by Bruce Johnson

Created on: August 13, 2009   Last Updated: August 16, 2009

In any sport, there will always be a question of who is 'the best'. After all, sport is, by definition, a competition. And competition demands that there be a winner.

In baseball, it is quite easy to get into (quite heated) arguments about who the best hitter of all time is. Or the best pitcher. Or the best fielder. And from the very beginning, it would seem that this could never be anything but a subjective discussion, with each person having their own idea of what makes a player the 'best' at whatever they excel at. But if your job is to evaluate players in order to put the best team together, using such subjective criteria is not an ideal situation. A better alternative would be to find a way to determine how 'good' a particular player is using an objective measure. It sounds like a difficult, if not impossible, goal to achieve. But this is exactly what Sabermetrics attempts to accomplish.

More precisely, the goal of Sabermetrics is to evaluate a particular player's past performance, as well as to predict the player's future performance. The term was originally coined by Bill James and is named after the Society for American Baseball Research (the SABR in SABeRmetrics). And while the idea has been around since the early 1970's (and become more prominent with Bill James' Baseball Abstract starting in 1977) it was not until the late 1990's when Sabermetrics became a part of the player evaluation process for Oakland and Boston that the term became commonly known.

The fundamental idea behind Sabermetrics is to identify those factors which are most likely to contribute to a team winning a game. For baseball, this is pretty obviously either runs scored or runs allowed are going to be the most important factors. But how does one determine the impact that a player has on either component. As it turns out, many of the frequently used statistics are not good predictors of success in terms of creating runs. Batting average, for example, is not good predictor. With a bit of thought, this makes sense. In the extreme case, a player could bat 1.000 (a hit every time at bat) and yet not score any runs if every other player on the team was striking out. Instead, Sabermetrics introduced terms like On-Base Percentage plus Slugging (OPS) and Walks and Hits per Inning Pitched (WHIP). When it comes to runs scored or runs prevented, it turns out that these statistics have a much greater predictive value than batting average or earned run average.

Whether you believe that Sabermetrics 'works' or not, there is no question that its introduction has had an impact on the game of baseball. And, in many cases, it is behind the decisions that determine whether a free agent should be signed or an aging superstar be let go. In other words, for baseball executives, Sabermetrics is most certainly here to stay.

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