Contents

List of Figures 23

1 How To Use This Guide 28

1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

1.2 The PDF Document . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

1.3 The Skater Spreadsheet . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

1.4 The Goalie Spreadsheet . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

2 Shooting Percentage as a Tool 30

2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

2.2 A Quick Discussion on Coin Flips . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

2.3 Applying Coin Flip Experiments to NHL Players . . . . . . . . . . . . . . . . . . . . . . . . . 32

2.4 Corey Perry’s 50 Goal Season . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

2.5 Lessons From the 2013-2014 Draft Kit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

2.6 Using SH% in Your 2014 Fantasy Draft . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

2.7 Using SH% in Your 2014 Fantasy Season . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

2.7.1 Claude Giroux: 2013-2014 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

2.7.2 Joe Pavelski: 2012-2013 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

2.7.3 Phil Kessel: 2011-2012 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

1

CONTENTS 2

2.8 Can it be Used in Reverse? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

2.8.1 Phil Kessel: 2012-2013 season . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

3 Penalty Kill Save Percentage 38

3.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38

3.2 PKSV% Is Non-Repeatable . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38

3.3 Implications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40

3.4 Is PKSV% Predictive? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40

3.5 2013-2014 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41

3.6 Final Thoughts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

4 Projecting A Goalie’s Save Percentage 43

4.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

4.2 The Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

4.2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

4.2.2 Looking at the League Averages . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44

4.2.3 How to Project . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44

5 When Are We Sure of a Goalie’s Talent Level? 46

5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46

5.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46

5.3 A Few More Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47

6 The Repeatability of Fantasy Hockey Stats - Part I 50

6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50

6.2 Correlation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50

6.2.1 Conceptual Arguments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50

CONTENTS 18

43 Philadelphia Flyers 249

43.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 249

43.2 Schedule Details . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 250

43.3 Player Movement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 251

43.4 Projected Lineup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 251

43.5 What to Expect . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 252

43.5.1 Key Forward Projections . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 252

43.5.2 Key Defensemen Projections . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 252

43.5.3 Key Peripheral Projections . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 252

43.5.4 Key Goalie Projections . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 253

43.5.5 Key Team Projections . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 253

43.6 Player Usage & Shot Metric Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 253

44 Pittsburgh Penguins 255

44.1 Schedule Details . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 256

44.2 Player Movement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 257

44.3 Projected Lineup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 257

44.4 What to Expect . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 258

44.4.1 Key Forward Projections . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 258

44.4.2 Key Defensemen Projections . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 258

44.4.3 Key Peripheral Projections . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 259

44.4.4 Key Goalie Projections . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 259

44.4.5 Key Team Projections . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 259

44.5 Player Usage & Shot Metric Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 259

45 St. Louis Blues 261

List of Figures

2.1 The Normal Distribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

3.1 Year-to-Year PKSV% Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

3.2 Shots Faced vs. PKSV% . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41

5.1 Simulation of 1000 Bad Goalies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47

5.2 Simulation of 1000 Average Goalies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48

5.3 Simulation of 1000 Good Goalies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49

6.1 Scatter Plots for Two Data Sets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51

6.2 Scatter Plots With Best Fit Lines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52

7.1 Year-to-Year Hits Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54

7.2 Year-to-Year Blocked Shots Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55

7.3 Year-to-Year ± Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56

7.4 Year-to-Year Goals Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57

7.5 Year-to-Year PPG Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57

7.6 Year-to-Year GWG Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57

7.7 Year-to-Year SHG Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58

7.8 Year-to-Year Assists Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58

23

CHAPTER 2. SHOOTING PERCENTAGE AS A TOOL 33

from 12% (much like the total number of heads ina coin flip experiment can deviate from 50%). But,the more and more shots a player takes, it becomesmore and more likely that his shooting percentagewill approach his talent level (12% in this particularexample). It will be your job in your fantasy hockeydraft to place your bets on players performing at ornear their career shooting percentages. These are theonly consistent, winning bets in fantasy hockey.

Let’s consider a 12% shooter in the NHL who takes275 shots in a single season. These kind of numberscorrelate well to 1st/2nd line players in the NHL, soit’s important that we understand how these play-ers behave. We can go through the typical standarddeviation calculations we described in a previous sec-tion and we’ll find out that the standard deviation forthis type of player is right around 2%. Recalling that68.3% of experiment results will yield values withinone standard deviation of the mean, we can say thatthis type of NHL player will have a shooting per-centage (for an entire season) between 10% and 14%in 68.3% of the experiments. What I really meanhere by experiment is an NHL season (275 shots ongoal). Extending this, in 95.4% of his NHL seasons,this type of player will have a shooting percentagebetween 8% and 16% (two standard deviations fromthe mean).

Since most NHL careers are about 10-15 years, itwould be very unlikely for a player with 12% scoringtalent to post a shooting percentage outside of the8%-16% range more than once in his career. It is thisconcept that forms the basis for why players who postshooting percentages in one season that are wildlydi↵erent from their career averages are expected toregress toward the mean. In our own studies (com-pleted over a four-year period), players with high one-season SH% typically have about a 90% chance ofexperiencing a strong regression.

2.4 Corey Perry’s 50 Goal Sea-son

In the 2010-2011 season, Corey Perry (who, up untilthat time, had never scored more than 32 goals in aseason) erupted for a 50 goal outburst. Overnight,he became every fantasy hockey manager’s dreampick for the 2011-2012 draft. There was talk of 60goals. Seriously. One fantasy hockey website claimedPerry wouldn’t reach 40 goals in the 2011-2012 sea-son. They were roundly mocked.

Let’s apply our coin flip knowledge to 2011-2012Corey Perry. Perry’s career shooting percentage (orodds that one of his shots on goal becomes a goal) is13%. He typically takes about 252 shots in a season.If you run these numbers using our coin flip meth-ods, you end up with about 2% for the standard de-viation of his shooting percentage. So, we can makethe following claims about Perry’s expected shootingpercentage during his career:

• 68% of the time, his shooting percentage shouldbe between 11% - 15%

• 95% of the time, his shooting percentage shouldbe between 9% - 17%

• 99% of the time, his shooting percentage shouldbe between 7% - 19%

When Perry scored 50 goals in 2010-2011, he wasriding a 17.2% shooting percentage. Our normal dis-tribution curve tell us that was an unlikely result.Perry should produce shooting percentages outsideof the 9% - 17% range in only 5% of the NHL seasonsthat he plays (basically once in his career). The mostlikely future performance for Perry would be in the11% - 15% range. So when our sta↵ was puttingtogether our projections for the 2011-2012 season,we expected Perry to score between 35-40 goals. Infact, Perry scored 37 goals in 2011-2012 and shotwith 13.4% success. This result caught many fan-tasy hockey managers by surprise - but it shouldn’t

CHAPTER 3. PENALTY KILL SAVE PERCENTAGE 39

Figure 3.1: Year-to-Year PKSV% Data

performance the following year.

The important take-away from these plots is the fol-lowing:

• if goalies could be good at PKSV%, you’d see alot more of them in the pink rectangles;

• if PKSV% were repeatable, you’d see a patternin the plotted data;

• because the PKSV% values are non-repeatable,they must be heavily influenced by luck.

Another way to test whether or not PKSV% is re-peatable is to observe goalie performance over many,many shots. If PKSV% is repeatable, then goalieswho are “good” at it should have high PKSV% valuesafter a lot of shots faced. And goalies who are “bad”at it will have low PKSV% values after a lot of shotsfaced. So, let’s do that. Let’s plot the cumulativePKSV% values of many NHL goalies vs. the numberof shots they faced while on the penalty kill. We’llput the shots faced on the y-axis and the PKSV% onthe x-axis. Thus, when you look at the plot in Figure3.2, goalies very low on the chart have faced few shotsand goalies very high on the chart have faced a lot of

CHAPTER 5. WHEN ARE WE SURE OF A GOALIE’S TALENT LEVEL? 49

Figure 5.3: Simulation of 1000 Good Goalies

CHAPTER 7. THE REPEATABILITY OF FANTASY HOCKEY STATS - PART II 54

Figure 7.1: Year-to-Year Hits Data

plots will have an R2 value. We’ve computed thesevalues and averaged them for convenience. For thehits category, the average R2 value is 0.81.

This is a great start. Remember, highR2 values meanthat the stat is repeatable. If the stat is repeatable, itis dominated by a player’s skill and not by luck. Andtherefore, the stat can be accurately projected forfuture seasons. If your league uses hits as a stat, thenour hit projections should be an important factor inyour drafting decision. What we’re saying here is thatour level of certainty in our hits projection is high.

7.3 Blocked Shots

Another example of a fantasy hockey stat with highrepeatability is blocked shots. In Figure 7.2, weplot data for NHL players over a four season pe-riod. Again, a clear relationship is established be-tween data from one season and the season immedi-ately following it.

The R2 value for blocked shots has been determinedto be 0.86 and is one of the strongest correlations ofany stat in fantasy hockey. If you’re drafting players

CHAPTER 11. ANALYSIS OF THE NHL SCHEDULE 71

Table 11.2: NHL Schedule Data 2014-2015

Team Back-to-back Sets

Anaheim Ducks 13Arizona Coyotes 13Boston Bruins 16Bu↵alo Sabres 19Calgary Flames 10Carolina Hurricanes 16Chicago Blackhawks 15Colorado Avalanche 12Columbus Blue Jackets 19Dallas Stars 12Detroit Red Wings 12Edmonton Oilers 11Florida Panthers 12Los Angeles Kings 9Minnesota Wild 13Montreal Canadiens 16Nashville Predators 11New Jersey Devils 18New York Islanders 16New York Rangers 13Ottawa Senators 13Philadelphia Flyers 14Pittsburgh Penguins 17San Jose Sharks 11St. Louis Blues 14Tampa Bay Lightning 13Toronto Maple Leafs 18Vancouver Canucks 12Washington Capitals 16Winnipeg Jets 9

11.3 NHL Schedule Quick Hits

• Enroth/Neuvirth should each get many starts asBu↵alo has 19 B2Bs.

• If Hiller or Ramo win the starting job, therearen’t many B2Bs available to the backup.

• Carolina’s B2B total drops from over 20 last sea-son to 16. Khudobin will see a jump from his 34starts last season.

• Varlamov should command many starts this sea-son with Colorado involved in a low 12 B2Bs.

• Columbus leads the league with 19 B2Bs thisseason. This could bite into Bobrovsky’s startingload a bit (he started 58 games last season, butsu↵ered a four-week injury in December).

• Lehtonen gave Dallas 64 starts with solid num-bers last season. With only 12 B2Bs this season,and Lindback riding shotgun, you can expect theStars to ride Lehtonen into the mid-60s again.

• Expect Luongo to get at least 65 starts giventhat Florida has only 12 B2Bs.

• One of the more interesting cases involves theLos Angeles Kings. They have a league-lownine B2Bs which surely means Jonathan Quickis primed for many starts.

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

• A healthy Rinne could be a sure-bet in Nashvillewith only 11 B2Bs and a shaky backup in Hut-ton.

• The Devils’ B2B total drops from 20+ to 18 thisseason. But Schneider is the #1 this year andwill get 60+ starts.

• 60-65 starts is typically the spot for Marc-AndreFleury. With Greiss on board as backup and 17B2Bs on the schedule, it looks like Fleury willhit his normal totals.

Chapter 15

Anatomy of a Yahoo Pro League

15.1 Background

Yahoo Fantasy Hockey o↵ers users the chance to com-pete in Yahoo Pro Leagues for a chance to win prizemoney. There are two levels of Yahoo Pro Leaguesdistinguished only by the cost of the buy-in (and sub-sequent payouts). This section of the draft kit pro-vides you with a behind the scenes look at how com-petitors win money in these leagues. Our emphasiswill be on Rotisserie style leagues, but many of thetips in this chapter can be applied to Head-to-Head(H2H) style Pro Leagues (with the caveat that Head-to-Head leagues are influenced to a greater extent byluck than Rotisserie leagues).

The data presented here have been compiled over aseveral year period and is found exclusively in thisdraft kit. We hope our users will be able to apply thisknowledge to dominate the Yahoo Fantasy HockeyPro League landscape.

15.2 Description of Leagues

The Yahoo Pro Leagues come in two flavors: PRO20and PRO100. There is a $20.00 (USD) buy-in for thePRO20 league and a $100.00 (USD) buy-in for thePRO100 league. You pay this entry fee (they acceptPayPal) and you are entered into a league with 11other random competitors. The only choice you haveover which league you join is the draft date and time

(e.g., you can choose to participate in the 8:00 PMdraft on September 29).

Yahoo Pro Leagues pay out money to the top threefinishers in the standings at the end of the season (orthe top three playo↵ teams for H2H leagues). Below,you can find the payout data for the two league types:

Table 15.1: Pro League Payouts

League 1st 2nd 3rd

PRO20 $120.00 $70.00 $30.00PRO100 $600.00 $360.00 $140.00

15.3 Scoring Settings

The Yahoo Pro Leagues use the default scoring set-tings on Yahoo. For starters, this means that ev-ery league will have 12 managers (any less, and theleague is folded before the draft and your money isreturned).

Rotisserie leagues use the settings shown in Figure15.1 (note: trade deadline date changes with eachseason), while H2H leagues use the settings shown inFigure 15.2. The scoring categories are identical, butthere are subtle di↵erences in how roster moves canbe made.

92

CHAPTER 15. ANATOMY OF A YAHOO PRO LEAGUE 97

Figure 15.3: FSI Draft Spreadsheet

CHAPTER 19. LEGEND FOR DRAFT KIT SPREADSHEETS 115

Table 19.1: Legend for Skaters Spreadsheet

Abbreviation Definition

NHLPOS Player’s position as defined by the o�cial NHL website.YPOS Player’s position as defined by the o�cial Yahoo fantasy hockey website.ADP Yahoo Average Draft Position.AUC Yahoo Average Auction Value.OWN Yahoo Percentage of all leagues in which player was drafted.EADP ESPN Average Draft Position.EAUC ESPN Average Auction Value.EOWN ESPN Percentage of all leagues in which player was drafted.DAY1AGE Player’s age on October 8, 2014.CSHPCT Career shooting percentage.AEVTOI 2013-2014 Average even-strength time on ice.APKTOI 2013-2014 Average penalty kill time on ice.APPTOI 2013-2014 Average power play time on ice.ATOI 2013-2014 Average time on ice.CG Career average goals expressed as a rate per 82 games played.CA Career average assists expressed as a rate per 82 games played.CPTS Career average points expressed as a rate per 82 games played.CSOG Career average shots on goal expressed as a rate per 82 games played.

3YGP Three-year average games played expressed as a rate per 82 games played.3YG Three-year average goals expressed as a rate per 82 games played.3YA Three-year average assists expressed as a rate per 82 games played.3YPTS Three-year average points expressed as a rate per 82 games played.3YSOG Three-year average shots on goal expressed as a rate per 82 games played.3YH Three-year average hits expressed as a rate per 82 games played.3YBS Three-year average blocked shots expressed as a rate per 82 games played.3YFOW Three-year average face-o↵ wins expressed as a rate per 82 games played.3YSHG Three-year average shorthanded goals expressed as a rate per 82 games played.3YSHA Three-year average shorthanded assists expressed as a rate per 82 games played.

P G Projected goals for the 2014-2015 season.P A Projected assists for the 2014-2015 season.P PTS Projected points for the 2014-2015 season.P H Projected hits for the 2014-2015 season.P BS Projected blocked shots for the 2014-2015 season.P FOW Projected face-o↵ wins for the 2014-2015 season.P PIM Projected penalty minutes for the 2014-2015 season.P PPG Projected power play goal for the 2014-2015 season.P PPA Projected power play assists for the 2014-2015 season.P PPP Projected power play points for the 2014-2015 season.P ATOI Projected average time on ice for the 2014-2015 season.PR Performance Rating. An overall score assigned to assess the player’s worth.

CHAPTER 19. LEGEND FOR DRAFT KIT SPREADSHEETS 116

19.2 The Goalies Spreadsheet

Table 19.2: Legend for Goalies Spreadsheet

Abbreviation Definition

ADP Yahoo Average Draft Position.AUC Yahoo Average Auction Value.OWN Yahoo Percentage of all leagues in which player was drafted.EADP ESPN Average Draft Position.EAUC ESPN Average Auction Value.EOWN ESPN Percentage of all leagues in which player was drafted.DAY1AGE Player’s age on October 8, 2014.EVSVPCT 2013-2014 even-strength save percentage.PKSVPCT 2013-2014 penalty kill save percentage.PPSVPCT 2013-2014 power play save percentage.CGP Career games played.CW Career wins.CGAA Career goals against average.CSVPCT Career save percentage.CSHO Career shutouts.

3YGS Three-year games started total.3YW Three-year wins total.3YSV Three-year saves total.3YGAA Three-year goals against average.3YSVPCT Three-year save percentage.3YEVSVPCT Three-year even-strength save percentage.3YPKSVPCT Three-year penalty kill save percentage.3YSHO Three-year shutouts total.3YTOI Three-year minutes played total.

P GS Projected starts for the 2014-2015 season.P W Projected wins for the 2014-2015 season.P L Projected regulation losses for the 2014-2015 season.P OTL Projected overtime losses for the 2014-2015 season.P SA Projected shots against for the 2014-2015 season.P SV Projected saves for the 2014-2015 season.P GA Projected goals against for the 2014-2015 season.P GAA Projected goals against average for the 2014-2015 season.P SV PCT Projected save percentage for the 2014-2015 season.P SHO Projected shutouts for the 2014-2015 season.

CHAPTER 22. ANAHEIM DUCKS 123

22.1 Schedule Details

The Anaheim Ducks have 13 back-to-back sets this season. In Figure 22.1, we’ve broken down their 2014-2015schedule by days of the week.

Figure 22.1: Anaheim Ducks Schedule By Day

CHAPTER 30. COLUMBUS BLUE JACKETS 176

Figure 30.2: Columbus Blue Jackets Player Usage Chart

CHAPTER 40. NEW YORK ISLANDERS 234

40.4 What to Expect

40.4.1 Key Forward Projections

2014-2015 Projections for Forwards

Player Goals Assists Points SOG

John Tavares 34 48 82 266Kyle Okposo 22 34 56 209Frans Nielsen 15 34 49 167Ryan Strome 18 28 46 227Mikhail Grabovski 14 26 40 121Josh Bailey 14 24 38 123

40.4.2 Key Defensemen Projections

2014-2015 Projections for Defensemen

Player Goals Assists Points SOG

Lubomir Visnovsky 10 26 36 130Calvin de Haan 5 21 25 114Travis Hamonic 4 19 23 154

40.4.3 Key Peripheral Projections

2014-2015 Peripheral Leaders

Player Hits BS PIMs FOW

Matt Martin 383Travis Hamonic 169Matt Martin 99John Tavares 776

40.4.4 Key Goalie Projections

2014-2015 Projections for Goalies

Player W GAA SV% SHO SA SV

Jaroslav Halak 32 2.41 0.918 8 1795 1648Chad Johnson 11 2.19 0.926 0 618 572