Quote:
Originally Posted by Bingo
Stats are an important tool to help eliminate bias in eye test.
Sample size is key for sure.
Also context ... stat selection.
What kind of player is it? If it's a fourth line player don't compare xGF% because they don't generate much xGF60 in their role. But how do they compare in xGA60 against their teammates knowing they have a sheltered role? How do they look against other fourth liners?
With the right context a stat isn't wrong. It's just important or not important.
Can you use one game sample sizes? Of course you can. If player x was on the ice for 8 HD chances against and no other player was on the ice for more than 2 it was likely a bad game.
The nature of the stat doesn't pin it on the single player though. So that's why longer data sets make more sense. If he's on the ice for more HD against over 40 games than any other player? Probably an issue.
We should try and think these things through, and hopefully land on something more constructive than "the stats are BS, I'll trust my eye test thanks!"
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Such a frustrating post because some of the comments are good and worth discussing, and some of them are not.
Yes, stats can be wrong, and often are.
Can you use one game sample sizes? Yes, for some things. But first, you always have to be careful with small sample sizes, and second, many of the stats that are quoted on here with single-game samples, should NOT be used in samples that small.
You use one example as an argument on behalf of all stats, but it isn't - each stat needs to be analyzed and evaluated on its own. Just because one example can survive a single-game data set, does not mean others can.
And rebutting any criticism with the standard: it's better than the "I'll trust my eye test" is not only lazy but is the opposite of what you are trying to claim with "land(ing) on something more constructive".
We've been around this a hundred times. You have faith that the stats are good enough. I think stats need to be held to a higher standard, because most people don't know how to interpret them, and put far too much faith in the results. And since they do, 'good enough' and 'they're the best we're got' isn't actually good enough in many cases. It is completely reasonable - and accurate - to criticize them when they warrant it.