Quote:
Originally Posted by Peter_Spaeth
I also question some "oh it's too small a sample size" arguments. Those always seem to me to reflect cherry-picking, to dismiss inconvenient stats that don't fit the theory. We used to see that argument all the time here to rebut the theory that Kershaw was not a good post-season pitcher; his lousy performances were just random events and couldn't possibly reflect that he wilted under pressure. Of course after a full season worth of postseason outings there's still a huge disparity so maybe that argument has been retired.
Of course when the stats do fit the theory, we don't see the sample size argument so much.
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And by any chance could the fact that pitchers end up facing the best teams during the playoffs and WS possibly be a factor in their not-so-stellar playoff performances also? Or how about those pitchers where age, injuries, the long regular season, and other factors would then catch up to them at playoff time? Corey Kluber may be a great example of that particular set of circumstances. A fantastic pitcher and Cy Young winner, Corey was always focused and ready to go. But as he'd head into the postseasons, it always seemed like he'd run out of gas or nagging regular season injuries finally caught up to him. Still, when he was right and rested, he could hang with the best of them.
The one other thing you said that I really like is in reference to the possibility that someone like Kershaw could also possibly wilt under post season pressure. Now I'm not saying he does, but I think ALL people realize and recognize that stress can and does affect every single one of us, and does so differently depending on the unique set of facts and circumstances a person is presented with. That is a prime example of one of those human elements I keep harping on about that statisticians can't possibly ever effectively measure and fully account for in their equations, formulas, and algorithms. They may try to tell that they in fact do have such variables and factors accounted for, until you ask them to show you and prove it to you, and then you get the excuses about how you wouldn't understand, or it would take too long, and so on.
Now I actually don't doubt that statisticians may in fact try to account for human variables like luck, stress, heart, competitiveness, or whatever, but since there is no real way to effectively measure and quantify such human variables in their work, the things they do to account for them are at best, SWAGs, and at worst, WAGs. And a guess is basically no more than someone's opinion, maybe an educated one, but still an opinion, no more, no less. Not ever saying a statisticians work is bad, just that they should be honest and admit what it actually is, their (very) educated guess more often than not.