Comparing regular season and postseason goal production

Should we be worried about where the Jackets stand?

It’s no secret that the Columbus Blue Jackets offense has been anemic over the last two months. Is it so bad that it threatens their playoff position, or their ability to make a run in the playoffs?

First, let’s look at where the Jackets stand at the moment: they are in second place in the Metropolitan Division coming out of the All Star break, but doing so despite one of the worst offenses in the NHL. They are averaging just 2.55 goals per game, which is the third worst, ahead of only Buffalo and Arizona.

For comparison, I looked at all of the playoff teams for the past four seasons (i.e., since the switch to the current alignment and playoff format). I tracked their GPG average for the regular season and postseason, their regular season rank, and their playoff win total (as a shorthand for how far they advanced).

Some things that stand out:

  • 34 of the 64 teams finished the regular season in the top 10 for scoring average. Only 6 finished in the bottom 10.
  • There’s less of a correlation in playoff success. Of the top 7 teams by regular season scoring, 3 made a conference final (1 cup winner) and 1 was eliminated in the first round. Of the bottom 7, 3 made a conference final (1 cup winner) and 3 were eliminated int he first round.
  • The top team on the chart is last season’s Pittsburgh team. So that makes me feel a little better about being unable to stop them from scoring in the playoff series.
  • The bottom team on the list is the 2014 Los Angles Kings, who won the Stanley Cup that season. How did they do it? They had great defense and goaltending in both the regular season and playoffs, of course. But in the playoffs their scoring improved by nearly a goal per game (2.41 to 3.38). Former Blue Jackets Marian Gaborik and Jeff Carter (ugh) both caught fire (14/8/22 for Gabby and 10/15/25 for you-know-who, in 26 games). /

What does this mean for the Blue Jackets? I have a few theories:

  • The renewed focus on defense since January 1 is a positive development. Ottawa last year showed how that kind of system can advance through the playoffs.
  • Obviously, we know this team lives or dies with the performance of Sergei Bobrovsky. Vezina Bob needs to show up in the postseason for once.
  • It’s possible - perhaps even likely - that the Blue Jackets will finish the regular season with a GPG over 2.55. It was higher in late November, after a hot streak and prior to the injury bug attacking. The return of Alexander Wennberg and Cam Atkinson can help. Perhaps lineup stability will lead to more production from players like Nick Foligno and Boone Jenner.
  • I mentioned that Marian Gaborik was huge for the 2014 Kings. They acquired him at the trade deadline from the Blue Jackets. Therefore it’s possible the Blue Jackets could add another forward between now and late February. Gaborik was a pending free agent, and that is the type of player I would expect GM Jarmo Kekalainen to pursue./

Here is the full chart. It is sortable by column. What, if anything, jumps out at you? What do you expect from the Columbus offense over the coming months?

Playoff scoring since 2014

TeamSeasonReg. GPGRankPost. GPGPost. WGPG Diff
PIT20173.3913.0816-0.31
DAL20163.2312.697-0.54
ANA20143.2112.697-0.52
MIN20173.2121.601-1.61
CHI20143.1823.0511-0.13
WAS20173.1832.777-0.41
TBL20153.1612.5014-0.66
BOS20143.1532.507-0.65
NYR20173.0942.836-0.26
TOR20173.0552.672-0.38
WAS20163.0222.426-0.60
NYR20153.0232.3711-0.65
CBJ20173.0162.601-0.41
COL20142.9942.863-0.13
NYI20152.9942.143-0.85
EDM20172.9682.777-0.19
PIT20142.9552.697-0.26
PIT20162.9433.04160.10
CHI20172.9390.750-2.18
SJS20142.9163.1430.23
STL20142.9162.332-0.58
STL20152.9152.332-0.58
NAS20172.90112.7314-0.17
SJS20162.8943.13140.24
CAL20152.8962.455-0.44
WAS20152.8962.007-0.89
VAN20152.8882.332-0.55
CHI20162.8562.8630.01
PHI20142.8482.293-0.55
NYR20162.8472.001-0.84
STL20172.84122.006-0.84
TBL20142.8392.500-0.33
FLA20162.8382.332-0.50
BOS20172.83132.172-0.66
OTT20152.8392.002-0.83
DAL20142.82103.0020.18
DET20152.82102.143-0.68
ANA20152.78113.56110.78
MIN20152.77122.404-0.37
NYI20162.77112.365-0.41
NAS20152.76143.5020.74
CBJ20142.76123.0020.24
TBL20162.73122.82110.09
NAS20162.73122.217-0.52
WPG20152.72162.250-0.47
LAK20162.72142.201-0.52
MTL20172.72151.832-0.89
CAL20172.71162.250-0.46
CHI20152.68173.00160.32
ANA20172.68182.94100.26
STL20162.67152.85100.18
SJS20172.67192.332-0.34
PIT20152.65191.601-1.05
DET20142.65161.201-1.45
ANA20162.62172.573-0.05
NYR20142.61192.5613-0.05
MTL20152.61202.086-0.53
MIN20162.60192.8320.23
PHI20162.57221.002-1.57
MTL20142.55213.00100.45
DET20162.55231.601-0.95
OTT20172.51222.4711-0.04
MIN20142.43242.6960.26
LAK20142.41263.38160.97

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