Plus/minus will lie to you about a defenseman. It gets dragged around by goaltending and shooting luck, and it tells you almost nothing about what a blueliner did to shape the game. Three "Against" metrics — Corsi Against, Fenwick Against, and Expected Goals Against — fix that. Each one counts what happens at the defensive end while the player is on the ice, and each answers a different question: how much offense did he allow, how much of it actually got through, and how dangerous was it?
Beyond Plus/Minus: Why Traditional Stats Fall Short
Plus/minus measures goals for and against at even strength, which sounds fair until you consider what a single player controls. A defenseman can be beaten cleanly, have his goalie bail him out, and finish the night plus-2. Or he can play airtight hockey behind a goalie having a bad month and sink to minus-5. The stat absorbs outcomes the player didn't produce.
Shot-attempt differentials were built to solve this. Charlie O'Connor, writing in The Athletic's advanced stats primer, laid out the classic case: a team that is +50 in shot attempts over twenty games but has been outscored by ten is, per the research he cites, more likely to post a positive goal differential over the next sixty games, because it "drives play." That's why a player with a great plus/minus but an awful Corsi is generally expected to regress. Goals are noisy; the process that generates them is not. Raw on-ice numbers still carry context problems — Evolving-Hockey notes that teammates, opponents, and zone starts all distort them — but shot-based metrics are the foundation that more sophisticated models build on.
Corsi Against: Measuring Shot Suppression Volume
Corsi Against (CA) counts every shot attempt the opposition directs at the net while a defenseman is on the ice at even strength: shots on goal, missed shots, and blocked shots. Nothing is filtered out. It is the broadest measure of how much offensive activity a player concedes.
That breadth is the point. Because it tallies every attempt, Corsi Against captures territorial control — whether a defenseman is spending his shifts hemmed into his own zone or pushing play the other way. Analysts describe Corsi as a plus/minus for shot attempts, and its comprehensive nature is what makes it a stable predictor of future performance even though it weighs every attempt equally. For a defenseman, a low CA relative to teammates is the first signal that he's suppressing offense rather than just surviving it. It's usually expressed per 60 minutes of ice time so players with different workloads can be compared.
Fenwick Against: Accounting for Shot Blocking
Fenwick Against (FA) is Corsi Against with one subtraction: blocked shots don't count. Only shots on goal and missed shots — the attempts that actually get through toward the net — are tallied.
That single exclusion changes what the metric means for a blueliner. Northwestern's Sports Analytics Group explains that Fenwick is especially useful when evaluating players on teams that deliberately trade shot volume for blocks — schemes that accept more attempts but ask defensemen to block them. Under Corsi, a team like that looks permissive; under Fenwick, you see whether the attempts reaching the goalie were actually reduced. There's a defensive caveat worth remembering, too: a defenseman who blocks a huge number of shots over a season is spending a lot of time in his own zone, which is itself a warning sign about the play in front of him.
Expected Goals Against (xGA): Evaluating Shot Quality
Expected Goals Against takes the final step: it weights each opposing shot attempt by its probability of becoming a goal. Instead of counting one, one, one, a point shot might count as 0.02 and a slot chance as 0.20. The xG value for a player is the sum of those probabilities on the attempts against him.
The idea has a long lineage. Alan Ryder published a methodology for studying the quality of hockey shots back in 2004, and Brian Macdonald presented an NHL expected goals model at the MIT Sloan Sports Analytics Conference in 2012. Modern models assign probability based on shot characteristics — location and shot type are core variables, and analysts continue to debate which factors belong in the model. For a defenseman, xGA answers the question Corsi can't: not how many chances did he allow, but how dangerous were they? A player who concedes many low-quality attempts and few high-danger ones is doing something right that raw volume would never show.
Choosing the Right Defensive Metric: A Decision Framework
The three metrics are not competitors; they're a funnel, narrowing from all activity to genuine danger. Use this matrix to match your evaluation question to the right stat:
| Metric | What It Counts | Best Used For |
|---|---|---|
| Corsi Against (CA) | All opposing shot attempts: shots on goal, misses, and blocks | Overall shot suppression and territorial control — is this defenseman limiting the opposition's offensive activity at all? |
| Fenwick Against (FA) | Unblocked attempts only: shots on goal and misses | Whether attempts actually reach the net — useful for judging shot-blocking schemes and defensemen on teams that trade volume for blocks |
| Expected Goals Against (xGA) | Each attempt weighted by its goal probability (location, type, situation) | Limiting high-quality chances — did he prevent dangerous opportunities, not just shots? |
Read the rows in order and you get a diagnostic sequence. High CA with low xGA suggests a defenseman allowing volume but little danger. Low CA with high xGA suggests the opposite: few chances, but the ones that get through are dangerous. Fenwick sits between them, revealing whether blocks are doing real work.
Interpreting Advanced Defensive Metrics Together
O'Connor's own practice captures how these stats combine: he uses Corsi and xG in tandem, with the former describing "a raw territorial advantage" and the latter showing "whether the shot quality battle was won as well." The same logic applies defensively, in reverse. Corsi Against tells you the volume battle; xGA tells you the quality battle; Fenwick Against tells you where blocks fit in.
Two habits make the comparison honest. First, use rate stats — per 60 minutes — rather than raw totals, so ice time doesn't distort the picture. Second, compare a defenseman to his own teammates and deployment, since raw on-ice numbers are shaped by partners, opponents, and zone starts. None of these metrics fully isolates an individual — that's the job of more advanced models like RAPM, which use these same metrics as inputs — but together they describe the shape of a player's defensive game far more truthfully than plus/minus ever could.
Applying Advanced Metrics for Deeper Defensive Insights
To accurately evaluate an NHL defenseman, move beyond traditional plus/minus and use Corsi Against, Fenwick Against, and Expected Goals Against together to understand both shot volume suppression and shot quality prevention. The measurable indicator to watch is a defenseman's consistent improvement in reducing opposing Corsi Against, Fenwick Against, and Expected Goals Against over time, particularly relative to their teammates and competition. When those three numbers trend down together while the player's role stays constant, you're looking at genuine defensive improvement — not goaltending luck wearing a plus/minus costume.
Sources
- An advanced stat primer: Understanding basic hockey metrics — The Athletic
- Evolving-Hockey — Evolving HOCKEY
- NHL Advanced Stats / Analytics — Hockey-Reference.com
- Beginner's Guide to Advanced Hockey Statistics — SITES
- Expected by Whom? A Skill-Adjusted Expected Goals Model for NHL Shooters and Goaltenders — ARXIV
- Expected goals - Wikipedia — EN
