It means teams in the West are tired from all the extra traveling they have to do and therefore are unable to put forth as much effort offensively as the East squads do, thus resulting in more OT and SO games.
I may be way off base, but the West is more defensive - like 1-0, 2-1 games rather than the higher scoring East - with 5-3, 4-2 type games.
As of now:
East; 140G, 400GF, 2.86GF/G
West; 140GP, 376G, 2.69GF/G
So while there is a small difference in total goals scored, I don't think it's large enough to explain a 5:1 ratio of shootouts.
However, if you look total the absolute values of the GF/GA differentials you get:
East - 108
West - 86
Which is a much larger relative difference than total goals scored between the conferences, perhaps suggestive of 'tighter games'.
However, it may actually be a result of Tampa's dominance of the SE, as they are the worst (best?) offender in the GF-GA totals. Likewise WPG, WSH and FLA are the next worst (worst.) offenders. So 22 out of the 108 (absolute value) total goal diff is due to Tampa and the SE, which has only been 4 games out of 140 - not really enough to influence the shootout stats.
TL;DR: Not scoring.
Unless someone wants to dig through every game for each conference and plot the goal diff, as there could be counteracting effects.
It's likely randomness but I would like to see the average goals scored by the winner and average scored by the loser. I imagine the west has a lower winners goals scored avg and a higher average for the losers. (or just a margin)
It really is. If there are like what, 500 statictical posts -- it would be extremely unnormal if one of them didn't stick out.
I read a very fascinating article on this in the NY Times a couple of years ago. There was a professor in the US who had done a case study I think on the death of a two twins in Finland. If anyone remember, there were some talk about a group of people someway connected to the defense industry in the US that died from accidents right after 9/11, and there were some speculations as if they had been assassined. Anyway, in connection with this, this professor commented on his research.
Two males in a small Finnish town, around say 85 y/o, died in separate car/pedestrian accident on the same hour the same day (like withint 30 minutes of each other or something like that). Subsequently, the police went out and said something like "this must be a murder, its just too unlikely that two guys who lived for 85 years then is killed within the same hour in separate accidents. One pedestrian in this city is killed in a car accident every third year, we can almost rule out accident". But it subsequently bascailly proved to be accidents.
This professor from the US started like with a number of all identical twins in Finland. He got a number of like a couple of millions.
-Then he looked at the number of car accidents in Finland and got a extremely small percentage for two identical twins to be killed on the same day.
-Still it seemed extremely unlikely that this would happen. But he found out that this winter day in Finland was extremely icy. So accidents was alot more likely on a day like that. The percentage went up a bit.
-He also found out that both twins had the same medical conditions. They were a tad demented, had very poor hearing and didn't see that well. The percentage went up even more.
-He found out that both twins as a routine went out around the same time the same day, which cut the number back more (if they were to die in a accident like this, it were on that day of the week at that time).
-Then he looked into other forms of accidents in light of the above. And the percentage for two identical twins, with the same medical condition, to be killed on the same day went up alot.
-Then applied that percentage to all identical twins in Europe. And the percentage for it to happend went up alot (he went from say 60k identical twins in Finland to 2m in Europe).
In the end he could basically prove that how if you looked at like a 20 year period, an accident like this were to be expected in Europe. Beyond that, he also noted that this probably was one of tens of thousands of accidents that would have been as newsworthy. Ie, if you just don't look at identical twins, but like accidents could happend were grandmother, mother and daughter to be killed in three seperate accidents on the same day -- that would also be a world wide story. Two sibblings being hit by the lightning. Falling airplane or whatever. His point was basically, if you live a full life you will experience a whole bunch of extremely unlikley things.
If it was purely random we could test the null hypothesis. I mean, we could find out if the variance was due to chance alone. But I don't want to do some data entry and then run a t-test, and I don't think I have to. Here's why.
On the surface, it looks big. I see about a 3:1 ratio from West to East (21:8) and the West plays a different hockey game than the East. But no teams face each other from different conferences, hence an OT game in a conference perpetuates this ratio.
I then see a team like Nashville having 8 OT games out 16 and then see a team like Toronto have 1 OT game out of 16. Is there something about these two teams that could explain the difference in games that went into OT? Possibly, they play two different games really.
So I dug a little deeper....
In between Toronto & Nashville are 28 other teams that vary from 3 OT games to 6 games then went into OT. No Western Conference team has less than 3 OT games, and no eastern conference team had more than 5.
That sounds legit, right?
Well, hang on.
6 teams went into OT 5 times (only 2 were Eastern)
4 teams went into OT 4 times (2 were East/West),
7 teams went into OT 3 times (only 4 were Western)
6 teams went into OT 2 times (only 1 were Western)
There's a lot of teams hanging around the median (3) and the mean (3.6). Hence the distribution should look pretty close to a bell curve. So the spread is normal and there's some overlap between the conferences.
I think we should look at this again at the end of the season to see if that ratio has reduced, but so far I would agree that this is likely a coincidence given the sample size.
Down to a mere 3.5% difference between conferences.
So a definite regression towards the mean to point i think we can state the differences between the 2 conferences is no longer significant.
If it remained at around 10%, we would of had something more substantial to discuss regarding real differences in each conference this yr. oh well. will do a final tally at season's end for those interested..