In the early 2000s, baseball changed forever when a scout for the Oakland Athletics franchise decided to use data to make leaner decisions while recruiting. The idea was simple: rely on statistics rather than human judgment in order to project which players would create long-term value for a franchise—then scout from the Minor Leagues accordingly.
The movie Moneyball, based on the life and decisions of Athletics’ general manager Billy Beane, helped popularize the concept to sports fans around the country. In reality, the idea of using stats to fuel decisions on the diamond has been around since the 1960s, when a man named Earnshaw Cook penned a book about the intersection of stats science and quality baseball.
In 1971, the Society for American Baseball Research (SABR) was founded by Bill James, establishing the field baseball fans know as sabermetrics. What continued with the Athletics under Beane was only a long-term extrapolation (and experimentation) with quantitative research and professional sports.
Changing the Game: Before & After Sabermetrics
Other fields of non-sports competition, were undergoing similar thought experiments in the 1960s and 70s around the time SABR took off. These focused on card and casino games, which also rely on mathematical systems and equations to build out strategies. Roulette pros, for example, rely on probabilities and percentages to glean an edge on the house, then exploit these odds depending on variables.
Though sabermetrics has to deal with unknown human variables versus roulette’s ever-consistent spinning wheel, Beane’s work proving the value of stats and pro sports has been far-reaching. In fact, it’s even contributed to a huge shift in how basketball is played—today, the sport emphasizes shooting accuracy and three-pointers to gain a statistical edge on the competition.
Over twenty years in from the application of stats in the MLB, where does sabermetrics fall today? Regardless of whether baseball fans love it or hate it, the practice is here to stay.
Building on the Fan Experience & Intelligence
When looking through the history of sabermetrics, it’s clear that early interest in a stats-based approach came from fans. Compared to other pro sports leagues in North America, the MLB has a dizzying amount of stats to work through. For some fantasy leaguers, this is a full-blown obsession—and the introduction of sabermetrics into the MLB only makes following the league more enriching.
Stats like wins above replacement (WAR), on-base plus slugging (OPS), and walks and hits per inning pitched (WHIP), present a unique challenge for armchair MLB fans. They want to glean the truth hidden in the data, then forecast a future star player before anyone else.
The reality today is that sabermetrics has been co-opted by billion-dollar franchises that want any leg up on the competition. In the hands of front-office executives rather than fans, sabermetrics is used to make cut-and-slash decisions for many teams.
Photo credit: Bill Stephan, Unsplash
Sabermetrics fans will harken back to the fact that data can be used to round out a fuller picture of baseball. Others might instead look at how a stats-first approach might eliminate chance or create room for misinterpretation.
The first point relies on the idea that human talent can’t be easily quantified with numbers. For example, take a player like Eduardo Escobar. After a sudden call-up from the White Sox without any background in the minor league in 2011, he would eventually go on to sign a two-year deal worth $20 million ten years later.
Sabermetrics can’t quantify the value for certain players like Escobar who lack early stats. They also don’t account for major setbacks (or comebacks), like those from the days of Rick Ankiel. And beyond a failure to predict human error or unexpected stars, the data in sabermetrics has also seen misinterpretation—especially in the early years.
Creating Room for Misinterpretation
Metrics like on-base percentage became invaluable for ranking hitters, while others, like the ability to get the ball in play or hit a homerun wasn’t weighed with the same care. The conclusions became lopsided. In other words, data in sabermetrics only has meaning as professional analysts assign meaning.
In the birth of a new industry, this is still being hashed out. At least today, there’s less confusion about what stats like ERA and wins shared mean for a team. Still, there’s much to learn when it comes to big data in baseball.
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