Critics of WAR calculation often argue that the formula has too many variations to be consistent. Just about every year, statisticians (or if some critics are to be believed, cyborgs) announce significant changes to various WAR components. In fact, the two most widely accepted authorities on the subject – Baseball Reference and Fangraphs – can’t seem to agree on the best way of deriving the number. A major step toward WAR uniformity came recently when the sites’ editors agreed to use the same replacement-level record (48-114) in deciding how many Wins Above Replacement to allocate across the whole of baseball (1000). The basic idea is this: in an entire baseball season, 2,430 games are played. If all teams were allotted 48 wins, establishing the replacement level, the total number of wins would be 1,440. This leaves 990 wins above the replacement level to be allotted amongst the teams. The first thing you likely notice is that 990 and 1000, while very close, are not the same number. Well, the agreed-upon performance of a replacement-level team is actually based on a set winning percentage – .294. Over the course of a 162-game season, a .294 record would actually yield 47.628 wins, which we all know is not possible. Allot that exact number of wins to 30 teams, however, and you’re left with something much closer to 1,000 (1,001.16) for the number of wins above replacement to be distributed.

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Those who accept and use Wins Above Replacement (WAR) as a tool to think about baseball have enough knowledge of and/or faith in its framework and methodology that they often treat WAR’s general accuracy more as a given than as a point of contention. On the other hand, a large number of baseball “traditionalists” refuse to acknowledge the possibility that the calculation could work at all. Still more are simply turned off by the cold indifference of numbers purporting to represent their heroes’ worth in the abstract. Unlike home runs or doubles, WAR is not something a player can do. It’s an artificial number derived by measuring a player’s performance against that of a person who does not even exist.

As a result, public debate about WAR (and sabermetrics in general) tends to focus on whether the calculation works and not how well. This isn’t exactly surprising. Given sports media’s widespread fixation on the yes/no rather than reasoned discussion of contested issues, the actual mechanics of such a polarizing thing can take a back seat to the perceived Big Picture. The “yes” crowd, while continuing to use advanced analytics to frame their discussion of the game, retreat to niche forums to discuss the ocean of grey that exists in baseball numbers. Naysayers soldier on, discounting advanced metrics as nerdy attempts to rewrite a narrative made clear through obvious stats while scoffing at WAR for its ignorance of mysterious baseball attributes such as “grit” and “the will to win.” The fertile middle ground, as it were, remains largely unplowed. Even as things like WAR and BABIP (batting average on balls in play) make more frequent appearances in mainstream baseball journalism, the presence of “sabermetric” stats in mass-consumption media often feels more like casually hip namedropping than an invitation to discuss why and how well they work.

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