| 26 March 2010
Baseball is clearly a game of statistics, but when you attempt to draw conclusions from a statistic you have used your entire life you are sometimes rebuked harshly.
Perhaps no other statistic is as hated in the sabermetric community as the RBI. Proponents of sabermetrics seem to rally around the RBI as a representative of everything that is wrong traditional statistics, and grimace every time they hear RBI cited.
Generally speaking, there are certainly better statistics to evaluate batters than RBI. This most recent MVP “race” between Joe Mauer and Mark Teixeira was a perfect example. Supporters of Teixeira argued that Mauer’s low RBI total was significant, and be considered a negative.
In reality, Teixeira had the higher RBI total in 2009 simply because he had many more opportunities to drive a runner home.
If bizarre visualizations work for you, see if this will help your understanding of why the RBI doesn’t work as well as some would like:
Two candy-bar salesman are competing against each other for a month. The rules are simple: Each salesman will stay in their own respective neighborhood, and whoever sells the most candy-bars at the end of the month wins.
Seems simple enough, right? Let’s add another layer of complexity: Salesman A’s neighborhood is chock-full of wealthy, upper-class families who could easily buy a chocolate bar. In fact, most do. Salesman B’s neighborhood, meanwhile, is populated mostly with families barely scraping together enough money to feed their families every day. A candy-bar would be a pleasant distraction from their current situation, but most simply can’t afford the luxury.
Even though Salesman B is a far superior salesman than his counterpart, he obviously sells much less candy than Salesman A. The environment Salesman B was forced to work in put him at a clear disadvantage.
The same can be applied to Mauer’s MVP candidacy in 2009. The Twins put fewer runners on base than the Yankees, and Mauer was at a clear disadvantage. Teixeira had many more opportunities, and as a result put up a much-higher RBI total.
RBI, more so than almost any other stat, is very reliant upon context. Even though comparing and contrasting the raw data is probably not the most effective way to determine a superior offensive player, the RBI is still a useful statistic when taken with an extra dose of context. For example, some prefer to use the percentage of converted RBI opportunities, which is a far better statistical tool.
When used correctly, RBI totals can be a very effective tool for measuring offensive prowess. More often than not, however, the user fails to add in the critical dose of context, which can spoil the entire result.
If you prefer to use RBI totals to evaluate batters, don’t forget to at least take a glance at their environments.
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