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07-31-2012, 01:34 PM
  #69
Iain Fyffe
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Quote:
Originally Posted by NugentHopkinsfan View Post
Sedins struggle to get the puck back and do a poor job of winning puck battles.
What's the relative value of puck-battle-winning to good-defensive-positioning? This is the forest for the trees problem. If you look only at a player's weaknesses, you undervalue them. It happens all the time. Martin St. Louis went undrafted not because of what he was, but he wasn't. People saw his size and wrote him off, regardless of what he could actually do on the ice.

The converse is also true. Looking only at a player's strengths overvalues them. It's a common bias of perception, and is one of the reasons that relying only on your perceptions is flawed. Similarly, if you over- or under-value certain aspects of the game, you under- or over-value players who are good/bad at those things.

People into statistical analysis are often admonished to keep it in context, that the numbers can't tell you everything. Put the same is true for those who tout watching the players. You can't see everything, and you can't always tell what something means even if you see it.

Quote:
Originally Posted by Canadiens1958 View Post
See your post #18 in this thread. the global sv% may remain fixed at 55B:45A or about but the "sub" sv%s that are the component strings of As and Bs may vary widely. Example globally sv% .920 < sv% < .925 but the component SV%s by zone, net, offensive style etc may vary greatly.
If you're trying to say that a set of data is less variable than various subsets of that same data, then yes, that's obviously true. That's sample sizes and regression to the mean at work.

Which is irrelevant, of course, unless variance is an important consideration in your analysis.

Quote:
Originally Posted by Canadiens1958 View Post
A season is just a starting reference point that is a base for a game, games, opponents, career. View it as the first bite of many.
Indeed, it's again sample size at work. But sample size is irrelevant unless variance is a significant consideration in your analysis.

The smaller the number of games, the less reliable the results are for drawing conclusions from that data. So if you try to explain why some team won a seven-game series (a tiny number of games from an analytical perspective), you need to consider that. You can try to say it's because all the centres on one team were right-handed, but demonstrating that that factor is significantly more important than simple variance due to the very small number of games is an uphill battle.

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