Big Data and Baseball: How Baseball Statistics Drive Major League Success
The crack of a bat, the roar of a crowd and the renewed hope of chasing the pennant: baseball is back. As ballparks across the country celebrate opening day, number crunchers nationwide will mark a new season of statistical analysis with unique acronyms ranging from RBI to VORP that may help predict a team’s run at the World Series.
The amount of data in sports continues to grow and the analysis of large data sets—AKA big data1 — remains an integral part of America’s favorite past time, from Henry Chadwick’s box score in the mid-19th century2 and the first use of a computer to compile baseball statistics in MacMillan Publishing’s “Baseball Encyclopedia” in 19693 to today’s sabermetrics which include OPS and WAR statistics.4
Introduced in 1980 by Bill James, sabermetrics is the search for objective baseball knowledge.2 These statistics go beyond traditional numbers such as batting average and earned run average to measure a player’s overall value and effectiveness, changing the game in major ways5. Made popular by Billy Beane, who managed the Oakland Athletics’ movie-worthy winning season6, the use of sabermetrics has transformed ball clubs, creating front office data management careers that are as key to a team’s winning formula as record-chasing sluggers.5
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Glossary of Baseball Acronyms:
RBI – Runs Batted In, a hitting statistic7
VORP – Value Over Replacement Player, or the number of runs a player contributes beyond what a potential replacement could be expected to contribute if given the same percentage of team plate appearances8
OPS – On Base Plus Slugging, or the sum of a player’s on-base percentage and their slugging percentage, accounting for all the different aspects of offense: contact, patience, and power9
WAR – Wins Above Replacement, or how many much value a player offers the team compared to a potential replacement player10