Anaheim – McKeen's Hockey

Kats Krunch: Examining the Stretch Pass

Gus Katsaros — Sat, 05 Mar 2016 14:40:45 +0000

In a post about drop passes to enter the zone on the power play in Arik Parnass special teams project , he commented about how a stretch pass leading to more quick strikes than sustained zone time.

The quote is here, but I would recommend reading that post for the effects of a drop pass in the neutral zone, personal no-no if I was coaching any team, regardless of the effectiveness.

Here’s Arik’s definition of a stretch pass.

My definition of a stretch pass was a little bit subjective, but I think hockey guys would agree that you kind of know one when you see one. Generally speaking, a stretch pass is a pass that comes before the defensive blue line that tries to stretch the defense, generally hitting a guy with considerable speed in motion at the offensive blue line.

Predictably, stretch passes lead to the fewest average seconds in the offensive zone. They are the highest risk plays and strive for shot quality on the rush over zone time.

I would include passes from the neutral zone, because of the nature of hitting the man on the fly while the speed element backs off the opposition in anticipation of breaking up a streaking man into the defensive zone. Fortunately, subjective as it may be, we have some data for that.

We can explore the stretch pass, considering there’s a small(ish) set of workable data via the passing project. Now we can isolate stretch passes and investigate their impact … if any.

Quick strikes from both teams leads to firewagon hockey, fun to watch but a nightmare to coach, and reliant on both teams abandoning defensive structure, looking for quick rushes the other way, or trying to catch the opposition off guard.

The 2013-14 Toronto Maple Leafs were the epitome of quick strike hockey, trying to take advantage of speed and shots from the rush, forsaking sustained zone time and pressure. Needless to say, the strategy, while indicating some early success, failed miserably.

Clearly, the style is not a sustainable model.

But do stretch passes make any difference? Let’s get into the passing project data for aggregation and context.

The data fortunately allows for event sequencing (labelled as A1, A2, or A3) along with originating zone (A1 Zone, A2 Zone, A3 Zone), beginning with three passes from an eventual shot event, a shot on goal, or shot directed to the net (missed/blocked) and a goal.

The project, to a detriment, doesn’t record passes that don’t lead to an eventual shot event, a void to analysis, since there is value in determining why or how plays broke down into non-events. The non-event could provide quite the bit of value, but with the amount of work involved and all done on a volunteer basis places a great onus on trackers to be focused.

Using the entire dataset from the project’s latest release, I looked at two types of stretch passes at 5v5:

a primary pass preceding an eventual shot event designated as a stretch pass as the from the defensive or neutral zone, stamped with a ‘s’ in the zone data
a primary pass prior to a shot event originating from the offensive zone, after a stretch pass allowed for entry into the zone either from the defensive or neutral zone.
If applicable, the percentage of rebounds generated from both above categories.

Starting with the table below, I’ve separated stretch passes into two categories we can calculate the shooting percentage or efficiency via each event.

Getting a stretch pass and fring a shot on goal has led to an 8.35% shooting percentage. When there’s a pass in the offensive zone after the initial stretch pass, shooting efficiency drops to 5.62%.

Situation	# of Events	SOG sh%	Stretch Pass sh%
Stretch Pass	587	8.35%	5.62%
Stretch + 1 OZ Pass	265	7.75%	4.41%
Rebounds	21/12	3.58%	4.53%

The problem is unfortunately is one of small samples. The Passing project has tracked approximately 270 games, or about 22% of 2015-16 games. With 587 events, there’s approximately 2.2 stretch pass events that leads to a shot on goal.

Intuitively, the results make sense. There’s a greater chance at scoring a goal off a solo rush and quick strike via a stretch pass. When it comes to rebounds, quick strikes lead to less rebounds, by a small percentage difference from a rebound off an offensive zone pass before the shot. An interesting note, of the 21 rebound events recorded - 11 shots on goal - with 10 players getting their own rebound, while 10 times a trailer, or another player, stealthily, or via a speed burst got to the net to take advantage of any loose pucks – without generating a shot on goal.

With the limitations in the amount of tracked games, it’s difficult to attain uniformity among all NHL teams, but we can do an estimate of the amount of stretch passes allowed per game at 5v5. The table below calculates stretch passes per game taken and allowed by all NHL clubs. The amount of individual games tracked is included for context.

From the project data, it seems like San Jose is the biggest culprit on both ends, taking advantage of stretch passes while allowing the most per game. Giving teams chances to score, even if it’s a couple of times per game at 5v5 could be detrimental.

Sticking to California, Los Angeles seems to allow a couple of stretch passes per game in comparison to the amount of passing events they generate (we know of the grinding style LA utilizes, featuring effective zone time and shot generation), while an hour down the way, Anaheim is among league leaders in generating scoring chances from stretch passing.

Tm	GP	Pass/GM Taken	SOG/Gm Taken	Pass/Gm Allowed	SOG/Gm Allowed
ANA	9	1.89	1.44	1.11	0.67
ARI	8	1.38	0.88	1.63	1.38
BOS	21	1.19	0.86	1.52	1.00
BUF	14	1.36	0.86	1.29	0.86
CAR	15	1.20	0.87	0.67	0.47
CBJ	14	1.36	0.93	1.50	1.21
CGY	15	1.07	0.67	2.00	1.20
CHI	49	1.27	0.69	0.76	0.53
COL	15	0.33	0.20	0.93	0.53
DAL	27	1.63	0.96	1.04	0.59
DET	17	1.29	0.82	1.06	0.76
EDM	20	1.15	0.80	1.85	1.15
FLA	17	0.35	0.29	0.59	0.29
L.A	9	0.78	0.56	2.00	1.56
MIN	12	1.58	0.83	1.50	0.75

Elliotte Friedman outlined in a 30 Thoughts blog about the Montreal Canadiens propensity to throw pucks into the neutral zone and fight for the puck outside of the defensive zone. It’s not exactly a stretch pass, but this is a similarity to the quick strike notion outlined in the stretch pass definition.

Tm	GP	Pass/GM Taken	SOG/Gm Taken	Pass/Gm Allowed	SOG/Gm Allowed
MTL	15	1.60	1.40	0.80	0.53
N.J	50	0.44	0.32	0.84	0.64
NSH	12	1.25	0.75	1.00	0.58
NYI	6	0.83	0.67	1.00	0.67
NYR	15	1.60	1.13	0.67	0.53
OTT	13	1.15	1.00	1.31	1.00
PHI	10	0.50	0.40	0.20	0.20
PIT	10	0.90	0.60	1.60	1.10
S.J	21	2.33	1.76	2.62	1.86
STL	14	1.57	1.07	1.07	0.71
T.B	33	0.58	0.30	0.61	0.33
TOR	24	0.79	0.54	0.83	0.63
VAN	16	0.81	0.69	1.06	0.63
WPG	14	0.71	0.43	1.43	0.93
WSH	29	0.79	0.48	0.31	0.21

This, along with a bunch of other interesting items should become a lot more clearer with a greater data set.

Get tracking.

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Anaheim Ducks 2014-15 Season in Charts

Gus Katsaros — Tue, 21 Jul 2015 02:55:24 +0000

For the Toronto Maple Leafs charts:

For the Minnesota Wild charts:

These are the preliminary charts I gather for each team before getting into each individual player (and before any video). These are starting points, not end results. Analysis that these jump off into will be within the pages of the Yearbook.

This doesn't happen without the sites for this data:

War On Ice Hockey Analysis and Behind The Net

Lets start with some PDO components.

Corey Perry recorded his final power play point of the season on Jan 30, so I split the Ducks season into two parts, pre- and post-Jan 30.
The results.

PRE:
50 GP - 29 goals – 163 opportunities - 17.8% efficiency
0.58 power play goals per game on 3.26 opportunities.
POST
32 GP – 8 goals – 74 opportunities – 10.8% efficiency
0.25 power play goals per game on 2.3 opportunities

These power play charts below accompany that trend.

Situational Corsi For percentage 10-game moving average for 1 year and then 2 years below that.

The below chart is a quarterly breakdown of points by players.

Differential Charts

Differential charts outline last season's production in relation to 1 year ago and a rolling 3 years.
With data from stats.hockeyanalysis.com I divided 2014-15 season into 2013-14 season to produce a differential. Players matching in both seasons production would receive a value = 1 which is also why the charts axes cross there.
Results greater than 1 signify outperforming the 1 year rate.
Results less than 1 means indicate underperforming their 1 year rate.
The 3 year results have a little twist.
There are two values:
-- 3-years ending 2014-15 (seasons of '12-13 thru to '14-15)
-- 3 year rate ending 2013-14 (seasons '11-12 ending '13-14).
The 3 year rate ending last season divided by the 3 year rate entering last season.
This ensures last season's rates don't influence medium term trends essentially isolating '14-15.
Divergent and scattered 1-yr rate differentials seemingly encase 3 year differentials as the latter converge closer to the middle, with players falling into career norms.
I called these relative differentials so on the charts you'll see 'rel'.
Bubble size is Pts/60

Minute per iCorsi & iFenwick and Miscallaneous

I like looking at time between events for clues. This chart measure the amount of time between each individual shot attempt for each player. Black dot denotes time on ice leader.

Situational Stats

These are all 10 game moving averages of goals or shots per 60 in various situations as denoted by the legend.
The dotted lines represent the NHL rolling 10-game moving average for comparison purposes.

Click to enlarge the images.

Kats Krunch: Tale of Two Power Plays

Gus Katsaros — Thu, 04 Dec 2014 21:24:13 +0000

Two teams, two power plays with two diverse results, while sharing a common affliction.

I had the opportunity to take in the Stars as they rolled in through the ACC to play the Maple Leafs on Dec 3, offering a glimpse into the abysmal Stars power play systems with some background through a live viewing to further decipher the reason for special teams futility. I had similar fortune with a live view of the Tampa Bay Lightning a couple of weeks back, offering the same opportunity to note the team’s power play systems – and potential flaws. The contrast in power play success here is striking.

They both have the same problem, a wide gap between shots on goal. Clearly, shot attempts are the best method of deciphering a power play and why it may not be firing on all cylinders, but what is presented below is a tale of two diverging successes despite the lack of shots.

This is the story of the potent Tampa Bay Lightning and futile Dallas Stars five-on-four power play.

I’m going to reserve the right to get into extensive video here, only because I’m trying to understand the power play in general on a more macro level. I’ll eventually get into the video breakdown but I’d like get it to another level with different teams playing different systems in zone – along with a different method of rushing through the neutral zone and gaining the blueline.

Below is a chart of the NHL power play as of Dec 2, 2014, after the Stars/Leafs game. Scales on either side represent power play efficiency and power play goals on the left and opportunities on the right.

Two bars are colored in red, two power plays with parallels despite two distinct styles. The Dallas Stars and Tampa Bay Lightning occupy a sizable gap in power play efficiency.

TampaBay has been humming along at a 23.6%, one standard deviation from the 18.57% NHL average and two standard deviations from Dallas sputtering at an abysmal 13.6%. both teams rank at the bottom of the league in power play shots with similar power play opportunities for each club. With a difference of eight minutes in 5v4 time has produced nine more goals for the Lightning than the Stars.

Team	GP	PP Opp	PP Shots	PPG	PP%	5v4 Time
TBL	26	89	91	21	23.6	131:50
DAL	25	88	79	12	13.6	140:02

Best illustrated in the image below, both teams take too long on average between shot attempts events on the 5v4 power play (for the purpose of this illustration I am only focusing on the one-man down situation, since a 5v3 carries a different dynamic). The Lightning and Stars take over 1:40 for a proper shot on goal.

In a direct comparison among the rest of the NHL, both teams are way above the pack in terms of time between shots on goal, comparing to the NHL averages as per the table below.

Tm	min/FF	min/CF	min/SF
TBL	1:08	0:50	1:43
DAL	1:15	0:48	1:48
NHL Av	0:53	0:38	1:12

The NHL average is 1:12 between a shot on goal, 53 seconds between Fenwick Events (unblocked shot attempts) and 38 for Corsi events. Historically, starting from the 2011-12 season, this is how the uniformity across the league looks year-to-year.

Fsh% and Csh% represent the percentage of shots that make up the underlying components of Fenwick and Corsi shot attempt metrics. I'll expand on the breakdown of Fenwick and Corsi events represented by shots for 2014-15 up to and including Dec 2, 2014 a little further down. For now this image is the comparison of time gap between shots on goal and the percentage of shots associated in Fenwick Events. Both Dallas and Tampa Bay are direct outliers here.

It’s different than a Pittsburgh that can set up and execute, or the Washington Capitals that are firing into a Corsi event every 28 seconds.

Some of the reasons I’ve observed to explain some of the team’s issues with the man-advantage are:

Tampa Bay Lightning

Lightning make it complicated to get into the zone from the breakout.
The setup for a breakout takes too long to get out of the defensive zone.
Options in the neutral zone aren’t always readily available and force the puckcarrier to gain the offensive zone as a result.
Wingers wait along the blueline to force back the defensemen, and may get a late pass to gain entry, otherwise set up as sentry posts without effect in the play.
Sometimes play with the puck at the top of the zone introducing a risk of penalty killers easily moving the puck out and forcing a regroup.
Less structure and more freelancing in the neutral zone on the regroup.
Once set up in the offensive zone, they pass it around too long looking for the perfect shot or the seam to send pucks through.
Too many blocked shots.

Dallas Stars

Dallas moves too slow through the neutral zone regardless of a formal breakout or a regroup from a dump out of the offensive zone by the penalty killing team.
Too many stationary players along the blueline waiting to get the zone instead of movement
Plays seem forced along the blueline.
Too much individualism, trying to gain the zone and not enough passing options.
Once gaining the zone, the tendency is to fire the puck around the boards to set up (seems a waste since they’ve already gained the zone.)
Stars also seem to switch the set up from new-wave traditional 1-3-1 into a modified 2-3 with two shooters at the top and three forwards a little lower in the zone (slot presence moves to the crease with the puck at the top of the zone and into the slot with the puck on either wing, with the weak side winger slightly edging towards the net as the set up to the slot man becomes the go-to play.)
Limiting the data to only shots on goal only limits the shot attempts function, so I wanted to see just what is happening with the shots in regards to overall shot attempts. The image below illustrates the percentage of shots comprising Fenwick (unblocked shot attempts) and Corsi (including blocked shots) events at 5v4. Both clubs fire less than 50% of their Corsi events as shots on goal. Tampa Bay has a slight edge over the Stars in terms of percentage of shots for their Corsi – although both are below the 50% threshold (Edmonton is the next closest bubble along the slope to the right of Tampa Bay).

Historically, within the BtN era (2007 - 2014), this is what the chart looks like up to and including 2014-15 data. Colorado has had the highest percentage of shots with Los Angeles, St. Louis and Dallas at the other end. Averages are in the blue box.

With the issue of gaining the zone eating up some valuable time off the clock during the 5v4, switching the focus from zone entry to in-zone time, both teams struggle to get shots on goal in comparison to the NHL average. Using data from BehindtheNet.ca sheds some light on what’s happening during shot attempts.

Dallas gets more shots on goal through to the net on a per-60 basis, yet fire more pucks that miss the net according to NHL average, but have half the rate of blocked shots at 5v4.

TampaBay, on the other hand, are almost three times as likely to have a shot blocked, pulling down the overall shots-for per 60 rate to almost half the NHL average. Not only are they taking a little too long to get shots through to the net, they’re struggling to get shots on goal.

Team	SF On/60	MF On/60	BF On/60
DAL	21.16	17.42	1.44
T.B	16.24	17.3	9.61
NHLAv	35.05	15.95	3.46

Taking it down one level from the team to position, the Lightning blueline seems to have a greater majority of shots blocked compared to the NHL average, while the forwards coast along the average, missing more shots than the average. It’s juxtaposition with the Stars and Lightning for missed shots, Stars forwards missing a lot more than defensemen and forwards firing past the net than Lightning defensemen.

Position	Team	SF On/60	MF On/60	BF On/60
Defense	DAL	18.69	21.71	1.35
Defense	T.B	13.69	14.78	12.53
Defense	NHLAv	36.65	15.98	2.47
Forward	DAL	22.48	15.13	1.49
Forward	T.B	21.58	21.78	3.43
Forward	NHLAv	34.18	15.94	3.99

There’s a possible shot quality argument that can be made at 5v4, exemplified by Tampa Bay, while Lightning writer, Kyle Alexander, makes a salient point of the Lightning’s power play.

@kalexanderRC I think shot quality matters more on the PP too but the data does suggest 5v4 SH% is almost all luck http://t.co/ZMlj1B2IHP

— Fear The Fin (@fearthefin) November 17, 2014

That chart in the link indicates that power play shooting percentages can be heavily influenced by ‘luck’ – a term I’m not apt to use without an explanation that ‘luck’ is more about the repeatability of a skillset to achieve similar results rather than pucks bouncing off of player’s asses into the gaping net.

More power play analysis is going to come down this pipeline.

Stats via Hockey Analysis and Behind The Net

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Kats Krunch: Q1 NHL Score-Adjusted Metrics & Situational TOI

Gus Katsaros — Tue, 02 Dec 2014 15:47:07 +0000

American Thanksgiving marked the unofficial end of the first quarter of the NHL season and time for some retrospection of some teams. Traditionally, I’d turn to typical Corsi and Fenwick along with derivative stats, but I've been enthusiastic by these newer score-adjusted metrics quickly establishing their value into the analytics discussion thus far around the quarter mark of the NHL season.

The original work by Eric Tulsky using the original data via behindthenet.ca has seen tweaks and enhancements to even suggestions that ‘close’ metrics may not necessarily be as predictive or originally though.

Steve Burtch expanded on this concept based off work by Micah Blake McCurdy and I’m intrigued to see where this is going. The nature seems to be to discard the limitations associated with ‘close’ parameters and supplement that with score, venue and schedule adjusted data.

There are even resources already doing the calculations for you, like Puck on Net, while and Puckalytics and original sister site Hockey Analysis already houses the raw data.

I like how the distinction here is between game-states, however, for the purposes of this writing, I’m not looking at the predictive value, only a snapshot of teams in different game states and keying in on some shorter team trends.

I like the fact we can break down specific game situations by time and events, when teams are tied, up or down by a goal, then by two or more goals. Let’s begin.

GAME TIED

Teams end up the majority of time with the game tied. There are outliers like the Pittsburgh Penguins who score first early and skew the time on ice by spending the majority of their time up a goal or two. The near past also shows a jump in the metrics based on time on ice with a deficit.

Minnesota, Carolina and Columbus also share this affinity, however that cluster of teams at the top of the chart, the bottom feeders of the NHL and the … Calgary Flames?

The chart (using data by Puck on Net) illustrates raw Corsi For and Corsi Against while the game is tied while bubble size represents time on ice. Teams in the lower left quadrant are playing less when the game is tied and more traveling along the x-axis. The cluster at the top is interesting due to the inclusion of the pesky Flames with that group. In short, while playing most of the game tied, the Flames along with the cluster of bottom feeding teams are allowing more Corsi Against events than Corsi For.

Time on ice is fairly consistent, the bubbles being very similar in size, albeit the ones in the lower left quadrant seem to be a bit smaller, mirrored by the low number of overall events. We will expand on the Hurricanes and Wild below.

GAME DOWN BY A GOAL

Here I’m more concerned with teams playing in traditional close situations and their individual components.

When the game starts to get away and teams start to lead by two or more goals, score effects kick in and we see teams with the lead press on a little less and teams playing without the lead apply more shooting pressure. Score effects are well documented and don’t need any expansion here.

The graph plots individual Corsi For events along the x-axis and the Corsi Against along the y-axis.

Most teams are clumped together in a range with outliers here are the Carolina Hurricanes and even the San Jose Sharks (while also playing at a high proportion of game tied minutes) with almost double the For events than Against. The Canes spend the majority of their game time down one goal and the cumulative effect over the season timeline has recently surpassed their time on ice while the game is tied (team charts are a rolling 3-game moving average).

The uptick with the team up by a goal and up by two goals, eerily corresponds to the return of team leader, Eric Staal from injury and even though the Hurricanes spend an inordinate amount of time playing from behind, the black line indicating being down by two or more goals has flatlined. Even with the Canes time on ice down by a goal their score-adjusted shot metrics are creeping up to over 50%.

The San Jose Sharks started off fairly hot, but signs over the near term are trending negative. Travis Yost, the analytics writer over at TSN does a good job expanding on the Sharks off season transactions after the disastrous playoff exit and just how well Joe Thornton has performed for the scrutiny he’s faced seemingly his entire career in the Bay Area.

This is how the Sharks situational season looks.

We can see early on how they played more with a wide lead and then (scoring 3.8 goals per game) and sputtered (down to 2.7 goals per game) – while even losing to Buffalo 2-1 at home. At the quarter point, the down one goal line begins to trend up – sharply, coinciding with a rise in time down two or more goals. Both metrics indicating playing with the lead are a lot flatter than a winning team desires.

A 3-game moving average of score adjusted Corsi, Fenwick and even shots, are all trending down after swooping upswing earlier on in the season. Something to watch for the California based club.

GAME UP BY A GOAL

Let's look at now at the game state of being up by a goal. There’s a little more separation here from the chart down one goal, with two main clusters.

It's no surprise Buffalo with a very small bubble is also shown your with very few Corsi For events, just over 50, and having almost four times the same amount of Corsi Against events. In fact, bubble size expands the further the bubble appears from the y-axis, with Winnipeg looking like Jupiter sized bubble compared to the Mars and Mercury-like size of the bubbles in the lower left quadrant.

Not pictured here, since these values are as of the American Thanksgiving, is the November 28 Buffalo game against Montreal featured the Sabres with a one goal lead for 30 minutes. Heading into the contest, having amassed an unassuming 118 minutes up one goal, the 30 minute increase represented an increase their time by almost 25% of the season total.

The real outlier here is the Winnipeg Jets, tracing the knife’s edge between winning and losing while only really being up a goal for the majority of their playing time (other than a tie game). Their situational season timeline is in the chart below.

The Jets were going down by more than two goals very early in the season and then between the fifth and 10th game something began to limit their defense affecting their down by two or more goals line to plateau, with a corresponding ascension in both game tied and up one goal situations.

Winnipeg started the season with a 1-4 record - scoring at a rate of 1.8 goals per game and allowing three - only to win six of their next eight games, allowing 11 goals (1.375 per game) and scoring only two per game – a very thin margin. They rallied along to a .500 record and a mark of (5-4-3) record in the last dozen games. To maintain any semblance of success the run after the break away from the pattern of being stuck in the zone between game tied in plus-1 situations. Their individual score-adjusted Corsi, Fenwick and Shots 3-game rolling average has seen the increase from the beginning of the season to peak just over 51% overall for all three metrics.

Let's move on to our final team, the Minnesota Wild. A hot start has become a staple for the Wild, only to tail off and struggle for a playoff spot.

Not so in 2014-15 where they started off with consecutive shutouts against last year’s surprising Colorado Avalanche. The Wild sustained some immediate success finding some scoring touch early on (averaging 3.4 goals in the first 10 games and only 2.3 since then up to American Thanksgiving) exemplified by their up two goals line in the chart below.

There’s a hiccup during a four game stand that saw them score only three goals, while allowing 14, indicated by the flat segment of the up two goals line and sharp upturn in the down two goals line. In the near past, that down two goals line has once again taken an immediate sharp upturn as the Wild defense hasn’t shown the same stinginess it did in the early part of the season.

When taking the individual Score-Adjusted Corsi, Fenwick and Shots moving average, the trend is negative from an unsustainable 60% clip early in the campaign. Even at this 55% clip, there is still some room for a negative correction with the result being a loss of standings points. It’s a good bet to keep a watch on the Wild.

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NHL Weekly Breakdown 2013-14

Gus Katsaros — Mon, 24 Mar 2014 05:29:53 +0000

For poolies, save those players games for late in the season, as the end of the 2013-14 season is very heavy in games and could be an opportunity lost. Take into consideration how many games your players are likely to capitalize on and plan accordingly.

This is especially true for those in head to head fantasy leagues since this period represents playoff time. Planning accordingly is a requirement for the final few weeks, especially if you can pluck key players that are likely to dress in late season games on teams with heavy schedules.

I’ve prepared a Google doc cutting it a little close but with the season set to start, it’s definitely a good time to publish the weekly schedule and make it available for a full season.

The Google doc has two tabs, with the suggestion to add the actual start and end dates for the weeks implemented.

Pay attention closely to the final five weeks feature five straight 100+ weekly games after the Olympics, while averaging 105 games per week. Weeks 22 and 26 are tied for the season high of 108 games.

Edmonton, Florida and Pittsburgh are all at home in the final week of the season, while Toronto and Colorado are on the road. San Jose has a back and forth pattern of home and road swings making for an interesting schedule that includes a lot of home dates.

Other highlights include.

The first 21 weeks average about 92 games with the high water mark at 102 (twice – in Week 9 and 11) and another 100 week once (Week 17). Week 1 has the lowest amount of games with 68 as the league opens up on rare Tuesday.

The Sochi Olympics take place between Weeks 19 and 20; things get jam-packed when they return.

In 2012-13 there were 110 games per week twice (Weeks 7 and 11).

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AHL: 2013-14 3-in-3 Sets

Gus Katsaros — Thu, 03 Oct 2013 05:21:33 +0000

This post highlighting the AHL schedules was scheduled earlier in the rotation, originally constructed before I presented the CHL 3-in-3 sets for all member clubs.

The NHL feeder league that schedules three games in three nights is the American Hockey League, housing the minor league affiliates for NHL clubs.

This post breaks down the number of 3-in-3 sets for each AHL team. There are a lot of key differences from the CHL, but I’ve kept the criteria similar and just present the data as is, with some commentary.

I had broken this down for 2012-13 which also has a historical look at from the previous season (2011-12).

2013-14 SEASON SETS

Teams average eight sets in ’13-14. Perennial leaders in the category, Providence Bruins (14) slipped to number two, while over two standard deviations from the average. The minor league affiliate for the Boston Bruins is actually tied for second with Hershey Bears (Washington) and Manchester Monarchs (Los Angeles).

This season, the Worcester Sharks, minor league affiliate of the San Jose Sharks is the AHL leader with 15 sets.

Providence and Manchester start off the 2014 calendar year with a 3-in-3 set every weekend in January.

Hershey has a killer stretch of 24 straight games of 3-in-3’s.

The breakdown by month has consequences as well.

For instance, a younger Toronto Marlies club in comparison to the previous seasons will play six sets of 3-in-3, with the first set after the turn of the calendar.

Toronto’s first set appears in game number 40, playing the entire first half of the season without a 3-in-3 set. The two straight sets begin in January 24 in Hamilton against the Bulldogs before completing the first set with two home dates. The second set has them traveling to Oklahoma City, then down to San Antonio and Texas.

The down side of half a season advantage are a bunched amount of four sets after March 7, including three straight sets over a 9-game period starting March 28 through to mid April, right on the eve of the playoffs.

In essence, the Marlies play 12 of their final 21 games as 3-in-3 sets – one set entirely on the road all with possible playoff consequences.

Portland (affiliate to the Phoenix Coyotes) dress for three sets in April, an AHL high. Check out Manchester as the opponent in Game 2 in two sets and the final game of the season.

GAMES 1 2 & 3

Of course, advantages also exist with similarities to the CHL scheduling. That is, scheduling has the residual benefit of playing an opponent in Game 3, a distinct advantage.

The Hershey Bears face an opponent playing in game 3 of a 3-in-3 set 18 times to lead the league. In a 76-game schedule, that amounts to about 24% of their schedule. Manchester (17) and Providence (16) trail the leader.

At the opposite end, the Rochester Americans (Buffalo Sabres) is the only team that will not face an opponent at all playing Game 3 of a 3-in-3 set.

Oklahoma City Barons (Edmonton) play three games and Abbotsford (Calgary) Charlotte (Carolina), Grand Rapids (Detroit), Hamilton (Montreal) and St John’s (Winnipeg) all have four (4) such games.

The full breakdown, including the monthly supplemental is located in the table below. The headings Road and Home signify if the sets are all at home or on the road. the VS GmX is the amount of games played with the opponent playing games 1 thru 3.

TEAM	3in3	OCT	NOV	DEC	JAN	FEB	MAR	APR	ROAD	HOME	VS Gm1	VS Gm2	VS Gm3
Abbotsford	4	1	0	0	0	1	1	1	4	0	3	4	4
Adirondack	9	0	1	1	0	1	4	2	2	1	13	14	8
Albany	11	0	0	1	3	2	3	2	3	1	9	10	13
Binghamton	8	1	2	0	2	0	1	2	1	0	11	5	6
Bridgeport	13	0	2	3	3	1	2	2	0	0	6	12	14
Charlotte	5	1	0	1	1	0	1	1	3	2	2	1	4
Chicago	6	0	0	1	1	2	1	1	0	0	2	6	10
Grand Rapids	6	1	0	0	1	1	3	0	3	1	7	6	4
Hamilton	9	1	0	2	2	1	3	0	2	1	3	4	4
Hartford	9	0	0	1	2	1	4	1	1	0	14	11	8
Hershey	14	0	1	1	4	3	3	2	2	0	9	11	18
Iowa	6	0	2	0	1	1	1	1	2	2	5	6	7
Lake Erie	5	1	1	0	1	0	1	1	2	0	3	3	5
Manchester	14	1	1	3	3	3	2	1	1	2	13	12	17
Milwaukee	9	0	1	2	1	2	2	1	2	0	6	4	10
Norfolk	10	1	1	2	2	1	1	2	7	0	6	5	8
Oklahoma City	5	0	0	2	1	1	1	0	3	2	6	5	3
Portland	12	0	1	1	2	2	3	3	0	2	11	12	11
Providence	14	0	2	2	4	0	4	2	1	0	13	10	16
Rochester	3	0	0	0	0	1	1	1	0	0	11	9	0
Rockford	6	0	0	1	2	1	1	1	0	0	9	12	5
San Antonio	3	0	0	0	1	1	0	1	1	1	8	5	7
Springfield	9	0	1	0	2	3	2	1	1	1	13	12	9
St. John's	6	1	1	0	1	2	1	0	6	0	4	6	4
Syracuse	7	0	0	0	1	1	4	1	0	0	11	13	5
Texas	3	1	0	0	0	1	0	1	1	0	6	7	6
Toronto	6	0	0	0	1	1	2	2	2	0	6	7	11
Utica	6	0	0	1	0	2	2	1	1	1	8	4	6
W-B/Scranton	10	1	0	0	3	2	2	2	3	0	10	12	10
Worcester	15	0	2	3	3	2	4	1	3	1	15	15	10

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Rate Stats Relative to 3-Year Average Ratios – Overperformers

Gus Katsaros — Tue, 24 Sep 2013 17:46:48 +0000

I put up a post describing the use of a comparative ratio using players ’12-13 production rate stats (per 60 minutes) divided by their 3- and 5-year averages – excluding the shortened season.

The point of the exercise was to have an independent comparison of the shortened season production, without extrapolating totals to simulate production per-82 games. I went into the details in the previous post so I’ll just put an excerpt here.

Data provided by www.stats.hockeyanalysis.com

The essential driving factor here is shots on goal per 60 minutes, tweaking the filtering criteria depending on the ratio.

To isolate underperformers, I used the following criteria:

SOG/60 > 1

Goals/60 <1

This returned a list of players that fired pucks at a rate greater than their 3-year average but didn’t score at the same clip than in the past (despite the uptick in shots/60 ratio)

For outperformers:

SOG/60 < 1

Goals/60 >1

The returned players fired less than their 3-year average, yet scored at a clip greater than their 3-year average.

The third filter was to determine consistency – particularly in shooting rates. This required incorporating 5-year average ratios as well, adding another long(er) term ratio filtering down the listings. In the end, 26 players made the final filter, some interesting names, some others negligible in the grand scheme

Today we are looking at players that outperformed their 3-year average of goals/60 while shooting at a reduced rate (SOG/60 < 1).

A total of 122 players make the cut here, split 89/33 forwards to defensemen.

Relative to 3-yr average - Defensemen
Player Name	Team	Sh%	G/60	A/60	1stA/60	Pts/60	SOG/60
FRANCOIS BEAUCHEMIN	Anaheim	2.57	2.42	1.95	1.37	2.06	0.94
SHELDON SOURAY	Anaheim	2.10	1.30	0.76	0.38	0.96	0.62
DENNIS SEIDENBERG	Boston	2.21	2.01	0.79	0.62	1.04	0.90
ZDENO CHARA	Boston	2.05	1.85	0.60	1.19	0.80	0.90
ALEXANDER SULZER	Buffalo	4.90	3.88	0.74	0.00	1.87	0.79
ANDREJ SEKERA	Buffalo	1.44	1.08	1.50	1.33	1.39	0.75
DENNIS WIDEMAN	Calgary	1.05	1.05	0.77	0.95	0.83	1.00
JOE CORVO	Carolina	2.16	2.08	1.21	0.43	1.43	0.97
BRENT SEABROOK	Chicago	2.72	2.03	0.58	0.54	0.85	0.75
FEDOR TYUTIN	Columbus	1.74	1.32	2.00	3.41	1.79	0.76
JAMES WISNIEWSKI	Columbus	1.50	1.29	0.97	1.61	1.00	0.86
NIKITA NIKITIN	Columbus	1.24	1.03	0.45	0.00	0.58	0.83
PHILIP LARSEN	Dallas	2.35	2.19	0.59	0.54	0.73	0.93
COREY POTTER	Edmonton	2.71	2.23	0.00	0.00	0.75	0.82
TOM GILBERT	Minnesota	2.72	1.93	1.13	0.65	1.25	0.71
ALEXEI EMELIN	Montreal	1.84	1.62	3.80	3.25	2.90	0.88
ANDREI MARKOV	Montreal	1.72	1.37	0.34	0.20	0.46	0.80
HENRIK TALLINDER	New Jersey	1.16	1.15	0.39	0.00	0.58	0.99
RADEK MARTINEK	NY Islanders	6.81	5.56	0.00	0.00	1.58	0.81
MARK STREIT	NY Islanders	3.95	3.44	0.97	0.60	1.15	0.88
MARC STAAL	NY Rangers	1.68	1.11	2.14	2.66	1.92	0.66
KIMMO TIMONEN	Philadelphia	1.10	1.07	0.91	0.27	0.93	0.96
PAUL MARTIN	Pittsburgh	9.45	7.85	1.24	1.33	1.84	0.82
KRIS LETANG	Pittsburgh	1.38	1.37	1.82	2.18	1.75	0.98
DOUGLAS MURRAY	Pittsburgh	2.25	1.41	1.34	0.57	1.34	0.63
MARC-EDOUARD VLASIC	San Jose	1.70	1.61	0.43	0.28	0.68	0.94
BARRET JACKMAN	St. Louis	11.76	9.82	0.81	1.13	1.11	0.83
KEVIN SHATTENKIRK	St. Louis	1.06	1.05	0.99	0.60	0.99	0.98
MATT CARLE	Tampa Bay	2.25	1.69	0.84	0.77	0.91	0.75
DION PHANEUF	Toronto	2.92	2.03	0.84	0.92	1.20	0.70
DAN HAMHUIS	Vancouver	2.00	1.61	1.18	1.61	1.26	0.81
ALEXANDER EDLER	Vancouver	1.44	1.34	0.82	0.41	0.91	0.94
TOBIAS ENSTROM	Winnipeg	2.48	1.71	0.76	0.35	0.93	0.69

Relative to 3-yr average - forwards
Player Name	Team	Sh%	G/60	A/60	1stA/60	Pts/60	SOG/60
BRAD STAUBITZ	Anaheim	2.20	1.76	1.76	3.09	1.77	0.80
DANIEL WINNIK	Anaheim	1.68	1.65	1.30	1.05	1.41	0.98
NICK BONINO	Anaheim	2.37	2.19	1.05	1.83	1.36	0.92
ANDREW COGLIANO	Anaheim	1.52	1.50	1.18	1.04	1.31	0.99
MATT BELESKEY	Anaheim	1.38	1.35	0.88	1.25	1.09	0.98
BRAD MARCHAND	Boston	1.29	1.15	1.34	1.70	1.26	0.89
JAROMIR JAGR	Boston	1.34	1.21	0.96	0.84	1.04	0.91
SHAWN THORNTON	Boston	1.37	1.20	0.92	0.28	1.02	0.87
STEVE OTT	Buffalo	1.41	1.30	1.29	0.93	1.30	0.92
STEVE BEGIN	Calgary	2.60	2.58	2.29	0.00	2.39	0.99
ALEX TANGUAY	Calgary	1.46	1.07	0.71	0.69	0.83	0.73
ERIC STAAL	Carolina	1.77	1.57	1.90	1.71	1.78	0.89
VIKTOR STALBERG	Chicago	1.07	1.04	1.22	1.63	1.13	0.97
MARIAN HOSSA	Chicago	1.15	1.05	0.89	0.79	0.96	0.92
DAVE BOLLAND	Chicago	1.58	1.23	0.71	0.81	0.93	0.77
DAN CARCILLO	Chicago	1.03	1.02	0.40	0.69	0.67	0.99
AARON PALUSHAJ	Colorado	1.54	1.17	2.05	3.51	1.88	0.76
TOMAS VINCOUR	Colorado	3.71	2.32	1.55	0.00	1.85	0.63
PIERRE PARENTEAU	Colorado	1.74	1.68	0.88	0.93	1.13	0.96
DEREK DORSETT	Columbus	1.55	1.34	1.72	0.48	1.56	0.86
ARTEM ANISIMOV	Columbus	1.86	1.75	0.78	0.75	1.17	0.94
VACLAV PROSPAL	Columbus	1.14	1.05	1.11	0.42	1.09	0.92
RYAN GARBUTT	Dallas	1.56	1.50	0.00	0.00	5.03	0.96
VERNON FIDDLER	Dallas	1.02	1.01	1.57	2.15	1.41	0.99
RAY WHITNEY	Dallas	1.56	1.29	0.95	0.85	1.06	0.82
ERIC NYSTROM	Dallas	1.28	1.11	1.00	1.66	1.06	0.86
JONATHAN ERICSSON	Detroit	1.97	1.34	1.39	1.46	1.37	0.68
TODD BERTUZZI	Detroit	2.19	1.94	0.64	0.00	1.16	0.89
JUSTIN ABDELKADER	Detroit	1.51	1.46	0.47	0.30	0.86	0.97
JORDIN TOOTOO	Detroit	1.24	1.01	0.40	0.53	0.57	0.82
LENNART PETRELL	Edmonton	1.13	1.08	2.03	0.41	1.63	0.96
MAGNUS PAAJARVI	Edmonton	1.63	1.52	1.25	1.51	1.36	0.93
SAM GAGNER	Edmonton	1.36	1.11	0.82	0.25	0.92	0.81
KRIS VERSTEEG	Florida	1.94	1.49	0.47	0.00	0.87	0.77
TOMAS KOPECKY	Florida	1.20	1.00	0.77	0.67	0.86	0.83
COLIN FRASER	Los Angeles	1.94	1.00	1.53	3.15	1.33	0.52
JEFF CARTER	Los Angeles	1.74	1.36	0.49	0.77	0.99	0.78
ANZE KOPITAR	Los Angeles	1.27	1.02	0.90	1.11	0.94	0.80
DEVIN SETOGUCHI	Minnesota	1.25	1.01	2.16	4.32	1.51	0.81
DANY HEATLEY	Minnesota	1.62	1.19	0.60	0.75	0.85	0.73
TORREY MITCHELL	Minnesota	1.45	1.16	0.53	1.03	0.77	0.80
DAVID LEGWAND	Nashville	2.31	1.65	0.80	0.66	1.10	0.72
NICK SPALING	Nashville	1.91	1.71	0.38	0.31	1.01	0.90
GABRIEL BOURQUE	Nashville	1.35	1.19	0.59	1.19	0.84	0.88
MIKE FISHER	Nashville	1.39	1.08	0.43	0.48	0.76	0.78
ANDREI LOKTIONOV	New Jersey	2.53	2.36	0.67	0.67	1.52	0.93
TOM KOSTOPOULOS	New Jersey	1.46	1.33	0.00	0.00	0.45	0.91
JOHN TAVARES	NY Islanders	1.70	1.69	0.72	0.98	1.13	0.99
MICHAEL GRABNER	NY Islanders	1.40	1.35	0.46	0.23	0.99	0.97
BRAD BOYES	NY Islanders	1.42	1.33	0.82	0.78	0.98	0.94
TRAVIS HAMONIC	NY Islanders	1.42	1.36	0.39	0.00	0.51	0.95
MATS ZUCCARELLO	NY Rangers	1.67	1.55	0.97	1.29	1.19	0.93
JIM O_BRIEN	Ottawa	1.70	1.67	0.00	0.00	0.84	0.98
JAKUB VORACEK	Philadelphia	1.50	1.41	1.17	1.09	1.26	0.94
MAXIME TALBOT	Philadelphia	1.19	1.06	0.84	0.80	0.94	0.89
KYLE CHIPCHURA	Phoenix	2.04	2.03	1.42	2.03	1.58	0.99
NICK JOHNSON	Phoenix	2.11	2.08	0.77	0.99	1.23	0.98
BOYD GORDON	Phoenix	1.25	1.10	1.05	1.37	1.06	0.88
RADIM VRBATA	Phoenix	1.55	1.48	0.65	0.66	1.02	0.95
ANTOINE VERMETTE	Phoenix	1.58	1.53	0.65	0.41	0.93	0.97
MICHAEL STONE	Phoenix	1.24	1.18	0.29	0.15	0.58	0.95
CHRIS KUNITZ	Pittsburgh	1.36	1.09	1.80	1.82	1.46	0.80
BRENDEN MORROW	Pittsburgh	2.17	1.64	1.26	1.47	1.42	0.76
JOE VITALE	Pittsburgh	1.31	1.02	0.57	1.02	0.73	0.78
TOMMY WINGELS	San Jose	1.40	1.14	1.19	1.14	1.18	0.81
JOE PAVELSKI	San Jose	1.49	1.10	0.62	0.63	0.85	0.74
SCOTT GOMEZ	San Jose	1.52	1.40	0.63	0.38	0.75	0.92
RYAN REAVES	St. Louis	1.53	1.45	1.21	0.60	1.36	0.95
CHRIS STEWART	St. Louis	1.20	1.00	1.01	0.72	1.00	0.83
PATRIK BERGLUND	St. Louis	1.59	1.01	0.85	0.93	0.93	0.63
MARTIN ST._LOUIS	Tampa Bay	1.38	1.05	1.12	0.88	1.10	0.77
RYAN MALONE	Tampa Bay	1.51	1.25	0.17	0.00	0.56	0.82
MATT FRATTIN	Toronto	1.80	1.69	2.25	0.75	1.93	0.93
JAY MCCLEMENT	Toronto	1.45	1.04	1.52	0.49	1.31	0.72
PHIL KESSEL	Toronto	1.29	1.07	1.25	1.26	1.16	0.83
JAMES VAN_RIEMSDYK	Toronto	1.35	1.28	0.95	0.65	1.09	0.95
TYLER BOZAK	Toronto	1.26	1.06	0.94	0.69	0.99	0.84
MIKHAIL GRABOVSKI	Toronto	1.23	1.08	0.23	0.21	0.56	0.88
ZACK KASSIAN	Vancouver	1.56	1.27	0.61	0.25	0.90	0.81
HENRIK SEDIN	Vancouver	1.35	1.18	0.65	0.50	0.76	0.87
CHRIS HIGGINS	Vancouver	1.44	1.05	0.50	0.46	0.75	0.73
JOEL WARD	Washington	2.11	1.85	1.58	1.15	1.65	0.88
BROOKS LAICH	Washington	2.81	1.14	0.98	1.48	1.03	0.41
MIKE RIBEIRO	Washington	1.65	1.10	0.76	0.52	0.85	0.67
MATT HENDRICKS	Washington	1.45	1.19	0.56	0.31	0.83	0.82
ANDREW LADD	Winnipeg	1.20	1.00	1.98	1.41	1.48	0.84
KYLE WELLWOOD	Winnipeg	1.38	1.17	0.98	1.36	1.06	0.85
CHRIS THORBURN	Winnipeg	3.41	1.57	0.72	1.01	0.98	0.46
ANTTI MIETTINEN	Winnipeg	1.47	1.12	0.62	0.00	0.84	0.76

Filtering this list down further to isolate players was accomplished by adding criteria for shooting percentage, splitting players with a ratio above or below 1.5.

The listing gets interesting for forwards and defensemen in this regard and takes a good look at the wide gap between ratios by position.

Defensemen shooting percentages skyrocket to an average of 3.29 times their 3-year average, while forwards averaged a ratio of 1.91. That’s a fairly significant distinction however two players with bloated ratios skew results. Removing Barret Jackman (11.76) and Paul Martin (9.45) reduces the overall average to 2.63, which is still higher than the forwards average.

The highest forward was Tomas Vincour (3.71) with Chris Thorburn following with 3.41. Tables below show the full listing.

Once again, to reiterate, these tables were starting points, jumping off into other parts of analysis that led to a better overall picture of the player’s performance isolated in ’12-13.

Relative to 3-yr average - forwards sh% ratio < 1.5
Player Name	Team	Sh%	G/60	A/60	1stA/60	Pts/60	SOG/60
JOE PAVELSKI	San Jose	1.49	1.10	0.62	0.63	0.85	0.74
ANTTI MIETTINEN	Winnipeg	1.47	1.12	0.62	0.00	0.84	0.76
ALEX TANGUAY	Calgary	1.46	1.07	0.71	0.69	0.83	0.73
TOM KOSTOPOULOS	New Jersey	1.46	1.33	0.00	0.00	0.45	0.91
MATT HENDRICKS	Washington	1.45	1.19	0.56	0.31	0.83	0.82
TORREY MITCHELL	Minnesota	1.45	1.16	0.53	1.03	0.77	0.80
JAY MCCLEMENT	Toronto	1.45	1.04	1.52	0.49	1.31	0.72
CHRIS HIGGINS	Vancouver	1.44	1.05	0.50	0.46	0.75	0.73
BRAD BOYES	NY Islanders	1.42	1.33	0.82	0.78	0.98	0.94
TRAVIS HAMONIC	NY Islanders	1.42	1.36	0.39	0.00	0.51	0.95
STEVE OTT	Buffalo	1.41	1.30	1.29	0.93	1.30	0.92
TOMMY WINGELS	San Jose	1.40	1.14	1.19	1.14	1.18	0.81
MICHAEL GRABNER	NY Islanders	1.40	1.35	0.46	0.23	0.99	0.97
MIKE FISHER	Nashville	1.39	1.08	0.43	0.48	0.76	0.78
MATT BELESKEY	Anaheim	1.38	1.35	0.88	1.25	1.09	0.98
KYLE WELLWOOD	Winnipeg	1.38	1.17	0.98	1.36	1.06	0.85
MARTIN ST._LOUIS	Tampa Bay	1.38	1.05	1.12	0.88	1.10	0.77
SHAWN THORNTON	Boston	1.37	1.20	0.92	0.28	1.02	0.87
SAM GAGNER	Edmonton	1.36	1.11	0.82	0.25	0.92	0.81
CHRIS KUNITZ	Pittsburgh	1.36	1.09	1.80	1.82	1.46	0.80
HENRIK SEDIN	Vancouver	1.35	1.18	0.65	0.50	0.76	0.87
GABRIEL BOURQUE	Nashville	1.35	1.19	0.59	1.19	0.84	0.88
JAMES VAN_RIEMSDYK	Toronto	1.35	1.28	0.95	0.65	1.09	0.95
JAROMIR JAGR	Boston	1.34	1.21	0.96	0.84	1.04	0.91
JOE VITALE	Pittsburgh	1.31	1.02	0.57	1.02	0.73	0.78
BRAD MARCHAND	Boston	1.29	1.15	1.34	1.70	1.26	0.89
PHIL KESSEL	Toronto	1.29	1.07	1.25	1.26	1.16	0.83
ERIC NYSTROM	Dallas	1.28	1.11	1.00	1.66	1.06	0.86
ANZE KOPITAR	Los Angeles	1.27	1.02	0.90	1.11	0.94	0.80
TYLER BOZAK	Toronto	1.26	1.06	0.94	0.69	0.99	0.84
BOYD GORDON	Phoenix	1.25	1.10	1.05	1.37	1.06	0.88
DEVIN SETOGUCHI	Minnesota	1.25	1.01	2.16	4.32	1.51	0.81
MICHAEL STONE	Phoenix	1.24	1.18	0.29	0.15	0.58	0.95
JORDIN TOOTOO	Detroit	1.24	1.01	0.40	0.53	0.57	0.82
MIKHAIL GRABOVSKI	Toronto	1.23	1.08	0.23	0.21	0.56	0.88
CHRIS STEWART	St. Louis	1.20	1.00	1.01	0.72	1.00	0.83
ANDREW LADD	Winnipeg	1.20	1.00	1.98	1.41	1.48	0.84
TOMAS KOPECKY	Florida	1.20	1.00	0.77	0.67	0.86	0.83
MAXIME TALBOT	Philadelphia	1.19	1.06	0.84	0.80	0.94	0.89
MARIAN HOSSA	Chicago	1.15	1.05	0.89	0.79	0.96	0.92
VACLAV PROSPAL	Columbus	1.14	1.05	1.11	0.42	1.09	0.92
LENNART PETRELL	Edmonton	1.13	1.08	2.03	0.41	1.63	0.96
VIKTOR STALBERG	Chicago	1.07	1.04	1.22	1.63	1.13	0.97
DAN CARCILLO	Chicago	1.03	1.02	0.40	0.69	0.67	0.99
VERNON FIDDLER	Dallas	1.02	1.01	1.57	2.15	1.41	0.99

Relative to 3-yr average - defensemen sh% ratio > 1.5
Player Name	Team	Sh%	G/60	A/60	1stA/60	Pts/60	SOG/60
BARRET JACKMAN	St. Louis	11.76	9.82	0.81	1.13	1.11	0.83
PAUL MARTIN	Pittsburgh	9.45	7.85	1.24	1.33	1.84	0.82
RADEK MARTINEK	NY Islanders	6.81	5.56	0.00	0.00	1.58	0.81
ALEXANDER SULZER	Buffalo	4.90	3.88	0.74	0.00	1.87	0.79
MARK STREIT	NY Islanders	3.95	3.44	0.97	0.60	1.15	0.88
DION PHANEUF	Toronto	2.92	2.03	0.84	0.92	1.20	0.70
TOM GILBERT	Minnesota	2.72	1.93	1.13	0.65	1.25	0.71
BRENT SEABROOK	Chicago	2.72	2.03	0.58	0.54	0.85	0.75
COREY POTTER	Edmonton	2.71	2.23	0.00	0.00	0.75	0.82
FRANCOIS BEAUCHEMIN	Anaheim	2.57	2.42	1.95	1.37	2.06	0.94
TOBIAS ENSTROM	Winnipeg	2.48	1.71	0.76	0.35	0.93	0.69
PHILIP LARSEN	Dallas	2.35	2.19	0.59	0.54	0.73	0.93
DOUGLAS MURRAY	Pittsburgh	2.25	1.41	1.34	0.57	1.34	0.63
MATT CARLE	Tampa Bay	2.25	1.69	0.84	0.77	0.91	0.75
DENNIS SEIDENBERG	Boston	2.21	2.01	0.79	0.62	1.04	0.90
JOE CORVO	Carolina	2.16	2.08	1.21	0.43	1.43	0.97
SHELDON SOURAY	Anaheim	2.10	1.30	0.76	0.38	0.96	0.62
ZDENO CHARA	Boston	2.05	1.85	0.60	1.19	0.80	0.90
DAN HAMHUIS	Vancouver	2.00	1.61	1.18	1.61	1.26	0.81
ALEXEI EMELIN	Montreal	1.84	1.62	3.80	3.25	2.90	0.88
FEDOR TYUTIN	Columbus	1.74	1.32	2.00	3.41	1.79	0.76
ANDREI MARKOV	Montreal	1.72	1.37	0.34	0.20	0.46	0.80
MARC-EDOUARD VLASIC	San Jose	1.70	1.61	0.43	0.28	0.68	0.94
MARC STAAL	NY Rangers	1.68	1.11	2.14	2.66	1.92	0.66

Relative to 3-yr average - forwards sh% > 1.5
Player Name	Team	Sh%	G/60	A/60	1stA/60	Pts/60	SOG/60
BRAD STAUBITZ	Anaheim	2.20	1.76	1.76	3.09	1.77	0.80
DANIEL WINNIK	Anaheim	1.68	1.65	1.30	1.05	1.41	0.98
NICK BONINO	Anaheim	2.37	2.19	1.05	1.83	1.36	0.92
ANDREW COGLIANO	Anaheim	1.52	1.50	1.18	1.04	1.31	0.99
STEVE BEGIN	Calgary	2.60	2.58	2.29	0.00	2.39	0.99
ERIC STAAL	Carolina	1.77	1.57	1.90	1.71	1.78	0.89
DAVE BOLLAND	Chicago	1.58	1.23	0.71	0.81	0.93	0.77
AARON PALUSHAJ	Colorado	1.54	1.17	2.05	3.51	1.88	0.76
TOMAS VINCOUR	Colorado	3.71	2.32	1.55	0.00	1.85	0.63
PIERRE PARENTEAU	Colorado	1.74	1.68	0.88	0.93	1.13	0.96
DEREK DORSETT	Columbus	1.55	1.34	1.72	0.48	1.56	0.86
ARTEM ANISIMOV	Columbus	1.86	1.75	0.78	0.75	1.17	0.94
RYAN GARBUTT	Dallas	1.56	1.50	0.00	0.00	5.03	0.96
RAY WHITNEY	Dallas	1.56	1.29	0.95	0.85	1.06	0.82
JONATHAN ERICSSON	Detroit	1.97	1.34	1.39	1.46	1.37	0.68
TODD BERTUZZI	Detroit	2.19	1.94	0.64	0.00	1.16	0.89
JUSTIN ABDELKADER	Detroit	1.51	1.46	0.47	0.30	0.86	0.97
MAGNUS PAAJARVI	Edmonton	1.63	1.52	1.25	1.51	1.36	0.93
KRIS VERSTEEG	Florida	1.94	1.49	0.47	0.00	0.87	0.77
COLIN FRASER	Los Angeles	1.94	1.00	1.53	3.15	1.33	0.52
JEFF CARTER	Los Angeles	1.74	1.36	0.49	0.77	0.99	0.78
DANY HEATLEY	Minnesota	1.62	1.19	0.60	0.75	0.85	0.73
DAVID LEGWAND	Nashville	2.31	1.65	0.80	0.66	1.10	0.72
NICK SPALING	Nashville	1.91	1.71	0.38	0.31	1.01	0.90
ANDREI LOKTIONOV	New Jersey	2.53	2.36	0.67	0.67	1.52	0.93
JOHN TAVARES	NY Islanders	1.70	1.69	0.72	0.98	1.13	0.99
MATS ZUCCARELLO	NY Rangers	1.67	1.55	0.97	1.29	1.19	0.93
JIM O_BRIEN	Ottawa	1.70	1.67	0.00	0.00	0.84	0.98
JAKUB VORACEK	Philadelphia	1.50	1.41	1.17	1.09	1.26	0.94
KYLE CHIPCHURA	Phoenix	2.04	2.03	1.42	2.03	1.58	0.99
NICK JOHNSON	Phoenix	2.11	2.08	0.77	0.99	1.23	0.98
RADIM VRBATA	Phoenix	1.55	1.48	0.65	0.66	1.02	0.95
ANTOINE VERMETTE	Phoenix	1.58	1.53	0.65	0.41	0.93	0.97
BRENDEN MORROW	Pittsburgh	2.17	1.64	1.26	1.47	1.42	0.76
SCOTT GOMEZ	San Jose	1.52	1.40	0.63	0.38	0.75	0.92
RYAN REAVES	St. Louis	1.53	1.45	1.21	0.60	1.36	0.95
PATRIK BERGLUND	St. Louis	1.59	1.01	0.85	0.93	0.93	0.63
RYAN MALONE	Tampa Bay	1.51	1.25	0.17	0.00	0.56	0.82
MATT FRATTIN	Toronto	1.80	1.69	2.25	0.75	1.93	0.93
ZACK KASSIAN	Vancouver	1.56	1.27	0.61	0.25	0.90	0.81
JOEL WARD	Washington	2.11	1.85	1.58	1.15	1.65	0.88
BROOKS LAICH	Washington	2.81	1.14	0.98	1.48	1.03	0.41
MIKE RIBEIRO	Washington	1.65	1.10	0.76	0.52	0.85	0.67
CHRIS THORBURN	Winnipeg	3.41	1.57	0.72	1.01	0.98	0.46

Relative to 3-yr average - Defensemen sh% ratio < 1.5
Player Name	Team	Sh%	G/60	A/60	1stA/60	Pts/60	SOG/60
ANDREJ SEKERA	Buffalo	1.44	1.08	1.50	1.33	1.39	0.75
DENNIS WIDEMAN	Calgary	1.05	1.05	0.77	0.95	0.83	1.00
JAMES WISNIEWSKI	Columbus	1.50	1.29	0.97	1.61	1.00	0.86
NIKITA NIKITIN	Columbus	1.24	1.03	0.45	0.00	0.58	0.83
HENRIK TALLINDER	New Jersey	1.16	1.15	0.39	0.00	0.58	0.99
KIMMO TIMONEN	Philadelphia	1.10	1.07	0.91	0.27	0.93	0.96
KRIS LETANG	Pittsburgh	1.38	1.37	1.82	2.18	1.75	0.98
KEVIN SHATTENKIRK	St. Louis	1.06	1.05	0.99	0.60	0.99	0.98
ALEXANDER EDLER	Vancouver	1.44	1.34	0.82	0.41	0.91	0.94

Rate Stats Relative to 3-Year Average Ratios – Underperformers

Gus Katsaros — Fri, 20 Sep 2013 19:54:37 +0000

During the time period when writing the Yearbook, some basic fundamentals used in some element of analysis don’t ever see the light of day. Summer 2013 was no different, particularly, with scoring in the shortened season requiring extrapolation over 82 games – which isn’t a very good indication of a true production, overlooking a variety of variables affecting game outcomes

There has to be a starting point however and instead of normalizing 2012-13 over 82 games, I took an approach that leaned heavily on ratios rather than hard numbers.

I also wanted to keep the shortened season data separate from the longer term averages to maintain integrity of independent running average directly compared to the short season. This meant using 2012-13 5v5 production rate stats (on a per60 basis) in relative comparison to the player’s 3-year and 5-year averages ending with the season 2011-12 – excluding ’12-13.

The key to this exercise wasn’t to make a definitive determination of the player’s future value, but rather an analytic starting point with a goal of answering the initial question, ‘how did players produce in the shortened season, relative to longer term trends. This was a beginning point, not the end result.

I won’t include a lot of commentary on players considering the amount of detail already prevalent in the McKeen’s Yearbook write-ups, however all those poolies (and writers looking for previews) may want to keep an eye on the players listed based on these preliminary results.

RATIOS

As an example, I will use goals/60. All the results here are based on 5v5 data via hockeyanlaysis.com.

Dividing ’12-13 goals/60 by the 3-year average (’12-13/3yr average) will result in a comparative ratio of the shortened season’s production relative to the player’s 3-year average. There are one of three possible results.

A one (1) indicates the player’s scoring ratio matched the 3-year goals/60 rate. A number greater than one meant the player outperformed his 3-year average. Less than one meant he underperformed.

SHOTS/60

The essential driving factor here is shots on goal per 60 minutes, tweaking the filtering criteria depending on the ratio.

To isolate underperformers, I used the following criteria:

SOG/60 > 1

Goals/60 <1

This returned a list of players that fired pucks at a rate greater than their 3-year average but didn’t score at the same clip than in the past (despite the uptick in shots/60 ratio)

For outperformers:

SOG/60 < 1

Goals/60 >1

The returned players fired less than their 3-year average, yet scored at a clip greater than their 3-year average.

The third filter was to determine consistency – particularly in shooting rates. This required incorporating 5-year average ratios as well, adding another long(er) term ratio filtering down the listings. In the end, 26 players made the final filter, some interesting names, some others negligible in the grand scheme.

Each category list can be quite big, so I’m going to split this into separate posts. This first one focuses on players that underperformed with a goals/60 ratio less than 1 and SOG/60 ration greater than 1.

Underperformed

A quick note here. Players like Matt Calvert has been developing over the 3-year period, so tht in itself must be taken into consideration when looking at the raw numbers. Bubble players like Kaspars Daugavins has an effect here too, with increased ice time instead of small samples comprising the overall 3-year averages. To reiterate, this analysis is a starting point, not the end means.

Across the NHL, 86 players underperformed their goals/60 ratio, a value less than one (with a shooting percentage greater than zero) while firing at a SOG/60 ratio greater than 1.

Of those 86 two NHL teams were unrepresented, Calgary and Toronto. When including players with a zero shooting percentage 149 were ranked, including multiple Flames and only one Leaf player made the list, Mark Fraser.

When prepping for your draft or looking at production comparisons for the shortened season, keep these ratios in mind (look at David Jones!!).

Here's the complete list broken down by forwards and defensemen.

Forwards Relative to 3-yr average
Player Name	Team	Sh%	G/60	A/60	1stA/60	Pts/60	SOG/60
KYLE PALMIERI	Anaheim	0.78	0.97	2.03	1.82	1.37	1.25
PATRICE BERGERON	Boston	0.67	0.76	1.14	0.77	1.00	1.13
KASPARS DAUGAVINS	Boston	0.41	0.52	1.74	0.00	0.98	1.27
TYLER SEGUIN	Boston	0.79	0.89	0.92	0.74	0.91	1.13
JORDAN CARON	Boston	0.43	0.48	0.81	0.81	0.66	1.14
RICH PEVERLEY	Boston	0.70	0.70	0.55	0.54	0.61	1.00
JORDAN STAAL	Carolina	0.89	0.94	0.75	0.69	0.83	1.06
DRAYSON BOWMAN	Carolina	0.72	0.81	0.63	0.63	0.73	1.13
JEFF SKINNER	Carolina	0.46	0.58	0.57	0.74	0.57	1.24
TIM WALLACE	Carolina	0.46	0.62	0.31	0.47	0.42	1.37
MICHAEL FROLIK	Chicago	0.68	0.76	1.18	1.16	1.01	1.12
MICHAL HANDZUS	Chicago	0.49	0.58	0.88	1.12	0.77	1.19
PAUL STASTNY	Colorado	0.86	0.86	0.45	0.13	0.59	1.00
DAVID JONES	Colorado	0.13	0.15	0.85	1.05	0.47	1.12
MATT CALVERT	Columbus	0.71	0.92	0.67	1.05	0.78	1.30
MARIAN GABORIK	Columbus	0.55	0.62	0.41	0.50	0.52	1.14
CORY EMMERTON	Detroit	0.85	0.92	0.86	1.29	0.90	1.08
VALTTERI FILPPULA	Detroit	0.61	0.69	0.64	0.77	0.66	1.14
JORDAN EBERLE	Edmonton	0.81	0.97	0.85	1.01	0.90	1.20
ALES HEMSKY	Edmonton	0.68	0.75	0.74	0.64	0.75	1.11
TOMAS FLEISCHMANN	Florida	0.88	0.88	0.97	0.86	0.92	1.00
MARCEL GOC	Florida	0.58	0.60	0.68	1.13	0.65	1.04
PETER MUELLER	Florida	0.52	0.73	0.58	0.68	0.64	1.41
BRAD RICHARDSON	Los Angeles	0.64	0.70	2.48	1.74	1.74	1.09
DUSTIN PENNER	Los Angeles	0.30	0.36	1.75	1.57	1.10	1.22
DWIGHT KING	Los Angeles	0.56	0.57	0.41	0.48	0.48	1.02
PIERRE-MARC BOUCHARD	Minnesota	0.87	0.90	0.96	1.07	0.94	1.04
CAL CLUTTERBUCK	Minnesota	0.69	0.70	0.91	1.02	0.79	1.01
ZACH PARISE	Minnesota	0.77	0.79	0.79	1.02	0.79	1.03
MATT HALISCHUK	Nashville	0.73	0.83	0.82	1.50	0.82	1.14
PAUL GAUSTAD	Nashville	0.35	0.37	0.58	0.44	0.49	1.06
DAVID CLARKSON	New Jersey	0.79	0.92	1.04	0.73	0.97	1.16
PATRIK ELIAS	New Jersey	0.80	0.89	0.70	0.77	0.77	1.11
ADAM HENRIQUE	New Jersey	0.90	0.98	0.28	0.59	0.50	1.08
MATT MOULSON	NY Islanders	0.48	0.55	2.24	3.21	1.25	1.15
MATT MARTIN	NY Islanders	0.88	0.97	1.09	1.86	1.03	1.11
FRANS NIELSEN	NY Islanders	0.47	0.50	1.24	1.02	0.99	1.07
KEITH AUCOIN	NY Islanders	0.57	0.91	0.49	0.73	0.62	1.60
RYANE CLOWE	NY Rangers	0.30	0.33	1.26	1.16	0.90	1.10
CARL HAGELIN	NY Rangers	0.71	0.91	0.85	1.10	0.88	1.28
MILAN MICHALEK	Ottawa	0.89	0.91	1.30	0.43	1.10	1.03
ZACK SMITH	Ottawa	0.58	0.64	1.12	1.28	0.89	1.10
ERIK CONDRA	Ottawa	0.70	0.72	0.85	0.37	0.80	1.04
CHRIS NEIL	Ottawa	0.62	0.70	0.60	0.69	0.65	1.14
CLAUDE GIROUX	Philadelphia	0.66	0.70	1.01	1.17	0.90	1.06
MIKKEL BOEDKER	Phoenix	0.45	0.54	1.37	0.61	1.01	1.19
LAURI KORPIKOSKI	Phoenix	0.66	0.92	0.59	0.78	0.73	1.39
JAROME IGINLA	Pittsburgh	0.76	0.76	1.02	1.21	0.90	1.00
JAMES SHEPPARD	San Jose	0.74	0.94	1.88	0.94	1.52	1.27
RAFFI TORRES	San Jose	0.89	0.93	1.43	1.63	1.19	1.04
T.J. GALIARDI	San Jose	0.70	0.81	1.34	1.87	1.10	1.15
JOE THORNTON	San Jose	0.58	0.65	0.82	0.91	0.78	1.12
PATRICK MARLEAU	San Jose	0.87	0.95	0.56	0.18	0.75	1.10
ANDREW DESJARDINS	San Jose	0.72	0.83	0.15	0.23	0.32	1.15
CHRIS PORTER	St. Louis	0.72	0.88	1.75	2.30	1.32	1.21
ADAM CRACKNELL	St. Louis	0.57	0.65	1.29	0.86	0.96	1.13
JADEN SCHWARTZ	St. Louis	0.73	0.99	0.85	0.00	0.92	1.35
ALEX BURROWS	Vancouver	0.84	0.96	0.82	1.11	0.89	1.14
JAY BEAGLE	Washington	0.43	0.47	3.32	4.96	1.32	1.09
MATHIEU PERREAULT	Washington	0.45	0.54	1.47	1.99	0.98	1.21
WOJTEK WOLSKI	Washington	0.61	0.67	0.49	0.67	0.55	1.11
NIK ANTROPOV	Winnipeg	0.50	0.59	1.00	1.23	0.83	1.17
ERIC TANGRADI	Winnipeg	0.83	0.87	0.43	0.29	0.52	1.04

Defensemen Relative to 3-yr average
Player Name	Team	Sh%	G/60	A/60	1stA/60	Pts/60	SOG/60
ADAM MCQUAID	Boston	0.79	0.79	0.56	0.68	0.63	1.01
CHRISTIAN EHRHOFF	Buffalo	0.77	0.79	1.19	1.93	1.06	1.04
JAMIE MCBAIN	Carolina	0.50	0.51	1.06	1.11	0.92	1.03
JAY HARRISON	Carolina	0.58	0.60	1.10	1.32	0.91	1.03
MARC-ANDRE BERGERON	Carolina	0.58	0.75	0.71	0.75	0.73	1.29
JAN HEJDA	Colorado	0.40	0.49	1.10	1.47	0.93	1.23
JACK JOHNSON	Columbus	0.76	0.79	1.26	1.38	1.13	1.05
ALEX GOLIGOSKI	Dallas	0.70	0.73	1.51	1.04	1.24	1.04
LADISLAV SMID	Edmonton	0.64	0.71	0.43	0.00	0.50	1.11
DREW DOUGHTY	Los Angeles	0.35	0.48	0.51	0.57	0.49	1.36
FRANCIS BOUILLON	Montreal	0.68	0.86	1.83	2.84	1.59	1.25
JONATHON BLUM	Nashville	0.31	0.38	1.41	1.13	1.01	1.21
ROMAN JOSI	Nashville	0.37	0.45	1.05	1.51	0.81	1.21
ANDY GREENE	New Jersey	0.80	0.91	0.65	0.23	0.68	1.15
LUBOMIR VISNOVSKY	NY Islanders	0.41	0.53	0.44	0.34	0.48	1.32
MICHAEL DEL ZOTTO	NY Rangers	0.18	0.28	1.59	2.93	1.11	1.53
DAN GIRARDI	NY Rangers	0.44	0.56	1.08	0.63	0.97	1.27
SERGEI GONCHAR	Ottawa	0.48	0.54	1.66	1.29	1.41	1.14
ERIK KARLSSON	Ottawa	0.57	0.88	1.30	1.63	1.15	1.54
BRUNO GERVAIS	Philadelphia	0.52	0.61	1.12	0.47	0.98	1.18
LUKE SCHENN	Philadelphia	0.34	0.41	0.73	0.43	0.66	1.20
OLIVER EKMAN-LARSSON	Phoenix	0.64	0.68	1.52	1.56	1.21	1.06
ERIC BREWER	Tampa Bay	0.76	0.78	1.55	1.98	1.20	1.04

NHL ’13-14 B2B Rested/Tired: Alberta Dirty Little Secret Edition

Gus Katsaros — Sat, 14 Sep 2013 03:35:45 +0000

Continuing the tradition, this is the annual back-to-back breakdown the NHL schedule between rested and tired teams. The 2013-14 version features an NHL post-lockout #1 record based around the Battle of Alberta .. sort of. I won't get into the heavy details here simply I'll just try to present breakdown by teams.

I’ve written about this extensively in the past including effects and impact on NHL realignment.

The underlying data is in a google doc here. There are three tabs in the document that correlate to the information described below.

The definitions are below.

Rested – team that has not played the previous night facing a team in the second night of a back-to-back set on consecutive nights.
Tired – a team playing its second game on consecutive nights versus a team that is rested and not played the previous night.

This season's leader is the Calgary Flames, with 20 games as a rested team. We’ll explore in a little more detail just a little further down the post the interesting little twist.

Divisional rivals Anaheim and San Jose rank second and third. Rounding out the bottom of the list is Washington with five games as a rested team, followed by Montréal, Nashville and Dallas (7).

In total, 325 games played features a rested versus tired team making up a slight uptick over 25% of the schedule.

Turning our attention to the other end, the New Jersey Devils lead the league with 16 games as a tired team. The Vancouver Canucks and defending Stanley Cup champions Chicago Blackhawks are tied for second with 15. Taken at the bottom is the Colorado Avalanche with six, followed by a four way tie between Pittsburgh, Minnesota, Tampa Bay and San Jose.

Over the entire spectrum of the NHL the average amount of games as a rested team is 10.8 meaning Calgary doubles the NHL average with 20.

NHL realignment missed the divisional impact to teams where an opponent would be traveling through an area, let's say Alberta, playing the Edmonton – Calgary or vice versa combination, effectively giving the team playing the first night a disadvantage over the team that's playing on the second night.

Rested teams have a tendency to win at about 0.596% clip.

Alberta's Dirty Little Secret

The Calgary Flames in 2013-14 are beneficiaries of a number of teams traveling through Edmonton first on the first night of a back-to-back and then skipping right over to Calgary for the second game in two nights.

Of the 20 games as a rested team, 13 feature a team that played Edmonton the night before - representing a new NHL post-lockout record. Five of those individual instances involve a divisional rival. Only four teams end up playing Calgary and then traveling to Edmonton, causing this to be one of the greatest imbalances perhaps even in the post-lockout NHL and a cause of concern especially with a different playoff format.

To put that into context how high 13 games really is, Edmonton on night one and Calgary on night two combination has occurred 28 times in the 6-year span between the lockouts not including the lockout shortened season of 2012-13.

Those 13 games represent slightly less than 50% of the total amount of games spanning six seasons. Reversing the combination produces 37 total games representing 35% of the six season total where Edmonton has the advantage of being a team on the second night.

Edmonton has led this category three times (Tied with Anaheim in ’11-12) over the six season span with the LA Kings leading twice – both instances in double digits, both against the Anaheim Ducks. LA had the previous high of 12 games in the 2008-09 season.

Now, one could try to make a case and justify this from a scheduling perspective that Calgary is in a position where they're trying to rebuild and the outcome here really doesn't matter. From a more sinister perspective, perhaps the schedule makers considered the impact it could have on Edmonton making the playoffs and tipping the scales to the team that isn’t as likely to be involved in the playoff race.

The situation could become very different if Calgary was to somehow make a run. Edmonton is already on the bubble to make the dance in Spring 2014 and it’s not like they need added divisional pressure. This is also an issue to monitor moving forward.

Florida and Tampa Bay have a similar back-and-forth, historically and that trend continues in 2013-14. Florida faces a team on the second night of a back-to-back after they've played Tampa Bay the previous night. That's three times less than the five teams that travel to Tampa Bay after playing Florida the previous night.

In general the Pacific division is affected most. In fact the Florida teams are the only others outside of the Pacific that feature this combination of teams playing through divisional rivals over three times.

Congratulations to the Flames setting records before the season even began.

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NHL Playoffs Preview: West

Gus Katsaros (stats/graphics), Carl Lemelin (writeups) — Tue, 30 Apr 2013 13:43:01 +0000

Welcome to the playoffs!

This preview is fairly simple to navigate through, but there are some elements that require some explanation.

Stats and graphics were compiled and prepared by Gus Katsaros and the writeups were written by Carl Lemelin

Each image can be blown up to a larger size by clicking on the graphic. The first click will take you to a splash page, and then clicking on the same image on that page will blow it up to its original size.

Every series preview has the same format.

All data was compiled using timeonice.com and NHL.com

The images are as follows:

A game-by-game Corsi breakdown by components, with the colors defined by the legend at the bottom.

Underneath is the head-to-head matchup broken down by their basic Corsi makeups.

The main image is a side by side comparison of the team's season plotted using the Fenwick Close. (Note; Archiving for FenClose began Feb 18 which will produce and N/A for games prior to that date.

Underneath all the visuals is a table with the head-to-head matchups. Most of the headings are self-explanatory, but the structure has the team that placed higher in the standings as the 'team', with the Decision, home/road and other columns based on that team versus their opponent.
Clicking on the team in the column will open a new window with the gamesheet for that game (hover over the team for a title).

TS is 'times shorthanded'.

The FenClo columns are the Fenwick Close for each of the sides, as they entered the game against their opponents.

Colored rows are as follows:
A BLACK row indicates the 'Team' column played the previous night as part of a back-to-back set, while the 'OPP' was rested.
A BLUE row indicates both teams played the previous night as part of a back-to-back set.

Enjoy the preview and if you're team is in the playoffs, enjoy round 1.

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Via Carl Lemelin

Most of the playoff previews you’ll read will post series opponents’ records during the season series. This has proven to be a very bad indicator of the eventual outcome in recent years; too many outside factors can influence the results, regular season series being spread out over 6 months (4 this year). But there is one thing the past 6 Stanley Cup finalists have in common: they’ve all finished the regular season on a high note.

All but one of these teams were at least 3 games over .500 during their final 10 regular season games. The 2010 Philadelphia Flyers (4-5-1) were the exception, but even they finished well going 3-1 in their final 4 games. The collective .642 points percentage of the group in the final stretch is enough to convince me that strong finishing squads have much better odds of making a significant run in the post-season.

In this year’s Wild West, Chicago St-Louis and Detroit fall into this category of momentum builders. Here are the Western Conference first round match-ups as we see them (Last-10 records in parentheses).

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Date

Team

Dec

Opp

PPG

PPOpp

PPGA

FenClo LAK

FenClo StL

2/11/2013

STL

LAK

#N/A

3/5/2013

STL

LAK

55.11

58.55

3/28/2013

STL

LAK

54.46

58.14

4-ST-LOUIS (7-3-0) vs 5-LOS-ANGELES (5-3-2)
This may be a homer series. Both teams are very comfortable on their own ice. The difference may be that the Blues are almost as confident on the road (14-9-1), but not the Kings (8-12-4). The Kings did sweep the Blues in last spring’s second round, but Ken Hitchcock is a master at making adjustments. Jonathan Quick and Drew Doughty, key playoff contributors for the champs, have been shadows of themselves in this short season. St-Louis may have the deepest overall roster in the league and they’re playing hungry; they look like last year’s Kings. Carl says: Blues in 5.

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Date

Team

Dec

Opp

PPG

PPOpp

PPGA

FenClo Van

FenClo SJS

1/27/2013

VAN

SJS

#N/A

3/5/2013

VAN

SJS

53.68

52.27

4/1/2013

VAN

SJS

53.67

51.54

3-VANCOUVER (5-4-1) vs 6-SAN JOSE (5-5-0)
Attention to details will determine the winner of this series. Of these two evenly matched teams, the Sharks have an edge in scoring depth. Derek Roy and Ryan Kesler must help spread the Canucks’offense, preventing San Jose from concentrating all their checking efforts on the Sedin twins. Kevin Bieksa must also find his 2011-12 form, help move the puck north efficiently and put shots on net from the point on the PP, a unit that has struggled all season. We believe these ‘ifs’ will materialize and like Vancouver’s depth on defense; Corey Schneider over Antti Niemi. Carl says: Canucks in 6.

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Date

Team

Dec

Opp

PPG

PPOpp

PPGA

FenClo CHI

FenClo MIN

1/30/2013

CHI

MIN

#N/A

3/5/2013

CHI

MIN

55.31

45.9

4/9/2013

CHI

MIN

55.7

47.88

1-CHICAGO (7-2-1) vs 8-MINNESOTA (4-5-1)
The Wild backed into the playoffs and were plagued by inconsistent play throughout the season. By contrast, the Hawks have had one of the most dominant regular seasons in NHL history. The major indicators all point toward the Windy City: 5-on-5 play (CHI-1st, MIN-24th), PK% (CHI-3rd, MIN-18th) and SV% (Crawford-.926, Backstrom-.909). Minny simply doesn’t have an answer for Chicago’s overall depth, especially on defense once you get past Ryan Suter. The three-headed monster of Patrick Kane-Jonathan Toews-Marian Hossa dominates this unfair fight. Carl says: Blackhawks in 5.

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Date

Team

Dec

Opp

PPG

PPOpp

PPGA

ANA FenCl

Det FenClo

2/15/2013

ANA

DET

#N/A

3/22/2013

ANA

DET

46.55

51.54

3/24/2013

ANA

DET

46.58

51.52

2-ANAHEIM (5-4-1) vs 7-DETROIT (5-2-3)
Besides the obvious points difference (10 more for the Ducks), there are only two key areas in which these well matched opponents have had a clear edge on each other: Anaheim’s 4th ranked PP vs Detroit’s 15th and The Wings’ Jimmy Howard out-stopping Jonas Hiller (.923 to .913 SV%). Both teams possess proven warriors on their top lines, but Mike Babcock has more quality forward depth to draw upon (specifically Johan Franzen and Valtteri Filppula). Anaheim’s defense is stronger individually, but Babcock’s system seems to have taken hold lately and his forwards are better backcheckers. Carl says: Red Wings in 6.

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