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Hockey Analytics

Shots Passing Percentages

by Gus Katsaros
Updated On: October 4, 2018, 4:04 pm ET

Colton Sceviour and Jonathan Marchessault have scored 11 goals between them, the rest of the Panthers accounting for 15 team goals. Two names that barely made a splash when the Florida Panthers signed them during the off season having the early team scoring lead. Marchessault is firing at 18% (all situation) and Sceviour at a 21% clip.

 

Shooting percentage induced performance is great, even in small samples, but there’s underlying components to these players that appeared outside of the scoresheet in 2015-16 prompting the interest from the Florida club.

 

We’re going to examine some less traditional micro-stats in the passing project, baring resemblance conceptually to individual percentage stats. Some may already be familiar with Individual statistics, specifically individual point percentage (IPP), and individual goals (IGP) and assists (IAP) and their use in analysis. I use these percentages extensively over the summer.

 

All three stats indicate the percentage of goals, assists or points that a player earned while being on the ice for a goal scored. IPP refers to points earned on all goals scored while on the ice. For goals, it’s the percentage of goals the player scored in relation to all on ice goals, and for assists it’s the number of assists earned on goals scored while on the ice.

 

I’ve written about IPP and its uses and there’s a wonderful article about the acquisition of Jonathan Marchessault by PDOcast host Dimitri Filipovic for a deeper dive into the logic of acquiring and usage of these cheap, serviceable discards.

 

PASSING PROJECT

 

When Ryan Stimson (contact him if you’re interested in helping track passes or an exciting new Forechecking project) and the passing project is completed amassing a full season’s data, we can go a lot further into this concept but for now, the crux of this is the percentage of times a player contributes to a shot event as the shooter, first, second or third pass from the actual shot event. The project data also contains an on-ice component that comes in really handy for these purposes.

 

The passing project tracks up to three passes leading to a shot attempt event, including filters for shots on goal and scoring chances, but for the purpose of this writing, we will solely focus on shot attempts.

 

We can isolate the instances in which a player contributed to a shot attempt event and then calculate the percentage of shot attempts made with this player contributing as either the shooter, primary, secondary or tertiary passer (I call this shot contribution passing percentage – SCP% for ease).

 

Note, the focus here is only at 5v5, special teams and other even strength conditions are omitted.

 

Last season – again, with limited sample size – I used this concept to justify the young Swede’s attempt to win the Norris Trophy, in light of just how important Erik Karlsson was in driving the Ottawa Senators offense.

 

This is how it looked like in mid-March

 

Total Shots Team Shots from Pass Sh A1 A2 A3 Percent Shot%
Doughty 339 30 30 12 5 22.71 8.85
Karlsson 379 75 111 57 34 73.09 19.79
Burns 713 174 79 50 33 47.12 24.40

 

 

Karlsson influenced the Senators shot generation almost single handed, with small samples to work with for the three defensemen.

 

For the Panthers, the first question we should ask is, do the Panthers have alternative data sources, such as packages from independent third party tracking companies such as Stathletes (Arizona Coyotes general manager John Chayka’s outfit) or Montreal based Sportslogiq, providing a data set supplementing more common openly available shot based metrics stats from noted sites like Corsica.hockey, Puckalytics, hockeystats.ca or Natural Stats Trick.

 

As a progressive management group, perhaps they even had some people tracking events internally – or with external help for hire.

 

Having data for your own team is ideal, but amassing data from all other teams – or focusing on players on a watch list – would be a distinct advantage.

 

Both free agent signings excelled as shot contribution passing percentages for their previous clubs respectively, Sceviour with Dallas, and Marchessault in Tampa Bay.

 

Jonathan Marchessault

 

Undrafted free agent signed by the Columbus Blue Jackets was traded to the Tampa Bay Lightning in March of 2014 and bolted to the other Florida coast in the summer of 2016 signing a two year contract. Breaking down the Tampa Bay Lightning (with 56 games tracked), Marchessault’s SCP%, contributing as a shooter, first, second or third passer in 75.94% of all on-ice shot attempts for the Lightning. The Bolts fired at a miserly 5.56% on ice shooting percentage with the former Blue Jackets discard on the ice. His IPP was the highest for the club, earning a point on 84.62% of the goals scored while on the ice, and an assist on a club high 61.54% of on-ice goals.

 

The NHL average SCP% according to the latest data release was 56.51% all round, without positional separation.

 

Players

Tot%

IPP

IAP

On Ice sh%

Tyler Johnson

77.91

56.41

30.77

8.61

Jonathan Drouin

77.50

61.54

30.77

14.44

Brian Boyle

76.57

56.00

24.00

6.91

Jonathan Marchessault

75.94

84.62

61.54

5.56

Valtteri Filppula

74.11

62.96

44.44

5.47

Nikita Kucherov

71.65

69.57

36.96

8.47

Steven Stamkos

70.82

67.39

30.43

8.95

Alex Killorn

69.72

56.25

41.67

8.99

 

 

Colton Sceviour

 

The former Dallas Stars forward stepped into the NHL full time after a successful stint with the Texas Stars. Texas won the Calder Trophy without the help of the skilled forward in 2014, notably because he graduated from the AHL to full time NHL duty in that same season. Sceviour sat near the top of the Stars with an 84.69% SCP%, leading the Stars shot generation in a support role.

 

Players

Tot%

 IPP

 IAP

 On Ice sh%

Radek Faksa

124.21

42.11

21.05

7.92

Tyler Seguin

92.76

68.52

29.63

9.00

Jason Spezza

86.07

76.74

30.23

7.68

Colton Sceviour

84.69

60.71

25.00

8.16

Cody Eakin

79.57

66.67

44.44

7.14

Ales Hemsky

74.59

75.68

48.65

8.22

Mattias Janmark

70.20

56.82

29.55

8.73

Jamie Benn

68.52

76.36

47.27

8.62

 

Sceviour has proven early to be a better asset than most depth forwards and the Panthers took a chance to bring him in for the added skilled depth on the forward units. The early season scoring surge is just a bonus – with the Panther big guns floundering offensively so far this season and Jonathan Huberdeau injured.

 

Pay attention to the 124% Radek Faksa, who really came into his own at the tail end of ’15-16 and one of the better Stars forwards during the playoffs. We will get to that next.

 

Both players have outperformed early expectations by leaps and bounds. Marchessault’s solid start culminated in a third star selection for the month of October.

 

Maybe the Cats had something special in their pre-signing analysis for these two players.

 

THE REST OF THE NHL

 

An interesting group of players emerge from this list, sorted by shot contribution passing percentage. These are players with greater than 100% contributions (which intuitively doesn’t compute). How can any player really have the ability to influence a shot event at more than 100%.

 

Well, since the project tracks three passes and a shooter if a player is listed as the tertiary passer, then the primary passer, there’s a doubling of involvement in the play for both categories. It speaks to the individual efforts in generating shots.

 

For instance, Evgeni Malkin can incorporate end to end rushes quite effortlessly, with a simple pass would be involved with a multiple contribution – boosting the percentages.

 

I don’t think this is the best method of statistical analysis, but a few notable names appear here (sample size issues). These players seem to contribute to shot generation on a more individual basis. The star power in this list is impressive.

 

Players

Tot%

 IPP

 IAP

 On Ice sh%

 CF%

Chris Kelly

183.33

20.00

0.00

11.63

39.13

Greg McKegg

180.00

66.67

0.00

6.12

43.75

Alan Quine

180.00

0.00

0.00

0.00

0.00

Logan Couture

152.94

65.38

30.77

8.78

53.10

Jordan Weal

150.00

0.00

0.00

3.92

47.40

Radek Faksa

124.21

42.11

21.05

7.92

53.61

Anze Kopitar

115.07

73.33

51.67

9.48

57.40

Evgeni Malkin

113.17

78.13

37.50

6.90

54.40

Ryan Getzlaf

111.26

71.79

61.54

7.01

55.00

Sean Couturier

110.96

77.78

55.56

8.61

54.21

Adam Cracknell

110.20

90.91

45.45

4.47

46.87

Linden Vey

110.00

70.00

60.00

4.27

44.74

Andrew Miller

110.00

0.00

0.00

0.00

48.00

J T Miller

109.33

77.27

38.64

9.11

49.51

Henrik Sedin

108.96

78.38

67.57

8.01

49.31

Brendan Gaunce

108.33

50.00

0.00

1.85

48.80

Jack Eichel

107.83

74.36

43.59

7.29

46.70

Brayden Schenn

106.12

76.74

44.19

8.02

50.48

Joe Pavelski

106.06

63.08

26.15

10.22

55.12

Sidney Crosby

105.18

82.26

53.23

8.88

54.39

Nazem Kadri

103.07

60.61

33.33

5.74

52.58

John Mitchell

102.67

77.27

40.91

6.55

42.07

Sean Monahan

101.39

66.07

32.14

10.22

48.37

  

I tried to see if there’s a correlation to SCP% and IPP or IAP, and while there’s a relationship, it seems to be fairly weak, meaning there’s little predictive power, but there’s something there.

 

Oh, and that Norris debate ended with Drew Doughty as the eventual winner. When more data was added, the tally as of the latest data available broke down like this:

 

View post on imgur.com

 

Maybe Brent Burns should have received a lot more serious consideration for the Norris after all.

Gus Katsaros
Gus Katsaros is the Pro Scouting Coordinator with McKeen’s Hockey, publishers of industry leading scouting and fantasy guide, the McKeen’s Annual Hockey Pool Yearbook. He also contributes to popular blog MapleLeafsHotStove.com ... he can be followed on Twitter @KatsHockey