I really dislike analyses like these because they masquerade as "science" when really there's no way to tell from the analyses if it's remotely accurate.
"Clutchiness is defined as blah blah blah... and here's the historical comparison blah blah blah."
Great. Now, what's the sample size for each team? How is it calculated exactly? What's the variance? What's the confidence interval? Did you adjust by park, by year, by league? There's correlation by following year, but what's the correlation with actual performance? What's the correlation with playoff performance? Whats the predictive value given a certain number of games?
Because, look, we have numbers. LOTS of numbers. An incredible amount. Which means two things:
1) A lot of correlations will happen randomly and hold no real meaning or predictive value, no matter how significant they seem to us 2) Your analysis is only as good as your data. Just because you have numbers doesn't mean that they mean anything, and a lot of the historical numbers are either slightly innaccurate (jsut enough to skew your analysis) or they need to be adjusted for different years, which is largely impossible (quick, how do you adjust BA to account for steroids? how do you adjust defensive metrics for the turf in Toronto, particularly that one year where it acted differently from the rest?)
In other words, if you look at any first place team, SOMETHING is going to be abnormal, probably a lot of things, by definition. That doesn't mean they're meaningful things. Some of them are just chance.
We're in this really weird era where there are so many numbers, and the knowledge of them is so well dispersed, and they're so easy to get to, that there are a million posts every day about some analysis someone did. Except that even if our stats are "new", we're using the same arbitrary, potentially meaningless, fairly random, data from before that we always had! And all these statistics are going to be proven completely full of crap in about 5, 10 years, when we get a glimpse into the statistics that the clubs are generating now.
Because the clubs are now gathering data at a whole other level that's never happened before and they're using it all the time. This is what Mo was referring to when he said "baseball card statistics" re: Moss trade. The stats us normal people are using? They're dead. When we have detailed and automated full video analysis (with machine learning), sub-CM accurate tracking, full facial recognition, accurate predictive models of injuries, and better weather simulation, all this "well, you take the BA, divide it by the number of stolen bases, times the average pitch count" stuff look like the stone age. And we're practically almost there now. Football and other sports are in the same position (for example, the new RFID tags all players will be wearing this season).
You know that scene in Moneyball where Bean can't believe all the ridiculous metrics his scouts are using to judge players (girlfriend hotness being the famous one)? We are still there right now, we just haven't realized it yet.