#MLB STATISTICAL TOOLS FOR DATA ANALYSIS PROFESSIONAL#

There are seemingly endless sabermetrics that attempt quantify player performance. Even in orthopedic surgery and sports medicine, we use many of these same performance metrics to predict injury, outcome, and return to play. At the organizational level, teams continue to use sabermetrics to build efficient rosters and predict player performance based on a seemingly endless list of performance metrics. In fact, many have been hired to work for various MLB organizations. At the fan level, websites like FanGraphs, Baseballsavant, Baseball Prospectus, and Pybaseball have become increasingly popular where writers analyze baseball using sabermetrics. Today, sabermetrics has led baseball to become one of the biggest data-driven sports worldwide. Bean favored statistics like on-base percentage and slugging percentage to build a winning team with a limited budget. As a result, sabermetrics did not truly take hold until the early 2000s when the Oakland “Moneyball” A’s made their fourth straight postseason appearance under the guidance of their general manager, Billy Bean. These scouts have become deeply ingrained into the fabric of baseball culture and have long argued that their assessments must be considered in concert with objective data.

#MLB STATISTICAL TOOLS FOR DATA ANALYSIS PROFESSIONAL#

In particular, many teams rely upon professional baseball scouts, who watch hundreds of players and write reports containing their opinions as to the potential of these players. Initially, this movement was met with resistance as a disturbance to baseball tradition. The term sabermetrics, coined from the SABR acronym, refers to “the search for objective knowledge about baseball” -how best to succeed in an in-game situation, determine a player’s value, etc. The utilization of baseball statistics as we know it today did not really develop until 1974 when Dick Cramer, Bill James, and Pete Palmer co-founded the Society of American Baseball Research’s (SABR) Statistical Analysis Committee. Afterwards, there had been many notable attempts at strategically utilizing baseball data, including the Dodgers hiring of a statistician to their front office in the 1940s.

Based on how many runners are on base and how many outs there are, this model provided the probabilities that the batting team would score. Ferdinand Cole Lane, a biologist turned editor-in-chief of Baseball Magazine in 1912, developed one of the first run expectancy models in the 1920s. Since the 1920s, people have been trying to utilize baseball data to their advantage to predict outcomes and develop winning teams.

Sabermetrics: a Historical Perspective on Data and Analytics in Baseball

Mlb statistical tools for data analysis