Saturday, July 26, 2014

Let's Talk Powerplay and Penalty Kill Numbers

This article takes a somewhat different approach than my others. I will explore Powerplay and Penalty Kill stats from last season and over the past 4 seasons. What is considered a "good" Powerplay? Penalty Kill? How do we figure this out?

I gathered Powerplay stats from last year at and compared them to each team's total overall points from last season in search of any correlation between the two.

All teams to the right of the "90" mark along the X-axis made the playoffs. The highest team Powerplay % last year was a tie between the Washington Capitals and Pittsburgh Penguins, both had a 23.4% Powerplay, but only one made the playoffs. The average Powerplay % from last season was 17.9%, with a standard deviation of 2.75%. So, using my definition of "above average", "good", and "elite" from my last article (good being 1 standard deviation or more above the mean, and elite being 2 standard deviations above), let's see who was what. A team with a Powerplay % over 17.9% would have an above average Powerplay. A team with a "good" Powerplay would have a stat of 20.65% or greater. For an "elite" team, their Powerplay percent would be 23.4% or greater. From this definition, there were two "elite" Powerplay units last season, Pittsburgh and Washington. I will address shot totals a little later in the article.

But in the mean time, let's look at last season's Penalty Kill stats.

Above the 83% mark along the X-axis, 9 of the top 12 teams made the playoffs. The highest Penalty Kill% in the league was the New Jersey Devils, at 86.4%, and yet they didn't make the playoffs. The correlation between the data points on this distribution is even lower than the correlation of the graph before this. The average penalty kill from last season was 82.08%, with a standard deviation of 2.4%. With these numbers, we can determine that a team would need a Penalty Kill% higher than 82.08% to have an "above average" stat. A "good" team would have a Penalty Kill% of 84.48% or greater. For an "elite" Penalty Kill stat, a team would have to have 86.88% or greater. With these parameters, there were no "elite" Penalty Kill teams from last season. New Jersey missed out on "elite" by 0.48%. 

Okay, so how does this recent season's special teams stats compare to the last 4 seasons? This is important because the sample size is much greater than 30 teams. I had to make this graph in Excel, and take out seasonal point totals due to the partial lockout season of 2012-2013. For the sake of clarity, I will view each specific point on the distribution as a team, as if there were 120 individual teams, and the only differentiation between two of the same teams will be the year.

To avoid any confusion, the data points are plotted in descending order, and the label of the X-axis is just the team count. The highest percentage is the 2012-2013 Washington Capitals, at 26.8%, and the lowest percentage was the 2013-2014 Florida Panthers, at 10%. The average Powerplay % was 17.84%, with a standard deviation of 2.76%. For categorizing special teams over the past 4 seasons, a team would need to have a Powerplay stat higher than 17.84% in order to have an "above average" Powerplay. For a "good" Powerplay, a team would need a stat of 20.6% or greater. For an "elite" Powerplay, a team would need a stat of 23.36% or greater. In this case, there are 7 "elite" Powerplay teams from the past 4 seasons, 2013-2014 Washington Capitals, 2013-2014 Pittsburgh Penguins, 2010-2011 Anaheim Ducks, 2010-2011 San Jose Sharks, 2010-2011 Vancouver Canucks, 2012-2013 Pittsburgh Penguins, and 2012-2013 Washington Capitals.

Let's look at Penalty Kill stats over the past 4 seasons. 

Like the graph above, each point is viewed as an individual team. The average Penalty Kill% over the past 4 seasons is 82.09%, with a standard deviation of 3%. So, a team would need a Penalty Kill higher than 82.09% to be considered "above average". For a "good" Penalty Kill, a team would need a stat of 85.09% or greater. For an "elite" rank, a team would need to have a Penalty Kill of 88.09% or greater. This means there were only two "elite" Penalty Kill teams over the past 4 seasons, 2011-2012 Montreal Canadiens, at 88.6%, and 2011-2012 New Jersey Devils, at 89.6%.

I think another interesting way to look at the effectiveness of Powerplays is the shooting percentage. The highest percentage from last season was the Tampa Bay Lightning, who had a 15.3% (The New Jersey Devils were a close second place with 15.2%). The average Powerplay shooting percent last season was 12.37%, with a standard deviation of 1.86%. An "above average" Powerplay shooting percent would need to be higher than 12.37%. A "good" Powerplay shooting percent would need to be 14.23% or greater. An "elite" Powerplay shooting percent would be 16.09% or greater. At this rate, there were no "elite" Powerplay shooting% teams last season.

Comparing this to the stats over the past 4 seasons, the top shooting percentage was the 2012-2013 Washington Capitals, with an astounding 20.2%. The overall average Powerplay shooting percent from these seasons was 12.7%, with a standard deviation of 1.93%. An "above average" Powerplay shooting percent  would be 12.7% or greater. A "good" Powerplay shooting percent would be 14.63% or greater. An "elite" Powerplay shooting percent would be 16.56% or greater. From this definition, there have been 5 "elite" Powerplay shooting percent teams in the past 4 seasons, 2011-2012 Edmonton Oilers, 2012-2013 Los Angeles Kings, 2012-2013 Edmonton Oilers, 2011-2012 Nashville Predators, and 2012-2013 Washington Capitals.

Of the 5 "elite" Powerplay shooting percent teams above, the 2011-2012 Edmonton Oilers were 3rd overall in the league, the 2011-2012 Nashville Predators were 1st overall in the league, the 2012-2013 Los Angeles Kings were 10th in the league, the 2012-2013 Washington Capitals were 1st overall in the league, and the 2012-2013 Edmonton Oilers were 7th overall in the league. So a high shooting percent on the Power Play is imperative to success.
One more thing to note: The two highest Powerplay shot totals over the past 4 seasons have been by the San Jose Sharks: 572 in 2010-2011, and 505 in 2013-2014. Yet, their Powerplay was 2nd overall (in 2010-2011) and 20th overall (in 2013-2014). The 2013-2014 San Jose Sharks Powerplay unit was 27th overall in the league in shooting percent, at 9.9%. The team in 2013-2014 with the 2nd most Powerplay shots was the Washington Capitals (469 shots). Yet, the Caps' Powerplay shooting percent was 14.5%. At what point is the Powerplay luck? Or is Alex Ovechkin just the league's best Powerplay forward? These are all points in which I will write further about later.

So yes, this post was a little different than my others. I just wanted to look into some recent numbers. Let me know what you think! I'd love some feedback, and to keep updated on my new posts, follow my Twitter account @DTJ_AHockeyBlog!

Wednesday, July 23, 2014

Setting A League-Wide Standard For Corsi

In my past posts, I've used a lot of numbers. This post will be no different. I began talking about what Corsi is in my last post. Essentially it's a statistic measuring possession. It is a recording of every corsi event for and against while a player is on the ice. A corsi event is when there is any type of shot on goal, whether it is a registered shot, a missed shot, or a blocked shot. When it happens for a player's team against the opponent's goal, it falls under the corsi for statistic. If the opponent's team has corsi events on the player's goal, a corsi against statistic is recorded.

As "advanced statistics" for hockey continue to develop, Corsi is just scratching the surface, as there are even more ways of measuring Corsi, which include iCorsi, expected Corsi, a new trend called delta corsi, and zone start adjusted Corsi. For the sake of my own sanity, this post will only focus on the total overall Corsi stat, known as Corsi For %.

Why is Corsi such an important statistic? As noted before, it is an indication towards the possession rate of teams. Looking at information from Extra Skater, 12 of the top 13 possession teams in the NHL last season made the playoffs. The exception to that would be the New Jersey Devils, sitting in 4th place. It is a way of determining just how good a player is with the puck (or without it), without taking point totals into account.

So what exactly is considered a "good" Corsi %? The conventional thought process behind it is that any Corsi rating above 50% is considered above average. After all, it makes sense, right? If you throw the puck more times at your opponent's goal than they throw the puck at yours, you're in good shape. I took the time to compile all the individual (completely unadjusted) Corsi For% from Extra Skater for every player that played 41 games or more (half the season) during the 2013-2014 season.

Above is the distribution of all 570 players involved, sorted from highest to lowest that I compiled.

So what is pictured above? It's not exactly a normal distribution. It's rather skewed to the right, in the positive direction. It's also graphed with a line of best fit with an R squared correlation value of 0.9487. Just a side note, the closer an R squared value is to 1, the closer it fits the given line formula (which is y = -0.0258x+ 57.164). Above, any player can be found plotted.

So let's get into some of the numbers of the distribution. Some quartiles to include have the minimum of 36.8% (Luke Gazdic), a 25th percentile of 47% (Jamie McGinn, Karl Alzner, Jonas Brodin, Thomas Vanek), a 50th percentile, or median of 50.3% (Nino Niederreiter, Ray Whitney, Ryan Ellis, Marty Havlat, David Desharnais, and Eric Brewer), a 75th percentile of 52.775% (Brian Campbell), and a maximum of 61.2% (Patrice Bergeron). The average of the entire distribution is 49.79% (Shawn Thornton, Cory Sarich), with a standard deviation of 4.36%.

There is a measurement in statistics called a Confidence Interval, which is a formula to determine the true population mean of a distribution. Using a formula for a 95% confidence interval, the interval is determined to be 0.36. This means that the range for the true population mean is between 49.43% and 50.15%. However, for argument's sake we will stick with the original determined mean of 49.79%.

It is important to note that Corsi, is just a single statistic, and should be taken with a grain of salt. It does not depict the entire image of a player. Just like "standard" stats like points, goals, and assists, a player's Corsi% can be inflated/deflated by his teammates, since hockey is a team sport. There is a measurement which determines how much a player's teammates help or hurt him. It's called Corsi Relative %, and essentially, it measures the difference of team average Corsi when the player is on and off the ice. I will not delve further into this stat, but just to provide an example, Mark Giordano, defenseman of the Calgary Flames, posted an overall average Corsi For% of 53.3% last season, but had a +10.3% Corsi Relative. This means that the team was 10.3% better with Corsi when he was on the ice. This is precisely why the Corsi stat must be taken with a grain of salt.

So what is considered good? What is considered above average? I will address "above average" first because it is the most straightforward. As the average is 49.79%, anybody with a higher Corsi than that would be considered above average. As for good, I would consider a "good" player to have a Corsi equal to or greater than 1 standard deviation above the mean. I would also even go far to say that a player is considered "elite" if he has a Corsi equal to or greater than 2 standard deviations above the average. This would make a "good" Corsi player have a value of at least 54.14%, and an "elite" Corsi player to have a value of at least 58.49%. This would make 13 "elite" Corsi players from last season. These players are: Patrice Bergeron (BOS), Jake Muzzin (LAK), Anze Kopitar (LAK), Justin Williams (LAK), Tyler Toffoli (LAK), Brad Marchand (BOS), Jonathan Toews (CHI), Jaromir Jagr (NJD), Michal Rozsival (CHI), Reilly Smith (BOS), Dwight King (LAK), Drew Doughty (LAK), and Travis Zajac (NJD).

The above information was determined by the entire amount of players. Let's break down how the numbers may differ at each position.


Above is the distribution of Corsi For% for Centers plotted with a line of best fit. The R squared correlation, 0.9353, is not as high as the previous graph (the entire league) due to a decreased sample size as well as a few outliers, but the formula for the line is y = -0.0915x +57.028.

Let's get right to the gist. The minimum value is 37.5%, the 25th percentile is 46.7%, the median is 49.9%, the 75th percentile is 52.4%, and the maximum value is 61.2%. The mean is 49.61%, with a standard deviation of 4.397%. How does this compare to the entire league? In short, the centers have a lower Corsi For% than the rest of the players.

A direct comparison of the numbers:
Value:                Entire League:            Centers:
Minimum                   36.8                      37.5
25th                          47                         46.7
Median                     50.3                      49.9
75th                          52.775                  52.4
Maximum                  61.2                      61.2
Mean                        49.79                    49.61
SD                            4.36                      4.397

While these are marginal differences, hockey is a game of inches, and the SD is larger for the centers due to a decreased sample size. What does this mean for the values of a "above average", "good", and "elite" Corsi value center? Any center with a Corsi For% higher than 49.61% would be considered "above average". Staying consistent with the determination of "good" and "elite" values, (1 and 2 standard deviations above the mean, respectively), a "good" Corsi center would have a value of 54%. An "elite" Corsi center would have a value of 58.4%. This would mean, relatively, that there are 5 elite Corsi centers: Patrice Bergeron, Anze Kopitar, Tyler Tiffoli, Jonathan Toews, and Travis Zajac.

Let's look at Left Wings:

The first thing to note is the higher R squared value, which is 0.9602. This is due to two main reasons: lower sample size (105 players) and better correlation along the formula: y = -0.1591x+58.244. Let's get a better look at the numbers: the minimum value is 36.8%, the 25th percentile value is 45.7% the median is 50.95%, the 75th percentile is 53.15%, and the maximum value is 58.5%. The mean is 49.72% with a standard deviation of 4.84%. How does this compare with the rest of the league?
Adding on to the chart before.

A direct comparison of the numbers:
Value:                Entire League:            Centers:                LW:
Minimum                   36.8                      37.5                  36.8
25th                          47                         46.7                  45.7
Median                     50.3                      49.9                  50.95
75th                          52.775                  52.4                  53.15
Maximum                  61.2                      61.2                 58.5
Mean                        49.79                    49.61               49.72
SD                            4.36                      4.397               4.84

So, in general, Left Wings have a higher Corsi For% than centers, with a few exceptions, including the minimum, 25th percentile, and maximum values. How does this play out with the evaluation of "above average", "good", and "elite" Corsi Left Wingers? Continuing off the definitions from before, a Left Wing with a Corsi above 49.72% would be considered above average. A "good" left winger Corsi For% would be 54.56% or greater. An "elite" left winger Corsi For% would be 59.4% or greater. Along those parameters, there is only one "elite" left winger: Brad Marchand.

Let's look at Right Wingers now:

As with the previous graph, the first thing I will note is the correlation value (R squared) being 0.9437, which is slightly lower than the Left Winger group, but still a very positive correlation. The formula for the equation depicted in this distribution is y = -0.1337x+57.458.
Let's take a closer look at some of the numbers:
The minimum value is 38.4%, the 25th percentile value is 47.2%, the median value is 50.3%, the 75th percentile is 52.3%, and the maximum value is 60.6%. The mean is 49.8% with a standard deviation of 4.33%. Let's add to the chart before.

A direct comparison of the numbers:

Value:                Entire League:            Centers:                LW:              RW:
Minimum                   36.8                      37.5                  36.8              38.4
25th                          47                         46.7                  45.7              47.2
Median                     50.3                      49.9                  50.95            50.3
75th                          52.775                  52.4                  53.15            52.3
Maximum                  61.2                      61.2                 58.5               60.6
Mean                        49.79                    49.61               49.72             49.8
SD                            4.36                      4.397               4.84               4.33

The Mean for the Corsi For% is higher for Right Wingers than any other group of the data at this point, but the median is lower than that of the Left Wingers.

What does this constitute for "above average", "good" and "elite" Corsi Right Wingers? Continuing the current trend, a Right Winger with a Corsi For% higher than 49.8% would be considered "above average". A "good" Right Winger, in Corsi, would have a value of 54.13% or greater. An "elite" Right Winger, in Corsi, would have a value of 58.46% or greater. This would mean there are 3 "elite" Right Winger Corsi values: Justin Williams, Jaromir Jagr,and Reilly Smith.

Finally, let's look at the defensemen:

There is not as strong of a correlation for the line of best fit in this distribution plot. The R squared value is 0.9456, which is lower than that of the Left Wing graph. The formula, however, for the plot is y =-0.0693x+56.711.
Let's look at some other numbers: The minimum value is 40.1%, the 25th percentile value is 47.65%, the median value is 50.2%, the 75th percentile value is 52.55%, and the maximum value is 61.1%. The mean is 49.92%, with a standard deviation of 4.01%.
Let's finalize the chart from before.

A direct comparison of the numbers:

Value:                Entire League:            Centers:                LW:              RW:              D:
Minimum                   36.8                      37.5                  36.8              38.4              40.1
25th                          47                         46.7                  45.7              47.2              47.65
Median                     50.3                      49.9                  50.95            50.3              50.2
75th                          52.775                  52.4                  53.15            52.3              52.55
Maximum                  61.2                      61.2                 58.5               60.6              61.1
Mean                        49.79                    49.61               49.72             49.8              49.92
SD                            4.36                      4.397               4.84               4.33              4.01

As one may have expected, the Defensemen have, just about, the highest overall Corsi For values, in comparison to the rest of the league and other positions.

Finally, for the "above average", "good", and "elite" corsi defensemen ratings: A Defenseman with a Corsi For value higher than 49.92% would be considered "above average". A "good" Corsi For Defenseman would have a value of 53.93% or greater. An "elite" Corsi Defenseman would have a value of 57.94% or greater. This means there are 4 "elite" Corsi Defensemen in the league: Jake Muzzin, Michal Rozsival, Drew Doughty, and Marc-Edouard Vlasic.

This is a lot to take in, so I will put in a chart what we have discussed:

Position "Above Average" "Good"  "Elite"
C 49.61 54 58.4
L 49.72 54.56 59.4
R 49.8 54.13 58.46
D 49.92 53.93 57.94
Overall 49.97 54.15 58.51

I will conclude by saying that contrary to popular belief, to be an "above average" Corsi skater does not mean one has to have a Corsi value over 50%, as proven by last year's possession statistics.

Thanks for reading! I gathered all the above information from, and compiled them myself.

Please follow my Twitter account @DTJ_AHockeyBlog and give me some feedback!

Monday, July 21, 2014

Mark Fayne Will Bring Much Needed Defense To The Edmonton Oilers

On July 1st, Mark Fayne was signed by the Edmonton Oilers to a 4 year contract with an Average Annual Value of $3.5 million per year. I will start by saying that this is a phenomenal signing for both the Oilers and Fayne. Over the past few seasons, Edmonton has gained a reputation for being a "top-heavy" team, with strong forwards, but severely lacking in defense. This has resulted in Edmonton having top 5 draft picks in 4 of the last 5 years in a re-building process that has been going on for what seems like forever to Edmonton fans. However, contrary to popular belief, Edmonton has some very promising defensive prospects coming through the system, such as Darnell Nurse and Martin Marincin.

(Image source: hockeyordie)
After watching a lot of Mark Fayne this past season, I can say that he is not a liability in any of the 3 zones he can play in. The 6'3", 210 pound defenseman does not bring a physical game. This past season he registered 77 hits in 72 games played. In the beginning of the season, Fayne sort of floated around the defensive pairings until he found his role on the top line with linemate Andy Greene. Fayne is not a strong offensive presence, as he had 11 points last season (in his 72 gp). What I've seen from Fayne is that he is very reliable at keeping the puck in the offensive zone when the team is attacking, and has great pinches. His significance on defense specializes in checking. Not conventional body checking, but checking in the sense of body positioning and defensive stick handling. While not a top-pairing defenseman, Fayne will be able to fit in wherever he is needed.

Above is what's called a Player Usage Chart. Essentially it tracks zone start percentage for a player (the further left a player is, the less offensive zone starts he has). It also compares that zone starting percent with the player's corsi and quality of competition stats. The size of the circle is indicative of the average ice time a player gets. So, there is a tremendous amount of information presented in the small graph above. Thinking about it all can really give someone a headache, so I will keep it simple: the higher a player's circle means the higher their corsi rating is. The corsi stat is a measurement of possession, and there is usually a positive correlation between higher (offensive) zone start percent and higher corsi (possession) stats. The fact that Fayne's corsi is so high, despite a low offensive zone start percent is a good sign that he is a defenseman with strong puck possession skills. Last season, Fayne posted an overall corsi rating of 55.3%, which was above the team (New Jersey Devils) average of 54.6%.

Why is this important? Because New Jersey was one of the best possession teams in the league last year. Their corsi percentage was 4th highest in the league. In today's NHL, possession is a strong element of success. (The only exception to that last season would be Colorado, but I will address that in another write-up). Edmonton sat in 28th in the league, last season, in possession ranking, with a corsi rating of 43.4%. So, immediately, Fayne will have an impact, in the sense of an improvement in the possession column.

So how do possession stats translate into defensive effectiveness? Corsi is a measurement of any shot attempt towards the goal, which includes saved shots, blocked and missed shots. If a player has a Corsi higher than 50%, then his team throws the puck toward the opponent's goal more times than the puck is thrown on his own goal when he is on the ice. For example. Hank is on Team A and steps onto the ice for a shift when the puck is taken through the neutral zone into Team B's zone. There, 2 shots on goal and 1 missed shot, and 2 blocked shots are recorded (this is a total of 5 corsi events). Team B then takes the puck down to Team A's zone and takes 2 shots and records a goal (this is a total of 2 corsi events). While a goal was scored, affecting his +/- stat, Hank has a corsi stat of 71.4% recorded during his shift. Many variables can affect a player's corsi stat, however, with a greater sample size than a single shift, a distribution starts to form and the player's average corsi really starts to take shape.

Something important to address here is a possible inflation of Fayne's stats from having such a good linemate in Andy Greene. To this, we turn to what is called WOWY stats, or With Or Without You stats. These are measurements of differences in performances between linemates. Found on, these stats can be determined. Below is a screenshot of the info.

The text is a little small, since I only focused in on Fayne's relationship with Greene. Here are the most important numbers: When the two were on the ice together, Fayne had a corsi percent of 55.3, and Greene had a 57.6% corsi. When the two were apart, Fayne had a 47.2% corsi, and Greene had a 54.2% corsi. So it is important to note that there was about an 8.1% inflation of Fayne's corsi numbers when playing with Greene. However, it is not exactly a one-way street, as Greene saw a 3.4% inflation to his corsi stats when playing with Fayne.

What is important to note is that, even independent from his steady linemate from last season, Fayne's independent corsi (possession) stats are higher than the Oilers' team average.

As for where Fayne will play, I can definitely see him matching up well with Andrew Ference, or whoever is on the second defensive pairing. His play on the Devils' 2nd penalty kill unit will also transcribe well into Edmonton. It is possible to see him spend time on the Oilers' 1st penalty kill unit.

All in all, Fayne will bring defense to the Oilers by bringing a consistent possession game, as a well-positioned shutdown defenseman role. With Ben Scrivens in net, and a year of Dallas Eakins system under the belt, as well as rising defensive prospects and the signing of Mark Fayne, we should see a much better Oilers team this coming season.

The numbers and services I used in writing this came from Extra Skater and Hockey Analysis.
Interested in more? I'm on twitter now, @DTJ_AHockeyBlog. Give me a follow, as well as some feedback!

Sunday, July 20, 2014

Second Post, what Cammalleri will bring to the Devils

In case you can't tell, I'm still figuring out the formatting of the site, and, in general, how to blog.

Earlier this month, on July 4th, the New Jersey Devils signed forward Mike Cammalleri to a five year contract with an AAV of $5 million.
(photo taken from

Cammalleri is 32 years old and scored 45 points in 63 games for the Calgary Flames last season, including 26 goals. The biggest and earliest impact that Cammalleri will bring to the Devils is a boost in even strength (5 v 5) scoring, an area where the Devils have severely lacked in the past few seasons. Looking at some info from, Cammalleri boasted an average 1.237 goals per 60 minutes of even strength ice time.
Above is a screenshot of his statistics in this category. While Cammalleri's 63 games played is a lower sample size, his 1.237 G/60 (of even strength) stat is comparable to the Devils leading goal-scorer for the 2013-2014 season, Adam Henrique. In 77 games, Henrique scored 25 goals, and his even strength goals per 60 minutes stat is 0.848.

So what does this mean? It means Cammalleri has big shoes to fill as a top-notch goal scorer for the Devils, which was the main reasoning behind his signing. Good news for Devils fans though, Cammalleri is a top-line left winger, something that the team has been looking for since the departure of Zach Parise and Ilya Kovalchuk to free agency and retirement, respectively. Why is this good news? Because as much as I like Dainius Zubrus, he (Zubrus) does not belong on the top offensive line. Cammalleri will fit in much better with the cycling system of Zajac and Jagr.

Another player lost to free agency recently was David Clarkson, a player fairly well-known to Devils fans. The main reason why his departure hurt the Devils (and why Cammalleri's signing helps the Devils) is because Clarkson was a "volume shooter". This means exactly what it sounds like, Clarkson shot the puck very often. Oftentimes in cycling systems, the emphasis is placed on passing over shooting. (The idea of a cycling system is to essentially keep passing the puck to get the defense out of position for the "perfect shot"). Cammalleri is a different scenario however, for the most part. While Cammalleri does pass the puck often, he had 191 shots on goal last season, including 65 missed shots and 87 blocked shots. So all in all, he had 343 corsi events last season. If his stats were directly compared to the Devils players this past season, Cammalleri would be in 2nd on the team in shots on goal and individual corsi events (both of which he is behind Jaromir Jagr).

Delving further into statistics, looking at the on-ice event statistics of the past season, there is a statistic called PShr, which stands for Point Share. This is an important measurement of the productivity of a player while he is on the ice. If a player is on the ice for 82 goals in a season, and records 61 points from those goals, this player would have a PShr of 74.4%. The past season, Cammalleri had a 81.1% PShr, which means he scored a point on 81.1% of the goals he was on the ice for. This was good enough for 22nd overall in the league last year.

He also had a 54.1% on-ice goal percentage, which means that of the goals Cammalleri was on the ice for last season, he scored 54.1% of them, which was good enough for 5th overall in the league.

(On a side note, it's interesting to see Michael Ryder on the on-ice goal percentage list considering how streaky of a scorer he was last season. I hope the team gives him another chance to prove himself.)

Compared to the Devils roster, Cammalleri's PShr would be the highest on the team, with Patrik Elias's 77.4% currently leading the team. The highest on-ice goal percentage player on the Devils from last season was Ryan Carter, with a 58.3% on-ice goal percentage, so Cammalleri would be 2nd in that category.

So, after looking at some preliminary offensive and possession statistics, Mike Cammalleri is going to do to wonders as a top-line winger for the New Jersey Devils. If Jaromir Jagr has the same type of season, production-wise (one can hope...the man is pretty much the epitome of physical fitness), and with a competent linemate on the top line, Jagr could most definitely top 70 points next season. Zajac's point production could also see a boost next season as well. Optimistically, I could see Zajac scoring in the 50-60 point range. Cammalleri will provide the finishing touch to top line that the Devils have been desperately craving. Is he the solution to the lack of offense? No, but this signing is most definitely a step in the right direction.

We've seen his stats, how about taking a look at how he plays. Now, I don't particularly care for highlight videos, as anybody can edit together a highlight clip, but this clip below looks specifically at Cammalleri's goals from the 2013-2014 season. Let's take a look: (It's 16 minutes, so that is promising, right?)

I'm not going to break down every one of his goals, but what I have noticed are two very important factors that lead to Cammalleri's scoring ability. The first is his ability to score from the right face-off circle. This can be seen in the very first goal shown in the video. The puck is passed across the crease to him, he goes down to one knee, and slaps it into the net. The second, and most important, thing to note when looking at his goal scoring is the ability he has to make space for himself and teammates in the slot. (The slot is the area of the ice directly between the faceoff dots, and stretches down to the goal crease). I'd like to quickly talk about the 4th goal shown in the video. This is the one where he passes to Monahan for the tying goal against the Red Wings. The reason why this goal in particular is exciting news for Devils fans is because Cammalleri makes a play off the puck being dumped in off the far boards. He then makes a great pass to Monahan going towards the goal, which creates space for him in the slot to make a well-placed shot over the goaltender's shoulder. Long story short, this is dump and chase hockey at its finest. No time is wasted, and a good scoring chance is created, and taken advantage of.
The other majority of Cammalleri's goals are rebounds and tip-ins, exactly the type of goals that thrive in the system of the New Jersey Devils. He also has quick hands, very quick hands, as can be seen in his goal against the Colorado Avalanche (around the 8:04 mark of the video).

My biggest concern about Cammalleri would be his ability to stay healthy. The last time he played 81 games in a season was in the 2008-2009 season (including 44 games in the 2012-2013 lockout shortened season). And while 5 years is a bit long for a 32 year old, the Devils especially know about having players 35+ years of age. And for the $5 million yearly cap hit? It may be a bit much, but that's what happens in free agency: salaries are slightly (or drastically) inflated. However, Cammalleri's $5 million cap hit is not too awful when compared to other recently-signed free agents such as Benoit Pouliot signing at $4 million per year, of Dave Bolland, signing at $5.5 million per year.

Cammalleri needs to have a healthy and productive first season with the Devils so the 5 year contract will not come around to bite them. All in all, though, signing Mike Cammalleri was a much needed step in the right direction for the New Jersey Devils this offseason. (Stats used were found at and I'd also like to note that the above charts were not compiled by me. The data in them was gathered from the aforementioned sites, but compiled by a user from /r/hockey.

...and yes, Cammalleri can score in the shootout. 

First Post: Most Important Free Agent Defenseman This Offseason

I've been meaning to start this blog for quite some time now, just as a place to clear my thoughts and get some ideas out about the game I love. I've been told I can talk one's ears off when it comes to hockey, so what better for me than to start writing a blog about it.
 A little about me: I've been watching the New Jersey Devils play since the early 2000's, when I was very young. It's been a decade of ups and downs, with the perennial hope that this every season is ours. The years go on, the hope goes on, the game goes on.

 Without further ado, let's talk about some free agents this offseason:

I'll start with the defensemen, because the class of free agents this offseason is relatively weak. In fact, the most important free agent defenseman for the Devils to sign, comes from within their own organization.

The most important defensive free agent is pending UFA Mark Fayne, who the Devils hope to re-sign before he hits the open market, July 1st. Let's take a quick look at his stat line over the past season. With 72 games played, Fayne had 4 goals and 7 assists for 11 total points and a +/- of -5. At first glance, those are not astounding numbers, however that is not Fayne's responsibility on the team. He is a shutdown defenseman and compliments his defensive partner, Andy Greene.
 Why I'd like Fayne back in NJ: He's a puck-moving defenseman with tremendous possession stats. According to, Fayne had the 4th highest Corsi % on the team this season. At 55.3%, Fayne's Corsi is higher than the team's average of 54.6%. It's not like Fayne had an easy competition, either. He was on the top line pairing with Greene, and had significant Quality of Competition %. Like any top defensive pairing, Fayne and Greene had the highest zone start percentage, as well as the highest Quality of Competition TOI percent of all the defensemen on the team, as seen in the graph below.
Comparatively to other UFA defensemen, Fayne has the 3rd highest Corsi %, behind NYR Anton Stralman in first and LA Willie Mitchell. Right behind Fayne is PIT Matt Niskanen, followed by SAN (now NYI) Dan Boyle, so Fayne is in good company. The most important thing to note about the chart above is Fayne's high corsi stats despite his low offensive zone start percentage.