Meet the 2012-13 Syracuse Crunch again for the first time: Defense

While we’re all sitting around bemoaning the potential loss of NHL games, the American Hockey League is preparing for its season. For Lightning fans, this means the opportunity to follow our boys as they hope that their success last season in Norfolk can be transferred to their new digs in Syracuse. This is the second in a series of previews of the new Syracuse Crunch players, done from a statistical perspective. Last week I looked at the goaltenders. Today I’m focusing on the defense.

But there’s a caveat here. There’s not much data available outside of the NHL.

The NHL and various hockey statistics sites maintain databases on each player that allow for a relatively complex analysis of the situations in which they played and how they did in those situations. This is just not possible with AHL data. For NHL players, we can determine, in an aggregate sense, what kind of players each guy played with most, what kind of players they played against most, what kind of territorial assignments they got, and how the team functioned with them on the ice.

For the American league, however, it’s not possible to obtain statistics like Relative Corsi or Quality of Competition simply because the shift and shot information needed to calculate them is not available to the public. (I would be surprised if no AHL team tracked any advanced stats, even if they don’t do it to the extent that it’s done at the NHL level. Nonetheless, that info is not compiled and made public in any form that I have ever seen. If you have seen it, I would love a link.)

Still, I’m gonna give it my best try.

If you haven’t had a lot of exposure to NHL advanced statistics, I recommend that you take a look at our Advanced Stats Primer for definitions of terms. I’m always willing to answer questions to the best of my ability, as well.

Three of the defensemen (four, if you count Evan Oberg, whose situation we’ll discuss in more depth in a moment) we’ll be looking at this week spent some time with the Lightning in 2011-12, which means that there is NHL data on their performance. In addition, Matt Taormina played 30 games with the New Jersey Devils.

[Keith Aulie played 17 games with the Toronto Maple Leafs; Brian Lee played 35 with the Ottawa Senators; Taormina, as noted, played all 30 with the New Jersey Devils.]

Note: All advanced stats below are drawn from behindthenet.ca and are for 5-on-5 play only.

This data includes only their time in the NHL, and I reviewed some of it at the the end of last season. In essence, Aulie was given the 3rd toughest assignments among Lightning defensemen, and Mikkelson and Lee faced much easier competition and started more of their shifts in the offensive zone. Aulie and Mikkelson did fairly well with those assignments. but Lee’s numbers were less impressive. Taormina had a higher Offensive Zone Start rate than any of the three Lightning players (58.7%) while facing roughly similar competition to Mikkelson and Lee (-0.357). His Relative Corsi was 3.3, indicating that he did pretty well under those conditions.

At the same time, all three Lightning players have slightly low but sustainable PDO figures. PDO is the team’s shooting percentage while the player is on the ice + the team’s save percentage while the player is on the ice. Both of these statistics are highly influenced by luck or randomness, and they are mirrors of each other, so their sum tends to move toward 100. It’s a measure of sustainability and, for players, as long as it’s between about 980 and 1020, there’s no reason to suspect that luck is playing a strong role in that player’s performance.

Of course, we much take all of these stats in context for them to make sense. Take Aulie’s stats, for instance. He tended to play against fairly tough competition (3rd highest Relative Quality of Competition among Lightning defensemen). At the same time, he was playing with only moderately productive teammates, in terms of shot generation. Take a look at Aulie’s most frequent linemates via behindthenet.ca.

It’s unsurprising that this combination of players would produce more shots against Aulie’s teams than for them, giving Aulie a negative Relative Corsi. Brian Lee, who played 20% of his time with Steven Stamkos (RelC 7.6) nevertheless also played 20% with Martin St. Louis (RelC 0.9) and 27% with Jared Cowen (RelC -2.5), which undoubtedly affected his Corsi numbers. [How much is a debate for another time.]

What does this all mean for Syracuse this season? Well, that will depend on how Coach Jon Cooper uses these players. And that we don’t have data for. What the Crunch are getting are two offensive defensemen (Lee and Mikkelson) and a guy with what BoltProspects.com’s Chad Schnarr called a “stay-at-home, simple game.” (Aulie)

As for Evan Oberg? Well, unfortunately, he only played 29:30 in 3 games. These kinds of stats simply don’t work very well for small samples, as all of the variations don’t get a chance to even out. In essence. until there’s more data on him, there is no way to know what is typical for him and what is an outlier. Advanced statistics are strictly a long-term, large-sample tool. While Oberg may have looked good in those three games, statistics can’t at this point add any context (or check any cognitive biases) in evaluating him.

What about the rest of the corps? I would first point you once again to BoltProspects’ preview of the defense. Chad tells you something about each of the guys and how they play, and he does it far better than I could. He also makes predictions about what the lineup might look like come the start of the season.

As far as the numbers go, here are their official stats from the AHL.

This is literally the extent of the data available outside of the NHL. Without even basic shift and play-by-play information, it is impossible to determine who’s on the ice when shots are taken, and so we are relying on measures like Plus-Minus and Points to evaluate performance. And we know those are flawed. Unless the AHL (and the European leagues) invest in making statistical data available to the public, it’s going to be a long, long (lockout) season for the stats nerds.

Twenty-eight’s a fluke, twenty nine’s a streak.