Wednesday, 17 September 2014

Shot Suppression Is The Name Of The Game

As the “Summer of Analytics” wraps up and various NHL camps get underway, there is a palpable urgency apparent from some NHL front offices to find the key to success by expanding their analytics departments. The task teams are currently undertaking is to identify their strengths and weaknesses by conventional and progressive means. Once they do this, they can then come up with a plan to exploit their strengths and improve the areas of weakness.

While there is no secret weapon or magic trick that will suddenly make a good team from a bad team, there are some team strengths that are more important to success than others. In looking at successful teams over the past several seasons, one such strength stands out from the rest: Shot suppression.

Shot suppression is a fairly basic concept, but because it is not as exciting as a high powered offense or as easy to identify as say an excellent penalty kill, it is not often discussed during broadcasts or major media analysis shows. Shot suppression is one of the true measures of the quality of a team’s defensive structure and systems. Even in analytics, this component of team play can be overlooked when we use percentages such as CF% (Corsi For) or FF% (Fenwick For). Percentages are terrific and useful for many things, but one of their shortcomings is that they mask Event Rates.

Event Rates are often expressed as whatever metric is being used “Per 20” or “Per 60”. To understand how aggressive an offense is, CF or FF rates are very useful. For example, the San Jose Sharks had the highest CF60 in the league at Score Close last season with mark of 63.6 and were third in the league in FF% (most popular team possession measurement tool) at 54.9%. The Ottawa Senators were second in the league in CF60 with a rate of 63.2, but were twelfth in the league in FF% at 50.8%.

When the CA and FA rates are added into the mix, we can see which teams allow more shots than others. When used in combination with the team’s CF and FF marks we get a picture of a team’s event rates.

You will note that the best or most successful teams in the league are not at either extreme in terms of event rates. They are not super low event like the New Jersey Devils nor are they super high event like the Ottawa Senators. Teams with very low event rates both in terms of shots for and shots against often struggle to produce enough offense to consistently win games. This was obvious last season, when Devil’s goalie Cory Schneider played very well but was consistently losing games because of a lack of offensive support. Likewise, teams with very high event rates in shots for and against tend to score quite a bit, but they also tend to give up a lot of goals.

The real question is: what is most important? Shot suppression or a prolific offense? Looking back over the past several seasons at teams that were successful both during the regular season and the playoffs may lead us to an answer.

Monday, 8 September 2014

Updates: Sept 8th

Shot Quality

After some serious fence-sitting I've decided to do what many have been asking for: Adjusting Shot Quality, and therefore Expected GF%, by the player's previous shooting% record. This means that Exp. GF%, shot quality and all the other statistics that rely on that model to predict shooting percentage will be stronger, as they don't just use the variables of the individual shot as previously was the case but also his shooting% from the past 3 seasons, if applicable.

Multiple Seasons

In the player and team stat pages, you can now compute statistics for multiple seasons at a time.

Enjoy the updates! Much more to come.

Tuesday, 2 September 2014

A Beginning

Hello and welcome to This site will be a place where you can access advanced hockey statistics and analysis.

You will notice some small and large differences to what you may have found at other fancystats websites. Here are a few big features to our stats.


Relative stats have taken on a whole new meaning. Instead of simply the entire team's performance when the player is off the ice , relative is calculated as the average performance of his actual on ice teammates and competition without the player in question, weighted by their ice time with him. This provides a much more meaningful metric to evaluate players with.

Exp GF%:

You will notice a 4th stat besides corsi, fenwick and goals for percentages: Exp. GF%, or expected GF%. Exp GF% is simply a player's on ice fenwick weighted by the quality of the shot. I outline the methodology for calculating shot quality here, and a player's own average shot quality is also listed.

Adjusting for Score State and Zone starts:

Score close metrics have been firmly rooted in advanced hockey stat methodology for some time now, but there are many issues:

1)  There is still variance in shooting rates even within the score close definition. I go into detail about this here.

2) Score close removes a whole swath of data, which significantly cuts down on the power of the sample size.

To solve this, and the effect of the variance in the ratio of offensive to defensive zone starts for players, a logistic regression is used to parse out the player-neutral odds that can effect shot differentials. If you wish to use this method when using the stats, simply click 'Yes' on the Adjusting for Zone Starts and Score State option.

I think there's a lot for hockey fans to love here, and this site will continuously develop so be sure to check back often!