Tuesday, 18 August 2015

xG Hexagonal Maps

First popularized by Kirk Goldsberry and then introduced to hockey via War-On-Ice's Hextally plots, hexagonal plots are a great tool for helping to visualize sports. I have created my own version's below in the form of apps. Two quick caveats, my current 2014/2015 data seems to have some bugs in it so take those seasons with a grain of salt and the individual attempts map also seems to be buggy for reasons currently unknown. I am working to fix both of those issues but just keep them in mind.

Here are some of the features of my xG Hexagonal Maps:

  • If you are unfamiliar with xG (Expected Goals) you can read my post detailing the methodology here. Simply, it provides the probability of any given shot resulting in a goal. 
    • A slight change between this xG and the one from that post is that these numbers also included missed shots now
  • The size of each hexagon is the frequency of shots from that specific location. The larger the hex, the more often a player shoots from that location
  • Each hex is coloured by the efficiency (xGe) of a player/team/goalie from that specific location.
    • Efficiency here is measured as the difference between how many goals we expected them to score from that location (their xG) and how many they actually scored from that danger zone.
    • A Blue Hex means that their xG was greater than their actual G, implying that they may have under-performed. 
    • Red Hex means that their xG was less than their actual G, implying that they may have over-preformed. 
  • Danger Zones are denoted by the light-pink and light-purple lines, high/medium/low. 
  • Not every red hex means a player over-preformed and not every blue hex means a player under-preformed. If you play in front of Henrik Lundqvist, your On-Ice Against xG is probably always going to be higher than your actually goals against. 
The links to all the different maps are posted below. Please let me know if you have any thoughts, questions, concerns, suggestions find anymore bugs . You can comment below or reach me via email me here: DTMAboutHeart@gmail.com or via Twitter here: @DTMAboutHeart  

Team Attempts Map


Goalie Map


Player On-Ice Attempts Map


Player Individual Attempts Map


Thursday, 23 July 2015

Corsi Plus-Minus: Individual Player Value Accounting for Teammates

***Reminder, if you are just interested in looking at the data/visualizations you can check out the separate page here instead of scrolling through this entire article.

Hockey stats have existed for about as long as the game itself. Simple boxscore stats such as goals and assists can be traced back almost a full century now. These stats have helped informed fans/coaches/managers of the value held by some players. Around the 1950’s the Montreal Canadiens began to track a player’s plus-minus. This stemmed from the idea that simple boxscore stats fail to capture many important elements of a game. Plus-minus was a good start to tracking impact that is not realized in traditional boxscore stats. Unfortunately, Plus-Minus has been recently shown to be quite incomplete and lacking by modern evaluation standards. 

The most famous stat to come out of the modern hockey analytics movement is probably Corsi (or just simply all shots). Corsi has numerous benefits over goal based metrics. It accumulates faster leading to more reliable information and is actually more predictive of who will be better in the future. Raw Corsi% (or CF%) tracks the share of shots (all shots, not just shots-on-goal) directed at the oppositions net versus how many are directed at a player’s own net when said player is on the ice, the higher a player’s CF% the better. This metric suffers from a key drawback however, each player’s CF% is heavily dependent upon the quality of his on-ice teammates. This is how a depth player on a good team (ex. Shawn Horcoff) typically has a higher CF% rating than a star player on a bad team (ex. Taylor Hall).

Issues with Current Metrics

Above, I have outlined some current issues with standard CF%, but these are not new issues. People have been aware of the effect playing on a good/bad team can have on a player for a while now, so next progression in the history of hockey stats resulted in Corsi%Relative. To calculate the CF%Rel of a Player Y on Team X is done as follows, Team X’s CF% while Player Y is on the ice minus Team X’s CF% while Player Y is not on the ice. This still runs into issues of comparing players on vastly different teams. Consider the example below:

Whose stat line is more impressive? It seems as though Malkin makes a pretty good team into a great team when he is on the ice while Hudler turns his team from awful to just bad. Which is more valuable? Or are they equally valuable? This is an improvement upon raw CF% but still shares many of the same problems. 

dCorsi fairly prevalent metric constructed by Stephen Burtch. dCorsi is calculated for offense as, Player X’s Actual Corsi For minus Player X’s Expected Corsi For. The real trick here is calculating what a player’s expected Corsi For is. I won’t dive too deep into this stat since it is not mine and you can read up more on it here or reach out to Stephen Burtch via Twitter here. Expected Corsi is calculated using a multivariate regression using five independent variables:
  • A dummy variable for each Team/Season
  • Time on Ice per game 
  • Team total time on ice that the player in question wasn’t on the ice
  • Offensive Zone Start%
  • Neutral Zone Start%
dCorsi moves the right direction here, especially with the dummy variable accounting for which team a player was on (if you don't know what a dummy variable is, just try to hold on until the Methodology section where I do my best to explain it). Unfortunately this doesn’t seem to help address the largest issue with CF% which is how heavily dependent an individual player’s CF% is on their teammates. 

The next evolution of CF%Rel is CF%RelTM, which stands for Corsi For % Relative to Teammates and is calculated by subtracting a player's average teammate CF% (calculated by weighting a player's teammates' CF% without him by their TOI spent with him) from his observable CF%. This sounds good but runs into issues of collinearity. Collinearity occurs in hockey data because of the way coaches use their players. 

The most famous example is probably with the Sedin twins on Vancouver. During the 2014-2015 regular season, of all 1100 minutes of 5v5 ice-time each the twins played in that season, 92% of their minutes were played with each other. This can greatly boost their teammate relative stats because their CF% is being boosted by playing with there also talented brother and is being compared to when one is instead playing with their lesser teammates. When Daniel wasn’t playing with Henrik last season his CF% was 29% but that was only about 77 minutes of ice time (or 8% of his seasonal total), unfortunately CF%RelTM will then weight this 29% heavily because of how much Daniel and Henrik play together. These rare instances where one player plays without the other can have disproportionately large effects on both player’s ratings.

This issue also occurs between players who are never on the ice together such as Jarret Stoll and Anze Kopitar on Los Angeles. Stoll played 905 minutes at 5v5 last season, how many of those were with Anze Kopitar? 1 minute and 30 seconds or 0.16% of Stoll’s total ice-time. When we look at Jarret Stoll’s most common teammates (the ones who will be weighted highest by CF%RelTM) we find that when they aren’t playing with Stoll they are playing with Kopitar. This unfairly punishes Stoll for playing the same position on the same team as one of the top centres in the game. Kopitar boosts Stoll’s teammates CF% when Stoll is on the bench while never providing the same boost to Stoll’s own CF% simply because they are never on the ice together.

This finally brings me to explain Corsi Plus-Minus, how it is calculated and why I believe it is a better metric for isolating a player’s contributions independent of the strength of that player’s teammates.


Using the play-by-play data provided by NHL.com I was able to look at every even-strength shift that took place from 2007-2015. From this data we set up a multivariate regression where our dependent variable is rate at which a Corsi event took place, our independent variables are which players were on the ice during that shift and each shift is weighted by how long it was. This model does not account for a player's zone starts for a variety for reasons (see. here here) and it also does not account for the strength of a player's opponents (see. here). Simply, there has been a lot of research to show that both of those components might not be as relevant in helping determine a player's value. 

For those of you that might not be familiar with multivariate regressions here is a relatively simple and hopefully helpful example to help you grasp the concept. 

  • y - Dependent Variable - How well you will do on your test? Measured in points
  • x - Independent Variables
    • X1 - How long did you study for? Measured in hours
    • X2 - How long did you play video games? Measured in hours
    • X3 - Did you go to the study session? Yes/No?
      • This is an example of a dummy variable 
  • B - Coefficients - The value of each independent variable 

So if you had a sheet of data filled out with how well everyone did on their test (dependent variable) along with the information on the 3 independent variables and then ran that data as a multivariate regression you would get something that looks like this (reminder I made all these numbers up): 

y = 75 + 2X- 3X+ 6X3

For the purposes of my methodology, the regressions coefficients are what we will be focusing on. Looking at coefficient β1, for every extra hour you studied (holding all other independent variables the equal) you can expect to score 2 points better on the test. Looking at the dummy variable, X3, it can either be a 1 (yes you did go to the study session) or 0 (no you did not go to the study session). If you go to the study session (holding all other independent variables the equal), you can expect to do 6 points better on your test.

Now back to how this relates to my methodology. Picture every player as a dummy variable in our data, that player was either on the ice during a shift or they weren’t. Running our regression will then give us coefficients that tell us (holding all other players equal) how much of an impact Player X has on his team’s CF%, a value I have coined Corsi Plus-Minus (CPM). Two of these regressions are run for each season, one for offence and one for defence. 

However, due to the collinearity nature of our data, as was touched upon earlier, we would be better served to use a ridge regression (also known as Tikhonov Regularization) to help account for this collinearity. This method adds a penalty factor to the regression for results being far away from the mean. This penalty factor, called lambda, is chosen based on a 10-fold cross-validation. This helps remove a lot of the noise accompanied with such a process. Players falling in the bottom 25% of the league’s playing time (measured by games played instead of TOI to not bias against forwards) are group together and treated as a single player to help reduce volatility in the results that would be caused by their extreme values. While randomness can still have an effect, the damage is less so due to this regularization. 

Repeatability and Predictiveness 

I ran some basic correlation tests to see how repetitive of a skill Corsi Plus-Minus (CPM), Offence CPM (OCPM) and Defence CPM (DCPM) is from year-to-year, players must have played at least 20 games in back-to-back years to be included in the correlation. I compared CPM to the other metrics (CF%RelTM and dCorsi) I mentioned earlier just as a means of reference. A correlation (Pearson R) rangers from -1 to 1. The closer to either -1 or 1 the stronger the relationship is, the closer to 0 the weaker the relationship.

I also ran some correlations to see how well CPM does at prediction future GF%. Reminder, negative correlations are a good thing for DCPM/dCA, so focus on the numerical values and not necessarily positive or minus signs.

  • OCPM -> GF/60
  • DCPM -> GA/60
  • CPM -> GF%

Reliability vs. Validity 

As I said above, having all 3 different metrics here is strictly for comparative purposes. Just because CF%TMRel is higher in most categories doesn't automatically qualify it as a better metric. I will summarize key parts from this Columbia article with regards to reliability and validity. 
“Reliability refers to a condition where a measurement process yields consistent scores (given an unchanged measured phenomenon) over repeat measurements.”  

This is a measure that quantifies how much random fluctuations can interfere with getting consistent results. As we have seen above, the collinearity of CPM samples decreases CPM's reliability. Compared to single-year With-Or-Without-You stats, CPM seems to be quite reliable.
“Validity refers to the extent we are measuring what we hope to measure (and what we think we are measuring).”  

CPM is completely valid, because it is directly measuring the result. Stats such as CF%, CF%Rel and CF%TMRel are not as valid, because they measure a proxy rather than the subject directly. So even while they may seem reliable and predictive they are not hitting the target of determining individual contributions.

Final Notes

Corsi Plus-Minus is just the next step in accurately determining a player's true value. There are many different versions of this methodology that could still be applied instead of Corsi, including goals, shots on net, Fenwick and xG. There is also a Bayesian version where instead of assuming every player starts the season with a rating of zero, we can tell the model what rating a player had the prior season so that it can more accurately estimate that player's true value. This version wouldn't help describe what happened during a given season quite so accurately but it could help provide an even great idea of a player's value. I have looked pretty heavily into this version of CPM and hopefully might release it shortly. In a follow-up post I hope to explore further correlations (ex. Y -> Y+3) as well as how CPM relates to time-on-ice and salary. 

The data is posted below in both as a spreadsheet and in the form of some Tableau visualizations. I also have noticed an error with the Comparison tab in my original Tableau that I am unable (for some  unknown reason) to update, so I created a new Tableau (posted below the spreadsheet) that now includes player salary data.
  • OCPM/DCPM/CPM are rate stats (per 60 minutes of ice-time)
  • The Impact version of these stats apply a player's ice-time to determine their actual impact in a given season.

Thanks to C. Tompkins for helping me with the Tableaus, as well as War-On-Ice and Puckalytics for the data. If you are interested in reading about topics like this in further depth, I will direct you to previous work done by Brian McDonald (here, here & here) that was an instrumental guide in my work.

Please let me know if you have any thoughts, questions, concerns or suggestions. You can comment below or reach me via email me here: DTMAboutHeart@gmail.com or via Twitter here: @DTMAboutHeart  

Monday, 22 June 2015

Clustering NHL Forwards (using k-means)

How do you classify hockey players? Many would argue to go by the classical six positions (C, LW, RW, LD, RD, G) while some would argue for a rover (see picture above). I suggest a different distinction. Obviously, goalies are their own identity so they're excluded from this analysis. That leaves players which I will further breakdown into forwards and defence. Forwards and defence tend to have very distinct roles with a few exceptions (D.Byfuglien and B.Burns). In this post I am going to focus on forwards. It isn't easy to decide just which position a forward plays, don't bother asking the PHWA (see. the Ovechkin debacle) because they obviously can't tell. NHL.com is no help either since many of their positional declarations are hilarious out of place (ex. Zetterberg is listed as a LW despite taking over 1000 face-offs last season which places him 48th in the entire NHL). Then there is there is the issue of 1st/2nd/3rd/4th line. These roles are usually overstated by most media types and then there is a designation problem. If a player preforms like a 1st liner but his coach players him on the 2nd line? What really are they? I know, deep stuff. Long story short, breaking players down into categories is easier said than done.

K-Means Clustering

Therefore, I set out to with a fun exercise to reclassify forwards based on their playing characteristics. I used k-means clustering to break the players down into 8 categories based on these characteristics. I want to stress that these measurements are meant to reflect a player's playing style not how well or poorly they preformed. The chart below shows the average of each measurement broken down by cluster. I arbitrarily named the clusters myself, you shouldn't read too much into those. Come up with your own if you want. (You should click on that picture if you want to look at the cluster characteristics more carefully.)

Here is a random sample of ten players and which cluster they belong to. Please don't get too upset if you don't like a certain player's cluster. Remember these clusters group players by "playing style" not skill level. 

Cluster Features

Below are some box-and-whisker plots which breakdown the clusters by Corsi%, Age, TOI/GM  and AAV. Here is a quick run through of how to read a box-and-whisker plot:
  • The big solid line going down the whole graph is the mean value for the whole same. Example: The mean Corsi% for all forwards is 50%.
  • Within each box plot is another solid line that marks the median value for that cluster (the middle value of that cluster). Median not the mean.
  • The box itself encompasses the upper and lower quartiles of values, from the 25% to 75% percentile. 
  • The whiskers mark the top and bottom 25%, excluding outliers. 
  • Dots denote outliers.


  • All-Around, seems like the group a player would want to be in but it still encompasses a range of players from Sidney Crosby to Manny Malhotra.
  • "Safe" Depth is  labelled as such due to them being populated of lower end players (see. AAV), yet their Corsi compares favourably when compared to the Depth cluster of players.
  • High Impact players, do a bit of everything including areas that don't involves scoring ex. draw more penalties than they take while dishing more hits than they receive.
  • Power Forwards,  are big guys (yes, I subjectively looked at the cluster of players for 30 seconds and thought I saw a bunch of perceived power forwards) who prefer to pass more than they shoot but also take more penalties than they draw, probably due to a lack of foot speed.
  • Depth players, while few in numbers (only 13) they dish hits like crazy yet clearly trail in the Corsi%, TOI/GM and AAV categories. 
  • Passers, create a lot of opportunities for their teammates and are wizards at taking the puck away from their opponents more than they give it up.
  • Depth scorers, these are typically young players who have been held down in the lineup by their coach yet can really shoot the lights out.
  • Scoring Wingers, are very similar do depth scorers yet have been given a larger playing opportunity.
  • I would love to do the same exercise for defenceman but their doesn't seem to be enough distinction using my current attribute metrics. Maybe I will discover a better way to classify them in the future, who knows.
Please let me know if you have any thoughts or questions. You can comment below or reach me via email me here: DTMAboutHeart@gmail.com or via Twitter here: @DTMAboutHeart

Wednesday, 3 June 2015

Updated xSV% - Save Percentage Accounting for Shot Quality

Goalies are voodoo. That should probably be added as the 11th Law of Hockey Analytics. Goaltending analysis is currently one of the most lacking subjects within hockey analytics. Great strides have been made however, with 5v5 SV%, AdjustedSV% and High Danger SV%. A few months back I revealed a statistic I referred to as xSV%. You can click there to read the article but, more or less, I calculated a goaltender's Expected SV% based on the quality of shooter for each shot faced based on a rolling average of the shooter's individual shooting percentage. xSV% is a goalie's actual SV% minus their ExpSV%. A higher (positive) xSV% is good while a lower (negative) xSV% is bad. I have thought more about that specific methodology since posting that article and have eventually decided that with some substantial changes I could greatly improve upon xSV% . Based on factors in my ExpG model combined with regressed shooting percentage for each shooter (same mindset but different process as the original xSV%) I basically started from scratch to develop this latest rendition of xSV%.


The basis of a goalie's Expected SV% comes from the same model I used in my latest ExpG model. Here is a quick breakdown of the different variables and a brief explanation of why they are included in the model:
  • Adjusted Distance
    • The farther a shot is taken from the lower likelihood it has of resulting in a goal 
  • Type of Shot
    • Snap/Slap/Backhand/Wraparound/etc...
    • Different types of shots have different probabilities of resulting in goals
  • Rebound - Yes/No?
    • A rebound is defined here as a shot taking place less than 4 seconds after a previous shot
    • Rebounds are more likely to result in a goal than non-rebounds
  • Score Situation
    • Up a goal/down a goal/tied/etc…
    • It has been proven that Sh% rises when teams are trailing and vice versa
    • This adjustment, while only slight, helps to account for a variety of other aspects that we are currently unable to quantify yet have an impact goal scoring 
  • Rush Shot - Yes/No?
    • Shots coming off the rush are more likely to result in a goal than non-rush shots
Now that we have the structure of our ExpSV% we need to add shooter talent into the mix since the model currently assumes league average shooting talent for each shot, which we know is not the case in reality. Generally, a shot from Sidney Crosby is more likely to result in a goal than a shot from George Parros. So I wanted to make a multiplier for each player in each season to get a best estimation of their personal effect on each shot's probability of resulting in a goal.

Using the Kuder-Richardson Formula 21 (KR-21) I was able to find that 5-on-5 Sh% stabilizes for forwards at about 375 shots while 5-on-5 Sh% for defenceman begins to really stabilizes around 275 shots. Therefore, for each season I added these shots (375 for forwards, 275 for defenceman) to a players total shots. I also added a certain amount of goals calculated as the added shots (375 or 275) multiplied by league average Sh% (forwards and defence had different league average Sh%) for that season. This would then allow me to calculate regressed Sh% based on these new shots and goals totals. I then divided rSh% by the league average Sh% (forwards and defence had different league averages) to give a Shot Multiplier. Then multiply this Shot Multiplier for each shot they took in that season. In case you didn't quite follow that rough explanation, here is an example of how this process played out for Steven Stamkos' 2011-2012 season, the highest rSh% season since 2007-2008:


The big issue with goalie metrics has always been how well they actually represent true ability. Sample size is a frequent issue with goaltenders and it has been shown countless times that the only way to truly get a good idea of a goaltender's ability is to take very large shot samples. These samples typically take multiple years to accumulate. Looking at year-to-year correlations, so far, it seems as though xSV% is on par with 5v5 SV%. The interesting difference I found, with the vital help of @MannyElk, is shown in the graph below. To paraphrase his earlier work, the experiment was done by drawing random samples of n games for each 40+ GP goalie season since 2007 and each sample was compared with the goalie's xSV% over the whole season. Essentially, the graph below shows that xSV% will show us more signal than noise sooner than 5v5 SV%.
**Disclaimer** Manny and I are not 100% sure of the results here so if anyone has suggestions please reach out. 


Quick review of the stats below:

  • xSV% = Actual SV% - Expected SV%
  • dGA (Goals Prevented Above Expectation) = Expected Goals Against - Actual Goals Against
Below is a wonderful Tableau visualization created by @Null_HHockey, as well as a spreadsheet with all of the relevant information. Once again big thanks to the guys at War-On-Ice for all their help (and data). Everything below will also be stored full time on a separate xSV% page. Enjoy!

Monday, 25 May 2015

Updated NHL Expected Goals Model

Here is the latest rendition of my Expected Goals model. If you haven't read the original post you probably should read it here before continuing.The only substantial change from the previous version is that this one now includes rush shots. As it has been previously shown that rush shots just by the very fact that they are rush shots result in a higher shooting percentage. My model currently only accounts for 5-on-5 situations and includes a total of five factors:
  • Adjusted Distance
    • The farther a shot the lower likelihood it results in a goal 
  • Type of Shot
    • Snap/Slap/Backhand/Wraparound/etc...
  • Rebound - Yes/No?
    • A rebound is defined as a shot taking place less than 4
  • Score Situation
    • Up a goal/down a goal/tied/etc…
  • Rush Shot - Yes/No?
    • Rush shots have a higher shooting percentage


Same sort of graphs below as in the previous post, along with the correlations for each. The ExpGF correlation jumped slightly from 0.58 to 0.61 yet the ExpGA correlation stayed consistent at 0.60. That isn't to say adding rush shots didn't effect the model. There is definitely some difference both positive and negative on certain teams, typically within the 10 goal range.


I still plan on adding some aspect of regressed shooter and goaltender talent somehow into the model. I am close to releasing ExpG at the player level, hopefully within the next week. Around the time I am able to incorporate goaltender talent into the model I should also be able to update my xSV% with the shot quality aspects of this model.

Expected Goals

Here are the updated results below. Note that, dGF/dGA/dGF%, are calculated as actual minus expected. Therefore, a positive dGF means that a team scored more goals than the model predicted they would. A positive dGA means that a allowed more goals against than the model would have predicted. I will update this spreadsheet in its own tab at the top of this site too. Please let me know any questions or feedback you might have. Enjoy!

Thursday, 21 May 2015

NHL Expected Goals Model

Did anyone ever consider shot quality? 

UPDATE: This model has since been improved upon and shown here. This post still provides good background on the basics of the model.

Shot quality and possession metrics have always been somewhat a point of contention. Expected Goals (ExpG) helps to combine these two facets in hopes of providing better information about the game. Expected Goals are not a novel concept, ones have been presented previously by Brian Macdonald for hockey and the original motivation for my study by Michael Caley's soccer version. I hope to lay out my ExpG model in a way that makes hockey sense, where everyone can understand why each factor was added into the model. The model works by assigning a value to each shot taken over the course of a season based on the model's predicted probability of that shot resulting in a goal. To calculate a team's final ExpG all you have to do is sum up all of these probabilities and there you have it. First I will breakdown the methodology that goes into this model. If you don't care and just want to see the results skip down to the Expected Goals section or check out the Expected Goals tab above.


My model uses a logistic regression to arrive at each goal probability. Basically, it uses a bunch of independent variables to produce the odds of binary outcome occurring, in our case, yes a goal was scored or no a goal wasn't scored. I reran the logistic regression for each season instead of using one big logistic regression. So far my model only accounts for 5-on-5 situations. This helps to account for minor changes in style of league play yet the regression coefficients didn't actually change much year-to-year. Here are the factors taken into account by the model:
  • Adjusted Distance
    • The farther a shot the lower likelihood it results in a goal 
  • Type of Shot
    • Snap/Slap/Backhand/Wraparound/etc...
  • Rebound - Yes/No?
    • A rebound is defined as a shot taking place less than 4
  • Score Situation
    • Up a goal/down a goal/tied/etc…


In the two graphs below you can see how well ExpG, both offensively and defensively, correlates with actual results. Each point represents one team from one season, except 2012-2013 was removed due to the lockout. 

There will always be some outliers in a given season but I think the model goes a relatively good job. The chart below shows that ExpG comes out on top when compared to Corsi and Scoring Chances in terms of correlation to real goals for and against in a given season.

Goals For Goals Against
ExpG 0.58 0.6
Corsi 0.493 0.57
Scoring Chances 0.53 0.562

Future Work

In the next coming weeks I will be focusing my efforts on two different aspects of this model. Firstly, I will investigate how well it predicts future goals, from one season to the next as well as something similar to Micah Blake McCurdy did with score-adjusted Corsi. Secondly, I will be looking at other factors to add into the model. I plan on adding rush shots as a factor, though the current state of my data will require some tweaking before I can do that. I also plan on exploring the effects of incorporating shooter talent and goaltender talent. I also plan on releasing ExpG at the player level and use aspects of this model to better xSV%. 

Expected Goals

I just wanted to thank War-On-Ice and Sam Ventura for the data used in this project. Finally, here are the results below. Note that, dGF/dGA/dGF%, are calculated as actual minus expected. I will give this spreadsheet its own tab at the top of this site too. Please let me know any questions or feedback you might have. Enjoy!

Tuesday, 31 March 2015

xSV% - Team Data

After my original post looking at xSV% exclusively at the individual goalie level I received a few requests to look at the same data but at the team level. Simple enough and presented below is xSV% team data from 2002-2014. If you didn't read my original post on xSV% you can do so here, but I am also going to follow up and reiterate what exactly xSV% entails.

What xSV% is:

  • Expected Save Percentage based on a 110 game moving average of the opposing shooter at the time of each shot faced by a goalie
  • Better players typically have a higher shooting percentage, therefore if a team limits their opponent's best players from shooting the puck, they will raise their own ExpSV%
  • Forwards typically have a higher shooting percentage, if a team can limit the amount of shots taken by an opposing teams forwards and instead force them to rely on their defenceman to generate shots, they will raise their own ExpSV%
  • ExpSV% is highly influenced by era. As shown in the graph below representing the league average Expected Save Percentage for each season with the lockout lost season shown by the red line, scoring has been down in recent years since the lockout.
  • This era influence is the big reason why the 110 game moving average is necessary. Simply using a single season worth of data can sometimes not be enough. Likewise, using a player's career average shooting percentage can provide misleading results.
    • For example, the ever great Jaromir Jagr has a career shooting percentage of 13.7% that is heavily influenced by his earlier playing days. Jagr hasn't had a season of shooting that efficiently since 2005-2006. Therefore a rolling average helps more accurately depict his current conversion ability. 

What xSV% is not:

  • An all encompassing, all-knowing stat that gives the exact Expected Save Percentage for each team
  • A definitive ranking of how well teams manage to play defence


Below is all the team level data. Play around with it and please send me any feedback/questions you might have. Enjoy!