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 HockeyAnalysis.com 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 (Stats.hockeyanalysis.com and ExtraSkater.com exclude goalie pulled situations, while I believe they are included at behindthenet.ca, 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 MapleLeafsHotStove.com 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 MapleLeafsHotStove.com.


For more information on specific advanced statistics and advanced statistics terminology, you can have a look at my glossary at stats.hockeyanalysis.com.

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.

If you enjoyed this article and found it useful, consider tweeting, liking, sharing or +1’ing it in the sharing area below.

  • Cloud09

    Welcome to the fold David. I’m looking forward to reading this when I have some more free time but thanks for putting the time into writing this for us ignorant stat folks.

  • Burtonboy

    Welcome aboard David . I would classify myself as a skeptic until recently. I may be old but I consider myself to be open minded about most things . Looking forward to learning more.

  • Cloud09

    Burtonboy Looks like they can teach an old dog new tricks.

  • Tim Horton

    Welcome and great read so far. I am at work so I will have to finish it later.  Some advice, if you really want to lure people to the statistical side, would be to take it a bit slow. Maybe go over one measure at a time. A wall of text may come across as a lot of jargon and scare some  away who may otherwise enjoy new ideas.

  • wiski

    I think wowy is my new favorite fancy stat, I feel all edumicated and shit now, welcome aboard David. 😉

  • wiski

    Cloud09 Burtonboy but not the new remote 😉

  • Tim Horton

    wiski   How long has WOWY been around in the hockey world? I have been saying for a while hockey needs a stat similar to WAR in baseball, this looks like the equivalent.

  • Burtonboy

    wiski Cloud09 Burtonboy Don’t even talk about it . Just finished doing a factory reset on my big TV . Now that thing has a ton of diagnostic tools let me tell ya.

  • wiski

    Tim Horton wiski It’s funny because I seem to remember Kessel and JVR taking a bit of a nose dive when Bozak was hurt?

  • dlb Mad

    there’s no big solid flat surface to pound a fist on anymore.

  • hockeyanalysis

    Tim Horton wiski WOWY has been around for a few years. It is more of an analytical technique than a stat but in my mind is the most useful technique. I know Ken Hitchcock, head coach of the Blues, uses a WOWY like analysis when setting his lines. He has mentioned looking at who performs best with who as a way of setting lines several times in interviews.

  • Burtonboy

    https://twitter.com/dstaples https://twitter.com/dstaples/status/496677211215515648
    https://twitter.com/LeafsNews https://twitter.com/hockeyanalysis nice work. Balanced, not over-stated.

  • wiski

    hockeyanalysis Tim Horton wiski and ours has problems making toast. lol

  • hockeyanalysis

    Tim Horton I will definitely try and keep my posts accessible to non-stats people as well. The truth is it really isn’t that complicated once you get the basics and that is why I started with an introduction to advanced statistics post. There is a lot of info in the post above but I tried to lay it out logically and explain not just what the stats are but why and how we use them and what their strengths and weaknesses are. If anything is not clear though, ask questions. I am here to help inform as much as entertain.

  • dlb Mad

    “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”
    you lost Fenton already.  Phanuef’s numbers are down because he’s down.  period.
    (sorry, i’ll keep reading…i’ve got a long way to go)

  • dlb Mad

    when talking about shot quality and comparing Thornton to Stamkos, for instance.  are we saying that Stamkos shoots from better places, or that he has a better shot?  or both?
    i see Stammer getting one timers from the face-off dot and i see Thornton shovelling pucks to the net from below the faceoff circle…but Stamkos also has an incredible shot, whereas Thornton isn’t known for his.
    i’m asking.  am i missing something?  or are we just only focusing on the one for the purpose of the topic?

  • The_Polish_Cannon

    dlb Mad Sometimes I wonder if Fenton reads any of the articles on here. He’s been coming on here pushing the same narrative for as long as I could remember (ie. Phaneuf sucks, Kessel isn’t a franchise player, Carlyle is a good coach because he’s won a cup, blah blah blah) That and continuously playing devil’s advocate in arguing with posters just for the sake of arguing.

  • dlb Mad

    i’ve said for the last two years now that if you’re going to put Stamkos on a pedestal you have to also put Kessel up there with him.  if one is a franchise player, so is the other.
    they’re different players somewhat, but Kessels’ critics have overlooked the same flaws in Stamkos’ game.  or rather, they celebrate what Stamkos is best at while pointing to Kessel’s deficiences instead of affording him the same comparison.
    Kessel shoots from far out a lot but he has a high level wrister that few others have.  Stamkos has a one-timer that few others compare to.  one relies on team mates to get him the puck, but his still is getting open and finishing the play, so he’s not just Johnny on the spot.  the other relies on players getting him the puck with time and space as well, though his strength is not soley in finishing the play himself.
    i don’t know, but can statistics show who is more valuable to their team?  can something decipher who is more suited to what one line needs or what one team is lacking, moreso than the other?
    i’m interested to see where it leads.

  • dlb Mad

    read the articles!

  • hockeyanalysis

    dlb Mad There has been difficulty in identifying shot location as a significant contributing factor to shot quality though it may be a minor factor (i.e. it can’t be reliably shown that some players have an ability to consistently have a higher percentage of shots taken from more dangerous areas when they are on the ice).

    For me, I think more significant factors are the quality of the shooter and the quality of the play makers that set up the shot. Stamkos has a good deceptive and accurate shot and that is a significant factor in his shooting percentage. He also plays with really skilled teammates who can set him up perfectly for those shots. The quality of the shooters and the puck skills of the guys on the ice drive shooting percentage more than ability to generate more shots from dangerous areas.

  • BigTO

    Welcome, David. I’ve checked your site for years, and find these numbers very interesting.
    I re-read this intro to AS just because everyone can use a refresher course. 
    It’s been interesting to see the League follow in lockstep once a few teams decide there’s value in having experts in these numbers on staff. It’s been a big summer for hockey’s numbers game.
    I’m always left wondering.. It would render much of the Corsi and Fenwick proxies as a thing of the past and we can stop debating over them.. Why can’t the NHL just begin recording time of possession? The EA NHL game does it FFS. Corsi and Fenwick correlate nicely to it when its been tracked by bloggers but knowing definitively who has the puck most of the time by the second would put an end to so much of the debate. No one will argue with a stat like that nor does it leave any room for misinterpretation really.

  • deedrag

    Thank you for your time explaining this David. I’ve been curious about Advanced Stats for awhile and how teams might use them. From these stats, I’m seeing that it is easier to build a team around a good defence first. Seems there are more variables when trying to create scoring than when trying to prevent it. Like the wowy stat. This can answer a lot of questions regarding who really is worth keeping on a team and who isn’t. Curious to know Kadri’s wowy?

  • hockeyanalysis

    dlb Mad Both Stamkos and Kessel are high end players but it is probably not too difficult to show that Stamkos is better. I also believe that teams are far better with an elite center than an elite winger which I think knocks Kessel down a notch when discussing overall value. Center is just a more important position overall.

  • hockeyanalysis

    deedrag I’ll do some WOWY analysis if Leaf players in future posts but yes, Kadri generally does pretty well especially relative to other Leaf centers.

  • Dan39

    hockeyanalysis deedrag Re Bozak, don’t the CF% and IPP% just show that he’s the one on the line responsible for defense? With IPP%, it’s obvious what I mean. With CF%, it seems to me his with-numbers may indicate more defensive assignments and the without% perhaps when Kessel is deployed on a rush or prolonged offensive zone period to add more offense.

  • MaxwellHowe

    Thanks for this.  I was in a “discussion” yesterday about Kadri and Lupul’s performance when playing together.  Is there a WOWY available for that combo?

  • MaxwellHowe

    I just looked at Stats.HockeyAnalysis.com.  The way I read it, Kadri is slightly better without Lupul.  Lupul is a little better when he is with Kadri.  They have a CF% of 44.2 together.  Kadri has a 46.6 without Lupul, Lupul has a  40.6 without Kadri.

  • Armchair GM

    A bit of information overload but I’ll keep it as a reference.  What comes to mind is the Russian hockey coach Anatoly Tarasov and his approach to posession and shots – shoot only when you have a good opportunity to score… a very high scoring percentage will deflate an opponent’s confidence…

  • vinoa

    So Ph9’s zone starts at QoC had nothing to do with his performance down the stretch? I guess there goes that defense.

  • vinoa

    hockeyanalysis Is Stamkos a traditional center though? Isn’t he more of a winger if he’s waiting for the set up?

  • dlb Mad

    thanks for the reply.  how often has Stamkos been a center?  he seems to be shuffled to the wing when other centres are present.
    he also seems to play more without the puck than with it.  Kessel plays like a centre in regards to drawing checkers and moving the puck.  Stammer seems to step always have getting a better shot on goal on his mind, so he’ll step around a guy and take a shot more while Kessel distributes the puck in some similar situations.
    of course, i’ve seen a lot more Kessel than Stamkos.
    if you need a guy to QB a PP then it’s Kessel.  if you need a guy to finish a high percentage of his PP shots it’s Stamkos.  if you need both, it’s Tavares.  :-)

  • dlb Mad

    thanks, i appreciate the response.  it helps me understand where you’re coming from a little and we can certainly find some common ground.

  • dlb Mad

    perhaps this is the sort of thing that Hitch uses to determine that Bozak is a better fit between JVR and Kessel than Kadri?
    can we use analysis to create a Cup winner that doesn’t require a #1 C or a #1 D?

  • http://mapleleafshotstove.com/ DeclanK

    Armchair GM http://blogs.thescore.com/nhl/2013/04/17/soul-of-a-new-machine-anatoli-tarasovs-fancystats/

  • dlb Mad

    it’s a little odd that Cameron’s not here commenting.  he’s had a lot to say on the topic of advanced stats lately.  in fact…he’s accused me of having a closed mind to it, yet here i am.
    wtf is the matter with people?  haha.
    get over here and read.

  • dlb Mad

    “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.”
    every time Fenton complains about Kessel going cold, and being the main reason why the Leafs go into the tank each year as a team, can we point to this and tell him he’s wrong?
    i mean, Kessel takes some low percentage shots (it’s part of the reason why he got the reputation for being a peripheral player so quickly) but so many of those go in when he’s hot … we only talk about it when it’s ringing iron or not deflecting off a skate blade and missing wide.
    Kessel and his linemates played some of their best hockey last year, and in the shortened season, when Kessel wasn’t having those chances count as goals.
    so my question is, are there any top scorers who don’t have hot and cold spells?  and among them, what allows them to succeed at a more constant rate?  and…does one help their team win more than the other overall by the time the 82 games are played (in which case, Kessel is not the elite sniper we should build around)?
    anything to argue either way?

  • dlb Mad

    Phaneuf didn’t look as energized as he did earlier in the year.  he lost his confidence, or his breath, or something.  last year was tale of two Dions.

  • dlb Mad

    “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”
    interesting.  i think i agree.
    when Kessel wasn’t scoring to begin the short season, i pointed to the fact his coach seemed happy with his efforts and contributions to the team (he wasn’t suffering for ice time or assignments, was on late in games whiel ahead or trailing) … as evidence of what i saw (that Kessel wasn’t scoring, but that he was playing well all the same).  guess who thought it was moot?

  • maximus_asinus

    MaxwellHowe but what does that mean? I interpret that as Kadri is more likely to pass off to Lupul when they’re together, and when they’re not together Lupul is “looked off” because there is a better shooter (Kessel) on the ice.

  • dlb Mad

    Doughty makes a lot of mistakes.  he also generates a lot of opportunity.  is it his ability to finish the one thing that seperates him from a big group of players or is it something else, like the percentage of time he’s doing something good out there?
    soooo…what can Rielly achieve?  what can Gardiner achieve?  they’re all slightly different, but what can they do best to contribute to the team (after reading the Phanuef article, where can they all go from here).
    Seabrook-Keith works.  what do we have potential to do with our players? 
    then take it further.  Granberg.  Percy.  Finn.  what are they, in fact?
    it’s funny trying to find a common language with so many people…scouts have a common understanding.

  • Dan39

    Hi David, thanks for the article. Can you address this question, might have gotten lost below?

    Re Bozak, don’t the CF% and IPP% just show that he’s the one on the line responsible for defense? With IPP%, it’s obvious what I mean. With CF%, it seems to me his with-numbers may indicate more defensive assignments and the without% perhaps when Kessel is deployed on a rush or prolonged offensive zone period to add more offense.

  • hockeyanalysis

    IPP is a purely offensive stat and is his point production relative to the number of goals scored when he is on the ice. Getting more defensive minutes playing with guys with poor offensive abilities won’t hurt his IPP. In fact it might help him as any offense generated is more likely to go through him than his defensive-minded line mates (i.e. McClemment) than his offensive minded line mates (i.e. Kessel).

    Bozak’s CF% could get hurt though by playing defensive minutes particularly when he is put out solely for a defensive face off. If he loses the face off he faces the likelihood of a shot against. If he wins the face off the team clears the zone and he goes to the bench. He was only there to win the face off. In this circumstance he is unfairly punished for being put out in a defense only situation. This would definitely have an impact in his “away from Kessel” stats though probably not enough to account for the difference.
    It is arguable whether Bozak is on the line to be the one guy responsible for defense. It may be the case but one would also need to evaluate whether he is effective in that role. There is certainly some doubt as to his effectiveness (exploring this might be well suited to a future post).

  • TML__fan

    I’m pleased you didn’t skim over the debate about shot quality, as this is a topic that sometimes gets downplayed when looking at analytics.  It’s unfortunate that shot quality is not tracked in a better manner and statistics readily available.  Although shooting averages may help to justify the importance of shot quality, they fail to fully quantify the influence of an individual player, and certainly when it comes to on-ice influence at both ends of the rink.   Even if a player consistently has an above average SH% (and their on-ice shot quality is high), that could be offset if their on-ice shot quality against is even higher. 

    Similarly we generally measure a goaltender’s contribution by their save percentage, with little or no attempt to consider shot quality.  Yet we often marginalize the numbers based on the number of shots they face.  Can we assume all goaltenders face the same percentage of shot quality regardless of the number of shots?

    Certainly hockey analytics are useful, but until they are somehow weighted by shot quality, they are only showing part of the picture.

  • StanSmith

    wiski Tim Horton That in itself is the problem with tracking shots instead of actual scoring.  Kessel and JVR did struggle to put points on the board when Bozak was hurt but they obviously still had good, if not better, shot totals for and against.  As a coach I want results, so regardless of the shot differential, if I’m scoring more goals than the opposition I’m going to with whatever is getting me results.

  • StanSmith

    I’m confused. If Corsi and Fenwick are tracking shot totals for and against, isn’t that exactly the information you get, is shot totals, not possession? I understand that you have to possess the puck before shooting it but in some cases you only have to possess it for a split second.  A player, or a team can possess the puck for long lengths of time and never even attempt a shot on net.  In terms of possession I would rather see the actual possession times. Despite the fact that the NHL doesn’t track it pretty much every game is televised and can be recorded so the only thing it takes to accumulate pure possession times is the time to do it. Even more importantly, at least team-wise I would like to see zone time stats, regardless of who actually possesses the puck in the zone.

  • Dan39

    hockeyanalysis Dan39 No, I’m saying on a line with Kessel and JVR, if his responsibility was defense for the line, presumably his IPP% would be expected to be lower than that of his team mates. Does that make sense? 

    As to the WOWYs, I think you explained why his CF% would be lower without Kessel and I explained why Kessel’s would be higher without him. Of course, Kessel is also a great player. 

    I don’t know for certain, it just seems like you can explain at least part of why Bozak’s advanced stats are so terrible in these ways. I don’t know to what extent, though.

  • hockeyanalysis

    StanSmith Yes, technically you are correct. Some people have done a comparison between actual possession time (watching games using a stop watch as you suggest) and shot totals and found good correlation. So, in addition to telling you shot attempts, fenwick/corsi can act as a proxy for possession time. It’ll never be a perfect replacement but can act as a proxy.

    In all honesty though, I am not convinced that if we had possession time it would be more useful than corsi/fenwick. The primary objective in hockey is to out score the opposition. Corsi/fenwick measures shots which is really one step back from scoring goals (i.e. you need a shot before you can have a goal). Possession time may be one more step back (i.e. you need the puck before you can get a shot which must happen before you can get a goal) which may in fact be a worse metric. I don’t think anyone has studied this to be sure either way though.

  • hockeyanalysis

    CanuckUKinToronto StanSmith This will be the future. It’s just a matter of when. Could be in the next year or two as this is already being done in basketball and several companies are trying to sell the technology to the NHL.

  • hockeyanalysis

    “No, I’m saying on a line with Kessel and JVR, if his responsibility was
    defense for the line, presumably his IPP% would be expected to be lower
    than that of his team mates. Does that make sense? ”
    Yes, that could be the case. If he were holding back and playing defense first. If this were the case one would expect Kessel’s defensive stats improve with Bozak than without. I am not sure this is the case. I could look into it in a future post.

  • Burtonboy

    hockeyanalysis CanuckUKinToronto StanSmith Trials of sports vu and or sportsvison to be tried this coming season in some arenas according to John Collins.