The Boys Are Back in Town!
No real insights for you today on the day before Pitchers & Catchers report to Viera. Federal Baseball already has some early photographic evidence of baseball returning to Viera. Highlights include Jordan Zimmermann and Drew Storen rocking the quasi-official Beastmode T-Shirt introduced to the ’11 Nats by Ian Desmond and made famous by Michael Morse. But who’s that shaking hands with Tyler Clippard? The #tigerbeatbaseball girls want to know. (It’s not Ryan Tatusko, though. I checked that already.)
Two statistically-related things that I’ve been thinking about lately, though:
Lost in Translation
Given the number of major league players and prospects who play in the Latin American winter-ball leagues in Venezuela, the Dominican Republic, and Mexico, it’s remarkable to me how hard it is to get reliable statistical information out of those leagues. The leagues have their own stats pages, to be sure. For instance, the Venezuelan League’s stats pages are pretty comprehensive. But it’s not exactly easy to find the player you’re looking for. Moreover, calculating advanced statistics like wOBA and wRC is pretty much impossible. The worst has got to be wRC, because it depends on calculating a league average wOBA. To do that for the Venezuelan league, I’d have to key in all the data for all players into another spreadsheet and run the calculations from there. The calculating isn’t too bad, but the data entry will take more time than I’m willing to commit (it’s not like sabermetrics is my job, y’know–and if it were, I’d be pretty terrible at it).
As an aside: reading statistical tables and box scores in Spanish reminded me that my Spanish isn’t as good as it ought to be. Baseball stats are cryptic enough in English, but they can be pretty opaque in Spanish. Glossaries do exist, but I’ve had to bring in an outside consultant for help with a few.
If you’re at all interested in Latin American baseball stats, PuraPelota has the most complete database I’ve been able to find, but they can be a bit slow on the update cycle. I haven’t been able to find anything nearly as complete or helpful for any of the Asian leagues (Japan, Korea, Taiwan). I can’t understand why that would be so–surely the Sabermetric revolution has spread all across the baseball world? Nothing makes you appreciate the excellent work that Baseball Reference and Fangraphs do quite like dealing with the sparse data available for foreign baseball leagues.
Eye in the Sky
…UZR, the measure employed to determine whether a fielder has more range than his teammates, and whether, on the whole, he can prevent opponents from from creating more runs. Joey Cora used to remind me how an infielder could be better depending on which pitcher was on the mound. This was due not only to the pitches, but also to the control the pitcher has over them. “What happens if a catcher calls for a sinker inside,” Cora asked. The shortstop moves a little, almost imperceptibly, towards the hole if the batter is right-handed. But if the pitcher leaves the ball outside, the roller could go up the middle of the infield. Result? A higher probability that the batted ball goes up the middle of the field and finds the shortstop further away from it–thus raising his UZR.
My initial reaction is that complaining that UZR may not describe that particular defensive alignment and situation like this is like complaining that the Ideal Gas Law won’t tell you exactly where to look for one particular carbon dioxide molecule in a tank full of compressed air.
Part of the problem, I think, is that UZR is the one baseball statistic in (quasi-) common use that is flat-out impossible to derive from other published statistics. As far as I can tell, the whole process depends on individual human beings watching game footage, noting where fielders are positioned, and noting where the fielder meets (or doesn’t meet) the ball.
Because I’m lazy, I figure that there must be a better way to do things–or at least one that isn’t so unbearbly tedious. We already have fairly sophisticated software that can track the location of, say, baseballs and baseball gloves as they move across a camera’s field of view. It should be a fairly simple matter to fix a wide-angle camera (or several) across a baseball field, record the whole game, and only have human intervention whenever the ball strikes the bat. An observer might tap one button when he sees the impact of the ball on the bat, and then tap another when the ball comes to rest (either in the glove of the fielder, or out of play). The end result might look something like the FlipFlopFlyball‘s defensive positioning infographic.
The genius of computing, however, would allow us to track each defensive move as a vector, with an origin point at wherever the defender started when the ball was put in play, and an endpoint at wherever he was standing when the play was over. I’m not so great at mathematics, but I imagine the resulting graphical representations (and statistical inferences!) that could be made from those data would be extremely useful in evaluating the range of any individual defender. Heck, maybe it wouldn’t be too hard to explain– if I only had a brain!