Pitching's cryptic nature has caused many headaches over the years, with fantasy owners representing a large portion of sufferers. We're here today to investigate how K-BB rates can alleviate the pain.
Instead of just looking at how often a player's at-bat ends in a strikeout, we can now see how frequently they swing and miss. Why limit ourselves to evaluating the player solely by the end result when there are many data points from within the at-bats?
If a player is hitting the ball hard, you should be paying attention. If poor surface statistics mask a strong batted ball profile, or vice versa, then there is a large profit margin to be capitalized upon.
Advanced baseball stats, or sabermetrics, are an integral tool for fantasy baseball managers. You don't need multiple advanced degrees in applied mathematics to use advanced baseball stats effectively, and this article will be strict a "no math zone."
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.