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This makes me think of my less responsible days when I would scarf down a slinger(this is a breakfast chili concotion which may or may not be specific to the Midwest) after drinking cheap beer after cheap beer. Ahh, the go old days.
Ken Rosenthal's new statistic: Jell = reached significant sample size
And why are things that a computer or statistic not measure called "funky"? Why did Rosenthal even waste his time writing this? I wouldn't even waste the time of a guy standing at a bus stop next to me with this garbage.
I want more! The book "Cardboard Gods" has more of this? I've got to check it out.
"The six wins from Holliday could have been achieved by Valverde, Wagner, Pineiro, and Damon at about the same economic cost, implying that the Cardinals did not and should not have paid a premium on a per win basis."
OK, I know what your analysis is trying to get at(valid alternatives),but I think it's too simplistic. First, I think it's quite a stretch to think that the Cardinals could have easily signed all four of the players you mentioned for those prices. There's things like market timing, the effect of the cardinals adding another bidder on these players, effectively forcing a team to pay more. Also, you're not considering the value of having high-end players on your roster. They have star power and thus draw more fans and interest to your franchise. Another thing is that I'm far more confident of getting six wins from Holliday than six wins your quartet. I just see him as lower risk. While the Cardinals don't have any true prospects coming up, filling the roster with these four players can block a potential breakout from a younger player(in particular, I think Hawksworth could be a good relief pitcher). In the Cardinals' situation, I like what they did.
I think you were going for a rather simplistic analysis, but a small change would have probably provided a better representation of consistency than FIGS range. Just calculate the standard deviation between the raw data and your LOWESS regression curve. Or simply use the standard way of calculating standard deviation from the mean(or median). Also, range can be skewed in this case. The higher the average FIGS score for a pitcher, then the higher the range will probably be. For example, a good pitcher who has typically high FIGS scores and then has one really bad game will have a larger range than an average pitcher with typically lower FIGS scores that has a really bad game. Either way, I think analizing consistency can be done a vast number of ways, but in the end you must see if those results are statiscally significant(i.e. using a chi-squared test,etc.)
One of my favorite players: 'Hard Hittin' Mark Whiten.
Not necessarily because of his hitting ability, although there was a game in Cincy when he was playing for the Cards where he hit four homers in a game. The guy had a cannon for an arm and I'm not sure a lot of people realized this. I would get to the ballpark early to see him warmup in the outfield playing catch. It didn't look like he was throwing very hard, but when the ball came out of his hand it looked like it was levitating above the ground. Like it never got above 10 feet off the ground and never below 4 feet no matter how far he threw it.