In my first blog of this semester, I discussed the benefits of technologically advanced cameras that can interpret data from the playing field. To elaborate on this field, it's important to discuss how MLB teams are using this knowledge. Sabermetrics is defined as the in-depth study of every in-depth baseball statistic, some of which are given by these cameras. Sabermetrics uses advanced functions and calculus applications to combine many traditional and advanced metrics (like the ones provided by the camera) to determine a new statistic that will more accurately assign a value to a given player. In the early 2000s, the Oakland A's implemented this new player analysis format to great success. A low market team, they lost 3 of their top players to free agency and didn't have enough money to sign any other expensive free agents. So, they quickly revolutionized their player analysis. Using sabermetrics, they determined that it is most vital to look for players that have a high on-base percentage (in other words, they reach with a walk more frequently). Overlooking traditional scouting of players, which involves the "eye-test" and batting average, they signed players that fit their model for mere pennies compared to the contracts given to the players they lost. With the lowest payroll in baseball, they won over 100 games. Today, Sabermetrics are used by virtually every team, leading to new techniques such as the increased usage of the defensive "shift" and less usage of the sacrifice bunt and stolen base.
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| More and more data is becoming available to GMs as Sabermetric continues to develop |
Relation to Computer Science:
Computer science has played a huge role for teams like the Athletics looking to gain a competitive edge. They used a program that told them how many games they would be projected to win with a given roster, which foreshadowed the implementation of WAR as an official statistic a few years later. If CS can project how well a certain team will do on the field, looking at traditional stats almost becomes useless. Today, a computer can even tell you how your team's ERA could change by signing a defensive wizard to play infield, because it knows all of his advanced defensive metrics, as well as those of the player he would be replacing. As the field of computer science continues to develop, more and more accurate ways of defining a player's worth will be realized, leading to more and more demand for the best possible programmers in MLB. Baseball is not the only avenue which should be in higher demand of them either, as companies world-wide have seen the benefits CS can bring, which leads to the whole issue of having too few CS majors for too many available jobs.
https://en.wikipedia.org/wiki/Sabermetrics#Calculus_in_baseball
https://en.wikipedia.org/wiki/Moneyball
