Sabermetrics is the empirical analysis of baseball, especially baseball statistics that measure in-game activity. The term is derived from the acronym SABR, which stands for the Society for American Baseball Research. It was coined by Bill James, who is one of its pioneers and is often considered its most prominent advocate and public face. (wiki)
Sabermetrics has revolutionized baseball. The game is being approached as a science. Throughout the game, statisticians are applying analytics to speed, strength, power, OBPs--everything they can evaluate to get an edge in the drafting of players, in matching them up on teams and matching them against each other.
Yet, when it is all said and done and all the information is in, they still play the game. Why?
First, unlike physics and other natural sciences, the variables the statistician can measure may not be the important ones. So they emphasize the few things they can measure because they are easier to study. No statistician would estimate that Kansas City would have been successful with their aggressive approach to hitting in the World Series last year. Their persistent success in the tournament last year was simply off the charts, in violation of most sabermetric hitting laws.
Second, while the statistician can produce general predictions, unlike physicists they cannot generate precise results. No statistician would estimate that Harrison would be Pittsburgh's leader in RBIs or McCutchen would be hitting .225.
The third reason baseball differs from physics relates to the first two: the information necessary to test detailed baseball predictions is impossible to gather. Usually right-handed hitters have an advantage against left-handed pitchers but that is not true with the Pirates' left-handed Watson who is much more effective against right-handed hitters. There is no material difference between the very average Arrietta from 2013 and the devastating Arrietta from 2015. St. Louis' Diaz' success had no augury in the minors. These distinctions are simply not analyzable. The factors are just not understood.
So we analyze as well as we can but still play the game because we know that our information, however voluminous, is flawed and incomplete. That is why we enjoy the competition after everything is analyzed: Significant uncertainty remains.
Now read the above again and substitute "economics" for "baseball."
Sabermetrics has revolutionized baseball. The game is being approached as a science. Throughout the game, statisticians are applying analytics to speed, strength, power, OBPs--everything they can evaluate to get an edge in the drafting of players, in matching them up on teams and matching them against each other.
Yet, when it is all said and done and all the information is in, they still play the game. Why?
First, unlike physics and other natural sciences, the variables the statistician can measure may not be the important ones. So they emphasize the few things they can measure because they are easier to study. No statistician would estimate that Kansas City would have been successful with their aggressive approach to hitting in the World Series last year. Their persistent success in the tournament last year was simply off the charts, in violation of most sabermetric hitting laws.
Second, while the statistician can produce general predictions, unlike physicists they cannot generate precise results. No statistician would estimate that Harrison would be Pittsburgh's leader in RBIs or McCutchen would be hitting .225.
The third reason baseball differs from physics relates to the first two: the information necessary to test detailed baseball predictions is impossible to gather. Usually right-handed hitters have an advantage against left-handed pitchers but that is not true with the Pirates' left-handed Watson who is much more effective against right-handed hitters. There is no material difference between the very average Arrietta from 2013 and the devastating Arrietta from 2015. St. Louis' Diaz' success had no augury in the minors. These distinctions are simply not analyzable. The factors are just not understood.
So we analyze as well as we can but still play the game because we know that our information, however voluminous, is flawed and incomplete. That is why we enjoy the competition after everything is analyzed: Significant uncertainty remains.
Now read the above again and substitute "economics" for "baseball."
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