Players are using “advanced analysis” to improve? Seems like a stretch

A recent article in ESPN the Magazine titled Saviormetrics described how Brandon McCarthy, current Oakland A’s starting pitcher, utilized advanced baseball metrics to shape his game as a mediocre pitcher to a diamond-in-the-rough.  I do believe it’s important for players to use the data available on them in conjunction with video to make improvements in their as I mentioned in a previous post, but this article seems to suggest McCarthy used some advanced analysis to improve his game.  In fact, one of the more absurd quotes in the article is when they compare McCarthy to Beane:

What Billy Beane was to GMs, Brandon McCarthy is now to players.

That seems like a very far stretch, if we’re to believe the article’s take on his recent development.  From the looks of it, Brandon saw that he was giving up too many homers, a fact he gathered from a high HR/IP, and relying too much on flyball outs, which he took from his low GB/FB ratio.  So, he analyzed that information and came to the conclusion that he needed to add a grounball-inducing pitch to his arsenal, a two-seamed fastball.  After adding it, he began to see a surprising amount of success and, ultimately, his amazing 2011 season.

That’s an acute analysis using some straight forward statistics.  That said, it seems like a stretch to me that a pitching coach couldn’t give him the same advice using only the basics of scouting.  According to the article, it’s all thanks to these two statistics that he was reborn.  I’m not suggesting that he shouldn’t be using those statistics to inform how he can better improve, or even saying that by understanding those metrics he didn’t find a method of development; my point is that he didn’t have to use anything outside of what has already been known in the “clubhouse,” not the advanced analysis suggested here.

What Billy Beane did (with the help of Michael Lewis’ book) is shape how front offices analyze the way they evaluate talent, whether it’s in-house, other team’s or amateur.  Front offices had always used reasoning based on basic statistics and scouting when making their decisions; it wasn’t until that Oakland A’s front office built their team of “misfit toys” with no money that others adopted deep analysis of data.

In the end, I hope this article does encourage more players to look at deeper into their own data, but that doesn’t refer to GB/FB or HR/IP.  I’ll get excited when I see them fully utilizing Pitch F/X data or maybe even some of the catcher ERA stuff…

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I am Director of Data Science at Two Six Capital, Co-Founder of Krossover, Ph.D in Stats from Wharton and a proud Savannah-ian.

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