Interestingly enough, one of the major postwar genres of Anglo-American literature was the academic comedy. Popularized in large part by Philip Larkin and the “Movement,” authors strove to poke fun at academic institutions and the conventions followed by the terrifically aloof professors. The most famous novel to fall into this genre is Lucky Jim by Kingsley Amis. The book features Jim Dixon, a poverty-stricken pseudo-pedant with a probationary position in the history department of a provincial university. A veritable alcoholic, Dixon attempts to solidify his position by penning a hopelessly yawn-inducing piece entitled “The Economic Influence of the Developments in Shipbuilding Techniques, 1450 to 1485.” Short novel made shorter, it doesn’t help him retain his position, but it does succeed in illustrating the banal formalities that academic writing necessitates.
In sabermetrics, there is a heavy reliance on sometimes inscrutable jargon, acronyms that sound like baby words (“FIP!”), and Mike Trout’s historical comps (Chappie Snodgrass is not a very good one in case anyone is wondering) that quite understandably renders the average fan mildly frustrated and the average fan over sixty wondering how we will ever make baseball great again. Typically, I enjoy those articles very much because they communicate news efficiently and analytically. Occasionally, however, articles stray into the Jim Dixon range of absolute obscurity, examining the baseball equivalent of “Shipbuilding Techniques,” whatever that may be. Such writings form the cornerstone of sabermetrics as they mesh history, theory, and sometimes economics.
Fortunately or unfortunately, my article today isn’t quite Dixon-esque, but it retains some of that style’s more tedious elements. It falls more closely into the category of two-minute ESPN quick sabermetric theory update. I don’t think that’s a thing. Seemingly pointless introduction aside, please consider what you know about DIPS theory. I won’t insult your intelligence, but it was developed by Voros McCracken at the turn of the millennium and has served as one of the principal tenets of the pitching side of sabermetrics ever since then. The theory, in its most atomic form, essentially posits that pitchers should be evaluated independently of defense because it’s something they cannot control. Hence “defense-independent pitching statistics.”
Certainly, it was a revolutionary concept and one that has even gained quite a bit of traction in the mainstream sports media. Announcers talk about how a certain pitcher would look a lot better pitching in front of, say, the Giants instead of the Twins. Metrics like xFIP only serve to quantify that idea.
But every grand theory or doctrine (DIPS is essentially sabermetric doctrine at this point) requires a corollary to frame it. And so I propose something I like to call the “WIS Corollary to DIPS,” where WIS stands for Weather Independent Statistics. The natural extension of evaluating pitcher performance independently of defense is to evaluate players independently of weather because it also exists outside of player control.
The basic idea of this is that weather plays enough of a role in enough games to superficially alter the statistics of players such that they cannot be accurately and precisely compared with the other players in the league because all of them face different environmental conditions. Taking that into consideration, all efforts must be made to strip out the effects of weather when making serious player comparisons. Coors Field is why Colorado performances are regarded with such skepticism, while the nature of San Francisco weather and AT&T Park is supposedly why that location serves as an apt environment for the development of pitchers.
Think about it — it’s something we already do. We look at home/road splits, we evaluate park factors, we try and put players on +/- scales. We talk about this constantly even at youth games. I have heard parents say many times, “If only the wind hadn’t been blowing in so hard he might have hit the fence.” It’s honestly a commonly held, yet generally unquantified, notion that the general public has.
Player X hits a blooper at Stadium C that falls in front of the left fielder for a hit. Player Y hits a blooper at Stadium D with the exact same exit velocity and launch angle as Player X’s ball, but it carries into the glove of an expectant left fielder. Should Player X really get credit for a hit and Player Y for an out? Basically all statistics, striving to communicate objective information, would say yes. If this kind of thing happens enough times over the course of a season, it can make a significant difference. A couple of fly balls that leave the park instead of being caught at the fence would put a dent in a pitcher’s ERA, while changing a player’s wRC+ by no small sum.
For that reason, players should be measured as if they play in a vacuum. One of the biggest goals of sabermetrics is to isolate player performance in order to evaluate him independently of variables he cannot necessarily control. Certainly, this has some far-reaching consequences if the idea gets carried out to its natural conclusion. Someone would likely end up developing a model that standardized stadium size, defensive alignment for varied player types, and other things of that nature. I’m not necessarily advocating for that, just for stripping out the effects of weather.
WIS by itself isn’t radical, but the extent to which it’s applied could be considered as such. As of now, it’s something consciously applied a relatively small portion of the time, but I think that it’s something that should be considered as much as possible. Obviously, there are issues with this. You can’t very well modify “raw” statistics like batting average or ERA so that they reflect play in a vacuum. What you could conceivably do is create a rather complicated model that requires a complicated explanation in order to describe how the players should have performed. And that’s something which brings us to an important point; the metrics that would employ this information would not be for the average fan; rather, they would be aimed at the serious analyst.
This is something I’ve already tried to employ with a metric I created called xHR, which uses the launch angle and exit velocity of batted balls to retroactively predict the number of home runs a player should have hit. The metric is still in development, but I think it’s something that works relatively well and can be applied to other types of metrics. For instance, an incredibly complex and comprehensive expected batting average could utilize Statcast information to determine whether a given fly ball would have been a hit in a vacuum based on fielder routes and the physics of the hit. By no means am I trying to assert that I have all, if any, of the answers. The only thing I’m trying to do here is to bring debate to a small corner of the internet regarding the proper way to evaluate baseball players.
Probably the most crucial thing to understand here is that the point of sabermetrics is to accurately and precisely evaluate players in the best possible way. Sabermetricians already do an incredible job of doing just that, but perhaps it’s time to take things a step further in the evaluation process by developing metrics that put performances in a vacuum. I know that baseball doesn’t happen in a void, but the best possible way to compare players is to measure them* as if they do.
WIS Corollary — One must strip out the effects of weather on players in order to have the most accurate and precise comparison between them.
*Oftentimes it’s necessary to compare players while including uncontrollable factors, like sequencing, especially when doing historical comparisons. It’s important to note that the WIS Corollary is applicable only in very specialized situations, and would generally go unused.
A busy person, but one who spends his free time in front of a computer screen, fiddling with statistics. And yes, that describes everyone who regularly visits this website.