The Corsi number in hockey measures shot attempt differential during even-strength play. It counts every shot attempt directed at the opposing net (shots on goal, missed shots, and blocked shots) minus the same types of attempts directed at your own net. The core idea: teams and players who generate more shot attempts than they allow are generally controlling the puck and creating more scoring opportunities.
What Counts as a Corsi Event
Corsi captures three types of shot attempts: shots that hit the net (shots on goal), shots that miss the net entirely, and shots that get blocked by a defender before reaching the goalie. By including all three, Corsi provides a much larger sample size than goals or even shots on goal alone. A team might only score two or three goals in a game, making goal-based stats noisy and unreliable over short stretches. But that same team might generate 30 or 40 total shot attempts, giving you a far more stable picture of who’s driving play.
Only even-strength situations count. Power plays and penalty kills are excluded because the extra skater creates such a lopsided advantage that including those situations would distort the numbers.
The Key Corsi Formulas
You’ll see Corsi expressed in a few different ways depending on the context:
- Corsi For (CF): The total shot attempts your team generates while you’re on the ice. This is simply shots on goal plus missed shots plus blocked shots.
- Corsi Against (CA): The total shot attempts the opposing team generates while you’re on the ice.
- Corsi For Percentage (CF%): CF divided by the sum of CF and CA. This gives you a single percentage that shows what share of all shot attempts belonged to your team.
CF% is the version most fans encounter. A CF% of 50% means shot attempts were perfectly even. Above 50% means your team is out-shooting the opponent in total attempts. Below 50% means you’re being outplayed in that regard. NHL statisticians generally consider a CF% of 55% or higher to be elite territory.
What Corsi Tells You About a Player
When applied to individual players, Corsi reveals something traditional stats often miss: whether a player helps their team control the game. A forward might not score many goals but could consistently tilt the ice in their team’s favor, generating chances and suppressing the opponent’s attempts. Corsi picks that up. Conversely, a player who scores on a highlight-reel play but spends most of their ice time pinned in the defensive zone will show a poor Corsi number.
You’ll also see “Corsi Relative” (Corsi Rel), which compares a player’s CF% to the team’s CF% when that player is on the bench. This isolates the player’s individual impact. If a team runs a 50% CF% overall but a 54% CF% when a specific player is on the ice, that player’s Corsi Rel is +4, meaning they’re clearly helping the team control possession more than their teammates do on their own.
How Well Corsi Predicts Winning
Corsi gained popularity because it’s a genuinely useful predictor of future performance, especially early in a season when goal totals are still small and unreliable. In the first 20 or so games, Corsi is comparable to more sophisticated metrics like expected goals and scoring chances at forecasting which teams will outscore their opponents going forward.
Analysis over 15 NHL seasons found that Corsi is particularly strong at predicting defensive performance. Teams and players who suppress shot attempts tend to keep doing so, making Corsi a reliable indicator of defensive play. For offensive prediction, scoring chance metrics (which account for where shots come from) tend to outperform Corsi as the season progresses. Still, Corsi remains a useful baseline because it’s simple, transparent, and built on a large sample of events that happen dozens of times per game.
Context Matters for Interpretation
Raw Corsi numbers can be misleading without context. A player who starts most of their shifts in the offensive zone will naturally post higher Corsi numbers than one who’s constantly deployed for defensive faceoffs in their own end. Linemates matter too: playing alongside skilled forwards inflates your numbers, while being paired with weaker players can drag them down. Coaches who use a particular player as their shutdown option against the opponent’s top line are effectively handicapping that player’s Corsi.
This is why analysts look at zone-start adjusted Corsi or Corsi Relative rather than relying on raw CF% alone. The number is most informative when you understand the conditions a player is playing under.
Corsi vs. Fenwick
Fenwick is a closely related metric that removes blocked shots from the equation, counting only shots on goal and missed shots. The logic behind Fenwick is that a blocked shot might say more about the defending team’s positioning than about the shooting team’s ability to generate offense. If a defender steps in front of a weak wrister from the point, counting that as a positive “shot attempt” for the offensive team may overstate their territorial control.
In practice, Corsi and Fenwick tend to track closely together, and most analysts use them somewhat interchangeably. Corsi remains more widely cited simply because it has the larger sample size.
The Biggest Limitation
Corsi treats all shot attempts equally. A desperate fling from the far blue line counts the same as a one-timer from the slot. It tells you nothing about shot quality, shot location, or how dangerous a scoring chance actually was. Two players can post identical CF% numbers while one is generating high-danger chances from the crease and the other is lobbing low-percentage shots from the perimeter.
This is why the hockey analytics community has increasingly supplemented Corsi with expected goals models, which weight each shot attempt by its probability of scoring based on location, shot type, and game situation. Corsi still serves as a useful, easy-to-calculate measure of puck possession and territorial control, but it works best as one piece of a larger analytical picture rather than a standalone verdict on a player’s value.