How the Yankees created a dynamic analytics department

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The Yankees went from payroll to analytics juggernaut over the past decade.

How do you survive as the General Manager for the New York Yankees for over 20 years?

“You have to grow a real thick skin and strong mental toughness.” —Brian Cashman

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While those attributes are merely the prerequisites for working under George Steinbrenner, the main reason for Cashman’s longevity as general manager is his eagerness to adapt. The turning point for the New York Yankees investment in analytics coincidentally came after losing to the Boston Red Sox in the 2004 ALCS after leading three games to none.

Once Cashman experienced his team’s competitive disadvantage due to the effects of the analytics explosion, he knew things had to change. The work of Theo Epstein caught his attention, because Boston’s roster seemed to be performing better than he expected. It was clear that the Red Sox created an advantage in their advanced scouting department using analytics to determine how to attack the Yankee hitters. Epstein also utilized analytics for player procurement in the draft as well as determining which key free agents to sign.

Cashman saw a deficiency in several key areas of the Yankees front office and went on a personal crusade to fix it. He completely changed how their pro scouting department functioned through the use of analytics. Cashman hired Michael Fishman, now the Yankees Assistant GM, as the team’s first analyst back in 2005 who helped create a quantitative analysis team. It’s now one of the largest in MLB employing over 20 analysts.

The Yankees bullpen is arguably the best in baseball, featuring key pitchers like Zack Britton, Adam Ottavino, Aroldis Chapman, and Dellin Betances. The analytics department prioritized relief pitchers that have high velocity and spin rates on their four-seam fastballs, often resulting in strikeouts. This is especially important in high-leverage situations later in a game.

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Fishman began changing the scouting department by creating a database that incorporated video analytics and qualitative analysis from scouts. This initiative eventually resulted in their proprietary analytics system which was called the Baseball Analysis and Statistics Engine (B.A.S.E.). There were several instances where B.A.S.E. would value players higher than scouts based on the system’s advanced metrics such as OPS or wOBA.

It should be noted that there was a time in MLB where home runs weren’t king. The reason that baseball is filled with so many home runs and strikeouts is because of progressive analytical departments like the Yankees. In an effort to create more efficient runs, Fishman and his team started to primarily focus on hitters who had either a lot of power or a high OBP. Nothing in between was seriously considered because it didn’t fit their model.

As time has gone on, seemingly every team has copied this exact method, which has caused the competitive pendulum to swing in the other direction. One of the ways the 2018 Red Sox cruised their way to another World Series championship was because of their ability to make quality contact, particularly with two strikes. The team prioritized players who were a tough out with two strikes, which benefited them immensely as it put constant pressure on the Dodgers. Due to his high contact rate, the acquisition of Luke Voit was the result of this new strategy implemented by the team’s analytics department.

Their army of analysts and Cashman’s relentless pursuit of excellence is why the Yankees are a favorite to win the 2019 World Series, even with their recent spate of injuries. It’s well known that they’ve always had one of the largest payrolls in baseball, but their recent success isn’t because of dollars: it’s data.

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