Author: Branden Stucky, Senior Engineer and CorrSolutions Team Leader
Baseball is arguably the most data-driven of all the major US sports, largely due to the sheer amount of data available in a 162- game season. In the 1970s, the baseball world coined the term SABRmetrics (now known colloquially as sabermetrics), which is the use of empirical data to measure in-game activity. Historically, it was thought that the team batting average (hits divided by the total number of at-bats) was the best metric to use when trying to determine a correlation to team runs scored. However, this measure doesn’t account for other ways of reaching base, so the metric of on-base percentage was created, which puts all manners of getting on base in the numerator and divides by the total number of plate appearances. From here, the world of sabermetrics exploded. The creation of slugging percentage, on-base plus slug, wins above replacement, and so many others were created to help teams understand not only the value of their players, but also how their players may perform against the competition. As they learned more about how to apply statistics and predictive models, computers grew from the old IBM S/360 with programs written in FORTRAN and Basic into cloud computing and current powerful languages like R and Python; teams are now learning how to take even more advantage of all the data they have been collecting over the past 100 years.
