Who We Be, Winnipeg HC? --- Centres

In the gradual lead-up to this year's NHL Draft (June 24th-25th), there is endless speculation on who's going where, when, and whether Brian Burke is going to even slightly admit that he regrets giving up Leafs 1st round picks for the next 30 years.  We're doing the same kind of song-and-dance at BTN, as we take the duties of Winnipeg H.C. bloggers.  But before we even get that far, it's important to assess the current state of the team.  As Robert Cleave pointed out, a reality check is in order, because you rarely inherit the Quebec Nordiques circa 1995-96.   This team will take it's lumps in the standings in the next year, though it won't necessarily be catastrophic.  It has a lot to do with what we have, and what we do this offseason.

That said...Who are these people?  Where do they get all those wonderful toys?

Just a heads-up: this post will include a glossary of terms, so you know where I'm coming from.  You'll see why.

Let's take a look at the men at the dot, our centres.

Okay, maybe there's a number of you saying, "Whoa, WTF is he talking about?"  So let's clear things up a bit...

The initial data you see there, goals, assists, +/-, etc., are interesting data, no doubt, but they are fraught with luck and in dire need of context.  The remaining statistics are various measures that have sought to provide the context and strip away some of the luck by looking at underlying statistics.  Let's start from the beginning, and work our way down the list:

• 5v5 O-ZoneS%: One of the more interesting developments in recent years is the conclusion that where a player begins their shift (or the percentage of time the first faceoff of their shift is in the offensive zone) effects their opportunities to generate offense (produce or help produce shots on the opposing goaltender).  A "protected" player will see their O-ZoneS% north of 54%; a "tough-minutes" player will be below 47%.  With Antropov, you can see he's close to the middle, which means his coach was not too concerned about when he was put out there.  Fair warning: it is important to look at this measure relative to teammates.  A whole team might have fairly low O-ZoneS%'s, in which case you'll want to see where that player places on the team before suggesting they are getting tough or easy shifts.
• 5v5 BZS: Once again working with O-ZoneS%, in this case observing also the percentage of time a player finishes their shift in the offensive zone and comparing the difference between O-ZoneS% and O-ZoneF% to the league-average.  Here's a bit more description.  Positive BZS denotes a bit above league average; when you get into the +2's you're looking at a solid BZS.  The range last year in BZS was from -7.85 to 5.766.
• FO%, #FO: Faceoff win percentage.  Obviously an important statistic for centres (and teams).  I also include the number of faceoffs to make sure our percentage isn't for all of 6 faceoffs.  League average last year (including all players taking 100 or more faceoffs) was about 50.4%, with a range from 31.2 to 63.1%.
• 5v5 PIMdiff/60: An awesome statistic developed from Gabe's data by determining how many penalties a player draws versus how many they take.  It's important to separate forwards and defencemen, as forwards draw more.  League average last year for forwards that played over 30 games was 0.08, with a range from 1.9 to -5 (let's hope the Oilers hold onto Steve MacIntyre).  Positive means the player drew more penalties than they committed.
• 5v5 TOI/60, 5v4 TOI/60, 4v5 TOI/60: Awhile ago, Tom Awad did an excellent series on ways to determine "good" players, and found an effective way to do this was to trust that coaches will reward a good player by giving them even-strength ice time.  These data also provide context for the other rate stats (i.e. Goals/60); if a player plays all of 0.02 minutes of an average 60 minute game, you'll know their Goals/60 won't really tell you much.  I also include 5v4 and 4v5 to let you know if the player is consistently used on the powerplay or penalty kill.  Why no 5v3 or 3v5 or 3v4, etc?  Because these are rare situations and I'm using a rate stat, these numbers can wildly fluctuate and not give you a clear picture on the players use (due to small sample size).  League-average for 5v5 TOI/60 for forwards (defencemen log more ice time) over 30 GP was 12.01, ranging from 17.66 to 2.98.
• 5v5 CrsOn, 5v5 CrsRel: Okay, here's a big one.  A player's 5v5 Corsi On takes the difference between the shots-for and shots-against when a player is on the ice.  5v5 Corsi Rel takes the difference between shots-for and shots-sgainst when the player's off the ice and subtracts that from the difference between shots-for and shots-against when the player's on the ice.  The idea is to provide a comparative between the team play when the player is on the ice versus when he/she's not.  The reason we use shots instead of goals is because a goal is a pretty lucky event.  Try watching a couple of games and observe the number of goals that resulted from lucky bounces, deflections, etc.  While you also see lucky shots, by using a more robust data set you reduce the impact of luck on your data (think about it this way: if you saw a goalie get a shutout, would you assume that they are a great goalie by that one game, or would you want to see them play that well across a larger number of games?).  Last year, the league average 5v5 Corsi On and 5v5 CorsiRel for skaters that played 30 or more games were -0.45 and -0.28, and ranged from -52.07 to 23.09 and 22.4 to -47.4 (thank you, Trevor Gillies), respectively.
• 5v4 SF/60: Shots-against on the powerplay are a pretty rare event, but we do want to know how good the player is at helping the team generate shots on the powerplay, so I'm including shots-for.  League-average is 44.6 shots-for/60 across players that played at least 30 games and had at least 1 minute per 60 minutes on the powerplay, with a range from 69.2 to 26.7.
• 4v5 SA/60: You'll notice I didn't include this for Antropov, as he played very little on the penalty kill, but had he I would've included this stat.  Same principle as before; shots-for are pretty rare on the penalty kill.  League-average is 44.95 shots-against/60 across players that played at least 30 games and had at least 1 minute per 60 minutes on the powerplay, with a range from 64.8 to 28.2.
• 5v5 G/60, 5v4 G/60, 5v5 A1/60, 5v4 A1/60: These are some of your main scoring metrics.  Regardless of whether the person played on the penalty kill or not, I wouldn't include these for 4v5, as shots-for and goals are rare on the penalty kill and variable.  You'll notice I used "A1", which refers to primary assists as opposed to steak sauce, and omitted secondary assists.  Secondary assists, particularly for forwards, are very reliant on your teammates and say less about a player's talent than primary assists.  The following league averages and ranges (once again, minimum 30 GP, in case of powerplay minimum 1 minute per 60 minutes of play, I separated forwards and defencemen):  5v5 G/60 - 0.68, 1.94 to 0, 5v4 G/60 - 1.45, 4.07 to 0, 5v5 A1/60 - 0.56, 1.51 to 0, 5v4 A1/60 - 1.19, 3.81 to 0.
• 5v5 QoC, 5v5 QoT: QoC and QoT refer to Gabe's measures of the Quality of Competition and Quality of Teammates that a player plays against and with.  This is developed by looking at the Corsi Numbers of the players that the person shares a line with and plays against, and is better explained by the guru himself, QoC here and QoT here.  League-average 5v5 QoC for players that played over 30 games last year was -0.0098, ranging from 0.128 to -0.257.  For 5v5 QoT in the same player population the league-average was -0.017 and ranged from 0.711 to -0.815.  For QoC, positive numbers denote tougher competition; for QoT, positive numbers denote better linemates.
• GVT: Goals Versus Threshold is a statistic developed by Tom Awad over at Hockey Prospectus that uses scoring to determine the value of a player above (in rarer cases, below) what a replacement player can contribute.  When we say "replacement", we mean a player that could be called up from the AHL.  Tom has a series of articles going into the intricacies of the statistic if you're interested.  It tends to favor scoring players over defensive players, as it revolves around scoring, but it controls for a lot of era and team effects.  For league average, I split forwards and defencemen as there's a slight discrepancy between the two; league-average for forwards playing 30+ games is 4.3, with a range from -3.4 to 19.7.