With another baseball season underway, I've been reflecting on the revolution within the game. Since Michael Lewis' Moneyball hit the shelves, baseball analytics departments have become the "thing" in front offices all around the game.
Coaches, scouts, GMs and fantasy baseball players are quickly changing their strategies to incorporate more player and team stats. Everyone is talking about it, everyone is embracing it, and everyone has seen the movie, Moneyball.
Although soccer has lagged behind other sports, such as baseball and basketball, in making broad use of data and analytics, the 2014 World Cup brings up numerous examples of data making an impact on many different aspects of the game.
With Major League Baseball's All-Stars now officially announced in the form of an awkward studio TV show, it seems like a perfectly good time to ruin the buzz of this year's All Star Game and instead, focus on the negatives.
The particular technique that we employ here attempts to profile an all-star player by identifying characteristics common to most players that make the team. For this study, we examined batting average, RBIs, etc., along with country of origin and the number of Twitter followers.
The playoffs being what they are, we knew that only one team -- and its fans -- would actually be happy when the whole thing was over. So what did the Tigers and all the other "losers" (and yes, that includes the Yankees) learn from the playoffs?
Grab a mass of data, like the 2011 baseball season's results. How can you glean useful information from all the numbers? This question sits squarely in the field of data mining, which is the science of extracting useful information from large sets of data.
As a fan, I sense a growing rift in the way baseball is discussed and appreciated -- those who hail the system of analytical tools as a godsend are younger, while those who decry it as hogwash are older.