Quote:
Originally Posted by metalfoot
I guess one of the fundamental questions here is what sample size in hockey is sufficient to develop statistics with adequate confidence levels? Is a season enough? a career? how long a career? Player A only played 180 games, but Player B played 290 so his numbers work for projection and extrapolation?

It depends. For example, in baseball, the various stats on range aren't considered to be particularly accurate below 3 seasons worth of data, but a batting average stabilizes to something usable after after a week or two's worth of game. It depends on how many data points you have, and how wide the variation is within your population. When you're performing operations with any number with uncertainty attached, the error multiplies.
The way statistical analysis works though, once you're above a threshold, how many points don't matter. Going from 95% certainty to 99% isn't a big jump, but going from 66% to 95%. If I'm remembering my bell curves right, 1 sigma gets you the 66%, 2 sigma gets you the 95%, and three the 99%. The returns you get in increased accuracy from adding new data diminish pretty rapidly, so that if 1 sigma turns out to be 150 games or so, player A's number won't be reliable while B's will. If it turns out to be 50, both will be equally valid.