Feb 052013
 

Before Leaf fans get all over me, let me say that there is nothing wrong with being a complementary player. Every team has and needs them and they can be valuable pieces of the puzzle. When I say complementary player I mean he is a player that needs others to help him get the most out of his game rather than someone who can elevate his game and those around him on his own. The complementary player isn’t as valuable as the guy who can elevate his game and the game of his line mates on his own (I call this a core player) but every good team needs a good cast of complementary players. Let me explain further with this chart of 2007-12 (5yr) even strength 5v5 data.

Sh% TOI/G
with Savard 13.9% 39:40
without Savard 8.9% 61:08
with Lupul 12.8% 46:34
without Lupul 9.1% 58:46
with Savard or Lupul 13.3% 43:07
without Savard or Lupul 7.7% 68:44

In my opinion, the two best (offensive) players that Kessel has played with over his career are Marc Savard and Joffrey Lupul so I focused on Kessel’s play with and without them. In the chart above, you can clearly see that Kessel has been substantially better when he is on the ice with either Savard or Lupul and in reality somewhat ordinary otherwise. When those two guys are on the ice Kessel’s shooting percentage, and thus goal production, sky rockets. Whatever Savard and Lupul are doing, they make Phil Kessel better. Does that make Savard and Lupul core players and Kessel a complementary player?  Maybe.  Let’s take a closer look at Lupul and see if his boost in Kessel’s performance extends to some of the other line mates he has had over the years (again, using 5 year 5v5 shooting percentages).

Linemate with Lupul without Lupul
Phil Kessel 12.8% 9.1%
Tyler Bozak 12.9% 13.4%
Scott Hartnell 12.1% 9.3%
Jeff Carter 12.4% 9.2%
Mike Richards 14.3% 9.0%

Aside from Tyler Bozak (and Kessel may be a factor as he has only played with Bozak when Kessel is also on the ice), he has improved the shooting percentage of each of his line mates over the past 5 seasons. This is fairly significant evidence that Lupul is in fact a core player that improves the performance of his line mates.

Every team needs core players, but there aren’t enough core players in the NHL to fill out your roster so every team also needs quality complementary players. From my perspective, Kessel is a good complementary player that guys like Lupul and Savard can elevate into very good very productive players, but because Kessel is also dependent on Lupul to be highly productive, Kessel isn’t worth the money that you would pay a core player. For this reason, if I were the Leafs management, I’d be very cautious about paying Kessel big money (i.e. in excess of $7M) on his next contract since, if something happens to Lupul (as is the case right now) he quickly becomes ordinary.

Now with that in mind, and the fact he is currently on a significant goal drought (12 games dating back to last season, mostly without Lupul) I think it is up to the Leaf coaching staff to mix up the lines and see if you can find another core player that can maximize Kessel’s production. Bozak and van Riemsdyk don’t seem to be the guys. Personally, I’d put him with Grabovski but it might also be interesting to see him with young energy players like Kadri and Frattin. The coaching staff has to do something but the current line is not working at all.

Feb 012013
 

Last week I introduced player TOI usage charts and one use I thought they had was to look at how a players usage changed during the downside of their careers. Today I will do just that by looking at Nicklas Lidstrom’s TOI charts over the last 5 seasons. Consider this an extension to my earlier article where I took a look at Lidstrom’s last few seasons of his career. Let’s get right at it with his 5v5 chart.

LidstromTOIChart

 

Lidstrom’s last big season was clearly 2007-08 and every year since he has been below his 2007-08 levels in terms of 5v5 ice time. What is interesting to note is how little (relatively) ice time he had during the 2010-11 season, the year he won the Norris Trophy. I think it was a big mistake that he was awarded the trophy that season and this is just a little more evidence of that. In fact, Lidstrom was 4th on the Red Wings in ESTOI/Game by defensemen which is why his TOI% in the chart above were so low that year. Rafalski retired in the summer of 2011 which meant Lidstrom would get a boost to his ice time in 2011-12.

So, what about his special teams play?

LidstromPPPKTOIChart

On the powerplay, Lidstrom maintained his level of playing ~60% of his teams 5v4 power play minutes but his penalty kill ice time dropped significantly over the final 2 seasons of his career.

Based on the above charts, the last year I think you could consider Lidstrom a true heavy work load stud of a defenseman was in 2007-08. He was still awfully good for a couple more years and quite good until he retired but his slow decline in ice time had begun.

 

Jan 302013
 

For those familiar with my history, I have been a big proponent that there is more to the game of hockey than corsi and that players can certainly drive on-ice shooting percentage. I have not done much work at the team level, but now that I have team stats up at stats.hockeyanalysis.com I figured I’d take a look.

Since shooting percentages can vary significantly over small sample sizes, my goal was to use the largest sample size possible.  As such, I used 5 years of team data (2007-08 through 2011-12) and looked at each teams shooting and save percentages over that time. During those 5 years Vancouver led all teams in 5v5 ZS adjusted save percentage shooting at 10.69% while Columbus trailed all teams with a 8.61% shooting percentage. What’s interesting to note is the top 6 teams are Vancouver, Washington, Chicago, Philadelphia, Boston and Pittsburgh, all what we would consider the teams with the best offensive talent in the league. Meanwhile, the bottom 5 teams are Columbus, Los Angeles, Phoenix, Carolina, and Minnesota, all teams (except maybe Carolina) more associated with defensive play and a defense-first system.

As far as save percentage goes, Phoenix led the league with a 91.83% save percentage while the NY Islanders trailed with an 89.04% save percentage. The top 5 teams were Phoenix, Boston, Anaheim, Nashville, and Montreal.  The bottom 5 teams were NY Islanders, Tampa, Toronto, Chicago and Ottawa. Not surprises there.

As far as sample size goes, teams on average had 7,627 shots for (or against) over the course of the 5 years which gives us a reasonable large sample size to work with.

Now, in order to not use an extreme situation, I decided to compare the 5th best team to the 5th worst team in each category and then determine the probability that their deviations from each other are solely due to randomness.  This meant I was comparing Boston to Minnesota for shooting percentage and Montreal to Ottawa for save percentage.

TeamShootingPercentageComp

As you can see, there isn’t a lot of overlap, meaning there isn’t a large probability that luck is the reason for the difference between these two teams 5 year save percentages.  In fact, the intersecting area under the two curves amounts to just a 6.2% chance that the differences are luck driven.  That’s pretty small and the differences between the teams above Boston and below Minnesota would be greater. I think we can be fairly certain that there are statistically significant differences between teams 5 year shooting percentages and considering how much player movement and coaching changes there are over the span of 5 years it makes it that much more impressive. Single seasons differences could in theory (and probably likely are) more significant.

TeamSavePercentageComp

The save percentage chart provides even stronger evidence that there are non-luck factors at play.  The intersecting area under the curves equates to a 2.15% chance that the differences are due to luck alone. There is easily a statistically significant differences between Ottawa and Montreal’s 5 year save percentages. Long-term team save percentages are not luck driven!

So, the next question is, how much does it matter?  Well, the average team takes approximately 1500 5v5 ZS adjusted shots each season. The differences in shooting percentage between the 5th best team and the 5th worst team is 1.27% so that would equate to a difference of 19 goals per year during 5v5 ZS adjusted situations. The difference between the 5th best and 5th worst team in save percentage is 1.5% which equates to a 22.5 goal difference. These are not insignificant goal totals and they are likely driven solely by the percentages.

Now, how does this equate to differences in shot rates? If we take the team with the 5th highest shot rate and apply a league average shooting percentage and then compare it to the team with the 5th lowest shot rate we would find a difference of 17.5 goals over the course of a single season. This is slightly lower than what we saw for shooting and save percentages.

What is interesting is this (the percentages being more important than the shot rates) is not inconsistent with what we have seen at the individual level. In Tom Awad’s “What makes Good Players Good, Part I” post he identified 3 skills that good players differed from bad players. He identified the variation in +/- due to finishing as being 0.42 for finishing (shooting percentage), 0.08 for shot quality (shot location) and 0.30 for out shooting which would equate to out shooting being just 37.5% of the overall difference. I also showed that fenwick shooting percentage is more important than fenwick rates by a fairly significant margin.

Any player or team evaluation that doesn’t take into account the percentages or assumes the percentages are all luck driven is an evaluation that is not telling you the complete story.

 

Jan 282013
 

I have updated stats.hockeyanalysis.com to include 2012-13 data (even though it is way too early to draw any real conclusions from it) and also to add several new features:

  1. Added team data.
  2. Added QoT and QoC (offense, defense and overall) stats based on Hockey Analysis HARO (offense), HARD (defense) and HART (overall) ratings. These QoT and QoC are essentially the average teammate or opponent HARO, HARD or HART rating.
  3. Changed WOWY pages so that both goal and corsi data are on the same page for easy comparison.
  4. Included individual stats in the WOWY pages so we can see how many goals Perry scored with Getzlaf on the ice with him.
  5. Also included each player in their on “with you” table so we can see that players overall individual stats for easy comparison with how he performed with his line mates (i.e. Perry scored 18 5v5 goals last year, 15 with Getzlaf on ice with him)
  6. The WOWY Against table is now split into two, one for opposition forwards and one for opposition defense and as a result have removed goalies from the list.
  7. I have merged 5v5up1 and 5v5up2+ situations into 5v5leading and 5v5down1 and 5v5down2+ into 5v5trailing.  Needed to do this to make my program more efficient and I didn’t think the distinction was all that important/useful compared to some of the other stuff.

There may be a few other changes that I don’t recall making but that should be the most important ones. Have a look around and if you see any issues or have any other features you’d like to see be sure to let me know and I’ll see what I can do.

 

 

Jan 252013
 

The last few days I have been looking at the percentage of a teams ice time for a given situation that a particular player is on the ice for.  So for instance, what percentage of the Leafs 5v5 even strength ice time was Joffrey Lupul on the ice in games in which Joffrey Lupul played. When I write a new program to calculate these numbers I need to to some testing to make sure the results are correct.  The first test is always the standard sniff test.  When the program runs I look at the output and ask myself “does the output make sense?”. When I first looked at the output the other day one of the numbers surprised me so much that I had to do some double checking to make sure it made sense. That number was the percentage of his teams power play ice time that Ilya Kovalchuk was on the ice for. That number was 87.25%.

That’s insane I thought so off to NHL.com to check and see if it could be at all possible. I first checked and noticed that the Devils had 439:59 minutes of PP ice time last year, including 420:36 minutes of 5v4 ice time. Next I checked out much PP ice time Kovalchuk had last year and see that he had 379:08 minutes of PP time. I do not know his exact 5v4 PP ice time numbers but 379:08 is about 86% of 439:59 so my calculation of Kovalchuk being on the ice for 87.25% of his teams PP ice time is perfectly within reason.

To me this seems like a crazy high number.  It means for every 2 minute penalty Kovalchuk is on the ice for 1:44 of it. That just makes me say “WOW!” but Kovalchuk is not alone in getting big PP minutes.  Here are some other players who have played in >70% of his teams 5v5 PP minutes (in games he played in) over the past 5 seasons.

Player 5v4 TOI%
Ilya Kovalchuk 87.25%
Alex Ovechkin 83.08%
Mike Green 76.86%
Mark Streit 75.35%
Sergei Gonchar 74.76%
Evgeni Malkin 73.83%
Sidney Crosby 73.01%
Dan Boyle 72.78%

I knew some players played a lot of PP ice time, but that still astonishes me. Oh, and for the record, in addition to being on the ice for 87.25% of his teams 5v4 PP ice time, Kovalchuk was on the ice for 89.66% of his teams 5v4 PP goals.

On the other end of things, over the last 5 years Willie Mitchell has played a whopping 59.2% of his teams 4v5 PK ice time which is might actually be more impressive considering how much more demanding playing on the PK is.

 

Jan 242013
 

The other day I introduced a new way of visualizing player time on ice and usage and today I am taking that one step further by superimposing a players performance on those charts.

So, with the TOI usage charts I presented the other day you can see how frequently a player was on the ice in any particular situation relative to how frequently the team plays during that situation.  So, a player might be on the ice for 30% of the teams 5v5 game tied minutes.  The next logical step is to take a look at his production during those situations relative to his teams production. If a player is on the ice for 30% of his teams 5v5 game tied minutes but he was only on the ice for 25% of the teams 5v5 game tied goals, that isn’t a good thing.  The team under-produced during his ice time relative to when he was not on the ice. We can also do the same for goals against and the resulting chart might look like this one for Zdeno Chara over the past 5 seasons.

The blue is Chara’s TOI usage percentages, the green is his goals for percentages and the red is his goals against percentages. You will notice that I have removed special teams play. The reason for this is because GA is not significant on power plays and GF is not significant on penalty kill so the chart ends up looking odd but in theory you could include them.

In an ideal situation the red box is smaller than the blue box (give up fewer goals than expected) and the green box is bigger than the blue box (give up more goals than expected). For Chara his results are a little mixed. When trailing he is very good having more goals for than expected and fewer goals against than expected when he is on the ice. His goals against relative to his teammates rises significantly when leading. I am not certain why, but maybe it has to do with his defense pairings when protecting a lead or opposing teams pressure him more when they are trailing.

Let’s take a look at another player who has been in the news lately, for both a contract signing and an injury.  Joffrey Lupul.

Strangely, almost the opposite of Chara. Lupul’s ‘leading’ stats are better than Chara’s while Chara is better when trailing. I am thinking maybe matchups are a factor here. When leading coaches are more diligent in matching Chara up against the opposing teams top line and keeping Lupul away from the opposing teams top line. Something to investigate further.

That said though, for Leaf fans if the Leafs get a better team that spends more time leading than trailing, Lupul’s numbers should, at least according to the chart above, get better. Especially goals against numbers.

Let’s finish off with one more superstar player, Sidney Crosby.

That is the chart of an offensively dominant player. Crosby’s offense is through the roof. Like Chara though, he is much weaker protecting a lead than any other situation.

As I said in my previous post, I am not sure where I will go with these radar charts, but they seem to be a valuable way of visualizing data so when appropriate I will attempt to make use of them. For example, it might be interesting to take a look at how a players usage and performance changes from year to year. In particular it might be interesting to see how ice time and performance changes for young players as they slowly improve or older players who are on the downsides of their careers.

 

Jan 232013
 

One of the challenges in hockey analytics, or any type of data analysis, is how to best visualize data in a way that is exceptionally informative and yet really simple to understand. I have been working on a few things can came up with something that I think might be a useful tool to understand how a player gets utilized by his coach.

Let’s start with some background. We can get an idea of how a player is utilized by looking at when the player gets used and how frequently he gets used.  Offensive players get more ice time on the power play and more ice time when their team is trailing and needs a goal. Defensive players get more ice time on the PK and when they are protecting a lead. This all makes sense, but the issue is some teams spend more time on the PP or PK than others while bad teams end up trailing more than good teams and leading less. This means doing a straight time on ice comparison between players on different teams doesn’t always accurately depict the usage of the player. If a player on the Red Wings plays the same number of minutes with the lead as a player on the Blue Jackets it doesn’t mean the players are used int he same way.  The Blue Jackets will lead a game significantly less than the Red Wings thus in the hypothetical example above the Blue Jackets are depending on their player a higher percent of the time with a lead than the Red Wings are their player.

To get around this I looked at percentages. If Player A played 500 minutes with a lead and his team played a total of 2000 minutes with a lead during games which Player A played, then Players A’s ice time with a lead percentage would be 25%. In games in which Player A played he was used in 25% of the teams time leading. I can calculated these percentages for any situation from 5v5 to 4v5 or 5v4 special teams to leading and trailing situations. The challenge is to visualize the data in a clear and understandable way. To do this I use radar charts. Lets look at a couple examples so you get an idea and we’ll use players that have extreme and opposite usages: Daniel Sedin and Manny Malhotra.

For those not up to speed on my terminology f10 is zone start adjusted ice time which ignores the 10 seconds after a face off in either the offensive or defensive zone.

The charts above are largely driven by PP and PK ice time but players that are used more often in offensive roles will have their charts bulge to the top and top right while those in more defensive roles will have their charts bulge more to the bottom and bottom left. Also, the larger the ‘polygon’ the more ice time and more relied on the player is. In the examples above, Sedin is clearly used more often in offensive situations and clearly gets more ice time.

Let’s now look at a player who is used in a more balanced way, Zdeno Chara.

That is a chart that is representative of a big ice time player who plays in all situations. We can then take it a step further and compare players such as the following.

In normal 5v5 situations Gardiner was depended on about as much as Phaneuf, but Phaneuf was relied on a lot more on special teams and a bit more when protecting a lead. Of course, you can also compare across teams with these charts:

Phaneuf and Chara were depended on almost equally in all situations except on the PP where Phaneuf was used far more frequently.

I am not sure where I will go with these charts but I think I’ll look at them from time to time as I am sure they will be of use in certain situations and I have a few ideas as to how to expand on them to make them even more interesting/useful.

 

Jan 202013
 

The Leafs announced today that they have re-signed Joffrey Lupul to a 5 year contract extension at an average salary and cap hit of $5.25M/yr.  Some Leaf fans are a little dismayed at both the value and the term of the deal as many people seem to view Lupul as a second line winger with a defensive liability that should have been traded, not re-signed.  I won’t deny that Lupul is a defensive liability (though wingers generally have less impact on defense than centers or defensemen), but I will dispute the claim that he is a second line winger.

Last season I wrote an article pointing out that Lupul’s production was not an anomaly and that he has always been that good of a player. In it I showed that he made almost all of his line mates more productive offensively when they were skating with him than when they were not.  I also showed that Lupul’s even strength goal production had not increased dramatically last year.  I won’t reiterate that here as you can go read it if you want, but I just wanted to post one more chart.  This chart shows the top 20 players in terms of goal scoring rates (individual goals per 20 minutes of ice time) during 5v5 zone start adjusted play over the last 5 years (minimum 3000 minutes of ice time).

Rank Player G/20
1 SIDNEY CROSBY 0.272
2 ALEXANDER SEMIN 0.259
3 STEVEN STAMKOS 0.240
4 MARIAN GABORIK 0.234
5 ALEX OVECHKIN 0.234
6 BOBBY RYAN 0.210
7 RICK NASH 0.209
8 ILYA KOVALCHUK 0.207
9 JEFF CARTER 0.201
10 PATRICK SHARP 0.200
11 ALEX BURROWS 0.197
12 PHIL KESSEL 0.196
13 JOFFREY LUPUL 0.196
14 JONATHAN TOEWS 0.196
15 DANIEL SEDIN 0.196
16 JAROME IGINLA 0.195
17 JAMES NEAL 0.195
18 MATT MOULSON 0.195
19 MARIAN HOSSA 0.193
20 EVGENI MALKIN 0.191

Lupul sits right there in 13th spot right behind Kessel and just ahead of guys like Toews, D. Sedin, Neal, Hossa and Malkin. That’s not too shabby if you ask me and certainly worthy of a $5.25M/yr deal if you ask me. The reason for Lupul’s perceived performance increase last year is largely due to more ice time, and more PP ice time in particular, and not because of luck or a one year wonder type thing.

Update: Edited to indicate the chart uses 5 years of data, not just last season.

 

 

Jan 172013
 

Earlier today I wrote a post about Tim Connolly and his offensive production at even strength. Shortly after posting that I thought a similar article comparing the performances of Bozak, Kadri and Conolly would be an interesting piece since they are sort of competing for roster spots (more so Kadri and Connolly than Bozak though). Of course, in the mean time Connolly has been put on waivers so to some extent he isn’t relevant anymore but I am including him for interest sake.

Here is a look at their individual offensive performances for the last 2 seasons for Bozak and Kadri and last year for Connolly (since he wasn’t with the Leafs in 2010-11).

Bozak:

Season ESTOI Goals Assists Points TOI/Pt
2011-12 1121:53 14 20 34 33:00
2010-11 1190:08 8 12 20 59:30
Combined 2311:01 22 32 54 42:48

Kadri:

Season ESTOI Goals Assists Points TOI/Pt
2011-12 263:24 4 2 6 44:04
2010-11 382:22 3 7 10 38:14
Combined 645:46 7 9 16 40:22

Connolly:

Season ESTOI Goals Assists Points TOI/Pt
2011-12 940:12 11 20 31 30:20

What is interesting is of the three, Connolly had the best TOI/Pt last year, even better than Bozak who benefited from playing primarily with Kessel and Lupul. Kadri’s most frequent line mates were Lombardi, MacArthur and Connolly while Connolly’s played with almost everyone but had the most minutes with Crabb, Kessel, Lupul, Lombardi and MacArthur (between 180 and 260 with all of them). It seems Connolly was far from the least productive Leaf forward at even strength.

That said, it seems irrelevant now what Connolly has done so more important is to look at Bozak vs Kadri. Overall they have had similar point rates over past 2 seasons but Bozak was much better last year. If all that came from playing with Kessel and Lupul then maybe Kadri is at least equally good.  And when you factor in that Bozak is a downright terrible defensive player I’d almost certainly give Kadri ice time over Bozak.

To put the above stats into perspective, here are Grabovski’s over the past 2 seasons.

Season ESTOI Goals Assists Points TOI/Pt
2011-12 1126:42 18 23 41 27:29
2010-11 1232:33 19 24 43 28:40
Combined 2359:15 37 47 84 28:05

Clearly Grabovski has produced much more at even strength than any of the other three and pretty consistent too. To put Grabovski into perspective though, Malkin had an even strength point every 16:37 last season. That’s domination.

 

Jan 172013
 

Yesterday evening James Mirtle from the Globe and Mail posted an article on The Curious case of Tim Connolly and the Leafs.  It’s worth a read so go read it but the premise of the article is how the narrative around Tim Connolly in training camp is he had a poor year last year and he needs to perform better this year.  Makes sense from most peoples view points but Connolly tries to present a different perspective.

Connolly can be prickly to deal with and wasn’t particularly interested in talking about last season, but when pressed, you could tell he felt he did more of value than the narrative – that he’s been an unmitigated bust in Toronto – would suggest.

Here was his answer when asked (maybe for the second or third time) about needing to “rebound” this season.

“Even strength, I think I had my second highest career points last year,” Connolly said. “I’d like to improve my play on the power play and maybe play a bigger role. Penalty killing, I think, my individual percentage was 89 per cent I read somewhere. I was able to lead the forwards in blocked shots.”

He makes two points in there.  The first is that he had his second highest even strength points last year and the second was something about individual percentage was 89 percent. Lets deal with the first one first by looking at his even strength points since the first lockout.

Season Goals Assists Points
2011-12 11 20 31
2010-11 7 16 23
2009-10 9 27 36
2008-09 12 16 28
2007-08 3 20 23
2005-06 9 20 29

(Note: Connolly only played 2 games in 2006-07 so I have omitted it from the table and discussion)

Tim Connolly is actually correct.  His best even strength point total came in 2009-10 when he had 36 points followed by his 31 even strength points last year.  But let’s take a look at those point totals relative to even strength ice time.

Season ESTOI Points TOI/Pt
2011-12 940:12 31 30:20
2010-11 840:31 23 36:33
2009-10 966:41 36 26:51
2008-09 631:26 28 22:33
2007-08 603:18 23 26:14
2005-06 708:47 29 24:26

The last column is time on ice per point, or time on ice between points.  Last year he was on the ice for an average of 30 minutes and 20 seconds between each of his even strength points. This was his second worst since the locked out season. So, while Connolly was technically correct in saying that he had his second highest even strength point total last season, it was a somewhat misleading representation of his performance.

Now for the individual PK percent. It generated a bit of twitter conversation last night questioning what it actually is.

One might think it is the penalty kill percentage when he was on the ice but that seems like a strange thing to calculate.  Is it goals per 2 minutes of PK time?  Is it goals per PK he spent any amount of time killing?  I really didn’t know so I dug into the numbers deeper by looking at the Leafs PK percentages on my stats site and noticed that Connolly had the best on-ice save percentage (listed as lowest opposition shooting percentage) of any Leaf last season during 4v5 play and that save percentage while he was on the ice was just shy of 89% (88.68%). It seems that maybe what Connolly meant to say was that he had an on-ice PK save percentage of 89%.

How good is an 89% save percentage on the PK?  Well, of the 100 forwards with at least 100 4v5 minutes of ice time last year, Connolly ranks 42nd in the league so league wide it isn’t that impressive but considering the Leafs weak goaltending it might actually be fairly good.

Here is the thing though. Single season PK save percentage is so fraught with sample size issues that it is next to useless as a stat for goalies let alone forwards.

One could evaluate Connolly based on PK goals against rate in which he came up 3rd on the Leafs (trailing Lombardi or Kulemin) but that is still fraught with sample size issues. More fairly we probably should evaluate Connolly’s PK contribution based on shots against rate or maybe even more fairly fenwick or corsi against rates. In each of those categories he ranked 5th among Leafs with at least 50 minutes of 4v5 ice time with only Joey Crabb being worse. Furthermore, among the 110 players with 100 minutes of 4v5 PK ice time last year, Connolly ranked 99th in fenwick against rate.

I don’t mean for this article to be a Connolly bashing article. I actually do think Connolly was a little misused and would probably do better with a more well defined role and not bounced around in the line up so much so in that sense I agree with the premise of what Connolly is saying. With that said though, it probably is fair to say that he didn’t have a great season and if he wants a regular role in the top six with time on the PP and PK he needs to perform better as his use of stats to attempt to show he had a good season is really just evidence to how statistics can be misused to support almost any narrative you want.  As they say, there are lies, damn lies, and then there are statistics.