1. Individual vs On Ice vs Team Statistics
  2. Possession Statistics: Corsi & Fenwick
  3. Shot Quality, the Percentages (Save and Shooting) and PDO
  4. Sample Size
  5. Corsi/Fenwick vs Goals
  6. Game State and Score Effects
  7. Usage – Zone Starts, QoC and QoT
  8. WOWY
  9. IPP, IGP and IAP

Exploring advanced hockey statistics & hockey analytics; how advanced stats work, how they relate to hockey as a whole.

I figured a good start would be to write up a brief (or maybe not-so brief) introduction to hockey analytics, which is something I have been intending to do on my own website but haven’t gotten around to it yet. It will get more attention here anyway, so this is as good a time as any to get it written.

This is certainly not an exhaustive list of everything going on in hockey analytics, but it should be a good overview of most of the major terms and concepts.

Individual vs On Ice vs Team Statistics

Before we get into specific advanced statistics,  let me mention the overriding concept of individual statistics vs on-ice statistics vs team statistics. Individual statistics are exactly what they sound like: Statistics an individual puts up. These are goals, assists, points and shots that the individual player produces. Phil Kessel scored 37 goals last year, 27 of which occurred during five on five even strength play, are examples of individual statistics. While these are called ‘individual statistics,’ they can still be heavily influenced by one’s teammates and an individual player can also influence his teammates’ individual statistics. As a result, it is often better to look at on-ice statistics.

On-ice advanced statistics are what the player and his team mates produce when the player is on the ice. When Phil Kessel was on the ice during five on five even strength play the Leafs scored 68 goals for a goals for rate of 1.013 goals for every 20 minutes of ice time — an example of on-ice statistics. Both individual and on-ice statistics can be used for individual player evaluation.

Team advanced statistics are just what they sound like: Statistics related to team performance.  “The Leafs scored 147 goals during five on five even strength play last season,” is an example of team statistics. Team statistics are, as you may have guessed, best used in team evaluation.

Possession Statistics: Corsi & Fenwick

This is probably the most commonly used concept in hockey analytics and yet often the most controversial. In many respects possession, Corsi and Fenwick have almost become synonymous with “hockey analytics,” although there is a lot more to hockey analytics than just these two metrics.

Possession is essentially defined as how much a team possesses or controls the puck. It could be represented as time (i.e. The Maple Leafs possessed the puck for 28:37 in last night’s game) or as a percentage (i.e. the Maple Leafs possessed the puck during 45.8% of the play in last night’s game). The idea behind possession: The more you control the puck, the more opportunity you have to generate scoring chances as well as less opportunity for your opponents. This is a good thing and something teams should want to do.

Unfortunately, the NHL doesn’t track possession time, which is where Corsi and Fenwick come in. Corsi and Fenwick are shot-based metrics. Corsi considers shots + shots that missed the net + shots that were blocked. Fenwick is the same but does not consider blocked shots. People generally use Corsi and Fenwick as a proxy for possession or puck control. Corsi can be presented as a counting stat (i.e. the Maple Leafs had 52 Corsi events for last night), but is more commonly represented as a percentage. If the Maple Leafs had a 52% Corsi percentage it would mean — of all the Corsi events that took place last night by either team — 52% of them were taken by the Leafs. Corsi percentage is often shortened to Corsi% or, as I tend to frequently use, CF% (corsi-for percentage). Fenwick percentage is the same but without considering shots that were blocked. In theory one could also just look at shots (ignoring both shots that missed the net and shots that were blocked), but doing so is far less common.

These statistics can also be described as rate statistics split between offense and defense. For example, I use CF20 (or CF/20) as indicating Corsi For per 20 minutes of ice time. CA20 would be corsi against per 20 minutes of ice time. CF/60 and CA/60 are also commonly used as indication per 60 minutes of ice time.

Although some people have preferences for Corsi over Fenwick or vice versa depending on use, I for the most part consider them interchangeable as they are extremely-highly correlated. For the most part I consider them to measure the same thing and using one over the other is  unimportant. That said, whenever I talk about whether I can/could drop one of them from my stats database, there is generally a group of people that want to continue to see both be made available.

Note: You may also see Corsi/Fenwick referred to as shot attempts, which is becoming a more user friendly and intuitive way of describing them.

Shot Quality, the Percentages (Save and Shooting) and PDO

One of the issues many (predominantly non-hockey analytic supporters, but myself to some extent as well) have with Corsi and Fenwick is that they are measuring shot attempts and not the quality of the shot attempt. There have been countless debates over this and to what extent shot quality exists and its relative importance. It is unfortunate, really, because neither side is absolutely right.

Let me first define the notion of shot quality. For me, showing that shot quality is real and is significant starts and ends with showing that a player or a team has the ability to maintain elevated shooting percentages. If a team year in and year out can maintain an elevated shooting percentage, shot quality exists. If a player year in and year out can maintain an elevated shooting percentage, shot quality exists.  We know that some shots are more difficult than others (i.e. a rebound shot from 8′ is far more difficult than a point shot from 45′), but what we want to know is whether a team or player can have a higher quality shot on average. Having shots that are, on average, more difficult to save and thus have a higher chance of resulting in a goal is essentially the definition of shot quality.  Now, does this exist at either the player or team level?

Let’s start by looking at players. Over the past 7 seasons, the players with the highest on-ice shooting percentage (i.e. the shooting percentage of all shots taken while the player was on the ice) during 5 on 5 play (minimum 4000 minutes of ice time) are Sidney Crosby, Steven Stamkos, Alex Tanguay, Marian Gaborik and Marty St. Louis, all with an on-ice shooting percentage above 10.2%. The five worst players, all with an on-ice shooting percentage below 6%, are Travis Moen, Nate Thompson, Samuel Pahlsson, Shawn Thornton, and Craig Adams. To not believe in shot quality at the player level one must believe that there is little or no difference between those two groups and they have achieved their elevated (or suppressed) shooting percentages by luck (good or bad) alone. If anyone believes that they are denying reality. Furthermore, if anyone believes that the difference between shooting 10% and shooting 6% is not significant they are denying reality as well (shooting 10% over 6% means scoring 66% more goals on the same number of shots). Shot quality exists and is an important consideration in player evaluation.

At the team level, shot quality is a little more difficult to show because it has generally been difficult for teams to assemble a group of players that can drive shooting percentage up and down the line up. High shooting percentage players are difficult to acquire and it would be cost prohibitive to assemble a full team of high shooting percentage players (in part because NHL teams have generally paid more for them). That said, there some teams that have shown an ability to maintain elevated or suppressed shooting percentages. The Leafs are generally one of those teams as they have finished 6th, 1st, 7th, and 5th in shooting percentage during 5 on 5 play over the past 4 seasons. Conversely, San Jose has finished 19th, 26th, 26th and 25th over the past four seasons.  The differences at the team level are less significant than at the player level, though, and thus Corsi is more effective as a team evaluation tool. The analytics bear this out.

It is my belief that players have an ability to influence their teams save percentage, although I do believe it is much more difficult to quantify this effect. Since any given year a player only plays in front of a couple goalies, it is extremely difficult to decouple the player’s impact on save percentage from their goalie’s. That said, I believe the ability is there, although less so than the ability to drive shooting percentage. I’ll get into this further later on when I discuss score effects.

PDO is an interesting statistic; it is essentially on-ice save percentage plus on-ice shooting percentage. Across the league the mean would be 100%, but individual teams and players can fluctuate a little from that point. Some people use PDO as an indication of luck or good/bad fortune by looking at how much PDO deviates from 100%, but one must take into consideration the quality of goaltending the player plays in front of or the players’ ability to drive on-ice shooting percentage. A PDO of 102% does not necessarily mean the player is lucky. Gaborik’s PDO over the last 7 seasons is 103.1%, while Crosby’s is 102.8%. So, while PDO can provide some indication of good/bad fortune, one must still consider to what extent the player’s talent or the circumstances play in as a factor.

Sample Size

This is probably a good time to bring up the issue of sample size because it is an integral reason why people would choose to use Corsi or Fenwick over goals. I just told you that shot quality is real right after telling you that possession/Corsi and Fenwick are important and valuable tools in analytics. Here is the issue: Goals are a relatively infrequent event in hockey. A team will score typically 2-4 goals per game and 200-250 goals per season. They will take between 25 and 35 shots per game and 2200 to 2800 shots per season, and they can have nearly twice as many Corsi events per game. These differences have a major impact in how confident we can be in the conclusions we can make and that has an impact on how we conduct analytics. Let me explain.

Since goals are so rare, a lucky bounce or two or a “hot streak” can have a huge impact on the results of a statistical analysis. If next season Matt Frattin starts the season with 5 goals in the first 8 games (he actually had 6 goals in his first 8 games he played in the 2012-13 season), we don’t immediately conclude he has a chance to lead the league at the end the season with 50 goals. We don’t do this because we know odd things happen over small sample sizes and there is no evidence Frattin has that kind of ability. As sample size grows, that “hot streak” will get averaged out by a “cold streak,” and eventually Frattin’s statistics will begin to become more representative of his actual ability. Over the next 12 game he may go on a cold streak and only get 1 goal giving him 6 in his first 20 games, which would put him on a 24 goal pace — still likely very high for Frattin, but much closer to what one might expect than the 50 goal pace he was on early on. It might take another 20 or 30 games or maybe the whole season for Frattin’s statistics to become representative of his true ability.

The significantly greater number of Corsi events that occur mean that we generate large sample sizes far more quickly and we get a better representation of talent far more quickly. With 20 games of data, goals are a very poor predictor of future performance but Corsi or Fenwick are far better predictors. This is true at both the team and player level. The greater number of events means we can draw conclusions far more quickly, which is a significant reason why people use Corsi and Fenwick.

Corsi/Fenwick vs Goals

To summarize the sections above we have the following:

  • Corsi/Fenwick have larger sample sizes and thus “stabilize” closer to true talent levels far faster than goals.
  • Shooting percentages do vary significantly across players (and to a lesser extent teams) and players likely have some impact on save percentage. As a result of this, Corsi/Fenwick will never be able to truly represent a players (or to a lesser extent teams) true offensive or defensive value (true value should always measure in terms of ability to boost goals for and suppress goals against because that is what truly matters in hockey).

As explained above, the people that tend to rally against analytics tend to do so on the idea that not all shots are created equal. The analytics people who fight back tend to argue that, at the team level, possession metrics like Corsi and Fenwick are the better predictor of future performance and thus it is fair to use Corsi and Fenwick as a primary talent evaluator (even if it doesn’t tell the whole story). Both sides have cases to be made, but as with most disputes the truth is somewhere in the middle. A team or (especially) a player evaluation that doesn’t include some consideration for the percentages is an incomplete and possibly incorrect evaluation and it is vitally important to be aware of this. Conversely, a team or player evaluation based largely on goal-based statistics that doesn’t include some consideration for sample size related errors and uncertainty is an incomplete and possibly incorrect evaluation (and we need to be aware of this, too).

Game State and Score Effects

To state the obvious, teams score goals at a significantly higher rate on the power play than they do at even strength and they score goals at a significantly higher rate at even strength than when killing penalties. Typically, hockey analytics is conducted using five on five even strength statistics unless one is conducting an analysis power play or penalty kill play specifically. Unless otherwise specified, advanced statistics such as Corsi or Fenwick or even goals and goal rates when conducting advanced statistical analysis are five on five even strength statistics and usually exclude goalie pulled situations ( and exclude goalie pulled situations, while I believe they are included at, which can lead to important differences).

The score of the game can have a significant impact on a teams and players statistics. Generally speaking, a team that has a lead gives up shots at a higher rate than they do in other situations but also has a higher save percentage, indication the shots they give up are of lower quality. Last season, only four teams had a Corsi percentage above 50% when leading (Los Angeles at 53.2%, Chicago at 50.7%, New Jersey at 50.1% and Boston at 50.0%). Conversely, only three teams (Maple Leafs at 48.0%, Buffalo at 47.7% and Edmonton at 47.5%) had corsi percentages below 50% when trailing. Last season, the Maple Leafs shooting percentage when leading was 9.03%, but it was 7.87% when trailing. A lot of the time score effects aren’t important, but for some occasions and for some teams they can have an impact on a team’s overall 5v5 statistics; therefore, at times they should be taken into consideration. For an analysis of how score effects can impact a players performance, have a look at my analysis of Dion Phaneuf when protecting a lead vs when playing catch up hockey.

I mentioned score effects in the section above in reference to a players ability to impact his teams save percentage. Score effects are evidence of this. Teams and individual players have a worse on-ice save percentage when playing catch up hockey than when protecting a lead. This can only happen if players have the ability to influence save percentage. The theory goes — when players play more aggressive offensive hockey when trying to play catch up, they give up more odd-man rushes against resulting in higher quality shots against and a lower save percentage. The opposite is true when a team plays more conservative defensive hockey when protecting a lead. This, to me, is clear evidence that players can and do influence save percentage at least based on style of play, if not by talent differences.

Usage – Zone Starts, QoC and QoT

Zone starts describe what zone a player starts his shifts in (not including on-the-fly line changes) and refer to the percentage of face offs the player was on the ice for in the offensive, defensive and neutral zones. These can be represented by Ozone%, which is the percentage of offensive or defensive face offs that the player had in the offensive zone (i.e. Ozone% = Offensive zone starts / offensive+defensive zone starts). My preference is to also consider neutral zone starts by looking at each zone separately. I do this by looking at OZFO% for percentage of face offs in the offensive zone, DZFO% for percentage of face offs in the defensive zone, and NZFO% for percentage of face offs in the neutral zone.

There has been much investigation into the impact of zone starts on a players individual statistics. Early research found that zone starts had a significant impact on a players overall statistics. While this sentiment is still floating around, it has largely been dismissed and most are now accepting that zone starts have minimal impact for most players overall statistics. Even the most extreme zone start usage will at most have a 1-2% impact on Corsi% (i.e. a 52% Corsi player with extreme offensive zone start usage is almost certainly still a 50+% Corsi player under neutral/average zone start usage). For most players it is not a significant factor in on-ice performance.

Like zone starts, Quality of Competition (QoC) is largely overstated when it comes to the impact it has on a players overall statistics. While a player playing against Sidney Crosby will have worse statistics than when playing against a typical third or fourth liner, the reality is that there are no players so consistently playing against high end players (or low end players) that their statistics will be impacted in a significant way.

The reality is zone starts and QoC metrics are of minimal importance in player evaluation  and are best used solely as an indication of how his coach views his skill set.

Conversely, Quality of Teammates can have a significant impact on a players statistics. David Clarkson in 2012-13 had a Corsi% of 61.1%. He dropped to 42.3% last season. He did get significantly more defensive zone starts, but a greater statistical analysis indicates that the main reason for this massive drop in Corsi% is the quality of his team mates. He went from playing on a very good Corsi team with some very good line mates (Patrick Elias and Travis Zajac) to a very poor Corsi team and playing on the second or third line (with Mason Raymond, Nazem Kadri, Jay McClement and Nikolai Kulemin). By far the only usage statistic that really needs to be taken under significant consideration in player evaluation is quality of teammates.


That brings us to what I consider the most important concept in hockey analytics: WOWY’s. WOWY stands for With Or Without You and looks at how players perform when playing on the ice together and when playing apart from each other. The value of WOWY’s is they tell us who is the more important player and who is making who better. Nothing shows this better than looking at Tyler Bozak’s statistics. Due to the much smaller sample sizes when looking at WOWY’s, I’ll look at the last three seasons of Bozak with and without Phil Kessel.

  • Bozak when playing with Kessel had a 46.8 CF% and 51.9 GF%.
  • Bozak when not playing with Kessel had a 32.9 CF% and 38.2 GF%
  • Kessel when not playing with Bozak had a 46.1 CF% and 50.0 GF%

In short, Bozak was terrible when not playing with Kessel while Kessel performed about the same when not playing with Bozak. This is clear evidence that Bozak was dependent on Kessel (along with Lupul and/or van Riemsdyk) and not the other way around.

WOWY’s help show who the production drivers are and who are not. To me, that answers the most important question in hockey. You want players who drive results, not those that depend on others to drive results. As an example of WOWY analysis, have a look at my analysis of the Hartnell/Umberger trade.


IPP stands for individual points percentage and is calculated by dividing the number of points a player has produced by the number of goals that were scored while the player was on the ice. This statistic tells us who is most involved in the teams offensive production when they are on the ice. Bringing this back to Kessel and Bozak, Kessel has an IPP of 78.9% over the last three seasons compared to Bozak’s 60.7%. This means — of all the goals that Kessel was on the ice for over the past 3 seasons (during 5 on 5 play) — he had a point on 78.9% of them, which is definitely among the league leaders. Conversely, Bozak had a point on only 60.7% of all the goals scored while he was on the ice which is near the bottom of the league. Like WOWY’s, IPP can help us determine which players are integral to their teams offense when they are on the ice and which players are more bystanders when it comes to offensive production.

IGP stands for individual goals percentage and is calculated almost exactly the same as IPP, but instead of using the points the player has we use the goals the player has scored. Phil Kessel has an IGP of 36.8%, which means he has scored 36.8% of all the goals scored when he was on the ice. This is also among the league leaders, although well below Stamkos’ 46.3% IGP.

IAP is the same as IGP except that it uses assists instead of goals and can be used to identify play makers rather than goal scorers. Henrik Sedin leads the league in IAP with an IAP of 61.4% over the past three seasons. Joe Thornton is right near the top as well with an IAP of 59.0%. These are probably the two best pure play makers in the league the last several seasons (they have very low IGP’s of 14.3% and 16.7%, respectively).

Looking Forward

There are a lot of other things happening in the world of hockey analytics, from projects tracking zone entries and exits to some of my recent work on “rush shots.” I hope to explore more of these in my future posts. There is still a lot to learn and explore in hockey analytics and I plan on using this as an outlet for bringing some of it to my fellow Leaf fans. For the time being, I hope that the above acts as a good introduction to hockey analytics and a general description of where hockey analytics is at right now.

I do want my experience here at to be an interesting and informative one for everyone involved, and that means I want it to be a bi-directional experience. I want to hear from you and want to know what interests you in the area of hockey analytics and how they relate to the Leafs. Whether you are an avid supporter of hockey analytics or a skeptic, I look forward to your feedback. If you have any questions you want answered, or players you want analyzed or want anything clarified, let me know. I have some ideas for future posts, but I am certainly leaving it open for input from all of you as to what I write about; definitely let me know either in the comments below or via e-mail. I will definitely read all of your comments but cannot guarantee that I will respond to them all. I will do my best as time permits and some responses may be in the form of a future post as opposed to an immediate and direct reply.

Hockey analytics can be interesting and informative and that is what I am hoping I can bring to


Looking for Leafs specific fancy stats? Go to our Toronto Maple Leafs Advanced Statistics page.

If you are looking for more—and different—metrics and different leagues to research, please read Rob Vollman’s post at Dobber Hockey on all the different websites for advanced hockey statistics.

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