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Can teams rely too heavily on analytics and sabermetrics?

Sabermetrics have always been a source of controversy in baseball, a trend that isn't likely to change. But for teams who do embrace analytics, can Moneyball go too far?

Butch Dill-USA TODAY Sports

Red Sox owner John Henry recently raised a lot of eyebrows by suggesting that his team, which famously used sabermetrics to find the likes of David Ortiz, had become too reliant upon analytics. After letting GM Ben Cherington go, they hired former Tigers GM Dave Dombrowski, who was relatively slow to embrace the sabermetrics trend in the Motor City.

Meanwhile, in Philadelphia, the Phillies hired Matt Klentak to be their GM, specifically hoping he can effectively ramp up the analytics department. While teams like the Oakland A's and the Tampa Bay Rays have managed to sidestep their low payrolls and succeed with the help of sabermetrics, clubs like the Arizona Diamondbacks have given a metaphoric middle finger to Moneyball.

The debate surrounding the validity of analytics in baseball is not a new one, and it won't be going away soon. The Kansas City Royals bunted, stole bases, and even had a leadoff hitter who openly said he would always swing at the first pitch, all things that have traditionally been discouraged by sabermetrics. Still, one might argue that their commitment to defense and a strong bullpen, in an age of declining offense and increasing strikeouts, is in fact an analytics-minded approach to building a contending team.

So can teams be too dependent on analytics? In my opinion, this is a bit of a trick question. A team can be at the forefront of the ever-evolving big data industry, but information alone isn't always enough. Even the process of executing an analytics-backed deal can take more than just crunching the numbers. Take the Yankees' acquisition of Nick Swisher as an example. In 2008, he had a disappointing .219/.332/.410 slash line with the White Sox. The Yankees, noticing his unusually low .249 BABIP and other anomalies, thought he was a candidate for a bounce-back season.

From 2009-2012, Swisher provided a very respectable 14.6 WAR for the Yankees. From a sabermetrics perspective, it was definitely a great trade. But more importantly, Brian Cashman managed to acquire Swisher for a utility player and two prospects who never spent significant time in the big leagues. The fact that Cashman actually managed to convince the White Sox to sell low on Swisher is very impressive, as teams rarely want to trade a player when his value is at its lowest.

In order to be successful, major league GMs need to do much more than look at the numbers. Working with the rest of the baseball operations department takes people skills and leadership. Executing the right trades and free agent signings requires an understanding of negotiating and leverage. Dealing with constant scrutiny from the fans and the media might not factor into the decision making process, but it is definitely an important part of the job. Scouting is an entirely different story.

At its core, having an analytics department is really about being informed. Generally speaking, knowing more than your opponents is a good thing. But a strong quantitative background is only as useful as the other facets that complement it. When the Astros traded for Collin McHugh because they thought his curveball's spin rate hinted at unseen upside, it was a great acquisition. They didn't reach the playoffs with a roster full of Collin McHughs though, and their hiring of former Baseball Prospectus scouting guru Kevin Goldstein exemplified the mix.

Instead of trying to find the perfect ratio of analytics to scouting, front offices should focus on trying to be the best at every facet of the game. It might mean pitting the smartest stat heads against the most perceptive scouts, but that really isn't the worst problem to have.