Big Data in Baseball

Just finished reading an excellent book I bought last May, but tucked away for a rainy day.  Okay, it wasn’t raining this weekend, but nevertheless … caught my eye again, and since it’s only 18 days until pitchers and catchers report to Spring Training, time to start gearing up!  The book is Big Data Baseball, and it centers on how the Pittsburgh Pirates turned around 20 years of losing (a North American record for consecutive years of sports futility) through a successful collaborative between number crunchers and old school baseball guys.  If you aren’t inclined to read the book, you can get a flavor for it by watching this video of the author’s presentation to the Pittsburgh Technology Council.

In a nutshell, low budget teams can’t buy winning clubs by acquiring star players. So what they’re increasingly doing is applying metrics to gain a competitive edge on aspects of the game that haven’t been widely discovered or applied yet.  It’s no coincidence that aside from aging, for example, the Phillies had three left handed hitters last year who were all dead pull hitters to RF – Ryan Howard, Chase Utley, and Cody Asche.  Teams used extreme shifts on all three of them, which knocked considerable points off their batting average and OBP.  Okay, Howard and Utley were riding off into the sunset anyway, but this certainly accelerated their hitting demise.  Asche is young enough that if he were able to use more of the field, he’d have a shot at prolonging his career.

So in essence the Pirates acquired hurlers who were ground ball pitchers, heavily employed the shift, and added a third edge which was obtaining a catcher skilled in the art of framing pitches.  The Pirates story centers on Russell Martin, who was the catcher they went after to aid their transformation.  After helping the Pirates to the post-season, he left for the Blue Jays.  But framing has become part of the Pirates culture, and they were at the top of the heap again in 2015 in framing stats with Francisco Cervelli having taken Martin’s place.  (Is it any surprise that the Phillies were near the bottom of the pack?)  Look at the second half of this short video, and you’ll see why it’s so easy to get enamored with these subtleties of the game.

As Travis Sawchik points out in Big Data Baseball, it’s not that these techniques are new.  It’s that with newer technology to record and analyze these nuances of the game and the wealth of data now available for number crunching, sabermetrics can quantify how good the various players are at these skill sets.  When a catcher frames a pitch and steals a ball from a strike, the difference between going 2-1 vs. 1-2 converts a hitter’s count to a pitcher’s count.  The typical hitter posts ups his average by 150 points when swinging on a 2-1 pitch vs. a 1-2 pitch.  And that is *huge*.

About Leonard J. Press, O.D., FAAO, FCOVD

Developmental Optometry is my passion as well as occupation. Blogging allows me to share thoughts in a unique visual style.
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2 Responses to Big Data in Baseball

  1. doctuhdon says:

    Another great post, Len. The video clips are outstanding. It’s good to see that the Phillies are finally entering into the world of analytics:

    Andy Galdi, a 30-year-old Google employee who was hired Thursday as the Phillies’ first-ever director of baseball research and development.

    “He’s the perfect guy to help grow us forward in this area,” Klentak said. “I’m excited about the fresh ideas that he’s going to bring. Baseball is a very traditional industry. It is very much a copycat industry. I’m excited about what somebody coming in from a place like Google, what outside ideas he’ll be able to provide and help us.”


  2. Thanks, Dan. Hopefully there is substance in the new order …

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