Were Analytics the Goat or the Hero of the Red Sox-Yankees Series?

Were Analytics the Goat or the Hero of the Red Sox-Yankees Series?

MLB

Were Analytics the Goat or the Hero of the Red Sox-Yankees Series?

The New York Yankees lost to the Boston Red Sox in a American League Divisional Series. Both teams are very good. That’s why each won over 100 regular season games. The better team likely prevailed after going into the Bronx and taking the final two contests, including a nail-biting Game 4.

The Yankees postmortem began the moment Steve Pearce kept his foot on the bag to secure the final out. YES’ Michael Kay took an interesting angle on yesterday’s show when he suggested analytics were at least partly responsible for New York’s demise.

Kay is absolutely correct in this respect: the game’s played by human beings and certain things can’t be quantified. Not all outs are created equal and it takes a special breed to close. But I am left to wonder — and perhaps others are too — how the Yankees failure to overcome a 4-1 ninth inning deficit against Craig Kimbrel can be attributed to an over-reliance on analytics.

One can argue that Giancarlo Stanton and Gary Sanchez should have had better two-strike approaches. Isn’t that more of a player attitude issue? And let’s not forget, Sanchez came about 10 feet short of blasting a game-winning home run.

The real question I have, though, is how we can have reactionary takes to the complicated field of numerical data when both teams playing are committed to using them. If being too concerned with analytics are why the Yankees lost, is that also why the number-crunching Red Sox won? If so, how can that be?

Critically thinking, analytics aren’t a zero-sum game. I’m not sure pundits understand that after all these years. It is possible that they would give both teams a greater chances to win or a greater chance to lose. This, of course, pretends it’s easy to discern the “goodness” or “badness” of using them based on result.

We’ve come a long way in understanding and accepting the new, sabermetric-friendly world. We have a long way to go in using it as fodder for the masses not particularly interested in the nuances. There may not be a good way to use it for above-average content and commentary.

A part of me remains skeptical that most people want to hear about it. There’s a chance using it as a boogeyman or scapegoat is the most useful, though not entirely honest, thing to do.

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