Or some way for users to set up subsets of one blog. Or at least have some co-writers for every blog to present multiple viewpoints.

JD, I didn’t miss that point. I have said this a million times: There are 2 sources of error in any sample metric. One is the fact that a player may have actually played better or worse than he would in the long run for whatever reason or no reason at all. The example you give is correct. Let’s say there is a ball that few SS other than the best get to on a regular basis. Betancourt may have gotten to that ball once in one opportunity or even twice in 2 opportunities (or 2 out of 3 or whatever). In the long run, he may get to only 20% of them, but in one opportunity, he may have succeeded 100% of the time. That is sample error. That is a guy hitting 1 HR in his first game or 10 HR in his first 100 PA or whatever. Sample error due to the random nature of sample results around a population mean.

The second source of error is measurement error and that is a completely different animal. In sampling statistics it may not even occur. When we flip a coin X number of times, there is no measurement error. All we have is sampling error. In the case of defensive metrics we have large and numerous measurement error. For example, that one ball that Yuni got to that is only gotten to 20% of the time by the average fielder, may NOT have had the characteristics we thought it did. While all hard hit ground balls hit in area X up the middle may be fielded 20% of the time, there are obviously lots of different types of balls in that bucket, from an easy, high chopper (albeit hard hit, if that is our bucket) to a ground skinner. As well, some of those balls in that bucket are hit directly up the middle and some are hit a little to the SS or 2B side. In addition to that, sometimes the SS is playing in position A and sometimes in position B because of the batter, pitcher, base runners and outs. Lot and lots of potential measurement error. So that ball that Yuni fielded may have been an easy chopper a little towards the SS side, even though it went into the "hard hit ball up the middle" bucket which is only fielded 20% of the time on the average by all SS.

So, to summarize, we have two sources of error in fielding metrics: One, when a player actually performs good or bad, but better or worse than he would in the long run. Two, when according to the data, the fielder performed like "X" but he really performed like "Y" because the data is not precise.

When we see an above or below average number for a fielder, we regress that number because of both types of error (sample and measurement). So if Yuni is -15 plays after 28 games, it is more likely than not that he played worse than he would play in the long run (than his true talent level) AND it is more likely than not, that he didn’t actually play as bad as those numbers indicate, because of measurement error (e.g., some of those balls that he didn’t get to were actually harder than their "buckets" suggested and/or some of the balls that he did get to were not as hard as their buckets suggested).

From MGL in this post correcting Poz about similar mistakes.