In article <mssC5y5u0.4Dn@netcom.com> mss@netcom.com (Mark Singer) writes: >In article <C5xwAI.3nu@news.cso.uiuc.edu> hbrooks@uiatma.atmos.uiuc.edu (Harold_Brooks) writes: >>In article <mssC5xB3I.2CG@netcom.com> mss@netcom.com (Mark Singer) writes: > >Actually, I think the large-scale sample size is part of the problem. >It seems to me that if we were to plot all the players in baseball >in regard to BA vs. Clutch BA deviation we would get some kind of >bell curve.  (The X-axis being the +/- deviation in clutch hitting >vs. non-clutch;  the Y-axis being the number of players.)  Certainly >there would be *some* players on the extreme ends of the bell.  My >*supposition* is that if we were to find the SAME players consistently >(year after year) at one end of the bell or the other, then we might >be able to make some reasonable conclusions about *those* players >(as opposed to all baseball players).  Let's be careful here.  If players' performance was completely random in (Clutch-No Clutch), then you would still expect some players to be good in the clutch every year and some to be not-so-good every year. With two years worth of data, you'd have 1/4 of the players good each year, 1/4 bad each year, and 1/2 would have one good and one bad year. We have 96 players for 5 years ('84-'88).  Just flipping a coin, you'd expect 3 players to be good all 5 years and 3 to be bad every year. This is what we actually get--  No. of good years    0    1    2    3    4    5 Clutch performers    4   10   37   24   18    3 Coin flip (random)   3   15   30   30   15    3  Essentially the distribution of clutch performers by number of years of good performance is the same as what you would get if the process leading to deviations from non-clutch performance was completely random. If there was anything to clutch hitting (at least in this definition) that had any predictive capability, you expect to see the number of players at the ends to be much larger than that predicted by flipping a coin.  Further, if you limit yourself to players who were a lot above or below average in clutch situations (say, 1 standard deviation from  the mean) more than one year, the random explanation still looks good. In the four years ('84-'87) that I looked at the data from Elias, there were 79 (29) players with a minimum of 25 (50) at bats in clutch  situations that were 1 sigma from the mean two different years.  Of those 79 (29) players, 38 (14) of them changed sign between the two years.  In other words, they were great clutch hitters one year and really horrible the other year.  If it was just a random process,  you'd expect those numbers to be 39.5 (14.5).    Everything that's been measured about clutch hitting over a period of years that could be used to predict any ability with any  proposed definition has looked like a random process (with the  caveat that there may be something related to platoon advantage that could be dragged out of the data--e.g., John Lowenstein  probably never had a "clutch" AB against a left-handed pitcher, but he might well have had some in blowouts, so that there would be a bias since his clutch ABs would be more geared to his  platoon advantage).  This is not a subject that has been glanced at casually.  A lot of people have put a lot of effort into  studying it and every one of them, with the exception of the Elias study, has been unable to find anything that would allow you to predict how someone will do in clutch situations better than flipping a coin.  (Self-serving plug follows:  some of the flaws in the Elias study are discussed in my paper in the forth- coming SABR book, _The Perfect Game_, by Taylor Publishing.  The authors are supposed to get a slice of the advance, so go bug your local bookstores now, and maybe I can get enough to take my wife to dinner once.:-)  Harold