The term sabermetrics feels as ubiquitous with today’s baseball as hits, strikes and home runs, and its influence can be found in just about every strategic decision that takes place in the game. But what exactly is sabermetrics, and how did it come about?

The truth is people have interpreted baseball statistics in creative ways dating back to Henry Chadwick’s first printed statistical lines in the 19th century. F.C. Lane, a biologist-turned-baseball magazine editor, developed perhaps the first example of a run expectancy model (probabilities a team will score based on how many runners are on base, and how many outs there are in the inning) in the 1920s. Branch Rickey famously hired an internal statistician for his Dodgers clubs in the 1940s. Giants manager Alvin Dark pioneered his own system of pluses and minuses to evaluate his players in the early 1960s, while Orioles skipper Earl Weaver employed an index card system to figure out his matchups, based on pitcher- and batter-handedness, around the same time.

All these practices fall under the sabermetric umbrella, but sabermetrics as we know them today began with the Society of American Baseball Research’s Statistical Analysis Committee -- a group co-founded by researchers Dick Cramer, Bill James and Pete Palmer in 1974. James in particular defined the term sabermetrics (taken in part from of the Society’s acronym, “SABR,”) as "the search for objective knowledge about baseball,” and he is known as the paternal figure of the movement. SABR’s internal discussions were first brought to the public with the first “Bill James Baseball Abstract” publication in 1982.

At its core, sabermetrics asks questions about how baseball is played and most efficient ways to succeed, and then goes about trying to answer them through empirical research. That can include anything from a simple data query (i.e. how do Major League hitters fare on 1-2 counts, as opposed to 2-1?) to something more abstract like, “What makes a player valuable to his team?” Every answer must be backed up with hard, quantifiable evidence, and that’s where sabermetrics can brush up against baseball strategies built more on feel or tradition. Because of this friction, James and other sabermetricians were largely mocked or ignored by many big league decision makers during the 1980s and early '90s.

As with most ideas that are new or that challenge the status quo, sabermetricians’ theories took years to gain favor in front offices. But the tide turned in a visible way when the “Moneyball” A’s favored metrics like on-base percentage and slugging percentage to build an unconventional, low-budget roster that found success during the 2002 season. The Red Sox hired James for their front office the following year, and they subsequently broke their decades-long “curse” to capture two World Series titles by the end of the decade. In recent years, the Rays have continually outperformed their payroll expectations by employing sabermetrics to build the most efficient rosters possible.

But sabermetrics today has expanded beyond James’ small circle of avid readers or the closed doors of front offices. Popular websites like FanGraphs and Baseball Prospectus have fostered communities in which writers analyze baseball on a daily basis, and so too do their readers in the comment sections and on fan pages on social media. Many of those sites’ analysts have gone on to work for Major League clubs in the style of James’ hiring to the Red Sox.

As analysts have transitioned to baseball’s front offices, sabermetric thinking has exerted a noticeable impact on how the sport is played. The entire language of evaluating player success has indeed changed, from batting average and RBIs to newer-age statistics like Weighted On-base Average (wOBA), Fielding Independent Pitching (FIP) and Weighted Runs Created Plus (wRC+). While these metrics can sound like a mouthful to the casual fan, they’re simply the next evolutionary step in evaluating which players help a team accrue the most wins and avoid the most losses.

Just as Chadwick’s statistics morphed into the new-age measures above, those metrics are already evolving again thanks in large part to emergence of Statcast player tracking technology. Statcast’s cutting-edge cameras and radar systems can instantaneously measure the movement taking place on the diamond -- from how fast a pitcher throws a baseball and the kind of spin he puts on it, to how hard a batter hits the pitch and how quickly the fielders react -- to paint a more comprehensive understanding of what’s actually taking place than observers have ever had access to before. Each passing year after Statcast’s public launch in 2015 has created an incredibly dense pool of tracking data that’s given life to new expected outcomes, like catch probability and expected batting average, that measure how players should fare based on similar events from the past. In the span of one half-decade, player tracking technology has transformed front offices’ mindsets and revolutionized scouting departments’ criteria once again.

That is the beauty of sabermetrics. Baseball’s simple rules give it the appearance of a simple game, but its ever-growing history, and the layers upon layers of decision-making and reactions that players enact on every pitch, create a seemingly infinite amount of data to pore through and questions to ask. A sabermetric mind dives into that immense challenge, seeking new and exciting discoveries from one of America’s oldest pastimes.