Updated “Games Behind” NHL Standings

Here is an updated look at how the divisional standings would shake out if the NHL were to adopt the Games Behind standings system (popular in the NBA and MLB):

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How to calculate games behind? Look here: http://en.wikipedia.org/wiki/Games_behind

This method is good because it takes into consideration how many games played each team has (see Ottawa’s jump above Boston because they have two less games played). Also, it doesn’t reward teams for losing in overtime which in my opinion is ludicrous to do so.

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Updated “Games Behind” NHL Standings

My Take on the NHL’s Flawed Point System

Many people have expressed their opinions about the point system the NHL currently follows, and there have been a few that have taken a look at how it has effected teams and the league as a whole (see William Loewen’s post on Hockey Prospectus as the most recent). What I am going to do is look at a couple of point systems of my own and compare them to what we are seeing in the league right now.

First, the reasoning behind why I think the current system is flawed. Well, have you ever heard of being rewarded when you lose? Specifically in sports? Well, the NHL basically hands out free points to teams that can end regulation time in a tie. By rewarding the same 2 points to a winner in overtime as the 2 points in regulation, you are holding the value of a win the same. However, by giving a point for a loss after regulation time (overtime or a shootout), you are increasing the total point returns by 50% and therefore teams have the incentive to force the game into overtime.

When the game is close in the latter stages, teams will be provoked to play more defensively because of this “loser’s point.” The team’s train of thought goes like this:

“Well, if we go to overtime we are guaranteed to get at least 1 point. And even if we win there we are going to get the same amount of points as we do if we win after the third period. Now, let’s not take any risks so we guarantee that single point with the chance to get another.” 

By increasing the returns of a game when it goes to overtime, teams are looking to make the decision that will be best for them. By weighing an overtime win the same as a regulation win, the incentive is to go into overtime rather than end the game in regulation.

The NHL is the only league out of the “Big 4” that uses a point system, and is the only one that recognizes a loss, in any sense, as a positive event as they reward points to overtime losses. What I am going to look at is two alternative point systems that are seen enacted in professional sport today that will help the nature of the game. For both approaches, the standings I am using are all games up to and including the games played on February 18, 2015.

The “Games Behind” Approach

This approach is widely known in the NBA and the MLB, and is often referred to as the number of “Games Behind” or the number of “Games Back.” This approach is good for a league that had a large amount of games (NBA with 82 and MLB with 162) as it puts into perspective how many games a team needs to win in order to catch teams above them. With teams often times have played different number of total games at any time throughout the season (which does not really happen in the NFL), this method is very useful because it allocates half games.  The number of games behind is the number of games the trailing team has to win and the number of games the leading team has to lose for them to be equal.

For example, if Los Angeles is 4 GB of Nashville, it indicates that if the Kings win their next 4 games and the Predators lose their next 4 games, they will be tied. It also represents how many more games the Kings need to win throughout the rest of the year in order to end up tied with the Predators at season-end.

We have to assume that the NHL will use the same playoff format in this alternative. For wins, I simply took all wins a team has for the entire season thus far. For losses, I combined all losses (regulation losses and overtime/shootout losses) to make a single “Loss” number. From here, I then used the “Games Behind” approach to look at the standings.

Eastern Conference

AtlanticGB

metroGB

EastWCGB

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Western Conference

CentralGB

PacificGB

WestWCGB

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I included the old rank (under the current standings) for each division as well to give some perspective about how the standings change. What this approach does is that it takes only wins. It weighs all losses equal, no matter if it was in overtime or not. The MLB or NBA doesn’t matter if you lost in 17 innings or in triple-overtime; a loss is a loss.

Looking at the NHL and team’s records as of now, the new method doesn’t have much effect on the divisional standings. In the East, only the Metro is effected (5th and 6th switch), while in the West, the Central remains unchanged but the Pacific sees some movement. Calgary drops to 3rd, while Los Angeles falls to 5th, and both the Canucks and Sharks are beneficiaries.

The changes in this method are more seen in the Wild Card race. While I do not have the current Wild Card positions right there, the order and rank of Wild Card positions under the current format is seen in the images for the next method (below). What you see when it comes to changes is that Columbus jumps to one spot out of the Wild Card, compared to being 4 spots out currently. In the West, Minnesota leapfrogs both Los Angeles and San Jose to take over the final playoff spot.

The differences you see here are a result of two things:

  1. The teams play games at a different rate, and therefore teams can be behind in points but still “on-pace” to jump ahead of leading teams. By assigning 0.5 games to an unplayed game, we are able to properly manage this difference.
  2. Teams are getting unnecessary points as a result of going into overtime and shootouts. Los Angeles, for example, has an overall W/L record of 27-30. There number of OTL’s, where they get 1 point currently, has them getting 12(!) extra points as a result. This forces them to drop and are then not rewarding for losing.

This method is one that should be considered in this time, as the league does not want to continue distributing useless points to teams that have to ability to simply sit back in tied games and force overtime, just to lose.

The “3 Point Win” System

This method is fairly prevalent in European Soccer Leagues, and can act as a way to reverse the incentive to play for a tie (or overtime). Under this system, the following point allocations are enacted:

  • Regulation Win: 3 points
  • Overtime Win: 2 points
  • Overtime Loss: 1 point
  • Regulation Loss: 0 points

We want to assume that the NHL wants stay with the overtime and shootout format they have, therefore not resulting in any tied results and every game has a winner (this is a difference from the system in European Football). Here is the results of this new proposed point system compared to the current point system:

Eastern Conference

Screen Shot 2015-02-19 at 8.42.12 PM

Western Conference

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RegW: Regulation Wins, RegL: Regulation Losses, OTW: Overtime/Shootout Wins, OTL: Overtime/Shootout Losses

For each chart there, the tables on the left are under the current point system (total points labeled “OldP”), and the tables on the right are under the “3 Point System” (total points labeled “NewP”). The biggest part of this system over the current is that it keeps the total point allocation equal no matter what the result of the game. If the game is ended in regulation, 3 points are given. If the game goes to overtime, still only 3 points are allocated. The returns of going to overtime are no longer increased and therefore teams that can find a way to win in overtime are rewarded.

One large differences you can see in the Western Conference standings is Calgary falling way down the ladder. Under the current point system, they are 2nd place in the Pacific Division. After adjusting for regulation and overtime wins and losses, they fall to two spots out of the playoffs and 5th in the Pacific. As a result, Los Angeles jumps into 3rd in the Pacific instead of being in the final Wild Card spot. The Central Division remains unchanged, but in general, the Wild Card race heats up and shifts quite a bit with this adjustment.

In the East, Tampa Bay is rewarded for their ability to get wins in regulation, jumping to the top of their division and the conference. Other than that, the divisional standings remain the same. The Wild Card standings here remain unchanged, surprisingly. This could possibly be because the majority of Eastern Conference teams head to overtime and shootouts around the same amount. For the top four teams in the Wild Card race, they all have between 16 and 18 games that went to overtime. This is only a swing of about 2 points, compared to Tampa Bay who has only gone to overtime or a shootout 10 times.

The main issue with this system compared to the current system is that teams will end up more dispersed in terms of point differences. I’m guessing that if the same adjustments were made for previous years, the playoff races may not have been so close because of the larger number of points earned (see that Nashville would have already been at 112 points with this approach). Another issue would be the NHL record books. Changing the point return of a game to 3 points, always, would force NHL team point records to be eliminated because of the large difference in total allocation.

It is hard to truly measure the difference that these systems would make on the NHL and its game. By changing the point system, you change the incentives for teams. However, teams are currently playing under the incentives that I discussed at the beginning of this piece. If you change the return of every game to 3 points, and have a regulation win worth 3 points, I am sure that teams would attempt to win in regulation more often and enforce more offensive tactics when it is late in the third period of a tied game. Similarly, if you adopt the “Games Behind” approach, teams would most likely attempt to win at all costs, since a loss is worth nothing no matter how it happens. Teams would no longer be content with an overtime loss because it gets them a point.

Basically, losing shouldn’t be rewarded in any sport. The NHL should actively take steps to adjust their point system to fix this flaw. My two approaches are just a couple of many different options that could fix the underlying problem.

My Take on the NHL’s Flawed Point System

Calder Trophy Race Analysis: dCorsi and Usage Comparison

Recently I contributed a piece to Hockey Prospectus where I took a look at the Calder Trophy race from an analytical standpoint and tried to point out who is standing out using advanced stats rather than traditional stats. Do me a solid and take a look at the article here:

http://www.hockeyprospectus.com/a-statistical-analysis-of-the-calder-trophy-race/

In short, I came to the conclusion that Filip Forsberg is the front-runner for the Calder thus far, but the strong rookie d-men, Aaron Ekblad and John Klingberg, are making cases for themselves as well. One thing I didn’t really get to do in that piece is look at all of the candidates usage numbers and how they are producing compared to what they are expected to do. A lot of the time, if not all the time, the Calder goes to the rookie who gets the most points or is the most productive to their team. However, rookies rarely play a large role with big minutes unless you are a top-5 pick or have developed through junior and multiple years in the AHL. For this reason, I want to look at the rookies of 2014-2015 and their dCorsi numbers. For those of you that aren’t aware of what dCorsi is, you can read a basic introduction to it here and you can also take a look at a more mathematical description of the methods and procedures in its development and deployment here.

I like what Steve Burtch (@StephenBurtch) has done with this method. It allows for us to look at not only how players are being deployed (looking at their Expected Corsi numbers), but it allows us to compare players using their delta numbers, which are the differences between their expected and observed corsi for and against numbers. What it lets us do is compare players across different usages in order to see which players are actually under- or over-performing. A high dCorsi shows that a player is contributed more to the possession game than he is expected, and vice-versa for a low (negative) dCorsi number. The dCorsiImpact number simply shows the cumulative sum of “dCorsi” a player contributes, based on their total ice time for the season. Now, on to the comparison.

The players I am looking at were determined in my HP piece, and if you are wondering why I selected the players I did I would suggest you read that piece to gain that understanding. The dCorsi numbers are available on war-on-ice.com, under the “Labs” tab, and it allows you to look at all players or a player across their career (since 2005-2006). First, lets take a look at the dCorsi60 the 8 rookies in my analysis:

dcorsi60
Rookie dCorsi per 60 minutes in 2014-2015. Numbers extracted from war-on-ice.com on February 17, 2015.

Looking at this, my previous Calder prediction may be a bit off. Forsberg is not doing so well in terms of his dCorsi numbers, as he is posting a corsi number that is 0.65 below what he is expected to produce. This may partially be a result of his tough minutes and increased ice time compared to the other rookies.

Anders Lee and Mark Stone are what stand out here. Lee has been fairly good production wise this season, finding his way onto a line with superstar John Tavares. Stone is making a big impact as well on the Senators, but I did not expect the usage numbers to be as high as they are here.

Ekblad is putting up a fairly low number in the dCorsi category, but this is probably because he is assuming top pairing minutes on the Panthers this year.Klingberg, who has outstanding over the past month or so, is living up to his big time minutes and is pulling ahead of Ekblad in this comparison. These are both highly talented defensemen who will be valuable pieces in the upcoming years.

The dCorsi number is only one way that Burtch has allowed us to view the usage of individual players. The dCorsiImpact number is very helpful when trying to quantify the possession impact of a player over an entire season or over a span of games. Here is a visual of the 8 rookies and their dCorsiImpact (which follows mainly the same pattern as the dCorsi60 number because it is that number multiplied by the number of 60 minute time periods they have played):

dcorsiimpact
Total dCorsiImpact for the 8 rookies in my analysis. All data extracted from war-on-ice.com on February 17, 2015.

The smart and clever guys at WarOnIce have split this number into two separate categories: dCFImpact and dCAImpact. This allows us to see if a player is getting that big impact number as a result of strong offensive play or as a product of good defensive possession work. The following scatter plot shows the dCFImpact and dCAImpact of the rookies thus far this season.

dCFimpactdCAimpact
dCFImpact vs. dCAImpact for the 8 rookies in my analysis. All data extracted from war-on-ice.com on February 17, 2015.

A positive dCFImpact number is good, and a negative dCAImpact number is good. The best players, or most well-rounded, should fall in the lower-right quadrant of the graph. This would mean they are driving both more possession for than expected and less possession against than expected. But really what this plot shows is where their dCorsiImpact number is being derived from.

Once again, Anders Lee and Mark Stone are looking good here, while my previous prediction (Filip Forsberg) isn’t faring to well. While he is good offensively, he does not meet his expected CA60 number and this effects his dCorsi60 and dCorsiImpact numbers accordingly. Lee is able to drive huge offensive numbers compared to the others, with a dCFImpact of 75.41. This is over twice as large as any other rookie in this group. Stone is strong defensively, posting a stronger dCAImpact number than Lee which allows him to gain some ground back in terms of total dCorsiImpact. Stone is a strong defensive force on the Sens this year, and is clearly performing the best in allowing possession against out of all forwards in this group.

But even with the strong numbers from these forwards, I am still so impressed with the numbers that John Klingberg is posting. In my initial analysis at HP, I was really close to pegging him as my favourite. After this finding, it is clear: my [revised] selection for the Calder Trophy in 2014-2015 goes to John Klingberg. After more research and analysis, I had to adjust my prediction. This guy is getting big minutes on a Stars squad that is looking to force its way into the playoffs. Klingberg will have to be a driving force behind this run if they want to have a chance, and if he keeps up his production thus far, their chances aren’t so bad. With the injury to Seguin, they may have to get some luck elsewhere, but I am confident this guy will be able to contribute to the team and help them get where they want to go.

Klingberg’s 5.36 dCorsi60 number is impressive, but with his big minutes, it drives his dCorsiImpact more than other skaters here. His total impact number isn’t driven by just one component (like Anders Lee’s); it is composed of a strong dCFImpact and a strong dCAImpact. This rookie d-man is driving and preventing possession better than expected, and this definitely warrants him as a candidate for the Calder Trophy.

Calder Trophy Race Analysis: dCorsi and Usage Comparison

Scoring Chances: Examining Team Shot Quality and Who is Doing It Best

For awhile now, a very controversial discussion has been circulating amongst the hockey analytics community regarding shot quality and how it can be effectively measured. As any hockey fan knows, there are low quality shots, high quality shots, and many different “categories” in between. However, it is hard to measure these over time and effectively measure which teams or players are getting the best shots or the highest amount of shots in “quality” areas.

Recently, war-on-ice.com has added scoring chances to their data on their site, and provided a fairly clear definition of how they classify a scoring chance, which can be seen here:

War On Ice Blog: Scoring Chances Defined

So for this post, I am looking at team play and which teams have been able to get quality shots so far in the 2014-2015 season. Using this definition (which has multiple dimensions and is very well explained), I can take data provided and compare teams to see if scoring chances contribute to team success.

The following plot shows team scoring chances for vs. team scoring chances against per 60 minutes of even strength 5v5 play. I have added labels for teams who are succeeding in the standings, and any of those that are significantly different from the majority of the group that is clustered in a fairly small range:

scf60sca60

Teams like Tampa Bay, Chicago, and the New York Islanders are evidently benefiting from generating a high number of scoring chances for while limiting the number of scoring chances against them. This has led these teams to the top of their divisions and allowed them to excel. Something that I specifically like are the numbers generated by Nashville and Florida, who do not necessarily generate a large number of scoring chances per 60 minutes but have put together good defensive systems that keep oppositional chances to a minimum.

Putting these numbers into a greater context is important, which is why I have used war-on-ice.com to compare the teams of this year to teams of past years. I examined teams from the 2005-2006 season to the present season, looking to see how well teams like the Islanders and the Stars are doing.

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Shown here are the top 15 teams since the 2005-2006 season in SCF60 at 5v5. Both the Islanders and Stars of this season are included in this group, and actually both rank in the top 11 of the list. This is impressive; basically, these teams are generating as many scoring chances as some of the most powerful offensive teams that have played the past few seasons. Washington ranks 1st and 2nd, which makes sense because Ovechkin tends to shoot from anywhere and gets to most shots in the league consistently. These two seasons would have been when he was really showing his offensive prowess. Included in this list are some pretty good teams of the past 10 years, and the Islanders and Stars should begin to get consideration for their dynamic offensive systems.

What I really want to look at now is how the bad teams are doing relative to past years; that is, the teams that struggle in the SCA60 category. Here is the top (or bottom) 15 teams in SCA60 since 2005-2006:

Screen Shot 2015-01-04 at 7.36.54 PM

This table shows two things: the Toronto Maple Leafs are bad, and the Buffalo Sabres are worse. Currently, the 2014-2015 Maple Leafs are allowing the most scoring chances per 60 minutes of even strength play since the beginning of the analytics era. What is even more astounding is that the Leafs of last season are second in this category! The defensive system under Randy Carlyle is just atrocious and they struggle to limit quality scoring chances against. This statistic can be linked to poor play; last season, the Leafs fell apart and missed the playoffs, and now are currently on a quick decline out of the Eastern Conference playoff race.

While Toronto may allow a substantial amount of scoring chances against, the 2014-2015 Buffalo Sabres are allowing just 2.1 scoring chances per 60 minutes less than these Leafs while only generating 19.9 scoring chances for! This comes out to a SCF% of 37.7%; this means that the Sabres only get 37.7% of scoring chances during 5v5 play. This is horrible; teams need to be able to generate scoring chances in order to score (you can score on low quality, “non-scoring chance” shots, but it is highly unlikely and impossible on a consistent basis). I need not say more; the Sabres are bad and are doing all they can to construct a rebuild.

I want to examine one final thing: how are the good defensive teams doing compared to past years? Here is the visual for this one:

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This list contains some of the best defensive teams of the past 10 years. Sorted by SCA60, these are the top 15 teams in limiting scoring chances against. Here you have the strong defensive New Jersey Devils (the “trap”), the always strong in the Detroit Red Wings (two-way players throughout the lineup), and two teams from 2014-2015: the Florida Panthers and the Nashville Predators.

So far this season, the Florida Panthers have been surprisingly strong. Although they have struggled to get wins, they have been fundamentally sound. This is evident by their SCA60 number; so far this season, their strong and sound defensive system has put them in 6th in the past 10 years. I like the makeup of this Panthers lineup, as they have combined a good group of young, talented players (Bjugstad, Ekblad, Huberdeau, Trocheck, Barkov) with the right veterans (Jokinen, Boyes, Bergenheim, Upshall, Campbell) to create a strong overall team that plays solid at both ends of the ice. I truly believe that the Panthers will make it into the playoffs this year, using a quality team system with everyone buying in as a group.

As I always mention, these numbers are simply one way to evaluate a team or a player, and many different statistics and viewpoints are needed to generate conclusions. For now, scoring chances are what we need to use to evaluate a team’s shot quality and how it is related to team success.

What I want to look at in the future is a weighted measure for scoring chances, that takes into account scoring chances for and against in all different situations (EV, PP, SH, etc.) and determines which teams are putting together a good overall system in terms of shot quality. Finding the right weights for PP, SH, and EV situations will lead to an accurate representation and will properly represent team shot quality for and against. This type of measure could explain a lot of different aspects of a team and could eventually lead to solving the shot quality issue (or at least I can hope it will).

Scoring Chances: Examining Team Shot Quality and Who is Doing It Best

Minnesota Wild: Dissecting Their Goaltending Catastrophe

So far through the 2014-2015 season, the Minnesota Wild have been struggling to move up the Western Conference standings. As many have noted, this is because of their atrocious goaltending thus far. As the 2014 calendar year comes to a close, and over a third of the season has been played, I felt it was a good time to dive deeper into the Wild’s goaltending problems. Being a Wild supporter since my early childhood, I really wanted to look at a specific team in my analyses. So look for more Wild-based posts in the future, but for now, let me take a look at their current issue: goaltending.

Throughout the season, the Wild have been a very “analytics-saavy” team, posting surprisingly high possession numbers (Corsi and Fenwick) compared to their past seasons. The table here shows their Corsi number thus far at all manpower situations:

minnesotacorsi

For the remainder of the post, I am referring to numbers during all situations (EV, PP, SH, etc.), giving a larger pool of data to work with, while also revealing some possible special teams issues that can be looked at in the future. These numbers here show that the Wild have been a very good possession team thus far; top 5 in the league in fact. However, they are struggling to win games, and becoming Corsi’s biggest nightmare. Those who support Corsi will have to look elsewhere to determine why they are struggling so much. For me, I think it can be shown by a single number: PDO. A team’s PDO is the sum of their shooting percentage and their save percentage. Throughout the “analytics era,” it has been shown that the PDO often regresses towards 100 over the long-term and “high” (102 and greater) and “low” (less than 98) are unsustainable and often times can be attributed to puck luck.

PDO

The PDO for the Wild is awful. They rank in the bottom 5 in the league in PDO, but this is only one number and the underlying cause is not always so clear. A PDO number can be explained by a few different factors. One could be luck (or “unluck”), but that is not very measurable, for me anyways. Seeing that their shooting percentage is not too bad (14th ranked), there is one other explanation that seems reasonable here: bad goaltending. The Wild rank 27th in the league in save percentage in all situations, which is terrible if they want to compete in the West. What makes this situation even worse is that the Wild allow the fewest shots on goal in the league! This means that they are constantly outshooting their opponents and limiting shots against, yet are receiving bad goaltending in the last line of defence. Here are Minnesota’s shooting numbers for the season thus far (in all situations):

minnesotashots

By constantly outshooting opponents while getting terrible goaltending, the Wild are not as bad when it comes to goals for, ranking 16th in the league as they get 51.1% of goals. Now I know that straight up save percentage is not a perfect indicator of goalie performance or strength, especially in all situations, which is why measures of “shot quality” must be examined in order to get a better idea of how the goalies are truly performing.

The following chart compares the Wild’s save percentage to the league average based on shot distance. This data is broken down into four blocks, as seen. The Wild are below the league average in each of these categories. This means they allow more goals, on average, than the league from any spot on the ice. We knew that, based on their save percentage, but what is revealing here is that they come in at least 3 percentage points below the league average from the closer ranges (<10 feet, 10-20 feet, and 20-30 feet). The Wild goalies are struggling with shots of “higher” quality and this is definitely a telling point.

minnesotashotdistance

Some of these numbers can be a little skewed, as distance only measures one aspect of the shot. It does not necessarily consider the location relative to the net, and therefore may not be a totally dangerous shot. That is why scoring chances must be evaluated as well. However, the numbers for Minnesota do not get much better. Their save percentage on “scoring chance” shots, those in the area shown in the picture below, is over 4% lower than the league average as you can see. This goaltending problem just keeps take more turns for the worse, revealing that the Wild net-minders are simply not getting it done compared to the league average and what it takes to win (especially in the potent Western Conference).

Minnesota Wild Shots Against- Scoring Chances
Minnesota Wild Shots Against- Scoring Chances

scoringchances

However, what may be a good sign for the Wild is that they typically do not allow as many shots from the scoring chance area, or from closest proximity for that matter, than the league average. Looking in the scoring chance table above, 39.41% of all shots against Minnesota are scoring chances, while for the entire league that number equates to 44.43%. This is also present in the table below, where about 42% of shots against the Wild come from within 30 feet, while for the entire league about 47% of shots come from within this distance. For the Wild, as a team, this can be a boost as it shows that they are limiting the “high-quality” shots, forcing teams into lower quality shots that should come with an associated higher save percentage. However, their save percentage is not lower and this has shown to be a problem for the Wild thus far.

Looking at a goaltending situation can be tough; a low save percentage can come as a result of poor goaltending, a bad defensive system, or simply luck. Trying to decipher the numbers can only get you so far in your analysis, and watching the games will lead to a better understanding of what is going on. For the future, I may look at analyzing the Wild’s individual goalies and their numbers to try to determine the root of the problem. Another area to consider is the idea of shot quality. One method is never truly “perfect,” especially when looking at shot quality. I have recently learned this after reading Rob Vollman’s Hockey Abstract chapter on the matter; there are simply too many factors that go into shot quality, such as location, rebounds, manpower situation, and game score, that it is difficult to determine one “good” measure.”

Also, Hockey Analysis has looked at rush shots and their effect on save percentage over time. You can read their article here to look at how they define rush shots and possibilities about how to use this data. They also have more recent articles that apply the concept and look to compare numbers across teams and seasons.

Stats retrieved from war-on-ice.com, stats.hockeyanalysis.com, and the Super Shot Search feature on somekindofninja.com

Link

Where Are Teams Actually Driving “Good” Possession?

Lately I have been developing a keen interest in advanced hockey stats and how they can be used in practical situations. I have developed this blog to share my opinions and findings  and develop a place for everything to be together. For the most part, discussions here will be focused on using the stats already available and trying to make sense of them. There won’t be much of the hard mathematical modelling that some analysts use because, frankly, I don’t have the capabilities to do such analysis, seeing that I am an undergraduate Sport Management student at the current time. However, I do plan on developing this aspect and possibly adding it into some posts here.

Now to the numbers. What I have been looking at lately is the score-adjusted possession numbers that are being circulated out there. Usually I’m looking at the numbers on puckon.net, and now that hockeystats.ca has incorporated it, these numbers can be viewed on a game-by-game basis.

I really like how puckon.net determines and presents their numbers, so for this discussion I am using numbers from their site. The formula they use is based off of Eric Tulsky’s method, but tweaked just a little bit. This tweaking just using the current league averages, rather than a fixed number, along with the specific team’s TOI according to the situation instead of the league’s average TOI.

It has been shown previously (i.e. in Tulsky’s analysis) that score-adjusted measures have greater predictive validity than “tied” measures, and even better than the “close” measures that have been widely used. What I have been looking at lately is the score-adjusted numbers at puckon.net, and really trying to find how they are being derived/generated.

So what I have done is taken each teams CF% for each score situation (down 2+, down 1, tied, up 1, up 2+) and compared it to the league average for that score situation. It’s one thing to look at a team’s possession numbers when they are down on the scoreboard, but it really means nothing unless you compare it to how the league performs at that score state. For example, Montreal gets 55.8% of Corsi’s when down by 2 or more. At a glance, this seems fairly good because they are getting more shot attempts that their opponent. However, it doesn’t really tell you anything because you can’t compare it to anything.

The following graph shows each teams performance when down by 2 or more and down by 1 relative to the league average at that score state:

cfdown2down1

For the most part, you can see that each team is either above average or below average in both score states. This shows what teams control possession when down compared to those who don’t. As expected, teams like Chicago, Nashville, and Detroit control large amounts of possession when trailing compared to the league averages (57.6% when down by 2+, 55.1% when down by 1).

Looking at teams who are below average when trailing, you’ll find the likes of Buffalo, Colorado, and Calgary, which you can expect. However, you can see that Pittsburgh finds themselves below average when trailing. For me, this is a good finding as the Penguins are typically seen as a “good” possession team, and are known to have the offensive firepower to perform when down. What you can see here is that this might not be the case, and Pittsburgh may not be driving the possession numbers that are expected of them.

The next two graphs here are relative CF% for each team when leading by 1 or 2+, along with Tied:

cfup2up1 cftied

When all three charts are presented, you must look at each team over each score state and compare when they are driving good possession numbers. Teams like the Blackhawks and the Lightning (top-4 teams in the league) are driving positive possession numbers relative to the league average at all score situations. What I like is my Minnesota Wild driving good possession numbers, compared to previous years, being positive relative to the league in all score states as well.

I want to talk about Toronto here for a second. In each situation, they are consistently below average relative to the league, yet are holding a playoff spot in the East. They are being outplayed and out-possessed on a consistent basis but are finding a way to get points. This is definitely not because of the possession numbers, but are a result of their PDO (which is a whole other discussion itself). With a high PDO to begin the year, many are projecting the Leafs to regress, especially with the stretch of opponents they are matching up against in upcoming weeks. This type of case is why possession numbers are not always correct in the short term, but this discussion at the end of the season would provide more of an inference regarding team possession numbers.

For one last chart, I am presenting the league-wide Score-Adjusted Corsi percentages, based on the formula used by puckon.net:

sacorsi

This just provides you with a picture of what teams are driving possession better than the league, weighted by their TOI per situation. A team that has possession numbers even with the league average at each score state would have a score-adjusted CF% of 50.0. This is a good baseline to measure a team’s performance at each state of the game throughout the season.

One thing to note with all of these charts: the situational relative corsi-for numbers get weighted in the score-adjusted formula based on how much a team spends in that specific score state. So a team like Buffalo, that has spent 360.8 minutes down by 2 or more at EV, will have their CF% in this situation weighted more than that of a team like Chicago, who has only spent 87.1 minutes of their EV ice time down by 2 or more.

Future discussions in this area should look into this factor. But for now, TOI data for each situation is available at puckon.net, which can be used to compare to the team numbers in the charts above. To look at these numbers the best, one should look at what situations teams are spending the most ice time in, look at their CF% in those situations and compare it to league averages, along with divisional and conference opponents.

Another possible future path for this discussion can look at division/conference-specific numbers and comparing teams to their divisional rivals. This can be telling because these teams play each other the most throughout the season.

Overall, these charts and numbers can tell a lot about teams and how they play based on the state of the game. If a team has an above-average CF% when trailing, but a below-average CF% when leading, with relatively similar TOI numbers for each situation, it can show that they may work harder when trailing and tend to sit back and “protect” when they get the lead. Tied score states can tell which teams dominate possession when the both teams are “equal” in terms of score effects (which is pretty much common sense).

Where Are Teams Actually Driving “Good” Possession?