xBABIP 2013 Style

Last year, I tracked Rockies players BABIP and then compared them to xBABIP to determine if a player's high or low BABIP was being driven by luck or jump/drop in actual performance. This is 2013's version.

As a review, or just to inform those who may not have read last year's articles here's an explanation of how these things work. Feel free to skip this and get to the actual data below if you're comfortable. BABIP is calculated using the following formula

$BABIP = \frac{H-HR}{AB-K-HR+SF}$

What BABIP does is calculate the number of percentage of hits from the balls put in play, or determine the "luck factor" of a player if you will. Now the league average tends to hover around .300 and it used to be though widely that all pitchers and hitters should normalize to a .300 BABIP. However, that's not quite true, as different profiles of pitchers and hitters tend to have higher or lower BABIP. So to determine a player's true "luck factor" you need to compare a player's BABIP to their expected BABIP (xBABIP).

A player with a .330 BABIP could actually be considered unlucky using this method if their batted ball profile produces a .400 xBABIP.

So to start, let's use a basic xBABIP formula

xBABIP = .15 * FB% + .24 * GB% + .73 * LD%

This one is fairly intuitive; it is derived by calculating the league percentage of hits on each type of batted balls. 73% of line drives result in hits 24% of ground balls and only 15% of fly balls. Now this does not mean that fly balls are worth less then ground balls due to them have a higher SLG%, but that's a topic for a different day. Now this formula is very effective for large sample size of hitters (like team xBABIP) and also for individual pitchers. However, since we're talking about individual hitters today I'll be using a more complex formula that was derived by slash12 from Beyond the Boxscore.

xBABIP =0.391597252 + (LD% x 0.287709436 ) + ((GB% - (GB% * IFH%) ) x -0.151969035 ) + ((FB% - (FB% x HR/FB%) - (FB% x IFFB%)) x -0.187532776) + ((IFFB% * FB%) x -0.834512464) + ((IFH% * GB%) x 0.4997192 )

Now obviously that's a lot more complex but it does tend to be more accurate for individual hitters. This is because it takes into account more of the individual players skill-set like speed out of the batter's box and how hard they hit fly balls. Now I would never want to calculate that manually, so instead I use excel spreadsheets to help out with the process.

I also like to keep in mind a batter's career BABIP (cBABIP) because while players don't always normalize to a .300 BABIP they do normalize to their cBABIP because it reflects their individual skill-set. This is especially true for players that are further along in their careers while players who are relatively young (think before age 27) are still developing and might have drastically changing BABIP from year to year as they improve or their holes are figured out by the league.

So enough of explaining (for a while at least) lets look at some numbers. But before we do, one big disclaimer; THIS IS A SMALL SAMPLE SIZE and because of that BABIP can be very misleading, one or two bad luck out or good luck hits and drastically change these number. However I think xBABIP is still a good stat at this sample size because it gives a nice picture of how a person is hitting the ball.

 Name BABIP xBABIP cBABIP Reid Brignac 0.333 0.257 0.289 Chris Nelson 0.360 0.342 0.344 Wilin Rosario 0.429 0.431 0.294 Carlos Gonzalez 0.367 0.309 0.348 Eric Young 0.235 0.369 0.317 Todd Helton 0.292 0.318 0.332 Dexter Fowler 0.286 0.378 0.352 Troy Tulowitzki 0.303 0.287 0.313 Michael Cuddyer 0.333 0.384 0.305 Josh Rutledge 0.229 0.315 0.303

So based on xBABIP, Rosario, Cuddyer and Fowler are probably hitting the ball the best right now, which isn't too much of a surprise based off the eye test. However, Rosario seems to be legit right now. Of course he's not going to sustain that .429 BABIP, however, we can see that it's legit for right now and not just luck based, so the expected drop in it will just be from him cooling off. Fowler's power is really carrying him right now, because on balls he's not knocking out of the park he's actually been pretty unlucky. However, the biggest tough luck guy right now seems to be EY2, a guy who profiles as a pretty high BABIP guy, who also has a nice high xBABIP, but sadly the hits just aren't falling yet.

The next step I like to take is to then see what would happen if all the players were hitting their xBABIP rather then their actual BABIP. I find this by solving the BABIP formula for hits, and then replace BABIP with xBABIP.

exH = HR + xBABIP(AB - K - HR + SF)

I then use this to find the expected slash line of the same hitters.

 Name xBA xOBP% xSLG% xOPS BA OBP% SLG% OPS Reid Brignac 0.214 0.255 0.325 0.581 0.278 0.316 0.389 0.705 Chris Nelson 0.259 0.339 0.259 0.599 0.273 0.351 0.273 0.624 Wilin Rosario 0.362 0.396 0.695 1.091 0.361 0.395 0.694 1.089 Carlos Gonzalez 0.292 0.393 0.578 0.971 0.333 0.429 0.619 1.048 Eric Young 0.285 0.371 0.376 0.747 0.182 0.280 0.273 0.553 Todd Helton 0.288 0.332 0.455 0.787 0.267 0.313 0.433 0.746 Dexter Fowler 0.353 0.437 0.779 1.215 0.298 0.389 0.723 1.112 Troy Tulowitzki 0.312 0.385 0.612 0.997 0.325 0.396 0.625 1.021 Michael Cuddyer 0.373 0.428 0.659 1.087 0.333 0.391 0.619 1.010 Josh Rutledge 0.261 0.286 0.348 0.634 0.196 0.224 0.283 0.507

Because I use ISO to help calculate the xSLG% part of this I did have one extra comment to make, Nelson needs to get some extra base hits soon, that .000 ISO is kind of painful to look at.

As always, I'll be hanging around in the comments section, so if you have any questions about any of this just post and I'll do my best to help.

CITF

Eat. Drink. Be Merry. But the above FanPost does not necessarily reflect the attitudes, opinions, or views of Purple Row's staff (unless, of course, it's written by the staff [and even then, it still might not]).

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