Much like any other industry, Major League Baseball’s organizations have historically been run by those with the most experience. Former players with an astute “knowledge of the game” became managers, while the most successful managers often went on to become general managers.
In 2002, a 40-year-old GM named Billy Beane constructed a low-budget Oakland A’s squad by using advanced statistics known as sabermetrics to evaluate players. That team went 103-59 and was the impetus for Michael Lewis’ 2003 book, Moneyball (made into a movie in 2011, pictured).
The Boston Red Sox attempted to lure Beane away from the A’s following Oakland’s spectacular 2002 season. When Beane declined Boston’s offer, the Red Sox made 28-year-old Yale graduate Theo Epstein the youngest GM in MLB history. Epstein’s playing career never extended beyond high school, but he was a stats geek who fully embraced sabermetrics.
A little more than a decade later, an astounding 11 general managers are products of Ivy League schools, with four coming from Harvard alone. Yet another went to MIT. That list doesn’t even include Epstein, who now serves as President of Baseball Operations for the World Series Champion Chicago Cubs.
“Not every team has an Ivy League whiz kid running things — Al Avila of the Tigers, for example, has a 25-year history in the game, primarily as a scout — but they all have an analytics department of various robustness and influence. In other words, the brain power in the game has grown in multiples since Lewis penned his ode to Beane.”
While Beane and the A’s were able to get ahead of the competition with statistical analysis 15 years ago, that’s no longer possible today. Schoenfield writes:
“It all suggests winning is harder than ever, and sustaining a high level of success even harder. Indeed, we can see this when comparing the five-year periods of 2011-2015 to 1998-2002. Conveniently, both periods had 42 teams win 90-plus games in a season, but what happened the following season reveals how smarter front offices have changed the game:
Teams in the more recent period not only had a lower average win total in the 90-win season, but suffered a bigger decline the following season, were less likely to win more games or to win 90 again and more likely to have a losing season.”
The title of Schoenfield’s piece is “Now that everyone is smart, GMs must go bold to succeed.” He makes the case that GMs need to take risks in order to put together winning ball clubs, by gambling with the future to succeed in the present. Schoenfield cites an example of the Boston Red Sox’s recent trade of top prospects Yoan Moncada and Michael Kopech for ace pitcher Chris Sale. While Sale could help guide Boston to another World Series in the near future, Moncada and Kopech might become stars for the next decade and beyond.
What if there’s another way to gain a competitive advantage that isn’t fraught with such uncertainties? A “Moneyball 2.0,” so to speak?
The next round of advanced statistical analytics may come from players’ physiological data. A recent Fangraph’s piece examining home-field advantage in baseball cited findings from the WHOOP MLB Performance Study, which observed a significant decline in players’ Recovery the day after traveling. Armed with this knowledge, teams can make efforts to ensure their players are properly rested.
The study also found a correlation between pitchers’ Recovery on game day and their fastball velocities, as well as with the speed of the ball off the bat for hitters. This is just the beginning of what can be learned from 24/7 physiological monitoring. As was the case with sabermetrics, franchises that embrace this before the rest will likely be the ones to get a leg up.