The goal of sabermetrics, as many of us enthusiasts believe, is to become as objective as possible in regards to baseball knowledge. In terms of offensive measures, we’ve made a whole lot of progress. That’s just because it’s very easy to isolate the hitter as an individual variable and approach his contributions as a random variable part of a probability model–it’s relatively simple to create an “expected value” of a hitter, aka wOBA. And then it was nearly perfected by adjusting for park and the run scoring environment to create wRC+, which many believe is a fantastic one-size-fits-all stat for a hitter’s contributions. And while there are going to be tweaks that will go even further at perfecting offensive contributions, one could say with a reasonable amount of confidence that we’re approaching the asymptote of what is the learning curve of offensive knowledge, saber-stat wise. I could be completely wrong on that one. But I do think in terms of our knowledge of hitters, it’s pretty well informed.
But our knowledge of fielders? That’s a completely different story. It’s almost become a hobby to become “skeptical of defensive metrics” at this point, but the concerns are real. With hitters, it’s pretty much known between WAR, OPS+, wRC+ and OFF how skilled a hitter is. While in defensive metrics, the comparison between UZR and TZ vary wildly in their observations–to one, a fielder is elite, and to the other merely average. How can these debates end?
Enter the new age of MLBAM, which will track everything about a batted ball. There are an infinite amount of possibilities regarding how this new data can be implemented, but there are a few off the top of my head that I think will make the biggest impact:
- Fielder Radii: Any fielder, including the pitcher, can have an average range, or radius, attributed to them so that it is well known the maximum potential of their range. It would then be easier to allocate certain fielders in certain positions by how much ground is covered. This radii can have multiple forms: maximum radius, median radius, minimum radius, to show the range of values in a player’s range.
- Route Efficiency: This has been touched on before, but it can be deduced into a number how efficient a player is at reaching their destination. This can compensate in some cases for a lack of speed, and there are definitely prospects who get overlooked because their speed is lacking, but possibly their route efficiency is elite. This could also be a teachable skill, something that can maximize the fielder’s possible radius.
- Better bunting: Although bunting is often the scourge of a sabermetrician’s existence, it still has its uses. It keeps the defense honest in shifts, keeps them honest in general, and certain times can extract a minimal run expectancy increase, which can be pretty valuable if utilized correctly over the long haul. But in this application, it would be used in teaching hitters to bunt. If we know the opposing team’s median fielder radii, can we not also find a hole in the infield defense? One could then practice on punching holes in infield defensive coverage. If a manager utilized this, (alongside bunting more in shifts!), I could definitely see them squeezing out 5-10 runs solely based on that. That’s nothing to scoff at.
- Effect of diving: When I mentioned the maximum fielder radius, that often includes the effect of a fielder diving. Under this new system, one could theorize the following: the defensive runs saved due to diving can be expressed as the probability of catching the ball by increasing my radius by x feet multiplied by the run value of the offensive event. On the other hand, it can be compared to the probability of causing an injury times the average number of runs lost due to having a player injured for x number of days. That type of data is wide open to interpretation, but the more information on the risks associated with diving can make coaches and players more informed when they practice and play.
As I said before, there are an infinite amount of practical uses for this new technology, many no one has even thought of yet. But, I do think that this will usher in a new era of innovation in the way of defensive metrics, and the team that best utilizes it can be on their way to being the subject of Moneyball 2.0.
Featured Image courtesy of www.deadspin.com
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