Goldstein: Baseball is bringing sports analytics to the forefront

From Phil Goldstein at BizTech Magazine on July 11, 2017:

What can data analytics tell a major league baseball team? These days, pretty much everything.

In early May, when veteran outfielder Curtis Granderson was struggling with a batting average that had dropped to .122, the New York Mets kept him in the lineup.

“That night, Granderson doubled twice to snap a 3-for-49 slump. In the following game, he parked his second home run,” SportTechie recently reported. “In the last two months, he’s hit 12 home runs with a .299 average and 1.037 on-base plus slugging percentage (OPS) that ranks in the National League’s top-three.”

What helped push Mets Manager Terry Collins to keep Granderson in the lineup? Despite Granderson’s surface offensive statistics being abysmal, “his underlying advanced metrics showed good process,” SportTechie reports. And Collins had faith in the slugger.


The secrecy surrounding each team’s analytics operation makes data security a paramount concern. Teams consider such technology to be proprietary and do not want anyone stealing their data or methods.

Some teams have divulged a few details. When asked last week at the annual convention for the Society of American Baseball Research if the Mets used neural networks and other machine learning technology, Joe Lefkowitz, the team’s senior coordinator for baseball systems, appeared caught off guard, according to SportTechie, and replied, “Umm … yes?” 

Lefkowitz said that it’s a “technique we use,” without going into specifics.

“It’s obviously a big part of our department’s purpose for existing: We’re trying to use the most advanced statistical techniques we can that hopefully not all the other teams are using,” he said.

Read the full article here:

Originally published: July 11, 2017. Last Updated: July 11, 2017.