Throughout the season, while covering the Philadelphia Flyers here at Broad Street Buzz, we have begun incorporating “fancy stats” into some of our articles. While we provided some basic information on what those stats entailed, I felt it’d be helpful to have a dedicated article explaining and detailing these advanced statistics.
While advanced hockey statistics are still in their infancy, there is a lot that can be gained by understanding even the most basic metrics like Corsi and Fenwick.
Through this series I hope to accomplish a couple of goals:
- Provide basic definitions and examples to help you folks understand the context in which the metric can help evaluate team and individual performance
- Attempt to help bridge the gap between “eye-testers” and “stat nerds”
- Introduce people to the growing advanced hockey stats community
- Present these new metrics without coming off as some smarmy douche
Today we will be looking at Corsi.
Where did the name come from?
The Corsi metric has been attributed to Buffalo Sabres goaltending coach, Jim Corsi. He originally used the metric to assess the amount of total shots taken on his goalies. This included shots that missed the net and those blocked by teammates.
What is a Corsi?
Corsi, as defined by ExtraSkater:
Corsi is the number of shot attempts by a team or player. In other words, it’s the sum of a team or players’ goals, shots on net, shots that miss the net, and shots that are blocked.
It’s worth noting as well that Corsi is quantified only at even-strength.
Corsi is used as a proxy for puck possession. And since possessing the puck is how you score goals in the NHL, it’s a useful statistic to determine how well a team or an individual player is creating offense.
What does Corsi tell us about a team’s performance?
Using Corsi can help us evaluate a team’s performance by helping us visualize which team possessed the puck more in the offensive zone.
So, for example, if the Flyers had registered 65 Corsi For while allowing 55 Corsi Against, you are able to extrapolate that the Flyers had a Corsi of +10. See? It’s not all that fancy or complicated. It’s very similar to the already existent +/- in terms of coming to a numerical value but inherently much more useful in evaluation.
In most cases though, when browsing advanced hockey statistic sites, you’ll find instead of a +/- value, a percentage value. So, using the example I provided above, just add up the CF and CA then divide by the team’s CF. You’ll then be presented with a team’s Corsi For %, which gives us a good indication of which team controlled play in their opponent’s defensive zone.
Before we move on to how Corsi can be used to evaluate a player’s individual performance, I want to introduce the term “Close”.
Close refers to a game situation where the score is tied in any period or a team is up or behind by one goal. This term is used to help eliminate variables when registering Corsi during a game by removing score effects.
Score effects can present themselves when a team is ahead one goal and turtles up defensively, thus negatively affecting their Corsi total. Conversely, when a team is ahead, that team may be keeping their foot on the gas trying desperately to tie the game by throwing EVERYTHING on net.
So, when navigating to a site like ExtraSkater.com you’ll find Corsi statistics sorted by different game situations. Corsi Close has shown to be a better indicator of each team’s (and player’s) performance during a game than using Corsi totals from all situations.
What does Corsi tell us about a player’s individual performance?
Corsi at an individual level can provide us with a broad stroke when it comes to how much the player contributed to their team’s offensive performance.
Just like team Corsi totals, each player during the game produces Corsi For events and Corsi Against events. The one difference with individual Corsi is that it is produced by combining the player’s own Corsi events and the Corsi events of the teammates he’s on-ice with. For example, If Jake Voracek was attributed with a CF of 20, it means while he was on the ice the Flyers produced 20 CF events. Conversely, if he has a CA of 11, he was on the ice for 11 CA events.
By using the same formula to get the Flyers team CF%, you can also calculate a player’s individual CF%.
Jakub Voracek CF% (64.5%) = CF (20) / (CA (11) + CF (20))
Taking all this into account, the greatest strength of individual Corsi statistics is being able to assess who on the ice was driving play to create scoring chances. Take former Flyer (that’s really sad to type by the way) Scott Hartnell this past season as an example. Even though he had a down year by only accounting for 20 goals after tallying 37 two years ago, he still was a possession monster for the Philadelphia Flyers. He was responsible for a 54.3 CF%, the second best percentage of any Flyers forward not named Jake Voracek.
Does that mean he’s a better player than Claude Giroux? Absolutely not. What it tells you about Hartnell is that he drives play. While he may not have been the finisher this year that we had come to expect of him, he did provide his linemates with numerous opportunities to score. Don’t believe me? Here’s some proof.
While there is a lot of factors to consider in addition to Corsi, like linemate skill, quality of competition, and zone-starts, it still is a very illuminating metric to get a basic idea of player skill. When you present all those factors together in one chart you get what you see above and below. Both of these charts come via the spectacular ExtraSkater. The interactive bits can be seen here.
Corsi loves you but you don’t have to love Corsi back.
Now with all this wonderful information I presented to you, you may be wondering, “Why should I care”? That’s a valid question and my answer is pretty simple. You don’t have to care.
Like I stated in my goals for series in the beginning of the article, I’m not writing this to convert anyone. If you want to continue ignoring advanced hockey statistics that is your prerogative and that’s okay. I think it’s still possible to have a polite discourse with someone even if they only rely on their eyes. My main intention is to just try to explain these new statistics to anyone who is curious about them and don’t know where to look.
Where can I go to find these stats and who can I follow to learn more?
In the next installment of my introduction to advanced hockey stats I will be explaining Fenwick and how it helped predict the Toronto Maple Leafs late season collapse. It’s going to be so much fun!