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# What you can expect from Seattle Mariner bats in the second half

What can we expect in the second half? Don't ask me. What am I, The Seer?

However, we do have the statistical wizardry of Alex Chamberlain over at Fan/Roto/Insta-graphs who provides a desperately needed update to an equation which I've always particularly enjoyed: xBABIP.

For those of you who were just born, first of all, welcome. Sorry about the world.

Second, BABIP is, of course, Batting Average of Balls in Play (which should never be pronounced as an acronym, regardless of what most people will tell you). As we know, sometimes you hit them where they are and sometimes you hit them where they ain't. Sometimes you hit a laser beam right at someone and sometimes you cue it off the end of the bat and you wind up with a double down the line. That's baseball.

BABIP has always provided context for that batting average. But xBABIP takes that a step further.

Follow that link above for the Full Mental Jacket version of the genesis of xBABIP and the current iteration, but xBABIP is basically there to predict batting average on balls in play using tightly correlated batted ball data. It is the context to BABIP, which feels redundant, but it's still fascinating.

Should you prefer to skip the 1500 word narrative of xBABIP, then here's the short version: xBABIP = .1770 — .3085*(True IFFB%) — .1285*(True FB%) + .3684*LD% + .0798*Oppo% + .0045*Spd + .2287*Hard% + Year Constant (-.01015). If you want an explanation of True IFFB%, True FB%, and Year Constant, you'll need to put your glasses on and hit the article, not to mention a few informational links for background.

What does this mean for Mariner batters? Turns out, for most of them, not a whole lot. But there are some tasty bites in here:

 LD% GB% FB% IFFB% True IFFB True FB% Pull% Cent% Oppo% Hard% SPD xBABIP BABIP Cano 20.70% 45.00% 34.30% 8.50% 0.029 0.314 36.90% 34.60% 28.50% 35.60% 2.5 0.309 0.323 Cruz 16.90% 44.30% 38.80% 7.60% 0.029 0.359 40.50% 35.00% 24.50% 35.40% 1.4 0.281 0.322 Seager 19.90% 35.00% 45.10% 5.60% 0.025 0.426 40.50% 29.80% 29.80% 39.10% 3.1 0.305 0.299 Lee 23.00% 51.10% 25.90% 5.60% 0.015 0.244 33.80% 34.50% 31.70% 26.60% 0.8 0.305 0.307 Smith 21.00% 48.10% 30.90% 8.90% 0.028 0.281 41.20% 40.70% 18.10% 34.60% 1.7 0.301 0.310 Gutierrez 16.50% 54.60% 28.90% 7.10% 0.021 0.268 45.40% 35.10% 19.60% 42.30% 1.5 0.306 0.295 Martin 13.70% 42.90% 43.50% 12.30% 0.054 0.381 50.00% 27.20% 22.80% 30.00% 5 0.261 0.305 Iannetta 22.20% 39.80% 38.00% 16.90% 0.064 0.316 40.70% 35.50% 23.80% 34.30% 0.6 0.288 0.262 Lind 17.10% 42.50% 40.30% 9.60% 0.039 0.364 41.40% 27.10% 31.50% 39.20% 1.6 0.293 0.244 Aoki 16.70% 67.10% 16.20% 17.10% 0.028 0.134 27.80% 40.40% 31.80% 16.60% 5.2 0.289 0.275 Marte 23.90% 50.50% 25.70% 15.80% 0.041 0.216 44.00% 30.80% 25.20% 23.10% 5.7 0.313 0.320

By and large, Cano, Seager, Lee, Smith, Guti, Marte are all probably in the margin of error range for the difference between expected BABIP and actual BABIP not meaning much of anything.

Cruz, however has a negative .041 difference between the two metrics. That might be mostly due to a 16.9% line drive rate. For perspective, he saw a 20.4% line drive rate in 2015, which led to a .350 BABIP, highest of his career. That said, a .281 xBABIP isn't hardly awful, and over almost 5,000 plate appearances, Nelson Cruz has maintained a .307 BABIP. So I wouldn't expect any kind of implosion, and that we can expect the line drive rate to regress as much as his BABIP does.

Leonys Martin's xBABIP predicts something quite reminiscent of his 2015 output, which is not the Leonys Martin that we currently crush on. Martin's line drive rate is at the lowest of his career at 13.7% -- and in fact among active players, it is the second worst in all of baseball. You can have success even with a not fantastic line drive rate, but it typically takes a more complete hitter to do so -- not necessarily one with such pull tendencies as Martin. I like Martin a good deal, and I suspect he's an ongoing project for Edgar and crew, but I don't think the Mariners can survive on less offensive output by Martin.

Lastly is the guy which the data backs up what the whites of your eyes have been telling you -- Adam Lind has been getting the shaft. By way of the equation which places value on guys who can go opposite field and hit the ball hard, Lind is among the best of the group. It's not that the predictor expects he's going to bonkers in the at bats to come, it's just that he ought to have been seeing about league average BABIP where in reality it sat at a measly .244. Versus right handed pitchers in his career, Lind has posted a .311 BABIP and that's what we can probably expect for the rest of 2016.

Honestly, it's not so much what will happen more than what ought to have happened given the batted-ball data available. Martin and Cruz and others could vastly change their batted ball profile in the following months and the predictor will melt down like it was Major Arnold Toht staring into the Ark of the Covenant. Regardless, it's somewhat comforting that some of the good hitting we've seen hasn't arrived entirely on the back of lady luck and that we ought to see some improvement out of our left-handed platoon mate at first base.

Should you have issue with the equation referenced herein, I encourage you to take that up with the creator, not me, because Math is hard.

Goms.