AN ARGUMENT FOR NHLE

Byron Bader
June 24 2014 08:00AM

NHLe is an equivalency formula used by some in the hockey analytics community.  It’s a method of standardizing scoring across various major and junior leagues.  Standardized scoring gives an idea of how players, generally younger prospects, perform at the NHL level. Some argue its merit as a valuable metric in assessing future performance.  The following provides a framework of how it can be used as a possible drafting qualifier.

The method was developed by Gabe Desjardins a number of years ago.  You can also find some more information about NHLe over at Matchsticks & Gasoline, our Calgary Flames blogging comrades.    

The equation looks like this …

[(Points ÷ Games Played) x 82] x League NHLe Value=NHLe

Here’s an example of how it works…

The translation for the KHL is 0.82, the translation for the NCAA is 0.41, the translation for the WHL is 0.3 and the translation for high school is roughly 0.07.  Let’s assume that four players scores at the same PPG in four separate leagues, let’s say 60 points in 50 games (PPG of 1.2).  The players' corresponding NHLe (when we scale it over 82 games) would be: KHL – 80.7; NCAA – 40.3; WHL – 29.5; HS – 6.9.  In summary, a soon to be drafted 18 year old player putting up those points in the KHL is a rock star generational-type prospect, a really good NCAA prospect, a decent WHL prospect and a very low probability high school prospect.

The knock on using NHLe as a method to project how a player is going to perform in the NHL is that it’s rarely completely accurate.  The equivalency is based on the average of how players have performed in the NHL the year after coming over and the standard deviation is huge (20-30%).  This is to say that most players don't score exactly where their NHLe suggests.

Where NHLe is beneficial, however, is as an indicator of future potential offensive scoring, in a general sense.  It’s not perfect but we will see in a minute why NHLe, and especially high NHLes, can be used to help predict future offense.

THE NUMBERS

Player

NHLe 

Games 

PPG 

Draft Year 

Number 

Draft PPG 

Age at Draft 

Crosby, Sidney

66.7

550

1.4

2005

1

2.7

17.90

Kane, Patrick

61.5

515

1.0

2007

1

2.5

18.61

Gagner, Sam

54.8

481

0.6

2007

6

2.2

17.88

Tavares, John

51.3

350

0.9

2009

1

2.1

18.78

Brassard, Derick

49.2

403

0.6

2006

6

2

18.77

Watson, Austin

49.2

6

0.2

2010

18

2

18.46

Hall, Taylor

45.7

246

0.9

2010

1

1.9

18.62

Kessel, Phil

44

586

0.8

2006

5

1.3

18.74

Schroeder, Jordan

43.2

56

0.3

2009

22

1.3

18.75

Stamkos, Steve

42.3

410

1.0

2008

1

1.7

18.39

Little, Bryan

41.9

486

0.6

2006

12

1.7

18.63

Cherepanov, Alexei

41.5

0

0.0

2007

17

1

 

Seguin, Tyler

41.4

283

0.7

2010

2

1.7

18.41

Granlund, Mikael

41.2

90

0.5

2010

9

0.9

18.34

Kane, Evander

38.7

324

0.6

2009

4

2.2

17.86

Tarasenko, Vladimir

38.4

102

0.6

2010

16

0.6

18.55

Turris, Kyle

37.4

316

0.5

2007

3

2.2

17.87

Giroux, Claude

36.6

415

0.9

2006

22

1.5

18.46

Backstrom, Nicklas

36.1

495

1.0

2006

4

0.6

18.60

Voracek, Jakub

35.9

449

0.6

2007

7

1.5

17.87

Couture, Logan

35.5

297

0.7

2007

9

1.4

18.25

Ryan, Bobby

35.3

448

0.8

2005

2

1.4

18.29

Bailey, Joshua

35.2

406

0.4

2008

9

1.4

18.74

Skinner, Jeff

34.6

259

0.7

2010

7

1.4

18.12

Stewart, Chris

34.5

382

0.6

2006

18

1.4

18.67

Kadri, Nazem

34.3

177

0.6

2009

7

1.4

18.73

Van Riemsdyk, James

34.2

324

0.6

2007

2

2.1

18.15

Duchene, Matt

34.1

337

0.8

2009

3

1.4

18.45

Hamill, Zach

33.1

20

0.2

2007

8

1.4

18.76

Zagrapan, Marek

33.1

0

0.0

2005

13

1.4

18.56

Ennis, Tyler

32

267

0.6

2008

26

1.3

18.73

Wilson, Colin

31.8

291

0.5

2008

7

0.9

18.69

Bourret, Alex

31.5

0

0.0

2005

16

1.3

18.73

Esposito, Angelo

31.3

0

0.0

2007

20

1.3

18.35

Glennie, Scott

31.3

0

0.0

2009

8

1.3

18.35

Toews, Jonathan

31.2

484

0.9

2006

3

0.9

18.17

Schenn, Brayden

31

192

0.5

2009

5

1.3

17.85

Hodgson, Cody

30.8

211

0.6

2008

10

1.3

18.36

Brule, Gilbert

30.6

299

0.3

2005

6

1.2

18.49

Sheppard, James

30.3

323

0.2

2006

9

1.3

18.18

Downie, Steve

29.4

336

0.5

2005

29

1.2

18.24

Caron, Jordan

29.4

123

0.2

2009

25

1.2

18.66

Perron, David

29.2

418

0.6

2007

26

1.2

19.09

Connolly, Brett

29.2

84

0.2

2010

6

1.2

18.16

Boedker, Mikkel

29

338

0.4

2008

8

1.2

18.54

Boychuk, Zach

29

96

0.1

2008

14

1.2

18.74

Schwartz, Jaden

28.4

132

0.5

2010

14

1.4

18.01

Lewis, Trevor

27.5

276

0.2

2006

17

1.3

19.47

Mueller, Peter

27.4

297

0.5

2006

8

1.1

18.21

Hishon, Joey

27.3

0

0.0

2010

17

1.1

18.69

Foligno, Nick

26.5

466

0.4

2006

28

1.1

18.66

Eberle, Jordan

26.4

275

0.8

2008

22

1.1

18.13

McArdle, Kenndal

26

42

0.1

2005

20

1.1

18.48

Burmistrov, Alexander

25.8

194

0.3

2010

8

1

18.69

Kassian, Zack

25.4

156

0.3

2009

13

1

18.43

Staal, Jordan

24.6

561

0.6

2006

2

1

17.80

Bennett, Beau

24.6

47

0.4

2010

20

2.1

18.59

Howden, Quinton

24.6

34

0.2

2010

25

1

18.44

Beach, Kyle

24.6

0

0.0

2008

11

1

18.46

Leveille, Daultan

24.6

0

0.0

2008

29

1.8

17.89

Holland, Peter

24.2

68

0.3

2009

15

1

18.46

O'Marra, Ryan

24.2

33

0.2

2005

15

1

18.06

Nemis, Greg

24.2

15

0.1

2008

25

1

18.07

Johansen, Ryan

23.9

189

0.5

2010

4

1

17.91

Cogliano, Andrew

23.9

540

0.4

2005

25

2.1

18.04

Pouliot, Benoit

23.9

371

0.4

2005

4

1

18.75

Okposo, Kyle

23.8

390

0.6

2006

7

1.2

18.20

Setoguchi, Devin

22.8

459

0.5

2005

8

0.9

18.49

Niederreiter, Nino

22.7

145

0.3

2010

5

0.9

17.81

Etem, Emerson

22.2

67

0.3

2010

29

0.9

18.04

Skille, Jack

21.8

194

0.3

2005

7

1.1

18.12

Paajarvi, Magnus

21.7

218

0.3

2009

10

0.3

18.22

Pacioretty, Max

21.5

319

0.7

2007

22

1.1

18.61

Colborne, Joe

21.4

96

0.4

2008

16

1.6

18.41

Josefson, Jacob

20.5

118

0.2

2009

20

0.3

18.16

Leblanc, Louis

20.2

50

0.2

2009

18

1

18.42

Coyle, Charlie

19.7

107

0.4

2010

28

1.5

18.33

Sutter, Brandon

19.7

415

0.4

2007

11

0.8

18.37

MacMillan, Logan

19.2

0

0.0

2007

19

0.8

18.07

Tikhonov, Viktor

18.8

61

0.3

2008

28

0.3

20.13

Grabner, Michael

18.4

283

0.5

2006

14

0.7

18.73

Oshie, TJ

18.3

371

0.7

2005

24

3.2

18.52

Paradis, Philippe

18

0

0.0

2009

27

0.8

18.49

Ashton, Carter

17.6

47

0.1

2009

29

0.7

18.25

Nash, Riley

17.5

110

0.3

2007

21

1.6

18.14

Kuznetsov, Evgeny

17.2

17

0.5

2010

26

0.3

18.11

Nelson, Brock

16.8

72

0.4

2010

30

2.9

18.71

Filatov, Nikita

16.8

53

0.3

2008

6

0

18.10

Tedenby, Mattias

16.7

120

0.3

2008

24

0.3

18.35

Sheahan, Riley

15.4

44

0.5

2010

21

0.5

18.56

White, Patrick

15.4

0

0.0

2007

25

0.8

18.44

Palmieri, Kyle

14.9

141

0.4

2009

26

0.4

18.41

Johansson, Marcus

14

263

0.5

2009

24

0.2

18.73

Hayes, Kevin

13.7

0

0.0

2010

24

2.4

18.14

Bjugstad, Nick

13.2

87

0.4

2010

19

2.3

17.95

Kreider, Chris

12.4

89

0.4

2009

19

2.2

18.17

Berglund, Patrik

12.2

436

0.5

2006

25

0.2

18.07

Hanzal, Martin

11.8

456

0.5

2005

17

0.2

18.36

O'Brien, Jim

11.7

68

0.2

2007

29

0.3

18.41

Eller, Lars

11.6

286

0.4

2007

13

1.4

18.14

Gillies, Colton

11.4

154

0.1

2007

16

0.5

18.38

Frolik, Michael

11.1

430

0.4

2006

10

0.2

18.36

Tlusty, Jiri

10.8

344

0.4

2006

13

0.2

18.29

Backlund, Mikael

10.7

246

0.4

2007

24

0.2

18.12

Bergfors, Nicklas

2.6

173

0.5

2005

23

0

18.15

Kopitar, Anze

0

604

0.9

2005

11

0

17.85

Gustafsson, Anton

0

0

0.0

2008

21

0

18.34

This is a list of all the 1st round forward draft picks from 2005 to 2010.  The table is sorted by highest NHLe to lowest.  I’ve highlighted in blue all players that have scored at a rate of 0.6 (approximately 50 points per 82 games) to this point in their career.  This is not to say scoring below this is a bust.  There are many forwards that put up 30-40 points a year but are incredibly valuable for what else they do.  But the thought here is that in the 1st round a team’s main goal is to find an offensive threat.  The threshold for this was arbitrarily set at 0.6 PPG over their career.

Also, you'll notice some players that have barely had a chance to play in the bigs yet, as they may be finishing a college degree or playing out the end of a Euro contract.  They could very well come in and light it up when they get to the NHL.  Players with lower ppg could also increase their scoring dramatically over the coming years. This is just the data we have right now.

NHLE 

 PPG of 0.7+ 

 PPG of 0.6+ 

 PPG of 0.5+ 

40+

53.8%

76.9%

84.6%

30-39

28.0%

60.0%

72.0%

20-29

5.6%

13.9%

27.8%

0-19

6.5%

6.5%

29.0%

What we find from just looking at the data is that very rarely does a team end up with a player who averages less than 50 points a year (very early in their careers) if the prospect has an NHLe north of 35, this seems to be the “can’t miss” range.  Perhaps the player doesn’t end up being the best of the draft but putting up 50 points + year after year in the toughest league in the world is pretty good. If we dive a little bit deeper, looking at some predictive regression modeling, the story gets better.

Here’s a brief crash course on how regressions work ...

Regression is a statistical process for estimating the relationship among variables. The adjusted R2 gives an indication of what type of impact one variable or group of variables (i.e., the independent variables) is having on another variable or outcome (i.e., the dependent variable).  You want this as close to 1.0 as possible as it indicates a very strong relationship and a strong model. With regards to the independent variables being assessed in the model, you want a p-value as low as possible (e.g., anything lower than 0.1 is good in this case); that indicates the variable is a significant factor and plays a role in the dependent variable you’re looking at. In this case, the dependent variable is the player’s current NHL ppg.  The independent variables are draft year NHLe, position taken in the 1st round and draft year PPG.

Adj. R2 = 0.24

NHLE: 1.90; P-Value: 0.06

Draft Number: -3.18; P-Value: 0.002

Draft Year PPG: 0.67; P-Value: 0.51

What we find is that this model explains about 24% of a player’s future PPG in the NHL.  It’s not the whole story but these simple three variables are explaining about a quarter of the story. In this model, NHLe and the draft position are helping to explain future performance in the NHL whereas draft year PPG is not in the least.  The higher a player’s NHLe and the higher they are chosen (often the highest NHLes go first … but not always) the higher their eventual PPG in the NHL should be. That's the main take-away.

Things like the player’s ability to drive possession, skating ability, compete level, attitude, willingness to learn, how they're developed after being drafted, QoT and QoC would likely make up the rest of this model. Those figures are much more difficult to track, especially at the junior/development level, however. One day we'll get there!

Back to this model. As you look back over drafts you'll often find players that end up “surprising” and far exceeding expectations based on where they were chosen (e.g., Giroux, P. Stastny, M. Savard, T. Fleury, Ribeiro, etc, etc, etc.) have the draft NHLe to suggest that they should be a very good player (35+).  They are skipped over for one reason or another (size, playing in an unknown overseas league, age, attitude, etc.) but surprises are more often than not players that should have gone higher if their NHLe was one of the main qualifiers for drafting. 

To illustrate this point, if you reverse the process and look at the highest scorers in the NHL over the past 20 years you find that draft year NHLe still comes out as a significant predictor (R2: 0.16; p-value: 0.002) whereas the significance of draft position goes away. There’s enough late-round surprises in the data to tip the scale the other way. 

SUM IT UP

Regardless of how you slice it, junior/developmental league scoring is, in my mind, the single biggest predictor of future offensive performance. These numbers, just simple standardized counting principles, can be leveraged by NHL teams to draft better by taking a significant portion of the guessing out.  Maybe a team loses out on getting “the best player” here and there but if a team follows these principles they would likely draft a very, very good player again and again and again.  

Start to factor in metrics like age (i.e., younger 35+ NHLes are better than older 35+ NHLes), massive NHLe jumps from their draft-1 year to their draft year (i.e., players that jump by 10-20 points) and individual primary, ev and total team point totals and the picture becomes clearer.  All this before ever even seeing a player play.

This year, there's five players (North American skaters anyways) that fit the "can't miss" 35+ NHLe mold in the draft class. They are: Sam Reinhart (43.1), Leon Draisaitl (40.4), Sam Bennett (39.3), Nikolaj Ehlers (39.3), Robert Fabbri (36.9). Dal Colle (34.9) is right there as well. Over-ager Louick Marcotte (35.5) also fits the bill but he's two years older than the rest of the crop.  He could be worth a late-round flier though. I bet we hear a lot about all of these guys in the coming years and they turn into very productive players for their respective teams.

Fb039371a1a1b706383cb72243cb4446
Byron has always been curious about numbers and stats, especially related to hockey. His background includes schooling heavily-focused on psychology, economics and statistics and a professional background revolving around reseach, segmentation, data mining and statistics. His love for hockey is as deep as the ocean is wide. Tell him your questions and let him into your heart. Twitter: @Baderader; Email: byron.bader@gmail.com