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 149^th 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.