Bruce Kluckhohn/via Getty images

While Santa’s elves were taking the day off to recover from toy-making and presumably exchange their own gifts, the elves working in Gabriel Desjardins’ workshop over at Arctic Ice Hockey gave me a good present to play with. Two of them, actually. They are advanced statistical graphs chronicling every NHL game played since the beginning of the 2007-08 season for every team.

While Behind The Net’s main website is a inhabitable wasteland of numbers and charts that can be pretty tough to get if you’re new to the stuff (we’ve all been there) the new graphs that were put up yesterday are a little more accessible.

The graphs allow fans to not only see shot counts, but also regressions to the mean over the course of a season. When you watch a few games in October, you tend to forget their surrounding circumstances. Behind The Net’s new shot graphs allow you to visualize the context using two simple statistics: shots and goals.

The green line, representing a team’s goal differential rate (ie: 5 for and 5 against would equal 50% while 6 for and 4 against would equal 60%) gets closer and closer to the blue line the more games are played. On the right graph, BTN tallies up team’s shooting percentage and save percentages at even strength into a number called PDO, which is expected to regress closer to one (or 100%) over the course of the season. These numbers are cumulative over the course of the season.

Above, the graph represents the 2011 Dallas Stars, who got out to a great start by the New Year but their successes were built on a house of cards. Their high PDO level, (looking to be at about 103% around Game 30) came back down to earth in the second half of the season, and with it, the team’s playoff fortunes.

Here is the frightening 2008 Stanley Cup Champion Detroit Red Wings:

A shot-differential over 50 is good. A shot differential over 53 is near-elite, and anything over 55 is positively terrifying.

There’s really no coincidence that the line representing PDO is almost in the same shape as the green line on the left graph: luck plays a major part in a team’s goal differential over a short series of games, but the more games that are played, the more it events out to the shot graph. While goals contribute far more to winning than shots do (a commenter once tried to prove this to me, which I thought was pretty funny) shot differentials have shown to be repeatable talents while maintaing high or low percentages isn’t.

What I like is how you can really see what we mean by regression when you look over the full slate of 82 games. It isn’t something that comes instantly. For instance, after seeing what we saw with the Dallas Stars, what do you think is going to happen to the New York Rangers as we trudge ahead in January?

Hint: The green line is going to get a little bit closer to the blue one.

To get these charts, the URL is very simple to reach:[Team]_[Year].html

The [Team] marker indicates each team’s three-letter code (San Jose is S.J, Dallas is DAL, Winnipeg is WPG or ATL…) and the year is the four-digit code indicating the fall half of the season (so the ’07-’08 season is written down in the URL bar as 2007, and the current season would be written in as 2011).

The second really cool thing that can be done with these is, if you click on all the individual games in the data chart below each, graph, you get a shot counter that is probably going to see it’s way into many online recaps in the near future:

This is from the Vancouver-Toronto game from a couple of weekends ago. The blue line represents the home team in all games, the black line is the visiting team, with the vertical lines representing goals for each team. What you have is second-by-second updates of shots (including missed shots) as you progress through the game, giving the fan a visual view at what happened over certain segments in the game, when one team pulled ahead, and when it was all over.

By extension, about half the games that are played end up with one team getting horrible goaltending and plays ahead on the shot clock throughout the game. Those are always fun to go and look back at, unless it’s your own team getting blown out on the wrong end of it.

So, these are just a couple of new toys that I’ll be using regularly in game reviews, and season-long analysis pieces. It’s a lot of fun to dick around and see how your favourite team did in a couple of years, or to look at how some of the best games you saw shook out.