Baseball Sabermetrics

Derek Lucente
Professor Allen
English Comp 1
December 16th, 2013

Sabermetrics Sabermetrics is the mathematical analysis of baseball records and data. Sabermetrics uses its own unique statistical categories to measure player performance instead of the more orthodox categories including: runs batted in (RBI), home runs (HR), and wins (W) for a pitcher. The theory behind sabermetrics produces data using complex formulas to determine the value of a player for the team as a whole rather than what his own individual end-of-season statistical line indicates. By far, baseball has both the longest seasons and the longest games, making it extremely hard to evaluate data from a single game at the beginning of a season and develop a prediction
A player was evaluated using averages from the following categories: hits, runs, runs-batted-in, homeruns, and stolen bases. While somewhat effective, these can be very linear at times considering the amount of variables involved within the game of baseball. If one looks at a player such as David Ortiz in 2003, his regular season stats alone would leave out much of what Ortiz did as a whole. While above average for a power hitter, his regular season batting average of .309 pales in comparison to the .760 average he achieved in the World Series. Statisticians began to realize that a blanket average of an entire baseball season was not enough to judge the worth of a player, thus the creation of more situational-based categories. Baseball began to look at a player for their strengths in particular situations rather than clumping all data to create a skewed average that does not reveal their usefulness in a specific role for the team. Sabermetrics takes these numbers and refines them as they pertain to filling a specific hole or a gap in a team’s
or Frank Thomas, for a particular team. However, like any theoretical system, it cannot guarantee victory one hundred percent of the time. After all, not every team can win the World Series every year. Again, the 2002 Athletics serve as a perfect example, winning twenty games in a row only to meet their demise upon entering the post season. Like any system based on projections, unforeseen events hold the potential to destroy any and all predictions of performance such as injury, overall health, and performance enhancing drugs. All three of these categories make using mathematics to evaluate players an imperfect science. For instance, former Red Sox player Jacoby Ellsbury provided a top-tier on-base-percentage of .426 this past season, contributing to a World Series victory. However, this statistic did little to help the team during the 256 games he missed due to injury from 2010 to 2012, an unpredictable