Chapter 3

Important Trends in the NHL

3.1 Introduction

After the 2004-2005 lockout, there was an upwardspike in the number of obstruction-related penaltycalls made in NHL games (see Figure 3.1). The in-crease in penalties resulted in an increase in power-play opportunities, which in turn, led to an increasein goal scoring.

Figure 3.1: Penalties Per Game (2005-present)

This increase in goal scoring would be short-lived.It’s debatable as to whether the o�cials slowed theirrate of penalty calls or if the players adapted and

are playing an obstruction-free type of hockey game.1

But, the end result is that overall scoring is down inthe NHL (as a result of fewer powerplay opportuni-ties) and this has important consequences for you asa fantasy hockey manager.

The goal of this chapter, then, is to explore the im-pact of this drop on how you draft as a fantasy hockeymanager and how you oversee your league as a fan-tasy hockey commissioner.

3.2 Powerplay Opportunities

One of the obvious results that stems from a decreasein penalty calls is a decrease in powerplay opportuni-ties. But seeing that in printed words is not nearly aspowerful as seeing the data represented graphically.Figure 3.2 reveals the massive drop in powerplay op-portunities for NHL teams since the 2005-2006 sea-son.

While the trend has leveled o↵ in recent seasons, theoverall drop amounts to nearly 50% over the pastdecade. This drop impacts fantasy hockey in waysyou may not have imagined.

With powerplay opportunities down, the fraction ofpoints scored by NHL players that come from thepowerplay is also down. This puts a premium on

1The reality is almost certainly a blend of these hypotheses.

45

CHAPTER 4. CHANGES TO THE OVERTIME FORMAT 51

Table 4.2: Games Requiring Shootouts

3-on-3 Goals/Min OTs Requiring Shootouts

0.103 59.8%0.168 43.2%0.267 26.3%

The data above is very telling. First, the 3-on-3 goalscoring rate of 0.103 suggests that the 4-on-4 goalscoring data from Table 4.1 is understated; and it is.Here’s why: the 4-on-4 goal scoring rates were mea-sured for all 4-on-4 even-strength situations insteadof just overtime situations. As it turns out, the over-time goal scoring rates in the NHL are closer to 0.116goals per minute.4 Additionally, this also means thatthe 3-on-3 goal scoring rate of 0.103 must also be in-accurate (there is no chance that more NHL gamesrequire a shootout using 3-on-3 instead of using 4-on-4). So, we can toss the 0.103 goal scoring rate for3-on-3 hockey out the window.

Throwing out the low-end goal scoring rate (whichhas to be inaccurate) leaves us with an approximaterange of 25% to 45% of overtime games next sea-son requiring a shootout. As a reminder, the currentamount of overtime games that require shootouts sitsat 56%. And it’s useful, again, to remind you that theAHL was able to drop their percentage of overtimegames requiring shootouts from 65% to 25%. But,the AHL switched to a seven minutes long overtimethat is a hybrid of 4-on-4 hockey followed by 3-on-3hockey.

Given that the AHL saw a reduction to 25% with aseven minutes long overtime, we do not anticipate thenumber of NHL overtime games requiring shootoutsto be that low. Instead, something closer to 40%looks much more appropriate.5

4This is computed using internal data at leftwinglock.com.5Small sample sizes are killing us here. There simply isn’t

enough 3-on-3 data to establish a firm goal scoring rate for thattype of hockey. But, we can say with pretty good odds, thatthe percentage of NHL overtime games requiring shootoutsshould drop from 56% to something in the range of 30-40% forthe 2015-2016 season.

4.5 Impact on Fantasy Hockey

Now that we’ve laid the groundwork for the reduc-tion in shootouts for the coming season, we can makesome statements about how this will impact fantasyhockey. This is a fantasy hockey draft kit after all.

4.5.1 Goals, Assists, and Points

The most clear path to take here is to consider howmany extra goals will be scored in the NHL in 2015-2016 as a result of the increase in overtime goals.6 Weknow from earlier that about 24% of NHL games goto overtime. This number is not expected to changein any meaningful way under the new overtime sys-tem. This tells us that about 295 games each seasonwill have overtime periods.7 Under the old overtimesystem, 44% of these games would end before theshootout (and therefore, an average of 130 overtimegoals are scored during the season). If the percent-age of overtime games requiring a shootout drops to,say, 30%, then we can expect approximately 207 over-time goals in the coming season. That’s a jump of 77goals over the old system, which amounts to about a1.2% increase in overall goal scoring in the NHL thisyear. Keep in mind that this is the high-end estimatethat assumes only 30% of overtime games require ashootout. If we use a more conservative 40% number,the approximate goal scoring increase for the seasonbecomes 0.72%.8

How can you use this for your draft? The numberof goals scored by a team in the NHL is 218 on aver-age. And assists are distributed at a rather consistentrate of 1.71 assists per goal. This means that a typi-cal NHL team is awarded about 591 points (goals +assists) in a season. What does a 1% increase looklike for these numbers? That means a team will earnan extra 7 points on the season. Breaking this down

6If the number of shootouts goes down (and it certainlywill), then overtime goals must increase.

7A full NHL season has 1,230 games.8It’s worth mentioning here that a goal increase of about

1% carries over to assists and points as well.

CHAPTER 8. SHOOTING PERCENTAGE AS A TOOL 65

8.2 Using SH% in Your 2015-2016 Fantasy Draft

We will now use the techniques described above toproject which NHL players are most likely to see adecline in their 2015-2016 SH% (and very likely expe-rience a corresponding decline in overall goal scoring).Note that we chose a cuto↵ requiring the players tohave taken at least 150 SOG during the 2014-2015season.

It is important to make the distinction here betweenregression and having a bad season. We are not pro-jecting the players in the table below to necessarilyhave bad seasons. We are not necessarily telling youto avoid particular players on this list. We are simplyprojecting the players below to see significant reduc-tions in their shooting percentages that fall more inline with their career averages.

Table 8.3: Likely Regression Candidates

Player Career 2014-2015

Justin Abdelkader 8.3 14.9P.K. Subban 6.3 8.8Mike Cammalleri 12.5 17.3Nick Foligno 12.4 17.0Jiri Hudler 15.1 19.6Corey Perry 13.4 17.1Mika Zibanejad 10.6 13.3Joe Pavelski 11.5 14.2Radim Vrbata 9.5 11.6Patrick Kane 11.9 14.5Mike Ho↵man 11.3 13.6Scott Hartnell 11.4 13.7Patric Hornqvist* 9.5 11.4Marian Gaborik 13.1 15.5

*Patric Hornqvist is probably the weakest candidateon this list. There is evidence that elite players canboost the SH% of their linemates by about 1%.1

1http://goo.gl/Z1ykQs

Hornqvist’s most frequent linemate from the 2014-2015 season was Sidney Crosby.2 Thus, it is possiblethat Hornqvist’s bump in SH% last season was somecombination of luck and playing alongside an elitelinemate in Crosby (recall that Hornqvist came overvia trade from Nashville prior to the start of the 2014-2015 season). This is precisely the situation we had inmind when we mentioned early on in the draft guidethat we prefer a blend of mathematics complementedby a situational awareness for both individual playersand teams

For a powerful 1-2 punch, combine the above list withthe fact that most NHL players experience a naturaldecline in their SH% after the age of 27.3 Your con-densed list would include names such as: Justin Ab-delkader, Mike Cammalleri, Nick Foligno, Jiri Hudler,Corey Perry, Joe Pavelski, Radim Vrbata, Scott Hart-nell, Patric Hornqvist, and Marian Gaborik.

History tells us that only 1 or 2 players from thetable above will buck the trend of dropping in goalproduction. Do you really like your odds (0%-10%)of picking that specific player to draft?

8.3 Case Studies in ShootingPercentage

8.3.1 Claude Giroux: 2013-2014

Claude Giroux remained goalless through the first15 games of the 2013-2014 NHL season. By mid-November, he had scored only one goal on 46 shotsfor a 2.2% shooting percentage. Frustrated by hisperformance (and that of the entire Flyers’ team),many fantasy hockey managers gave up on Girouxand either dropped him or traded him for weak re-turns.

2http://leftwinglock.com/line-combinations/?player1=Patric+Hornqvist&flag=y

3http://www.arcticicehockey.com/2010/4/15/1418423/shooting-percentage-vs-age

CHAPTER 9. INDIVIDUAL POINTS PERCENTAGE 69

We mentioned earlier that the average IPP for de-fensemen is about 30%. Carlson’s values havebounced around above and below 30%, but he’sclearly an above average defensemen. But what re-ally stands out in Figure 9.1, is Carlson’s 54.2% IPPin 2014-2015. This is well above the norm for NHLdefensemen and more importantly, well above Carl-son’s own career average. This 2014-2015 IPP valuesuggests to us that Carlson was on the receiving endof favorable luck and that it would not be wise to ex-pect the same production level in the coming season.

9.3 Case Study: Michael DelZotto

Michael Del Zotto posted 32 points in 64 games forthe Philadelphia Flyers last season. His breakdownwas 10 goals (with a SH% of 8.4) and 22 assists. DelZotto is being largely in fantasy drafts until aboutthe 13th round.

We’re going to take a look at Del Zotto’s career his-tory from an IPP perspective. We’ve plotted six yearsof data in Figure 9.2

Figure 9.2: Michael Del Zotto (2009-present)

Del Zotto’s chart looks very similar to John Carl-son’s IPP chart. Both players have fluctuating valuesabove and below the 30% indicator and then explodeover 50% in the 2014-2015 season. The 2014-2015season data obviously looks well out of the normalrange for Michael Del Zotto and managers shouldkeep this in mind as they draft him in the comingseason. While we do think he is capable of 30+ pointseasons, we expect them to happen over the courseof 82 games and not 64.

9.4 Case Study: Jiri Hudler

2014-2015 was a career year for Jiri Hudler as he setpersonal bests in goals (31), assists (45), and points(76). That he did this at age 31 is amazing but also aconcern for fantasy hockey managers going forward.Let’s have a look. In Figure 9.3 we’ve Hudler’s careernumbers for IPP.

Figure 9.3: Jiri Hudler (2008-present)

What you see here is a steady ride at, and around,70% IPP (what you would expect from a typical for-ward in the NHL). But in 2014-2015, Hudler’s IPPjumps to 90%. It is clear that Hudler earned points

CHAPTER 14. THE REPEATABILITY OF FANTASY HOCKEY STATS - PART I 92

a long discussion about them.

Imagine drawing a line of best fit through the data forStat X and another line of best fit through the datain Stat Y. These lines of best fit are shown in bluein Figure 14.2. Consider these lines of best fit to betheoretical models that attempt to predict the 2012stats using the 2011 stats. While it’s obvious fromthe plots which model works better at predictions,what isn’t clear is how much better it is.

Figure 14.2: Scatter Plots With Best Fit Lines

We can all agree that the points for Stat X are closerto the model than the points in Stat Y. This is nocoincidence. In fact, this is actually a good way todetermine which model is more accurate (that is, bet-ter at predicting). We could send a Left Wing Locksta↵er into an o�ce and have him measure the dis-tance between each plotted point and the blue linefor Stat X. He could then be forced to perform thesame assignment for Stat Y. Whichever model (blueline) ends up with the smallest combined distancebetween all of the points and the model is the betterprediction.2

2Math nerds of the world: don’t throw a fit here. I amreally glossing over the ugly details about how one would ac-tually perform this calculation. For starters, it would includesquaring the distances first before adding them together.

It turns out that you can assign a number to these dis-tances measurements3 that we have been discussing.And this number will tell you how good your modelfits your data. Put a more useful way, this numberwill tell you how well your 2012 data is explainedby your 2011 data. This number is R2 (pronounced:r-squared) and is known as the coe�cient of determi-nation.

R2 can take on values from 0 to 1. If all of your datapoints were found to be in complete alignment withyour line of best fit (model), then the R2 value forthat model would be 1. That would indicate thatyour model is perfect and your predictions will al-ways be exactly right. Instead, if your R2 value were0, your model would be completely useless and haveno predictive power whatsoever. Real life situationsalmost always fall somewhere in between.

Going back to our two sets of data and models (thebest line fits in blue), we can determine the R2 val-ues. We’ve done this and found that the R2 value forStat X is 0.55 and for Stat Y is 0.14. Thus, we canclaim the following for Stat X : 55% of the 2012 Stat

X data can be explained by the 2011 Stat X data.And for Stat Y : 14% of the 2012 Stat Y data canbe explained by the 2011 Stat Y data. If you wereusing projections for a fantasy hockey season basedon these models, you would feel more confident withyour Stat X numbers than your Stat Y numbers.

14.3 Putting It Together

We can compute a single, numerical value to assesshow accurately a model fits the data of a particu-lar fantasy hockey statistic. This value, R2, will bea powerful tool in understanding which stats are re-peatable and which stats are not.

3It’s not correct to call R2 a distance, but this is not theplace for a rigorous discussion of the topic. Instead, I wantmost readers to come away with a conceptual feel for the topic.

CHAPTER 15. THE REPEATABILITY OF FANTASY HOCKEY STATS - PART II 101

Figure 15.14: Repeatability Data for Fantasy Hockey Stats

sion, fantasy hockey) interesting. It would beboring if everything were predictable.

• The data presented here only represents the lim-its on projections of future statistics if you’reusing past data of the exact same statistic.

Take a minute to digest that second point. Considerthe goals category. Several years of data suggeststhat about 60% of future goal data is explainable bypast goal data. If you were to build a model project-ing future goals (using past goal data), your modelwould be subject to significant variations (⇠40%) un-explained by the data of past seasons. To do bet-ter than this, you would need to develop a model

(that used something other than past goal data asthe input) to improve the R2 relationship betweenthe model and the observed data.

Can it be done? Yes, in some cases, significant im-provements can be made in models that use inputdata di↵erent from simple past data. We’ve beenworking on these models for a number of years to im-prove the projections that all draft kit clients receiveand some of our biggest gains in R2 have come withinthe Goals and Powerplay Goals categories. We’rehappy to report that our projections consistently out-perform all other draft kits and our competitive ad-vantage comes from developing models that improveupon these R2 limits.

CHAPTER 21. ANALYSIS OF THE NHL SCHEDULE 124

to get a little bit of work in relief for JonathanQuick.

• Montreal has 16 B2Bs this season which couldlead to a drop in starts for Carey Price.

• Expect Rinne to carry a heavy load again thisseason with only 12 B2Bs for Nashville.

• New Jersey gets a big break this year as theirB2B total drops from 18 to 12. That’s goodnews for Cory Schneider owners.

• Ron Hextall is on record stating Neuvirth willget more starts than a typical backup goalie.Philadelphia has 15 B2Bs, so that will be thefloor on the number of starts by Neuvirth. He’slikely to see something closer to 22-25.

• Pittsburgh has 17 B2Bs again this season. Thatshould pave the way for a typical 60-65 start sea-son for Marc-Andre Fleury.

21.4 Days of the Week

For managers in leagues with no ceilings on gamesplayed (such as many head-to-head formats), it iscritical that you are familiar with the NHL scheduleon a day-to-day basis.

For example, in the 2015-2016 season, 60.74% of allNHL games will occur on Tuesday, Thursday, or Sat-urday. That’s a rather crowded schedule and it re-sults in a lot of scheduling conflicts for your rosterplayers throughout the week. Thus, it is to your ad-vantage to draft players who play a large fraction oftheir games on “o↵-days” such as Monday, Wednes-day, Friday, and Sunday. This would allow you tomaximize the number of starts you get from your ros-ter players. And in some league formats, more startsequals victory.

We’ve broken down the NHL schedule by days andfound the following:

• Monday - 246 games (10.00%)

• Tuesday - 470 games (19.11%)

• Wednesday - 208 games (8.46%)

• Thursday - 446 games (18.13%)

• Friday - 278 games (11.30%)

• Saturday - 578 games (23.50%)

• Sunday - 234 games (9.51%)

Figure 21.2: NHL Schedule by Day of the Week

So, which teams play the most games on the o↵-days?Be sure to check out the chapters on each team inthis kit for a detailed breakdown of their respectiveschedules.

While knowing this data over the course of the seasonis useful, why not use our Roster Maximizer Toolwhich will compute all schedule conflicts for any datesand teams of your choosing? It is the only such toolavailable on the web and it is free to use.5

5http://www.leftwinglock.com/roster-maximizer/

CHAPTER 22. KEY INJURIES 129

Darren Helm - Detroit Red Wings

September 20Is expected to miss 2-4 weeks with a shoulder separation that resulted from a collision during trainingcamp.25

Adam Henrique - New Jersey Devils

April 27Underwent wrist surgery. His recovery was expected to take 4-6 weeks.26

July 15Mentioned that he had one week left in his rehab.27

Ales Hemsky - Dallas Stars

April 27Had hip surgery. His recovery time is estimated at 4-5 months.28

September 14Very doubtful he will play the first four or five exhibition games. Dallas is hoping to get him in the lastcouple games, which is the best-case scenario.29

Chris Higgins - Vancouver Canucks

September 23Is expected to miss 3 weeks after su↵ering a fracture of his right foot while blocking a shot (3 weeksseems optimistic to us!).30

Tyler Johnson - Tampa Bay Lightning

September 04Is expected to be ready for camp after su↵ering a broken wrist during the playo↵s.31

September 23Says he feels no limitations in his wrist following his injury last Spring. He suited up for the September23 pre-season game.32

25https://twitter.com/AnsarKhanMLive/status/64522918767509504126https://twitter.com/ledger_njdevils/status/59026138853203558527https://twitter.com/tgfireandice/status/62254526523338342428http://goo.gl/dGLlXA29http://www.twitlonger.com/show/n_1snf2ju30https://twitter.com/benkuzma/status/64650401340850585631https://twitter.com/tbtimes_jsmith/status/63981075631851110432https://twitter.com/bburnsnhl/status/646713682785902592

CHAPTER 23. PLAYERS TO WATCH 139

Figure 23.1: Point Production Rate of Rookies Compared to All NHL Players

23.1.2 Top Rookie Forwards

Connor McDavid (EDM) 26 G 39 A

McDavid enters his rookie season as the most highlytouted prospect since Sidney Crosby in 2005. He wasthe OHL rookie of the year in 2012-2013 and has beendominant in international tournaments. He finishedthe 2014-2015 OHL season with the third most pointsin the league, despite missing over 20 games. He isthe one rookie you should draft early in both keeperleagues and one-year leagues. He is expected to cen-ter the 2nd line in Edmonton this season alongsideTaylor Hall.

Artemi Panarin (CHI) 28 G 33 A

Panarin will be 24 by the end of October and, assuch, is one of the more mature rookies eligible for theCalder trophy this season. The expectation is that if

Panarin makes the Chicago roster, he’ll be playing asthe second line left wing on a line with Patrick Kaneand one of Anisimov/Teravainen. Panarin played inthe KHL in 2014-2015 where he outscored Ilya Ko-vachuk. But he has more accomplishments than thatoft-cited factoid; for example, Panarin has been apoint-per-game player in international tournamentsand the 2015 KHL playo↵s. Don’t overpay for thisgamble. We realize we have Panarin projected highly,but his ADP (14th round) suggest he’s being over-looked by most fantasy managers.

Jack Eichel (BUF) 20 G 36 A

Eichel will contend for the Calder trophy this seasonas he enters the NHL after an impressive season withBoston University. He is a skilled skater, but alsostrong at 6’2” and nearly 200 pounds. Eichel is ex-pected to center the 2nd line in Bu↵alo which mightfree him up to play against weaker defenders. Unlike

CHAPTER 37. CHICAGO BLACKHAWKS 217

2014-2015 Players Who Left

Player Position New Team

Adam Clendening D PITBen Smith R SJSBrad Richards C DETBrandon Saad L CBJDaniel Carcillo LJoakim Nordstrom L CARJohnny Oduya D DALKimmo Timonen DKlas Dahlbeck D ARIKris Versteeg L CARKyle Cumiskey DMichael Paliotta D CBJPatrick Sharp L DALPeter Regin CAntti Raanta G NYR

37.7 Projected Lineup

Once the 2015-2016 season begins, you can find all of the detailed line combinations for the Chicago Black-hawks here:

http://leftwinglock.com/line-combinations/chicago-blackhawks/

Here is a preview of what the lines might look like in Chicago this season:

Projected Forwards

Left Wing Center Right Wing

Teuvo Teravainen Jonathan Toews Marian HossaArtemi Panarin Artem Anisimov Patrick KaneBryan Bickell Andrew Shaw Kyle BaunAndrew Desjardins Marcus Kruger Ryan Garbutt

Projected Defensemen

Left Defense Right Defense

Duncan Keith Brent SeabrookTrevor Daley Niklas HjalmarssonTrevor Van Riemsdyk David Rundblad

CHAPTER 40. DALLAS STARS 235

Figure 40.1: Dallas Stars Schedule By Day

40.6 Player Movement

Recent Acquisitions

Player Position Former Team

Jason Demers D SJSJohnny Oduya D CHIPatrick Sharp L CHITravis Moen L MTLAntti Niemi G SJS

CHAPTER 47. NASHVILLE PREDATORS 284

Figure 47.2: Nashville Predators Player Usage Chart

for the blue line. Laviolette didn’t disappoint and Jones was able to post a respectable 27-point season withstrong possession numbers.

One key way in which Laviolette di↵ered from Trotz was that former used a feast-or-famine approach too↵ensive zone starts. While many of the top six forwards (Mike Ribeiro, Filip Forsberg, Craig Smith,James Neal, Colin Wilson) received favorable o↵ensive zone starts in 2014-2015, the fourth line was put ina very di�cult position. Paul Gaustad, Eric Nystrom, and Gabriel Borque saw o↵ensive zone starts in theteens! Laviolette’s player usage gives you a prescription for how to handle his teams from a fantasy hockeyperspective: draft his top players and stay very far away from everyone else on the team.

Expect the player usage chart for 2015-2016 to look reasonably similar to the one you see in Figure 47.2.The Nashville roster had very little turnover and the team had a very strong season until losing Shea Weberearly in the playo↵s.