game Change in goals per face-off after adjustment Change in goals per game
Even-strength Off. zone 29.84 0.000113 0.00337
Power-play Off. zone 2.384 0.000307 0.000731
Shorthanded Off. zone 0.639 -0.000233 -0.000149
Sum 0.00395

Multiply that goals per game sum by the 1,230 games in a full season and we are talking about approximately 4.9 additional goals per year across the entire league. It’s an almost unnoticeable difference, but in a league desperate to increase goal scoring, it may be better than nothing.

Blue Jackets/Lightning face-off photo by Paula Lively, used under CC BY 2.0

Redefining face-off success using shot data

Face-off percentage is a great statistic but there’s more to face-off success that it does not capture. In my article at Hockey Prospectus I explain how I created a new face-off statistic, Net Shots Post Face-off (NSPF), measuring success using shots after face-offs rather than the scorer awarded face-off win.

You can view the leaders for NSPF by offensive zone and defensive zone.

Introducing face-off percentage opponent hand splits, and revealing hidden biases

jacketswings

Have you ever wondered whether certain players win more of their face-offs when a left-handed (or right-handed) opponent lines up against them? It’s a request I’ve gotten numerous times here, so I’m happy to announce that player profiles on faceoffs.net now contain split stats against left/right handed opponents. You can also view this data across the league’s top face-off takers here.

To show what you can do with this information, here are two interesting charts that may be of particular interest to Anaheim Ducks and Nashville Predators fans:

getzlaf_web
ribeiro_web

Ryan Getzlaf, an approximately average overall face-off taker, has consistently shown an ability to win more of his face-offs against opponents who shoot right-handed — a bias that has persisted as his performance has slightly improved each of the past four years.

On the bottom, Mike Ribeiro‘s overall face-off numbers display his struggles, but one of the key reasons behind them may have gone unnoticed until now. Ribeiro goes from merely mediocre against left-handed shooters (who take about 63% of the NHL’s face-offs, but that’s a topic for another day) to downright awful against right-handed shooters, and this spread is consistent across all four years that are currently in my database.

Most players do not display such a wide disparity in their performance, and in fact for most any bias they did have would be overwhelmed by the simple random variation that occurs in year-to-year face-off data. But there appear to be some, like Getzlaf and Ribeiro, for whom the handedness of their face-off opponents does matter, and this will help in unearthing such cases.

Below are splits (FO% vs. left-handed minus FO% vs. right-handed) for the 52 players who have taken at least 100 face-offs against both right- and left-handed opponents every season since 2011-12. I ordered them by increasing standard deviation, so the players towards the top are the ones with the most consistent splits across the four years.

Player 2014-15 2013-14 2012-13 2011-12 Average Std.dev.
Adam Henrique 4.0 3.8 3.9 2.6 3.6 0.7
Claude Giroux 0.5 -0.3 0.8 -1.7 -0.2 1.1
Nicklas Backstrom 5.7 2.2 3.7 3.0 3.7 1.5
Ryan Getzlaf -8.7 -9.0 -7.5 -5.6 -7.7 1.5
Antoine Vermette -2.0 -3.6 -4.8 -1.1 -2.9 1.6
Mike Ribeiro 10.2 9.3 8.9 12.6 10.3 1.7
Henrik Zetterberg 1.8 -1.0 3.5 0.6 1.2 1.9
Mike Richards 2.5 -2.4 -0.8 -0.8 -0.4 2.1
Jay McClement -2.8 -1.4 -4.7 -6.2 -3.8 2.1
Jarret Stoll 5.1 3.1 1.2 0.1 2.4 2.2
Sidney Crosby -3.7 1.0 0.0 -2.7 -1.4 2.2
Eric Staal -3.3 -4.5 0.0 -0.1 -2.0 2.3
Paul Gaustad -0.1 4.3 4.8 1.6 2.7 2.3
David Desharnais -2.8 -1.8 2.8 -0.7 -0.6 2.4
Mikko Koivu -3.1 -2.0 -2.4 2.7 -1.2 2.6
Nate Thompson 4.9 5.0 4.4 -0.8 3.4 2.8
Ryan O’Reilly 0.4 -3.4 2.0 3.2 0.6 2.9
Paul Stastny -6.6 -5.4 -12.0 -7.5 -7.9 2.9
Martin Hanzal 4.3 -0.1 -2.4 2.4 1.1 2.9
Tomas Plekanec -2.7 -1.0 -4.0 -7.9 -3.9 2.9
Sean Couturier 1.2 1.7 -4.8 -1.0 -0.7 3.0
Brandon Sutter -2.6 -5.9 1.4 -1.4 -2.1 3.0
Tyler Bozak -1.4 -2.5 -7.4 -7.0 -4.6 3.1
Sam Gagner 1.1 -4.2 -0.4 3.3 -0.1 3.2
Vernon Fiddler -2.0 0.2 4.8 3.9 1.7 3.2
Brian Boyle 2.4 -5.1 -3.3 -1.4 -1.9 3.2
Derek Stepan 0.4 0.5 7.1 5.1 3.3 3.4
Joe Pavelski -9.3 -3.6 -3.1 -9.4 -6.4 3.5
David Backes 4.1 -3.4 0.7 4.4 1.5 3.6
Patrice Bergeron -2.5 3.6 6.5 1.6 2.3 3.8
Brad Richards -5.7 -9.1 0.0 -3.3 -4.5 3.8
Jonathan Toews -3.3 -1.1 2.5 5.9 1.0 4.0
Kyle Turris 4.0 -2.9 -6.2 0.9 -1.1 4.4
Derick Brassard 8.1 0.0 -1.4 -0.8 1.5 4.5
Joe Thornton 2.6 -6.0 -2.4 3.6 -0.6 4.5
Boyd Gordon 5.0 -6.4 -0.2 0.2 -0.4 4.7
John Tavares 0.5 0.4 9.9 7.0 4.5 4.8
Bryan Little -3.8 0.3 -9.5 1.1 -3.0 4.9
Steven Stamkos 1.2 3.4 11.8 1.8 4.6 4.9
Logan Couture 5.5 0.8 8.2 12.6 6.8 4.9
Gregory Campbell -4.4 7.6 5.0 1.2 2.4 5.2
Ryan Nugent-Hopkins 7.1 -5.7 -1.6 -2.2 -0.6 5.4
Henrik Sedin 6.7 1.5 -6.9 0.1 0.4 5.6
Matt Duchene 3.7 -2.3 7.0 -5.6 0.7 5.7
Pavel Datsyuk -14.0 1.4 -4.0 -3.2 -5.0 6.5
David Legwand 11.3 -2.0 2.1 -3.1 2.1 6.5
Marcus Kruger 13.8 -0.1 -1.1 4.1 4.2 6.8
Frans Nielsen 12.4 -3.9 -4.9 1.1 1.2 7.9
Anze Kopitar -11.6 4.1 6.2 2.4 0.3 8.1
Chris Kelly -18.4 -11.6 -6.1 1.4 -8.7 8.4
Valtteri Filppula 0.2 0.9 -7.0 14.8 2.2 9.1
Cody Hodgson -17.6 1.7 8.2 6.2 -0.4 11.8


Photo credit: mrlaugh@flickr