Eastern Edge: Primary Assist Rates to Monitor

by Cam Metz on May 22, 2018

Completely outside the topic of the Eastern Conference but how about what is going on in Vegas right now?  I mean in all actuality if you try to wrap your head around making The Cup Final in year one it’s almost impossible. There will be a movie made about this one – that’s for sure. 

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This summer for me has been all about trying to find the little idiosyncrasies that you can use to gain an advantage one year leagues next year.  I’m excited about some of the modelling I’ve done around shooting percentage and expected goals (ixGF).  I’m really looking forward to seeing how stripping out majors and misconducts from penalty minutes can create a more predictable stat line (PIMs).  I’ve mentioned before valuing certain positions more than others tends to lend me to draft centers much later.  

The data for all of this work came from Corsica.

For that reason it seems unlikely that goal scoring from a center is going to be the big stat you’ll be trying to get from them – instead assists are usually the wheel house for this position.   Assists are great except that a secondary assist is usually the luck of the draw and not ratable from year to year.  Looking at the last four seasons here is the primary assist rate for the Top 100 scorers each season:

Primary Assist Rate for Top 100 Point Producers by Season

Season

Rate

2014-2015

58.4%

2015-2016

58.5%

2016-2017

58.6%

2017-2018

57.8%

 

If you tighten this list up the average percentage for the Top 50 scorers in each of these seasons become 60%.  This is valuable information!  We know that primary assists are a great indicator of point production.  I ran the primary assist rate for the top 500 point producers for the last 4 seasons and predicted the point-per-game of those players.  The model has a significance level that says it is a predictive value.  Check out this graph – basically we see that the higher the Y axis gets the more likely it the primary assist rate will be equal to or greater than 58%. 

 

 

Similarly if we look at point totals and primary assist totals we can see a nice correlation forming:

 

Ok so excuse all the graphs but the point is primary assists are a great predictor of point totals.  So we know the top 50 scorers tend to have around 60% of their assists being primary assists.  We also tend to see that players can sustain in the same ballpark the rate of their primary assists.  So using this as a measuring stick we can look at players that off their 3 year average can reasonably increase their primary assist total and as correlated their point totals as a whole.

Since it’s the summer I’ll dump the top 20 players from each division in a table and then over the next couple weeks look a little closer at specific players.  The requirements for this table are that the player scored 40 points.

 

Primary Assist Rate 2017-2018 Metro

Player

A

A1

A1 Pct% 2017-18

A1 % 3 year average

Delta

JEFF.SKINNER

25

13

52.0

71.4

19.4%

KEVIN.HAYES

19

10

52.6

65.9

13.3%

JAKE.GUENTZEL

26

15

57.7

70.0

12.3%

TAYLOR.HALL

52

30

57.7

67.9

10.2%

VLADISLAV.NAMESTNIKOV

26

11

42.3

50.5

8.2%

KYLE.PALMIERI

19

8

42.1

49.5

7.4%

TEUVO.TERAVAINEN

39

23

59.0

64.5

5.5%

EVGENI.MALKIN

56

31

55.4

60.2

4.9%

JOHN.TAVARES

46

28

60.9

65.6

4.7%

J.T..MILLER

35

19

54.3

58.7

4.4%

SHAYNE.GOSTISBEHERE

50

23

46.0

49.1

3.1%

JOSH.BAILEY

51

29

56.9

59.4

2.5%

SIDNEY.CROSBY

59

33

55.9

58.3

2.4%

JORDAN.STAAL

27

14

51.9

54.2

2.3%

TRAVIS.KONECNY

24

13

54.2

56.5

2.3%

ELIAS.LINDHOLM

28

18

64.3

66.6

2.3%

KRIS.LETANG

43

17

39.5

41.6

2.1%

CLAUDE.GIROUX

67

32

47.8

49.8

2.0%

PATRICK.MAROON

25

15

60.0

61.5

1.5%

THOMAS.VANEK

31

22

71.0

71.7

0.7%

 

Primary Assist Rate 2017-2018 Atlantic

           

Player

A

A1

A1 Pct% 2017-18

A1 % 3 year average

Delta

RILEY.NASH

27

13

48.1

61.7

13.6%

TYLER.JOHNSON

28

14

50.0

61.7

11.7%

VINCENT.TROCHECK

44

22

50.0

60.6

10.6%

MIKE.HOFFMAN

34

19

55.9

64.2

8.3%

VLADISLAV.NAMESTNIKOV

26

11

42.3

50.5

8.2%

STEVEN.STAMKOS

57

36

63.2

70.4

7.2%

ALEX.KILLORN

31

17

54.8

61.3

6.5%

NIKITA.KUCHEROV

61

34

55.7

61.9

6.2%

ALEX.GALCHENYUK

31

16

51.6

57.5

5.9%

JONATHAN.DROUIN

33

17

51.5

57.1

5.6%

NAZEM.KADRI

23

12

52.2

56.9

4.8%

J.T..MILLER

35

19

54.3

58.7

4.4%

DYLAN.LARKIN

45

25

55.6

59.4

3.9%

BRENDAN.GALLAGHER

23

13

56.5

60.3

3.8%

JAMES.VAN RIEMSDYK

18

11

61.1

64.7

3.5%

BRAD.MARCHAND

51

30

58.8

62.3

3.5%

AUSTON.MATTHEWS

29

18

62.1

65.5

3.4%

JACK.EICHEL

39

23

59.0

61.8

2.8%

VICTOR.HEDMAN

46

23

50.0

52.6

2.6%

ALEKSANDER.BARKOV

48

26

54.2

56.7

2.6%

 

It is crazy to imagine that Taylor Hall has compared to his three-year career average more room to increase his primary assist rate.  Same goes for Vincent Trocheck, after his remarkable season he also has the opportunity to improve on his career average.  These percentages give a great baseline for trying to understand where we may see corrections next year; the flipside could be said for the rate of secondary assists produced by a player.  I’ll dig a little deeper into that rate next week and look at the specific players on these lists as well.

So to conclude – so far I have written about shooting percentage differential, stripping out penalty minutes from majors/misconducts, expected goal production, and now primary assist rates.  

Any questions?

 

Follow me on twitter at @DH_jcameronmetz