“Of course, with this method, a ball hogger will get slightly more credit than he deserves, but as long as his ball-hogging is done on easy fly balls, he isn’t going to get much credit anyway. For example, if a certain type of ball and location is caught 90% of the time, whoever catches it is only going to receive .1 (1 minus .9) times .83, or .083 runs in credit.”

To be a -9, Granderson is not getting to some balls that some CF’s get to, and they are landing for hits.

One major problem with UZR in WAR is simply sample size. Granderson could be seeing a disproportionately high number of tough plays that are just outside of his range. That kind of thing isn’t going to even out over the course of one season.

Another issue is positioning. UZR makes no account for where the fielder starts. If you’re a coach, the effectiveness of your outfield positioning strategy will be determined not by a player’s individual UZR, but by the combined UZR of the outfield. Of course if Granderson is being positioned differently, you would think it would allow him to make plays other centerfielders cannot to balance out the plays he isn’t making that other centerfielders do make (over a large enough sample size, of course).

The biggest problem of all with UZR is simply the measurement error. You need a human being to make a judgment as to the nature of the ball being hit. There are various categories of fly balls, and of course the ball needs to be assigned to the appropriate zone on the field.

The measurement error and positioning issue (though, in theory those should be a wash) is really what field f/x will solve.

Remember 2009 and how 3 players on the same team cancel each other out? Not in Beantown it doesn’t.

Granderson’s year is completely exposing the fielding flaws in WAR. I watch every game, and I think I might recognize a bad CF if I saw one, in errors, or at least misplayed balls.

This whole debate, believe it or not, reminds me a lot of the debate teachers have about how to teach kids to read. When I entered kindergarten thirty-six years ago, reading was taught exclusively through phonics. If kids could sound out words, they would become readers. By the time I became a teacher, people realized that while some kids learned fine that way (we’ll call them the Garveys) others (the Buckners) did not. So everything was turned upside down. Phonics were thrown out, and the Whole Reading movement took over. The idea was that if kids were exposed to lots of good text through read alouds and independent reading, they would magically learn to read, as if by osmosis. Lots of kids actually learned this way — my bet is that some of your own kids have magically learned to read while sitting in your lap in front of Dr. Seuss. But guess what? While the Buckners liked Whole Reading, all the Garveys fell by the wayside. It took a long, long time for people to realize that what we actually need is BOTH approaches at the same time.

And so it seems to be with defensive metrics. I think the moneyballation of player analysis has been a great thing, especially for the hitters, but I still believe that there are some aspects of defense that have to be measured with the eyes. Think back two years before the installation of this current outfield. Yankee outfielders were old and plodding. I can’t tell you how many times I watched a Yankee hitter launch a rocket into the gap only to have it run down by a speedy opposing outfielder, and each time I’d be pissed off for two reasons: one, an out was made; two, I knew the Yankee outfield wouldn’t have been able to make the play. I didn’t need defensive metrics back then to tell me the Yankee outfield was a defensive liability, just like I don’t need them now to tell me the opposite.

I’m sure this information is out there (and I know this idea appears numerous times earlier in this thread), but it seems like it would make more sense to use defensive stats for an entire outfield. You can debate the relative importance of Granderson vs. Gardner, but what matters most, especially in terms of wins and losses, is the Yankee outfield is light years ahead of where it was two or three years ago.

Oh, and for all the voters who are reading, please give Brett Gardner the Gold Glove. Please.

Only problem is that the data is likely to remain accessible only to MLB teams and maybe a couple of the largest data crunching organizations.

Also, the dataset generated is freaking huge, something around a terabyte from every game, IIRC.

There’s an inherent limitation. Once you break things down into such fine-grained categories, the sample sizes for any particular type of ball are miniscule. You would have to make assumptions (or maybe just try out different models) for how the difficulty of the play depended on the various attributes of the ball, so you could combine all of the different hits coherently. Daunting.

Also, I hadn’t thought about the non-zero-sum problem, but that’s also huge. Take an extreme case: you have a the fastest OF in the history of the world. Now add the second and third fastest to his left and right. Their contributions must be less than the sum of their individual abilities, because you’re getting Willie Mays stealing hits that Duke Snider would have snagged anyway, and so on.

There’s a bit of this in offense, too, insufficiently appreciated, but the effect is much smaller.

Well, one issue is that it’s doubtful the alignment would be the same every day. So you might see Granderson shading to right-center, but then if you saw extra hits landing in short right-center you would have to be sure they hadn’t re-shifted the outfield for the particular batter.

Here’s a possibility. If there were a record for every single PA of where the given OFer was positioned, you could plot every ball that landed in the OF and every ball that player caught, and you’d get a kind of cloud shape around him representing his range. The cloud shape would not be a region of the field, but would be defined relative to the fielder as he moved around.

You’d still have to factor in overlapping fielders — somewhere you’d have to account for the balls that the other fielders caught. And obviously you’d have to separate line drives, soft flies, and so forth.

Hmm. It would be an interesting piece of information, but I still wouldn’t use it the way I’d use offensive stats.

In many ways the relationship between pitching and defense is a chicken-and-egg proposition, just like the link between RBs and an offensive line.

On FanGraphs, Swisher is worth +10 runs to the yanks, Gardner +19, and Granderson -9. i’d like to know where these balls in play are landing for hits and extra bases given that array.

Maybe baseball defense is more like football or basketball, and not like baseball offense. Each player contributes to the team, but the whole is not simply the sum of the parts. And that’s why sabermetricians have been thwarted.

Just speculating.

Defensive metrics and derivatives like WAR are very valuable, but they do require discretion, something of which I think too many lose sight.

[1] Granderson is a truly elite outfielder, but it’s worth noting that when he played CF, he didn’t have a version of himself vulturing balls.

I think if you look at them together and w/ swisher, you see a successful outfield that happens to catch balls in a way that the defensive stats are not sophisticated/nuanced enough to track accurately.