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How Robinson Cano beat the shift

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Oakland Athletics v Seattle Mariners Photo by Stephen Brashear/Getty Images

Throughout this entire series of articles on defensive shifts, we’ve been examining team-wide data. The common refrain has been, “the data isn’t very granular,” so the analysis has been necessarily shallow. Luckily, FanGraphs has recently made their individual player splits incredibly customizable. This granular data that we’ve been striving for is now right in front of us. We can now see how a particular batter fared when pulling groundballs into the shift.

First, let’s take a broad look at the four Mariners who put more than 200 balls in play when facing a shift in 2016:

The names probably aren’t a surprise but look at those BABIPs. Each one of them fared better when facing a shift. Some of the sample sizes are pretty small but it’s encouraging to see that these hitters weren’t dramatically impacted by the defense.

There is one line above that stands out above the rest, Robinson Cano’s. Not only is there an almost 50 point difference in BABIP when facing a shift, he’s doing more damage with those hits. Out of the four players above, he’s the only one who is hitting the ball to the opposite field more often when facing a shift. Robbie is an incredible hitter who has enough bat control to execute his plan at the plate more often than not.

Let’s dig a little deeper into Cano’s performance. Here’s a table with his BABIPs while the defense has a shift employed. I’ve split them into nine buckets—the three batted ball types split by direction. The number of balls in play for each bucket are in parenthesis.

I don’t think any of this data is surprising. When facing a shift, Robbie was most successful when spraying line drives to all fields. But when he hit the ball to the opposite field, more than 40% of them fell in for hits. Even when he was hitting groundballs into the shift, he fared better than I would have expected. We don’t have any league averages for any of these granular splits so it’s hard to come to any hard conclusions.

Since I haven’t had much time to play around with this data for individual players, this article is somewhat of an experiment. I’ve laid out the data as I see it but I need your help for where to go next. What questions does an exercise like this bring up? Are there aspects of analysis you feel like I’ve missed. Let me know in the comments and I’ll do my best to follow up.