Posts Tagged ‘fantasy hockey’

Hockey is a game, and fantasy hockey is a game based around a game (though not a game within a game, that would be Inception!)

In game theory, an important distinction is whether the players have perfect information or imperfect information.

In games with perfect information (chess and checkers are good examples,) all the factors are known by all players. Each player can see all of his opponent’s pieces, and there is no ‘hidden’ information except for the plans and strategies inside the other guy’s head.

In games with imperfect information (most card games, blackjack and poker for example,) some of the factors are unknown by the players, which is where the complexity, the skill, and the intricacies of the game come into play. If everyone knew the dealer’s face down card in blackjack, or what everyone else at the poker table had as their pocket cards, those games would be a lot less fun.

So, do we have perfect information or imperfect information in fantasy hockey? Some would foolishly say it’s the former–they look at the stats for last night’s games or a guy’s production last season, and think it’s perfect information. After all, there’s the numbers right there in black and white, there’s everything that happened in the game, right in the box score. But when I think about how advanced stats like Corsi, PDO, zone starts, and qual comp contribute to fantasy, I think about how they make those boxcar stats look imperfect in an awful hurry. Sure, this guy got that many points, but he did it against really soft competition, or he got some really good puck luck, and those points may not really be an accurate reflection of his skill or a predictor of how many points he can put up going forward.

But on the other side of the coin, fantasy doesn’t care about a player’s QoC or his PDO…goals are goals. Let me clarify: if you drafted Patrick Marleau or Thomas Vanek this season, you got yourself a whole heap of goals in the first few games of the season, and you almost certainly won your first couple matchups. Whether those goals came on ridiculously high shooting percentages (they did) or came easier because of favorable zone starts (they did,) they still counted. Last April, I got myself on board the Pascal Dupuis express during my fantasy playoffs, and I enjoyed a nice little hot streak, to the tune of around a point per game over a couple weeks. Did I know that production was unsustainable? Certainly. Did I keep sending him out there night after night? You’re damn right I did! But I digress…

The point I’m trying to make is that the role of more sophisticated stats (or advanced stats, or underlying stats, or fancy stats, or whatever you want to call them, ultimately it doesn’t really matter) is to provide more *context* to a hockey player’s production. And in fantasy, that context can be supremely helpful. It can give us strong signals on whether to buy low or sell high on a team or player, and those signals (if we choose to heed them) can give us a leg up on our competition who is ignorant to even basic stuff like individual Sh%.

These stats don’t give us perfect information, by any means. No self-respecting stat guy (or gal) would tell you that. And we as a community are constantly trying to improve our methods, to develop new numbers and metrics that are meaningful and useful and not downright crazy (if you’re plugged in to the #fancystats community on Twitter, you may have heard about the paper that got accepted to the Sloan Sports Analytics Conference that makes that case that Alexander Steen is one of the most important players in the league. But I digress again…)

Bottom line: in fantasy hockey, we are dealing with imperfect information. But the value of Corsi and Fenwick and the usage charts and all that is that they give us slightly *less imperfect* information. One step closer to knowing when the dealer is about to flip over that suicide king, or that the guy on the button is working with deuce-seven offsuit.


^^The podcast is embedded in this page. Click the play button above to begin streaming, or click here to download an .mp3 file.

Today I interview one of my favorite hockey researchers, Rob Vollman of Hockey Prospectus and Hockey Abstract. Rob has developed a number of great tools and stats that have become a major part of the hockey analytics community.

“A good hockey stat first of all has to be useful.”

“The real achievement is to take something that’s complex and make it simple.”

LINKS:

Hockey Abstract

Rob’s personal page where he puts all his work. This site is a wealth of information–everything from Quality Starts to “Oz Coke charts” (AKA player usage charts) to historical comparisons. Your one-stop-shop for Vollman’s writing.

Hockey Prospectus

Another great site with a ton of great hockey writing from a bunch of today’s smartest minds.

>>E-mail me your fantasy hockey questions at hashtaghockey [at] gmail [dot] com, and follow me on Twitter, @Hashtag_Hockey


^^Podcast is embedded in this post. Click here to download the .mp3 file.

Can’t wait to get on iTunes so I don’t have to embed the files! I’m this close too, there appears to be a problem on the iTunes side…waiting to hear back from their tech support >_>

A player spotlight podcast today, with a twist: I did a top-10 list of forwards and defensemen by CorsiOn (min 10 GP). Talked a bit about how underlying stats like Corsi don’t always illuminate how well a player might do for fantasy, and gave lots of examples. For reference he’s the list.

Defense

  • 10. Davis Drewiske (LA) 16.4, (1+2=3), +1, 4 PIM, 8 SOG, 13:46, 6.9% on ice
  •  9. Dennis Wideman (CGY) 17.5, (3+6=9), -1, 4 PIM, 33 SOG, 24:53, 7.5%
  •  8. Paul Postma (WPG) 18.8, (1+4=5), -2, 2 PIM, 17 SOG, 16:04, 7.1%
  •  7. Zdeno Chara (BOS) 19.2, (2+3=5), +2, 14 PIM, 31 SOG, 25:29, 9.5%
  •  6. Johnny Boychuk (BOS) 19.3, (1+1=2), +3, 6 PIM, 22 SOG, 20:33, 10.9%
  •  5. T. J. Brodie (CGY) 19.6, (0+4=4), +4, 0 PIM, 11 SOG, 17:57, 9.1%
  •  4. Erik Karlsson (OTT) 23.0, (6+4=10), +6, 8 PIM, 63 SOG, 27:04, 5.8%
  •  3. Marc Methot (OTT) 23.6, (0+3=3), +1, 14 PIM, 14 SOG, 22:22, 7.0%
  •  2. Slava Voynov (LA) 26.8, (2+4=6), +6, 2 PIM, 24 SOG, 21:41, 7.7%
  •  1. Alec Martinez IR (LA) 29.7, (1+1=2), +/- 0, 4 PIM, 14 SOG, 18:56, 5.4%

Forwards

  • 10. Anze Kopitar (LA), 25.4, (4+4=8), +/-0, 8 PIM, 19 SOG, 20:55, 6.1% on ice
  •  9. Eric Fehr (WAS), 26.4, (3+2=5), +/-0, 4 PIM, 13 SOG, 9:15 ATOI, 13.3%
  •  8. Brad Marchand (BOS), 26.5, (7+1=8), +4, 4 PIM, 16 SOG, 16:40, 8.6%, (43.8%)
  •  7. Tyler Seguin (BOS), 26.8, (2+5=7), +8, 6 PIM, 37 SOG, 17:39, 11.2%
  •  6. Patrice Bergeron (BOS), 27.4, (2+5=7), +6, 4 PIM, 44 SOG, 18:43, 7.3%
  •  5. Jeff Carter (LA), 28.8, (6+1=7), -2, 6 PIM, 31 SOG, 18:34, 7.5% (19.4%)
  •  4. Kyle Clifford (LA), 30.8, (2+5=7), +/-0, 12 PIM, 12 SOG, 11:37 ATOI, 13.4%
  •  3. Henrik Sedin (VAN), 33.1, (0+10=10), +9, 8 PIM, 19 SOG, 20:0 ATOI, 11.3%
  •  2. Daniel Sedin (VAN) 34.1, (4+8=12), +5, 0 PIM, 34 SOG, 19:03 ATOI, 9.1%
  •  1. Justin Williams (LA) 35.7, (1+4=5), +3, 12 PIM, 44 SOG, 16:29 ATOI, 9.0%

Hey! If you like the Hashtag Hockey podcast, check out the NHL Numbers podcast! They’re not on iTunes yet either so in the meantime check out their pod here.

Join me next week when I will have a special guest, fancy stat innovator extraordinaire, Rob Vollman!

In the mean time, send me any show ideas or fantasy hockey questions at hashtaghockey [at] gmail [dot] com and make sure to follow me on Twitter, @Hashtag_Hockey

hashtag hockey podcast logo chiller


^^The podcast is embedded in this post. Click the play button above to begin streaming, or click here to download the .mp3 file.

I’m still working on figuring out how to get the podcast on iTunes, so please bear with me as I get the technical details figured out.

Today, I look around the league and suggest a couple of Anaheim Ducks and New Jersey Devils to pick up, plus a stat toolbox on PDO and a feature on how to make effective trades. Enjoy!

Make sure to follow me on Twitter, @Hashtag_Hockey or e-mail me at hashtaghockey [at] gmail [dot] com.

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^^^Podcast embedded in this post. Click the play button above to stream the podcast or download the Hashtag Hockey Episode 3 mp3

I recorded today’s podcast at a lower audio quality so apologies in advance if it’s tougher to hear…I’m still getting this figured out.

Today I gave a quick stat workshop where I covered on-ice shooting percent/save percent, offensive zone start percent, and a general description of corsi-based stats.

Links:

www.behindthenet.ca

Your one-stop shop for advanced stats, including all the stats mentioned in today’s podcast.

Behind the net FAQ: 10-part advanced stats tutorial

A great series of articles explaining the advanced stats that I use in my writing and podcasts.

Hashtag Hockey Stats Glossary

A description of the stats written by me

Thanks for checking out the podcast, you can e-mail me at hashtaghockey [at] gmail [dot] com, and make sure to follow me on Twitter, @Hashtag_Hockey

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***The podcast is embedded in this post. Hashtag Hockey Podcast Episode 2 Final

Episode 2 of the Hashtag Hockey Podcast is officially up! Today I take a look around the league and talk about who’s off to a hot start, a cold start, and the most surprising player in the new 2013 NHL season. Then, in my feature segment, I talk about how to play fantasy hockey like a poker player, and finally, I talk about shooting percentage…why you should care and how it can make you a better fantasy hockey player.

LINKS:

www.hockey-reference.com

^^Good statistical database, including shooting percentage and year-to-year comparisons

Armchair hockey.net: On Shooting Percentages

Arctic Ice Hockey: Luck vs Shot Quality in Shooting Percentage

Quanthockey.com: NHL Average Shot Percentage from 1967-68 to 2012-13

^^Awesome charts! League Average Sh% for forwards and defense over time. Love these graphs!

Click here for last week’s show, and don’t forget to follow me on Twitter, @Hashtag_Hockey

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It’s a rainy day in Southern California–a perfect time to stay inside and look at some early returns from the Minnesota Wild’s first three games. I’m having trouble navigating the behindthenet.ca site, so for now I’ll have to settle with just a basic look at Corsi-related stats. Once I figure out what I’m doing wrong over there I’ll be able to dig deeper and get into some more thorough analysis. All numbers posted below are even-strength.

Top Line: Parise-Koivu-Heatley

The first thing I see is that Mike Yeo has been capitalizing on the club’s three straight home games and using the top line in an opportune way. All three skaters have been started heavily in the offensive zone (65.2% for Parise and Heatley, 62.5% for Koivu) and all have faced soft competition (CorsiRel QoC of around -2.0 to -2.1 for Parise and Heatley and -1.9 for Koivu.) The skaters have jumped on the opportunity and directed pucks on net–Corsi On right around 7 for the wingers and an astounding 19.05 for Koivu. Obviously the sample is small that that’s impressive. The line has looked very good to my eyes, but I see that they haven’t had great puck luck, the three have on ice Sh% just over 4. This line is absolutely driving the Wild offense right now with CorsiRel of 14.5-14.7 for Heatley and Parise, and 31.4 (!!) for Koivu. We’ll see how the coach continues to use the top line on the road, but if the team wants to make the playoffs, they’ll need to get some offense out of the rest of the roster, which brings us to…

Second Line: Cullen-Granlund-Setoguchi

I wrote earlier this week about how I don’t particularly like Cullen on this line, but I see why Yeo has him paired with Granlund. I hope Yeo mixes up the pairings as the season goes on so for now I’ll hold my tongue. This line has been getting the tough competition–CorsiRel QoC of 2.8 for Cullen, 3.0 for Seto, and 3.3 for Granlund! Their possession numbers are paying the price for it, Granlund has a CorsiOn of -4.5 but Cullen (-10.8) and Setoguchi (-13.9) are seeing the ice tilted against them quite severely. Mike Yeo seems to be sheltering Granlund (OZ Start 60%) for sure and Setoguchi a bit (54.5%) with Cullen getting the nod defensively (45.5%). Granlund and Setoguchi have had a bit better luck, but their on ice Sh% are still pretty low (5.88 Setoguchi, 6.667 Granlund, and a big fat goose egg for Cullen.) I’m sure these usage numbers will change as the sample gets larger but I’m surprised to see this line get the brunt of the tough minutes when I thought they would go to better-established two-way players like

Third Line: Bouchard-Brodziak-Clutterbuck

Brodziak and Clutterbuck have a rep for playing solid 200-ft games, so I’m a little surprised to see their middling competition numbers–Bouchard has 0.5 CorsiRelQoC and the others actually negative, though just barely. Bouchard and Clutterbuck have a CorsiOn just over 5 right now while Brodziak is seeing a lot of rubber flying his way, with a -13.3. All three have OZ Start% north of 50 (56.5 Brodziak, 59.1 Clutterbuck, and 61.9 Bouchard) but they are winding up at the other end of the ice–OZ finsih% approaching 40. No es bueno. I like the composition of this line with the three bringing different playstyles to the table, so perhaps they need some more time to gel, but I’ll be keeping an eye on this line because these early numbers point to them being somewhat of a liability.

I don’t really want to get into the fourth line of Powe-Mitchell-Konopka, but the only number I’ll point out is their on ice Sv% of .909 (TM, ZK) and .917 (DP). Ouch.

Blue Line

The Wild blue line is hurting right now, but Jonas Brodin is slated to get the start tonight against Detroit. Scandella is down in Houston but I liked what I saw from him last year so I hope they bring him up soon. Dumba has been practicing with the team but hasn’t seen game action yet–I’d bet the money in my wallet that they don’t burn a year of his ELC but I sure want to see him get a couple games.

Spurgeon and Suter have seen the toughest competition of the defensemen, though their CorsiRelQoC are only about 0.7 to 0.8. Their CorsiOn is not great though, -9.5 for Spurgeon and -16 for Suter. The goalies haven’t helped either, as Suter has an on ice Sv% at .900 and Spurgeon .889. It looks like Spurgeon has drawn a couple penalties, which is good to see, but until the blue line gets a little clearer I don’t think there’s too much to squeeze out of these numbers.

I’m gearing up for continuing my podcast this weekend, should have it ready by Monday–if you have fantasy hockey questions, send them to hashtaghockey [at] gee mail dot com or tweet me @Hashtag_Hockey. Thanks for reading!

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***The podcast is embedded in this post. Click the PLAY button above to start listening!!***

Or, if that doesn’t work, here is a direct link to the MP3 file: Hashtag Hockey Podcast Ep 1 Goalies

 

Today, I launched the Hashtag Hockey Podcast! I talked about how goalies can make or break a fantasy hockey season, and how to look at goalie stats differently than just the simple Win-Loss record, overall Sv% and GAA.

Here are the show notes, with some of the information I presented on the podcast and relevant links:

 

Even Strength Sv% vs Overall Sv%

DEFINITION: Even strength Sv% is simply a goalie’s Sv% when the game is at 5-vs-5 (not including Power Plays or Penalty Kill). Overall Sv% is the stat used most commonly, a goalie’s Sv% for the whole game.

LINK: A look at Niklas Backstrom’s Even Strength Sv%, from The Providence

 

Niklas Backstrom

Even Strength Sv%

07-08              08-09              09-10              10-11              11-12

.925                .923                .912                .928                .931

Overall Sv%

07-08              08-09              09-10              10-11              11-12

.920                .923                .903                .916                .919

PP Sv%

07-08              08-09              09-10              10-11              11-12

.905                .918                .880                .859                .864

 

Jonas Hiller

Even Strength Sv%

07-08              08-09              09-10              10-11              11-12

.940                .910                .930                .931                .915

Overall Sv%

07-08              08-09              09-10              10-11              11-12

.922                .900                .918                .924                .910

PP Sv%

07-08              08-09              09-10              10-11              11-12

.867                .867                .874                .891                .870

Quality Starts Explained

LINK: Hockey Prospectus, Quality Starts 2011-12

LINK: Hockey Prospectus, Quality Starts 2010-11

LINK: Hockey Prospectus, Quality Starts 2009-10

LINK: Hockey Prospectus, Quality Starts original article

 

Quality Start Definitions:

Quality Start (QS): A game in which the starting goalie records a .917 Sv% or better; OR a game in which the starting goalie allows two or fewer goals and records a .885 Sv% or better. Generally speaking, a team will win around 75% of the games where the goalie posts a QS.

Quality Start Wasted (QSW): A game where the starting goalie records a QS but the team loses the game.

Bailout (BO): A game where the starting goalie does not record a QS but the team wins the game.

Terrible Start (TS): A game where the starting goalie records a Sv% less than .850.

Terrible Start Bail Out (TSBO): A game where the starting goalie records a TS but the team wins the game.

For more on Ev Sv% and QS stats, see some of my earliest posts on Mike Smith, Jimmy Howard, Jaroslav Halak, and Jonathan Quick

 

Thanks for checking out the podcast, and remember to follow me on Twitter @Hashtag_Hockey

Recently, an interesting article was published to the NHLNumbers website examining the behavior of skaters and their shooting tendencies, or how much they could be considered a “Puck Hog.” The article, written by Ben Wendorf, proposed an elegant way of measuring how frequently a player opts to shoot the puck.

Simply, the metric looks at the proportion of On-ice Fenwick (A’) that was represented by a specific player’s Fenwick shots (A). This number can be thought of as the Attempted Shot Ratio.

Ben found that this number did not correlate to things like shooting percent or quality of teammates, but that players showed the same tendency year after year. Thus, he concluded, “With high repeatability, and little connection to shooting talent, player development, or teammates, this seems much more like a behavioral activity.”

The article got me thinking about some of my own research that I did way back in the first month or so of writing this blog. I was messing around with players’ shots on goal versus missed shots, and determined what proportion of Fenwick shots were shots on goal (SOG/FEN), which I called Shooting Ratio. I had similar findings—that players could be productive even if they missed the net a lot, and the behavior was stable year after year. Something that stuck out to me during that research was the almost perfectly normal distribution of skaters’ ability to hit the net, so it was not much of a surprise to me when I checked with Ben and found that Attempted Shot Ratios also showed that perfect bell curve that we stat heads swoon over.

After mulling things over, I thought it would be insightful to combine Ben’s “Puck Hog” metric with my missed shot data to provide more of a two-dimensional look at player behavior. Or, simply,

How often do players choose to shoot, and how often do they hit the net?

Method:

I decided to look at just Minnesota Wild forward skaters from 2011-12 for this post, although I am working on a bigger dataset to look at more of a sample. I crunched all the numbers to determine players’ even-strength Attempted Shot Ratios (how often does the player take the shot when he’s on the ice) and Shooting Ratios (what proportion of those Fenwick shots are shots on goal) using the formulas listed above. All computations use 5v5 data.

Population Parameters

After initially just comparing the 11-12 Wild players to each other, I realized that I would be better served using ‘population’ data that came out of Ben’s and my research. Specifically,

Attempted Shot Ratio: Mean = 0.244, SD = 0.05 (N = 2177 NHL forwards, 2007-2012)

Shooting Ratio: Mean = 0.737, SD = 0.074 (N = 492 NHL forwards > 10 GP, 2011-12)

Results:

The tables below list Minnesota forwards with their attempted shot ratio and shooting ratio. The z-scores associated with each are calculated using the population parameters listed above. Due to several players who wore the same number at different times, some numbers have been substituted for dummy numbers (Wellman, Taffe, Veilleux.)

Name AttShRatio ASR-Z Sh Ratio ShR-Z
Almond, Cody (27) .205 -.789 .778 .551
Bouchard, Pierre-Marc (96) .265 .414 .803 .885
Brodziak, Kyle (21) .266 .430 .712 -.338
Bulmer, Brett (19) .233 -.229 .700 -.500
Christensen, Erik (26) .205 -.787 .727 -.131
Clutterbuck, Cal (22) .317 1.462 .686 -.684
Cullen, Matt (7) .229 -.302 .872 1.083
Heatley, Dany (15) .255 .221 .716 -.280
Johnson, Nick (25) .281 .747 .817 1.083
Koivu, Mikko (9) .218 -.513 .748 .144
Latendresse, Guillaume (48) .314 1.391 .703 -.464
Mcintyre, David (34) .214 -.594 .667 -.951
Mcmillan, Carson (45) .215 -0.572 .571 -2.238
Ortmeyer, Jed (41) .383 2.774 .645 -1.241
Palmer, Jarod (79) .265 .426 .846 1.475
Palmieri, Nick (17) .323 1.575 .747 .128
Peters, Warren (43) .273 .581 .662 -1.010
Powe, Darroll (14) .279 .700 .697 -.538
Rau, Chad (36) .256 .248 .700 -0.500
Setoguchi, Devin (10) .284 .796 .780 .578
Taffe, Jeff (97) .286 .834 .750 .176
Veilleux, Stephane (98) .228 -.326 .696 -.559
Wellman, Casey (95) .309 1.302 .706 -.421
Zucker, Jason (16) .345 2.017 .800 .851

When charted, the z-scores can be informative because they fall into one of four quadrants on the chart. Attempted Shot Rate is on the X-axis, and Shooting Rate is on the Y-axis. For lack of better terminology, they are I – shooting inclined + accurate shot; II – shooting inclined + inaccurate shots; III – shooting averse + inaccurate shot; IV – shooting averse + accurate shot. (I really dislike using “accurate/inaccurate” here but I just don’t have a better pair of descriptors right now. If you have suggestions please share!)

*Click the chart to view the full-size version.

Discussion:

The first thing that jumps out to me is that the majority of players fall into quadrants I and II…so it’s a team full of puck hogs? Apparently so. Recall that the ASR data here are compared to the five-year population parameters, so the Wild have a lot of guys who choose to shoot the puck. I am trying to avoid value judgements here since a higher value ASR doesn’t necessarily represent “better hockey,” just a different playstyle. On the other hand, Sh Ratio is less subjective–making the goalie make a save is almost always better than missing the net. I know some times a player will shoot wide on purpose to try to make a play, but for now I am using the assumption that SOG > missed shot.

So the Wild had guys who were looking for shots but they had the fewest shots per game and goals per game. What to make of this? I wouldn’t necessarily expect a team-wide pattern to emerge when looking at this kind of plot, but it would seem to make sense that having more of a balance between shooters and set-up guys would produce more quality scoring chances. I would also think that a team distribution like this would indicate that the Wild players were predictable–the opponents probably figured out quickly that guys would be looking to shoot instead of making the extra pass, so perhaps that predictability suppressed their shot totals by making them easier to read. I’m just spitballing here, but if the Wild ranked high in a stat that measured blocked shots against, there might be something to this theory. But honestly at this stage in the research I’m still just digesting the numbers so again, if you have thoughts let’s hear them!

At the individual level, it is important to keep in mind that there isn’t really a “good play vs bad play” distinction here, it’s all just a measurement of behavior. Because we know that both ASR and ShR are normally distributed, it makes the indivudal results more interpretable, but a player who has a high attempted shot ratio isn’t necessarily doing his team any favors if he consistently misses the net more than his teammates. I do like the quadrant setup because it helps classify players. Matt Cullen doesn’t shoot much anymore, but when he does pull the trigger he hits the net more than any of his teammates. Mikko Koivu, known as a playmaker, prefers to make an extra pass rather than shoot, and is about average at hitting the net. Pierre-Marc Bouchard, another playmaker, actually shoots the puck more than I would have guessed, and is an accurate shot. Nick Johnson, despite having a strong tendency for the uncontrolled zone entry, pulls the trigger often and makes the goalie make a save. Dany Heatley is as close to zero on both scales as any player, which is probably a surprise to most of us. Cal Clutterbuck and Guillaume Latendresse have a strong tendency to shoot, but are somewhat wild.

In the end, it is my hope that this kind of multidimensional shooting analysis can be helpful in our understanding of players’ tendencies. This kind of stuff can’t be used in isolation, but rather it all works together to paint a picture and gives us a peek inside the head of the players we enjoy watching. The game of hockey is just a series of decisions, nearly all of which must be made at a speed where there is no time for conscious thought but instead are instinctual. I will be excited to continue this analysis to expand to different teams and different years. If you have questions or comments I would love to hear them. You can comment here on this page or though Twitter, @Hashtag_Hockey. Thanks for reading!

The Anaheim Ducks have not made the playoffs two of the last three years, and their top line of Ryan Getzlaf, Corey Perry and Bobby Ryan vastly underperformed last  year as the team finished fifth in the Pacific division. It’s a safe bet that each of these players will be available in your draft a fair bit lower than last year, providing a good opportunity for value if they can bounce back. I believe the Ducks’ woes last year were due to a combination of factors: a slow start (they went 7-13-4 through November before firing head coach Randy Carlyle) combined with a weird case of vertigo for Jonas Hiller, and just pure bad luck all mashed up for a disaster of a season. Unless your league is made up of Ducks homers, expect your owners to pay more attention to other teams in the Pacific division: the Stanley Cup champion Kings, the surprising Coyotes and their new super goalie Mike Smith, or the perennially-enticing San Jose Sharks. With more stability behind the bench and a chance to press the reset button on last year’s bad luck, I am willing to bet that the Ducks’ stars will return to form. I think Perry will still be drafted in first two rounds (people don’t forget a 50-goal scorer so quickly) and Ryan’s year wasn’t so bad last season, but I am looking for Ryan Getzlaf to fall the most in drafts and thus potentially return the most value.

Fantasy Stats

The RPG line were all coming off great years in 2010-11, which certainly inflated their expectations going into 11-12. Corey Perry in particular had a phenomenal 2010 campaign, lighting the lamp a cool 50 times (32 coming at even strength) to go with 48 assists, putting him within spitting distance of the century mark with 98 points on the season. Despite suffering a really nasty facial laceration (fair warning) that put hin out of commission for 15 games, Getzlaf still managed to record (19+57=76,) good for better than a point-per-game. Bobby Ryan posted a career-high 270 shots on his way to a 34-goal, 71-point season. Then it all came crashing down in 2011-12, when Perry scored only 27 goals, though still managed a 76-point campaign, while Getzlaf posted only 9 points in his first 17 contests. Ryan, however, managed to net 35 goals on 258 shots, salvaging some value for his fantasy owners. What went wrong for the Ducks? It’s hard to say exactly–like all things their crappy season was multi causal so we can’t point to just one thing. However, a look at the advanced stats can help shed some light on the subject…

Advanced Stats

The first thing I want to point out is the RPG line’s on-ice shooting percentage, which measures the percent of shots by any teammate that turn into goals when a player is on the ice.

After two seasons of seeing on-ice Sh% in the double digits (10 to 12 is pretty standard for star-level players,) Getzlaf and Perry both tanked into the 7-8 range, which you would expect to see from the grinders on the team. Ryan’s on-ice Sh% did not drop as drastically–he was still in the upper-9 range in 2011, but a look at his TOI with Getzlaf & Perry shows that he only skated with those two around half the time, which explains part of that difference.

Another notable factor for the trio was their increased level of competition: all three players have seen steadily tougher competition over the years, as measured by CorsiRel Quality of Competition, where league average is about zero, plus-one represents very tough opponents, and negative-one is what we would lovingly classify as “Cupcake.”

In 2009, Bobby Ryan got the toughest minutes of the three with .501, representing middle of the pack competition, while Getzlaf saw .348 opposition and Perry a rather low .203–it’s safe to say he was not skating against the other teams’ stars. Then, in 2010-11 the linemates saw slightly tougher opposition, and in 2011-12 Perry was being matched by .905 competition and Getzlaf .843. You can imagine the opposing coaches saying to themselves, “Derp, there’s that guy that scored 50 last year, I better send out my shutdown line when he’s skating herp.” Bobby Ryan’s QualComp remained pretty stable, staying in about the .500 range. Special teams are not a factor, as the statistic only uses 5v5 data, and even with a coaching change, it’s notable to see that consistency.

Despite much stiffer competition, the RPG line was still driving play for the Ducks last year, with Getzlaf and Perry posting CorsiRels of 14.5 and 15.9 respectively, and moving play into the offensive zone (each had an OZ Start % of about 48 and OZ Finish % of just over 52. With Anaheim’s depth chart looking mostly the same as last year, we can expect their top line to keep getting the bulk of the tough minutes. If their younger players can step up, it would go a long way towards freeing the RPG line to focus on putting the biscuit in the basket.

A brief aside here to look at the difference between CorsiOn and CorsiRel. In 2010-11, the year the RPG line all had 70 point seasons, they all posted good CorsiRel numbers, 7.9 for Ryan, 12.5 for Getzlaf, and 14.2 for 50 Shades of Goals Corey Perry (sorry.) Recall that CorsiOn is simply the rate of the team’s combined S+MS+BS per 60 mins when a player is on ice, while CorsiRel looks at a players’ Corsi while on the ice compared to all other players cumulatively when the player was off the ice. Each player’s CorsiOn was negative in 10-11, meaning that although relative to the rest of the Ducks they were driving possession, they still saw more pucks directed at their own net than the opponent’s.

What this tells us is that Getzlaf, Perry and Ryan had to really capitalize on their opportunities that year, which they certainly did. Perry shot 17.2% after converting 10.0% the previous year, which to me hints that for all the bad luck in 11-12, their 10-11 numbers were the result of some favorable puck luck.

After seeing all negative CorsiOn values in 10-11, all three posted positive Corsi numbers in 11-12: 1.31 for Ryan, 6.82 for Getzlaf, and 7.6 for Perry. So while they were on their heels but scoring profusely one year, they were skating downhill the next year but not finding the twine. What this suggests to me is that going forward, it’s safe to think their production will wind up somewhere between those two years–with increased QualComp they may not achieve what they did two years ago but they aren’t as bad as they appeared last year.

Offseason Moves

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Teemu Selanne re-signed with the team for another year, and while he did lead the team in points last year with just 66, the guy just turned 42 years old. He wouldn’t have signed on for another campaign if he didn’t think he could produce, but let’s be really real right now, his days of scoring on an elite level are over. As I write this, the Lubomir Visnovski kerfuffle has not been completely sorted out, but it appears he is likely gone. But for the most part, the roster will remain mostly the same as last year.

Bottom Line

Corey Perry will be drafted highest of the three, possibly still in the first round, but I think he could fall into the second or maybe even farther. Depending on how the early picks shake out, I’d take him as early as seventh overall (keep your eyes peeled for my preseason ranks in the next couple of weeks,) but in the unlikely scenario he was still available in the second or even third round I would snatch him up faster than a fat kid gets out in dodgeball. Bobby Ryan has scored in the mid-thirties for two years in a row now, while throwing a ton of rubber at the net. Owners will notice his consistent production, and stat-heads will notice that he is still facing middling competition. Ryan was drafted in the early rounds last year (ADP 22.1 ESPN, 12.4 Y!) but I could see him slipping a few rounds this year because people will likely not notice his consistency. I’ve seen him on as the 75th ranked *forward*, so he could be a fantastic value pick if he’s still on the board in the double digit rounds. I really want to get into some mock drafts to see where he starts going.

After Getzlaf’s free fall last season, he will very likely experience a similar fall down the draft board. Obviously the lower he goes, the more potential value he could return if he bounces back, so while I think somewhere in the seventh or eighth round would be alright, I will be inclined to pass him over and see just how far he goes. There is nothing wrong with a little gamesmanship on draft day, so I might loudly bring up just how bad he was last year and then scoop him up a few rounds later. He is being ranked in the same ballpark as guys like Jeff Carter and Jeff Skinner, both of whom I would rather have. My home league counts hits (not my call, don’t judge me) so he does have a little more appeal to me than in standard leagues.

All three of these guys are still in their mid-twenties, so I don’t take their bad performance last year as much indication that their skills are deteriorating. As I mentioned before, I think it was sort of a perfect storm for the Ducks, and between a slow start that led to a coaching change and Hiller getting messed up between the ears, the whole team just fell into a funk. I’m willing to bet that last year was just bad beats all around and this year will be better. As a man much wiser than myself once said, “Sometimes you eat the bear and sometimes the bear, well…he eats you.” <That some kind of Eastern thing?>

Having said that, I do think the Anaheim roster is thinning out (just like 15′s hair BOOM!) After the top line, they have Koivu and Selanne who are a combined 79(!) years old, and their bottom six is nothing to write home about. Expect opposing coaches to continue to focus their best shutdown players on the RPG line this year, but there is such a thing as positive regression to the mean, so I am willing to bet on a bounce back season. Getzlaf in particular could be a great value pick if his production doesn’t continue to…<sunglasses>…recede.