Advanced Stats: Projecting Nash’s Production

Glen Miller

In a deal nearly everyone sees as a clear win for New York, the Rangers at last were able to acquire winger Rick Nash from Columbus. To be fair to Columbus GM Scott Howson, the package he received in exchange for Nash was probably the best he could do given the situation. Even though he likely could have made the same deal at the trade deadline he was under no pressure to do so. There was always a chance that by waiting until summer to move Nash the market would improve. It isn’t as if not trading him in February cost the Blue Jackets a playoff berth or anything.

Nash is the player whom Sather long ago targeted to add the scoring punch needed for the Blue Shirts to take the next step in the playoffs. His offensive ability can’t be questioned though critics point out he was never able to put the Jackets on his back, so to speak, and carry them into the playoffs single-handedly. In truth I don’t believe there are many non-goaltenders that have the ability to turn a mediocre club into a playoff squad on their own.

The one legitimate question that has been raised is how has Nash’s performance been affected by the lack of a quality team around him in Columbus. Proponents of the deal from the Rangers perspective are of the belief that with better talent to play with in New York Nash will produce bigger numbers. Others feel that three straight seasons posting fewer goals than the previous year is indicative that Nash’s offensive production has peaked and Ranger fans should temper their expectations.

So which is true; can Nash be expected to elevate his production based on having better talent around him in New York? Let’s analyze the numbers.

First, there really isn’t much of a doubt the Rangers were a much better team than Columbus last year. Still, let’s see just how much better New York was in terms of standings points, goals scored, total team GVT and team Goals Created (GC).

New York Rangers

Columbus Blue Jackets

Variance

Standings Points

109

65

66.7%

Team Goals Scored

226

202

11.9%

Team GVT

167.9

34.5

386.7%

Team GC

223

199

12.1%

 

The Rangers finished 43 points ahead of Columbus in the standings but from a goal scoring perspective they were only 12% more prolific than the Blue Jackets. However, using the all-encompassing player valuation metric, GVT, the Rangers collectively outshined Columbus by a wide margin. This suggests the Rangers were light years better than the Jackets at preventing goals.

It’s clear the Rangers were the better team no matter what number you looked at. However, does simply playing with a better cast of players guarantee that Nash will perform better? It’s not as simple as saying since the Rangers scored 12% more goals than Columbus therefore Nash should score 12% more this season as a Ranger. In order to get an idea whether Nash will be a better performer in New York let’s look at other players who moved at approximately the same point in their careers to a more successful environment and see how their performance was impacted.

Looking back to a previous post questioning the logic of including Derek Stepan in the package for Nash we projected their immediate future using comparables. For this post we will use some of those same players and in order to expand the pool I’ve added several others using Hockey Reference’s similarity scores which are based on adjusted point shares.

In the following table I’ll list the age which each player was traded, compare the point totals of the team the player was with and the improved team he was dealt to (using same season standings points) and the player’s performance in terms of GVT before and after. I’ve also included Nash’s numbers with his projected totals based on the results gleaned from the comparables.

 

Age When

Prev. Team 

New Team

 

GVT/GP with

GVT/GP with

Player

Traded

Points

Points

Variance

Previous Team

New Team

Variance

Rick Nash

28

65

109

67.69%

0.134

0.188

 

Brendan Shanahan

28

77

131

70.13%

0.199

0.279

40.46%

Keith Tkachuk

28

90

103

14.44%

0.188

0.266

41.24%

Pat Verbeek

25

66

79

19.70%

0.042

0.158

278.98%

Dave Andreychuk

29

86

99

15.12%

0.188

0.243

29.49%

Luc Robitaille

28

66

101

53.03%

0.131

0.250

90.37%

Adam Oates

29

83

84

1.20%

0.110

0.269

144.59%

Ron Francis

27

73

88

20.55%

0.158

0.069

-56.61%

Joe Mullen

29

83

89

7.23%

0.144

0.200

38.74%

Rick Tocchet

27

75

87

16.00%

0.967

1.363

40.87%

Dale Hunter

27

72

86

19.44%

0.115

0.078

-31.88%

Brent Sutter

29

60

106

76.67%

0.071

0.106

49.71%

Dany Heatley

29

83

117

40.96%

0.165

0.213

29.63%

Brian Bellows

28

70

93

32.86%

0.123

0.141

15.48%

Average:

27.9

75.7

97.2

28.35%

0.200

0.280

39.80%

In this comparison the average player was 28 years old (as is Nash), moved to a club that was 28.35% better in terms of where they finished in the standings versus the player’s previous team, and their performance according to GVT per game played improved by nearly 40%.

That might seem significant but there is something else to consider; Nash is moving to a team that was 68% “better” than his old squad. Some of the other players didn’t go to a team that was that much better than his prior club. In fact I considered many more players for this study but eliminated them for that reason alone. I left the table as is to demonstrate that Nash is going to a team that is significantly better than the one he left.

Let’s now eliminate the players whose new club isn’t at least 40% stronger than his previous team and see what is left.

 

Age When

Prev. Team 

New Team

 

GVT/GP with

GVT/GP with

Player

Traded

Points

Points

Variance

Previous Team

New Team

Variance

Rick Nash

28

65

109

67.69%

0.134

0.201

 

Brendan Shanahan

28

77

131

70.13%

0.199

0.279

40.46%

Luc Robitaille

28

66

101

53.03%

0.131

0.250

90.37%

Brent Sutter

29

60

106

76.67%

0.071

0.106

49.71%

Dany Heatley

29

83

117

40.96%

0.165

0.213

29.63%

Average:

28.4

70.2

112.8

60.68%

0.141

0.212

50.06%

The slimmed down list reveals players improved their GVT rate by better than 50% when changing teams to one at least 40% better using our criteria than their previous club. Of course Nash is going to be expected to produce points for the Rangers; how did the point production of the players above change with their new teams?

 

Points

Pts/Gm

Points

Pts/Gm

Pts/Gm

Player

Old Team

Old Team

New Team

New Team

Variance

Rick Nash

59

0.72

62.9

0.77

6.68%

Brendan Shanahan

78

1.05

87

1.10

4.48%

Luc Robitaille

86

1.04

42

0.91

-11.88%

Brent Sutter

53

0.71

60

0.87

23.05%

Dany Heatley

72

0.88

82

1.00

13.89%

Average:

69.6

0.92

67.8

0.98

6.68%

Surprisingly the offensive production only increased 6.68% with their new teams. If we project the same increase for Nash we can expect his output to jump just four points to 63 over a full season in New York. Now we should also realize that two of the four comparable players averaged better than a point-per-game before being dealt to their new team. Perhaps it shouldn’t be a surprise their point production didn’t improve more.

Let’s go back to our original pool of players this time eliminating players who posted an average of better than a point-per-game before joining their new team.

Points

Pts/Gm

Points

Pts/Gm

Pts/Gm

Player

Old Team

Old Team

New Team

New Team

Variance

Rick Nash

59

0.72

74.3

0.91

25.91%

Pat Verbeek

47

0.61

89

1.11

82.26%

Rick Tocchet

59

0.97

109

1.36

40.87%

Dale Hunter

39

0.85

59

0.75

-11.91%

Brent Sutter

53

0.71

60

0.87

23.05%

Dany Heatley

72

0.88

82

1.00

13.89%

Brian Bellows

75

0.94

88

1.07

14.47%

Average:

57.5

0.82

81.2

1.03

25.91%

Using this table to project what Nash might produce next season we see a bump up to 0.91 points/game. Using his career goal/points ratio we can calculate Nash to post a scoring line of 39.3 goals, 35.0 assists and 74.3 points. That’s a total most Ranger fans could live with.

Remember, Nash has averaged 0.453 goals-per-game during his career. Over an 82-game schedule that projects out to be 37 goals. Intuitively it stands to reason even an average performance by Nash with no consideration given to playing with a superior cast would result in the talented winger netting 35 – 40 goals.

Another way to look at this is to analyze the “luck” elements involved. Shooting percentages can vary tremendously from season to season. Last year Nash recorded a career-low shooting percentage of just 9.8%. His career average, including last season is 12.7%. If he had simply converted his career average number of shots into goals last year he would have finished with 38.86 goals.

We can also look at something called “PDO,” which represents the sum of the shooting percentage and save percentage while a player is on the ice. Analyzing years of data reveals that this figure tends to regress heavily toward the mean number of 1,000. Anything higher than a total of 1,000 is indicative of good luck. A figure less than 1,000 is considered unlucky.

Columbus posted a save percentage 0.909 while Nash was on the ice. The team’s shooting percentage while Nash was on the ice was 0.0848. If you add those numbers together and multiply by 1,000 (to get it into proper terms) we end up with 994. That means Nash was somewhat unlucky last season.

To get an idea of just how unlucky Nash was he finished 149th out of 237 forwards who played 70 or more games last year in PDO. Since we know this number tends to regress to the mean we can anticipate Nash will be luckier next season.

Now it’s quite possible any improvement in Nash’s luck will come solely from on-ice save percentage since he will now have Henrik Lundqvist in goal behind him. If Nash’s on-ice save percentage increases to just 0.915 he wouldn’t have to see an improved on-ice shooting percentage to reach a PDO of 1,000. Thus there is no guarantee that a regression to the PDO mean will result in more offensive contributions from Nash.

While the different components of this analysis may vary somewhat quantitatively it does suggest it is likely Nash will see an uptick in overall performance and a moderate increase in offensive output solely by virtue of the move to a higher quality team. Again, as I’ve said many times, this type of analysis is only useful in conjunction with traditional evaluation. Many experienced evaluators feel the move to the Rangers will yield better scoring totals from Nash and in this instance, the analysis supports that conclusion. Exactly how much his production increases remains to be seen but the statistical analysis suggests solid offensive production.