Editor's Note: Over the summer we're going to try to put together some primers on some of the statistical measures that fall under the "advanced statistics" umbrella in order to help lay the groundwork for a lot of the analytical discussion that takes places on the site and throughout a lot of the Bettmanosphere (trademark pending). This is a great chance to ask any questions, seek any clarifications, and share any links that can help increase everyone's undertanding (ie no "advanced stats are stupid" links).
Fenwick
What does the stat actually describe?
SHOT ATTEMPT DIFFERENTIAL EXCLUDING BLOCKED SHOTS
Where did the weird name come from?
Named after Calgary Flames blogger from the Battle of Alberta, Matt Fenwick
What it means and why it's useful
Fenwick is best used as a proxy differential in scoring chance opportunities. Players and teams with higher Fenwick numbers are typically getting more scoring chances than the opposition. In another form Fenwick Percentage (aka Fen%) is a solid representation of the percentage of scoring chances a team is getting while specific players are on the ice, or overall.
Fenwick is actually a slight modification of Corsi (which we described in the last posting). Basically Fenwick excludes blocked shots. So instead of ALL shot attempts, it only includes shots on net and missed shots. It is just the differential between the for and against for these values.
Fenwick = (SOG FOR + Missed SH FOR) - (SOG AGAINST + Missed SH AGAINST)
Follow on after the jump for more detail, some examples, and discussion of why Fenwick is relevant.
Like Corsi, Fenwick is calculated game by game - for example here is an individual game sheet from the last game of the season between Toronto and Montreal courtesy of TimeOnIce.com. On the sheet you can see the players in the game, and all of the goals, saved shots on goal, missed shots on goal, while each player was on the ice. As mentioned previously Fenwick EXCLUDES the blocked shot component used in Corsi.
Fenwick correlates slightly more highly with scoring chances than Corsi tends to, though not necessarily by a wide margin and it doesn't particularly do a better job of indicating possession.
What it can tell us
Matt Fenwick was the main proponent of this modification of the Corsi statistic, which has now gained wide usage around the fancy-stats community of Hockey Analysts. There is a slight reduction in total events being measured so you do get slightly smaller samples of data. There have also been objections around the value of Corsi in other corners on occasion. But below is Matt's concise justification for the modification he was advocating to Corsi:
"My argument is basically:
1. The whole (or perhaps best) use of Corsi is to have objective figures that can be used as a proxy for scoring chances (what else are you using it for?).
2. A shot that is blocked is either a) not a scoring chance at all, or b) on average from a worse scoring area than shots/posts/missed shots.
Yes it affects the "sample size" but that only means anything if what you are sampling is relevant to what you are trying to represent. You could include Penalties Drawn (or hell, faceoff wins/losses!), but I'm not sure that the connection to a scoring chance is as obvious as for shots/posts etc.
That's my opinion anyway, take it or leave it."
- Matt Fenwick, Nov. 22nd, 2007.
The argument above generally supports the logic for using Fenwick as a proxy for scoring chance differential. Blocking shots may in fact be a skill, so from a defensive standpoint, excluding them removes a penalty many defensive D men face as skilled shot blockers. Similarly, players that excel at getting a high proportion of their shots past shot blockers are rewarded in this metric.
We often categorize Fenwick percentages in different score situations as a way of monitoring score effects. Teams that are behind tend to get more shots and scoring chances because they press to get back into the game, and often the team with the lead naturally sits back and absorbs pressure. Conversely when the game is tied, or close (within a goal) teams tend to play a much more balanced approach, giving up as little as possible, and working to score more goals on offense. Thus when we compare teams by their Fenwick Percentages while close, we get a good window into their overall ability to both produce and suppress offensive chances.
At the team level, Fenwick percentages are probably one the best predictors we have of future success. Teams that can sustain a high Fenwick percentage over the long term tend to be out playing their opposition at a high level. This also belies some teams that are succeeding due to unsustainable luck.
Take for example the first third of last season where the Minnesota Wild were leading the NHL standings, and the Los Angeles Kings were mired at the bottom of the Western Conference. Those Shot Reports from TimeOnIce.com cover the 2011-12 season from opening night until the end of Dec. 17th 2011. At that point last season, Minnesota had 45 points through 33 gp, with a 20-8-5 record and were leading the NHL. Los Angeles had a 14-14-4 record through 32 gp, and were ranked 11th in the West, 5 points out of a playoff spot.
At that point in the season though, Los Angeles had a Fenwick Percentage of 0.513, while Minnesota had a Fenwick Percentage of 0.426. This means LA was getting approximately 51.3% of the scoring chances at Even Strength in their games to that point in the season while Minnesota was only getting 42.6%.
Obviously there's a question around sustainability here. Minnesota went on to finish the year with 81 points in the standings, and a 35-36-11 record. Their Even Strength Close Fenwick Percentage to close out the year was 44.9%, the worst in the NHL. Los Angeles finished in 8th in the Western Conference with 95 points and a 40-27-15 record (digging out of an early hole can be hard to do), but they eventually went on to win the Stanley Cup (as most of you hopefully are aware). L.A.'s Close Fenwick Percentage at the end of the season was 53.6%, 4th best in the NHL.
Their numbers improved even more drastically in the short portion of the regular season after the trade deadline, when they added Jeff Carter to their already solid line up. Carter is an excellent possession player, who generally drives offense and scoring chances.
Team Fenwick Percentages sorted by scoring situation are available at behindthenet.ca. Generally speaking, teams that have the best Fenwick Percentages while close are the best teams in the NHL To wit - the top 5 teams in order last season were: St Louis (55.1%), Pittsburgh (55.0%), Detroit (54.4%), Los Angeles (53.6%), and Chicago (52.8%).
At the other end of the spectrum we have the NHL bottom feeders, who for whatever reason can't figure out how to. The worst 5 teams in the NHL by Close Fenwick Percentage last year were: Calgary (47.5%), Montreal (46.8%), Toronto (46.7%), Nashville (46.1%), and Minnesota (44.9%).
Many might be wondering, how the hell does Nashville do so well with such atrocious Fenwick Percentages? My answer would be Pekka Rinne, Shea Weber, and Ryan Suter. They also generate virtually no solid offensive scoring chances with regularity... i.e. they lack offensive weapons.
Individual Fenwick Numbers also provide a solid perspective on how a player does with regards to scoring chances. Using game numbers found on NHL.com game sheets, you can sort through portions of the season and enter data into TimeOnIce.com in order to run a script that describes individual Fenwick Percentages for portions of the season.
http://timeonice.com/mplayershots1112.php?team=TOR&first=20001&last=20474
As can be seen by clicking on the above link - we're exploring the fenwick and corsi percentages for the Leafs during the 2011-12 season between games 20001 and 20474 (around Dec 31st, 2011).
During that early stretch, Mikhail Grabovski, David Steckel, Dion Phaneuf, and Carl Gunnarsson were leading the way for the Leafs. You'll notice that again, these numbers align closely with Corsi. Overall this is just another tool in our box of ways of examining the play of the players and teams we're interested in.
Hopefully this posting sheds a bit of light on another random statistic that many of you have heard quoted regularly, but haven't necessarily understood in detail.
