The best way to estimate face-off ability in the NHL, in my opinion, is with my statistic NSPF (Net Shots Post-Faceoff). NSPF estimates a player’s ability to win face-offs, and more importantly, his ability to influence shot flow, by looking at how often shots occur during the 10 seconds following his face-offs. You can read more about why I use 10 seconds in my original introduction of NSPF at Hockey Prospectus.
Since the introduction I’ve made some small changes to NSPF. Specifically:
- I’m now basing the numbers by default on all shot attempts (previously it was unblocked shots only), although on the stat page you can switch between other shot types like unblocked shots, shots on goal, and goals.
- I’m now including face-offs in the neutral zone.
- I’m now adjusting the numbers in comparison to the league average. Special thanks to Rob Vollman for suggesting this.
Here’s how NSPF is now calculated from start to finish, using Patrice Bergeron‘s 2014-15 regular season as an example:
First, we count the number of shots (for and against) during the 10 seconds following the puck drop on all of Bergeron’s even-strength face-offs, according to whether the face-off was in the offensive zone, defensive zone, or neutral zone:
|(B) minus (C)|
While it’s nice to know his net shots, it doesn’t tell us by itself how much better he is than other players in the league. Almost all players will have positive net shots in the offensive zone, negative net shots in the defensive zone, and close to zero in the neutral zone.
So now we form a basis of comparison to the rest of the league by calculating the amount of net shots we would expect Bergeron to have, which is the league average rate of net shots after face-offs times the number of face-offs he took.
For the neutral zone that’s easy; the net shot rate is zero across the league by definition, because every shot-for counts as a shot-against for someone else’s neutral zone face-off. For the other two zones we’ll find the shots-after-faceoffs rate by calculating the total number of shots in the 10 seconds after face-offs (minus Bergeron’s) and the total number of zone face-offs (minus Bergeron’s).
Note that the league values for offensive and defensive zone net shots are equal in magnitude and opposite in sign; like with the neutral zone, everyone’s shot-for is someone else’s shot-against.
|Zone||League Face-offs||League Face-offs
|League Net Shots||League Net Shots
|(E) minus (A)||(G) minus (D)|
We use these numbers to form a shot-per-face-off ratio that we multiply by the number of Bergeron’s zone face-offs to obtain the expected net shots. With the expected value we can come up with an adjusted number that reflects Bergeron’s ability in relation to the rest of the league.
|Zone||League Shots per Face-off
Expected Net Shots
Adjusted Net Shots
|(H) div. by (F)||(I) × (A)||(D) minus (J)|
With the adjusted net shots calculated per zone, the total adjusted Net Shots Post-Faceoff is simply the sum of the numbers in column K: 96.9
To form a basis of comparison with other players with other quantities of face-offs, we can divide that number by Bergeron’s even-strength face-off count, 1424, to get his NSPF per face-off: 0.0680.
While it might be hard to ascribe meaning to a small number like that, here’s a simple way to do it. 0.0680 is approximately 1/15. So every 15 face-offs at even-strength, Bergeron’s face-off taking ability compared to an average player gains his team an additional shot attempt (or denies the opposing team a shot attempt) within 10 seconds of the face-off. For a player who averaged 17.6 even-strength face-offs per game last year, that’s a nice bonus for the Boston Bruins. And that’s just compared to an average player; his value compared to a below-average player would obviously be even greater.
However I do want to emphasize that NSPF is an just estimate of a player’s face-off winning ability, because we are judging a player by all shots that occur within 10 seconds (whether or not the face-off had any bearing on them) and ignoring any events that occur after 10 seconds. We do so because statistically the window of face-offs influencing shot flow has been shown to be strongest within 10 seconds, and that’s the best we can do at the moment. When advanced player tracking lets us apply a better filter, we’ll come up with a better way to judge face-off talent by the events that follow.