The NHL Advanced Stats Cheat Sheet


This guide is an overview for the media and newcomers to the NHL’s advanced (or “fancy”) stats. It includes definitions of the key advanced stats concepts, plus an FAQ to clarify some of the typical inquiries about these measures. It is not to meant to be completely comprehensive; only a useful introduction to possession-based analysis.

Possession Stats 

Corsi numbers are proxies for offensive zone puck possession. That means the higher the number, the more time a player or team spends in the attacking end of the ice. 

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Corsi – Total shots at the net for and against at even strength, including missed shots, blocks, goals and saved shots. Can be expressed as a differential (+/-) or a ratio (%).

Named after goalie coach Jim Corsi who initially developed the measure to track goalie performance. Can be expressed as a differential (+/-) or a ratio (%). Differentials are often converted into a rate stat to correct for ice time (corsi differential/60 minutes of ice time). 

Corsi For (CF) – All the shot attempts at the net for a given player or team at even strength. The “offensive” half of tyical corsi differential.

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Corsi Against (CA) – All the shot attempts at the net against for a given player or team at even strength. The “defensive” half of typical corsi differential.

Relative corsi (corsi rel) – The difference between a team’s corsi rate when a player is on and off the ice at even strength. For example, if a team generates a corsi of +2 corsi per 60 minutes with Joe Hockey on the ice and that drops to -3 corsi per 60 minutes when he isn’t, his relative corsi is +5 corsi per 60. 

Fenwick (FF) – Total shots at the net for and against at even strength except for blocked shots. 

Named after hockey blogger Matt Fenwick, who hypothesized removing blocked shots from corsi would result in better correlation with scoring chances.

Fenwick relative (FF rel) – same as relative corsi, except without blocked shots.

Corsi/Fenwick close – A team or player’s corsi or fenwick rate when the game is within a goal. Created to correct for playing to score effects. (see below)

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Corsi/Fenwick tied – A team or player’s corsi or fenwick rate when the game is tied. Created to correct for playing to score effects. (see below)

Percentage Stats

Goal scoring is controlled by two primary processes in the NHL: volume (possession) and frequency (percentages). These stats measure the rate at which the puck goes in the net (frequency) with a player on the ice. They tend to heavily regress to the mean over time, so they are considered proxies for luck or variance.

PDO – The sum of on-ice save percentage and on-ice shooting percentage at even strength. League average PDO is 100. Sums considerably higher or lower (+/-2.5) tend to regress towards 100 over large samples for both teams and players. Named after the internet alias of the man who conceived the stat. Could be considered an acronym for “Percentage Determined Outcomes”.

On-ice Save Percentage (SV%ON) – The save rate for an individual player at even strength. Skaters have no discernible effect on this number. League average is around .920 (92.0%).

On-ice Shooting Percentage (SH%ON) – The rate at which a player’s team scores at even strength. The quality of player and his linemates does seem to have some effect in this number, such that we would expect a modest spread around the league mean of of 0.08 (8%) due to skill effects. However, it takes many thousands of shots to differentiate skill from random variance.

Circumstance Stats

Aside from a players skill level, there are number of circumstances that influence corsi results, including tactics, quality of linemates, quality of competition and where the player tends to start his shifts. The following stats attempt to account for many of these factors.

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Zone Starts (ZS) – the ratio of offensive zone faceoffs to defensive zone faceoffs for a player at even strength. Usually expressed as a percentage. A rule of thumb is each extra zone start is worth about (+/-) 0.3 corsi. For example, if a player sees 300 more offensive zone faceoffs than defensive zone faceoffs over a season, his corsi will be inflated by approximately (300 X 0.3) +90 net corsi.

Quality of Competition (QoC) – The aggregate quality of competition a player faces at even strength. Calculated in a number of ways:

1.) Total Ice (TOICE) – The combined, averaged percentage of even strength ice time per game of a players’ opponents. For example, PK Subban averaged 19:17 at even strength in 2013-14, which is roughly 43% (19.33/45) of Montreal’s per game even strength ice available. A number of 30% or over usually indicates a high quality of competition. A number below 25% usually indicates a very low quality of competition.

2.) Corsi Quality of Competition (Corsi QoC) – The combined, averaged corsi rate/60 of a players’ opponents.

3.) Relative Corsi Quality of Competition (Rel QoC) – The combined, average relative corsi* of a players’ opponents. This number tends to give the more accurate quality of competition ranking over regular corsi QoC.

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Note – Corsi quality of competition metrics are best used to rank players within a certain team, rather than compare players across teams.

*(See relative corsi above for a definition)

Quality of Teammates – The aggregate quality of teammates for a player over time. Calculated in the same manner(s) as quality of competition above. Because players see a lot more time with regular linemates than they do opposition players, quality of teammate is hypothesized to have greater influence on corsi than quality of competition.

Playing to Score Effect – The persistent tendency for teams who are leading to cede possession to teams who are trailing. This effect tends to accelerate the higher the goal differential in a game. For example, a team leading by three goals tends to give up more possession than a team leading by one or two goals (and vice versa). Can “wash out” over time, but can be very pronounced in small samples, such as a single game or a brief series of games. Corrected for by using corsi/fenwick close or tied (see above for definitions).

With or Without You (WOWY) – A form of analysis that tries to determine an individual’s contribution to corsi by looking at his effect on frequent linemates. This is done by looking at each linemate’s corsi with the player at even strength and then without him. For example, with Joe Hockey, Johnny Blueline has a corsi ratio of 54%. Without Joe Hockey, Johnny Blueline’s corsi ratio drops to 47%. This process is repeated across Joe Hockey’s linemates to see if there is a persistent pattern of improvement or decline.

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Zone Entries – A stat that measures how frequently skaters enter the offensive zone with control of the puck. Usually broken down into component parts (passing, carrying, dump-in, give-away) to determine a ratio or differential of controlled entries (passing, carrying/dump-in, give away).

Zone Exits – Similar to zone entries, except looking at how skaters exit the defensive zone rather then entering the offensive zone.

Zone entry and exit stats are relatively new were developed by Eric Tulksy and his landmark study which showed that neutral zone play likely has a strong influence on possession rates. The database of zone entry and exits stats collected by Corey Sznajder for the 2013-14 will help make further in-roads in this area of study.


1.) Why corsi? Why don’t we just use goals and shots?

The reason corsi is useful is because of statistical power: the larger the sample size of data, the higher the power of the analysis. Meaning, in hockey, there are far more corsi events (all shots at the net) than there are individual goals or even shots on goal. Over the course of a single season, for example, this increases our sample size from 100 or less (goals) or several hundred (shots) to several thousand (corsi events) for each team and player.

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2.) What is the main take away of all these new stats?

The primary relationship to understand with hockey’s advanced stats is the interplay of volume (corsi) and frequency (percentages). The latter (percentages) has far more influence on outcomes (like goal differential and wins), but tends to

a.) vary around the mean erratically in small samples and

b) regress towards the mean over large samples.

The former (corsi) has a less obvious effect on scoring and wins in the short term, but tends to be more repeatable and predictable at both the skater and team level. Especially when accounting for various moderating effects like quality of line mates, opposition, score effects, zone starts, etc.

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A metaphor to understand the relationship between corsi and percentages when it comes to roster building: PDO is to goals as the temperature is to the weather: although it tends to settle into a generally predictable average over the course of a particular season, it nevertheless will vary wildly around that mean in short outbursts. In Calgary, Alberta, for example, it can be 30 degree Celsius during the day and then 12 degrees that night in the summer. Or it can change from -25 degrees one day to +10 degrees the next during the winter. 

Building a strong possession team is like building a robust shelter that can weather the changes in PDO. A club that depends on percentage driven outcomes to succeed is a straw hut erected on the beach – trouble when the weather turns. On the other hand, a team that controls the play at even strength is like a brick house – far less likely to experience catastrophic failure when the percentages fall.

3.) Are there limitations to corsi analysis?

Of course, since there is no one kind of analysis or data that can paint a complete picture of the game. Here’s a few of the caveats to keep in mind:

a.) Corsi is almost exclusively concerned with even strength play. This excludes special teams outcomes, which tends to be about 25% of the game.

b.) Tearing apart individual contributions is difficult. While we have developed some tools to help factor in the influence of moderating variables like linemates, opposition, tactics and usage, the nature of hockey means assigning outcomes to one particular skater of the 10 that are on the ice at any one time is challenging. This is also why considering a player’s corsi in context of his team and circumstances is vital.

c.) PDO isn’t entirely “luck”. While we consider the fluctuations in on-ice SH% and SV% to be a proxy for random variance, the reality is there is some influence of skill and usage in these measures. The problem is it is almost impossible to identify the “skill” from the “luck” in small samples (less than several thousand shots).

d.) We haven’t determined with certainty the particular player skills or coaching tactics that directly influence corsi yet. Although we can determine with some certainty good and bad possession players and good and bad possession teams, the particular collection of skills or strategies that cause these outcomes are still not entirely clear. Parsing these relationships would involve careful observation and video scouting and is likely the “next step” in the evolution of advanced stats.

4.) What is an example of a “good” or “bad” corsi rate?

The starting point for any possession rate is zero (0 corsi/60 or 50% if it’s expressed as a ratio). This indicates the skater is more or less spending an equal amount of time at both ends of the ice.

All things being equal a player with a corsi rate in the positive double digits (+10/60) or with a ratio of 55%+ is probably an elite possession player. On the other hand, anyone in the negative double digits (-10/60) or below 45% is probably a lousy possession player. Similarly, any team at 55% or above is likely elite. Anyone at 45% or less is probably in the draft lottery (unless they have an elite goalie).

The important caveat here is “all things being equal”. It’s important to always consider a player’s corsi numbers relative to his team and his circumstances. When referencing corsi, it’s essential to look at things like zone start ratio, quality of competition and relative corsi (and relative ranking) within the context of his team. Be sure to note the number of games played by a skater as well: results can be skewed by only playing a limited number of contests (30 is a good minimum cut off point).

Case Study: Sheldon Brookbank

Here’s an example of how to use some of the concepts and tools noted above.

Sheldon Brookbank played 48 games for the Chicago Blackhawks in 2013-14. His basic corsi rate was +3.28/60, which considered alone seems pretty decent. However, here are his circumstantial factors:

1.) a zone start ratio of 61% (meaning he started more often in the offensive zone)

2.) A relative corsi QoC of -0.86 (3rd easiest on the team)

3.) A relative corsi rate of -10.1/60 (3rd lowest on the team)

So now we can see that Brookbank played lesser competition, started more often in the offensive zone and yet the team’s ability to direct pucks at the net/possess the puck in the offensive zone dropped more than 10 shots per hour when he was on the ice (versus when he was on the bench).

Conclusion: despite his seemingly above board possession rate, Brookbank was no doubt floated by the quality of his team and the relative ease of his circumstances. He’s probably not a very good possession player.

This excellent look at the world of hockey analytics was written by Kent Wilson. For those with more questions about how to understand, find, or use advanced stats, feel free to contact Kent via email ([email protected]) or on twitter (@Kent_Wilson).

  • The Soup Fascist

    One stat that I have been tracking is the correlation that when one team scores more goals than their opponent over a sixty to sixty five minute span, an inordinate amount of times they win the game. Conversely, the team scoring fewer goals than their opponent tends to not emerge victorious.

    I have coined this the Soup Fascia Effect. I think it merits more of a look.

    The results are quite compelling.

  • Mangiant

    Its going to really infuriate the hockey nerds when the behind the curve hockey pundits on CBC and Sportsnet start using these terms in their broadcasts this coming year. Bloggers are going to have to come up with new metrics and terminology.

    There needs to be work on the measurement of truculence. Maybe call it the TBM or Truculence Barometer Meter. Possession numbers of Skill players when nuclear deterrents are riding pine verses not having a knuckledragger. All kidding aside I’d like to see some correlation of Corsi/Fenwick stat developed in accordance to size. LA obviously is a strong possession team, but a team like Chicago is as well albeit being generally smaller.

    • Mangiant

      Value of truculence is already factored in.

      It’s called WOWY.

      Run the comparisons and you’ll now have a measurement of importance of truculence. Don’t show Burke though, it might make cause his hair to disheavel.

  • Mangiant

    We need a Corsi Adjusted stat.

    e.g. the article shows a measurable difference for zone starts. So, Corsi adjusted for zone starts would give more immediate comparison. From article, if Brookbank had 300 more O zone starts, then Corsi Adjusted would just have the 90 net tacked on.

    The same could be done for QoC and relative Corsi.

    The new Corsi Adjusted could then show Brookbank’s number without studying the other measures.

  • Mangiant

    When will Dellow have impact on MacT?

    MacT defended contract for Schultz against analytics by saying it is useful as team stat. Kent explains above how it is used to identify individual contribution.

    • Serious Gord

      MacT’s comments on advanced stats are what is known as “lip service”.

      From the online dictionary:

      “Verbal expression of agreement or allegiance, unsupported by real conviction or action; hypocritical respect”

      Just as “bold” and “core” and “norris candidate” are meant to be lip service.

      The problem with lip service is that it can really come back to haunt you and the backlash can be a rapid drop in respect and thus power.

  • Serious Gord

    Advanced stats are the future.

    Presently they are crude and there is a pitched battle as to which ones will become enduring metrics. Sort like the early days of computing when it was imagined that all science grads would have to know how to program in code like cobol and pascal.

    Right now they are confusing, overlapping and stultifying. Again, not unlike early computer languages.

    Thanks for the guide above, i will be bookmarking it for reference when needed, but like those early computer languages i will refrain from trying to learn them in depth and by rote and use my time and energy elsewhere for now and wait for the statistical dust to settle.

    • Craig1981

      Yeah, but the constructs
      (like loops, if statements etc.)of the early languages, are the same now as they were then and form the basis of all modern languages.

      The basics of advanced stats are being formed now and will form the basics forever, just like the constructs of early languages.

  • Serious Gord

    My only real problem with all of the Analytics in hockey is when the Leafs finish 14th in the East, but the numbers say we did much better and somebody screams “We’ve Won, We’ve Won”.

    • Serious Gord

      All posts with handle OiledStatGuy from this post about Leafs and on where forged, except me complaining here.

      OilersNation please fix your software. I registered with an email, so least you could do is block posts that don’t have my originally submitted email. This isn’t 100% verifiable, but at least it will lessen chance of making a complete sham of your site.

      Did get a laugh at forged post that included previous forged post.

  • Serious Gord

    A suggestion to the operators of ON: perhaps there is a way to create an advanced stats glossary for the site that can be updated as these stats wax and wane in usage.

  • Serious Gord

    The one thing I don’t understand is how a guy like Ryan Smyth can have a higher corsi than Taylor Hall. Everyone knows Hall is a possession monster.

    • Serious Gord

      When the Corsi and Fenwick guys eventually convince all the GM’s to dump the half dozen face punchers and the “rah rah effort” guys in favour of actual hockey players, the games will be “gooder”.

      This stuff is not boring. The relentless petulance of flat-earthers such as yourself at having to think is indeed boring. You are the “tea party” of hockeydom, the Sarah Palins of sport thought (word used loosely). Dellows, and Extraskater and Charron and the others certainly know the game better and enjoy it more than any of us. Before all of this knowledge becomes proprietary, learn from it.

      • Serious Gord

        The tea party and sarah palin should not be conflated. Some very serious, advanced thinkers are a part of the true conservative wing that is the tea party (and they take a considerable amount of their inspiration from the reform movement here in canada). Guys like Rick Santelli, Ted Cruz and Bobby Jindal are the antithesis of Flat-earthers.

  • Serious Gord

    I understand that the advance stats are great tools for GMs, coaches, scouts and geeks.

    There are 18,000 fans in the stands, and I would hazard to guess that only 180 of them care about corsi, fenwicks, etc.

    At the end of the day/game/season if Taylor Hall has 80 points, that’s all that matters to the average fan.

      • Dan 1919

        John , no harm at all, I am just saying its not for everyone. Maybe one day when the NHL publishes these stats by [ player/team etc],might develope interest for the other 17000 fans.

        ” you have Hall blasting down left side coming in on goal.. your thinking… heck he’s got 30 goals hope this will be 31. You are not thinking what his corsi his at that point.

        But it helps the coach and GM to see how he got those 30 goals thus far.

  • Serious Gord

    This is awesome stuff! great work.

    So sick of the average hockey fan wining about advanced stats – crying about their lack of usefulness. Stats are used fore everything, at some level, on this planet. Why not hockey? I will point them towards this article and tell them to ‘educate yourself bro!”

  • Good to see all the defining terms…I really rarely read the actual stats but instead read the article authors synopsis of them and trust that they know what they are talking about…..

    Who is tracking the stat info though? Who is keeping track of ice time and shots for and against while a player is on the ice? I have never understood this? Is someone watching and timing every player?

  • Dan 1919

    Is this for real? I have nothing against advanced stats or people who track them, it’s just another section to entertain and interest people derived from a hockey business with the entire business modo being to entertain people… so advanced stats are fine.

    But let’s face it, good hockey people and GM’s will only use them as info. Going back in time, would advanced stats of allowed CHI to be defeated, what about, DET, or COL in the Sakic/Fors days? The answer is no, good teams are good teams and the advanced stats although helpful, could be eliminated from the game with little ill effects.

    What is completely tacky, nerdy and unprofessional is how these geeks decided to name the stats after people. Why don’t we change the “Assist” to the “Gretzky,” and the “Goal” to the “Richard.” under this logic.

    Tayloer Hall had 27 Richards and 53 Gretzkys last year, what a player. Or likewise, a “Goal Against” could be called a Dubnyk.

      • Dan 1919

        I’ve seen plenty of professionals and professional groups do unprofessional things… irrelevant.

        IMO it’s tacky and doesn’t go well with sports stats. G, GA, GW, S%, Corsi? Then the Fenwick is the exact same as Corsi without blocked shots but with a completely different name?

        Again the stats are really just another thing to pull from the game, that’s fine, and the names aren’t a huge deal, but I do think they’ll need to be cleaned up. At the end of the day they’re just another hockey stat, not individual Transformers that all need their own name.

        • Point is, millions of things are named after people by extremely professional people. Calling it unprofessional doesn’t make a lot of sense.

          If you want to say that you wish the names were more descriptive I could understand.

          Just for you, Corsi is now “on-ice shot attempt percentage”. Fenwick = “Unblocked on-ice shot attempt percentage”. Much more elegant.

    • Dan 1919

      But let’s face it, good hockey people and GM’s will only use them as info.

      I love how some people think this is some kind of ultra-contentious revelation. What else would they be used for?

      • Dan 1919

        I actually agree with you. I’m slightly confused as to why there’s two sides and some people don’t like the “fancy stats.”

        You often hear the people preaching the stats act like they’re revolutionizing hockey, when they’re not actually doing anything besides pulling info from the game that’s always been there (we’ve always managed to figure out who was good and who wasn’t without out it).

        Then you hear the other people jump all over the advanced stats like they are useless, when OK, they might not be that important, but it’s just another stat so who cares.

        I think most people know stats are just additional info if looked at in an objective manner.

        • (we’ve always managed to figure out who was good and who wasn’t without out it)

          As an Oilers fan I take issue with this statement. So do many Leafs fans I assume.

          I’d also like to add that most analytics proponents are very specific in describing stats as data pulled from game sheets. It is people calling the numbers useless who inexplicably believe we claim they are anything more than that. Analytics comes into play when the numbers are used, and how they are used is what can potentially change things. You would not believe how much player evaluation has changed in Basketball since SportVU has come into play and the analytics groups have all that data to play with.

    • Zarny

      Advanced stats were NOT derived from a hockey business with the entire business modo being to entertain people.

      That is quite possibly the dumbest comment I’ve ever read on here which is saying something.

      Advanced stats actually derived from the complete opposite. There literally was no hockey business or drive to “entertain” people involved.

      The pioneers of advanced stats were in fact decidedly from outside the hockey business for the most part. They used statistical analysis to determine correlations with winning.

      The naming conventions have nothing to do with ego and everything to do with simplicity. Corsi, Fenwick and almost every advanced stat are compilations unlike goals and assists etc. It’s simply easier to refer to Corsi as Corsi in lieu of “Total shots at the net for and against at even strength, including missed shots, blocks, goals and saved shots”.

      • Dan 1919

        Your reading comprehension is terrible, possibly the worst I’ve seen on here, and that’s saying something.

        Nobody ever said they were derived to entertain people. They are now being used to entertain people. Ex. JW writes an article about a new player to the Oilers like Pouliot and uses advanced stats to back up his opinion. Indirectly, his articles and use of advanced stats entertain people (in a good way).

        The naming convention may or may not hold out as it lacks simplicity, contrary to what your poor comprehension skills may believe. I really don’t care either way, and it’s not what I would choose, but IMO I doubt the current names will hold out if they are ever added to a stats column on I think we will see it go to something more conventional like an abbreviation of what it actually represents.

        Now I haven’t really said anything new in this post, but because of your poor comprehension, I’ve had to reiterate it.

        Advanced stat 17-3, The Zarny: having to explain to someone in a long drawn out fashion what the context was actually referring to because they jump to conclusions while paying little attention to what was actually said.

        • Zarny

          Good grief.

          You’re right, you haven’t said anything knew. And you clearly have a poor understanding of what statistical analysis actually is.

          The naming convention will hold specifically because Corsi or Fenwick are in reality much simpler than a long winded description of what the statistic is compiled from. It may not be what you would choose, but that doesn’t really matter because you contribute nothing to the development of hockey analytics. Those that do contribute will continue to use the common naming conventions already established. If you see an abbreviation used it will be no different than G or A for goals and assists.

          P.S. Advanced stats aren’t numbered so citing “Advanced Stat 17-3” literally makes no sense.

    • Dan 1919

      The funny thing is, and correct me if I’m wrong, there’s nothing binding about naming info. So if the NHL did ever decide to add a few columns for these advanced stats it’s highly unlikely they’d have two completely different names for Corsi and Fenwick, or even use those names at all.

      They’d probably just use some generic abbreviation so when you hover over it, it reminds you of what it actually is, instead of having to google Corsi and Fenwick to find out blocked shots differentiates them.