So now that the calendar has turned over to May, I though it might be a good time to post the current xBABIP calculations for the Rockies as well as their related adjusted stats. If you have read any of my previous posts from last year or this, I'd encourage you to read my April post so you have an idea of how I arrive at these calculations. I'll spend more of this post discussing implications of the data.
To start it off here's the players BABIP chart.
Name |
BABIP |
xBABIP |
cBABIP |
dBABIP |
Reid Brignac |
0.333 |
0.300 |
0.290 |
0.033 |
Chris Nelson |
0.333 |
0.314 |
0.342 |
0.019 |
Wilin Rosario |
0.375 |
0.394 |
0.299 |
-0.019 |
Carlos Gonzalez |
0.360 |
0.307 |
0.348 |
0.053 |
Eric Young |
0.354 |
0.339 |
0.324 |
0.015 |
Todd Helton |
0.294 |
0.316 |
0.332 |
-0.022 |
Dexter Fowler |
0.338 |
0.341 |
0.352 |
-0.003 |
Troy Tulowitzki |
0.303 |
0.297 |
0.313 |
0.006 |
Jordan Pacheco |
0.392 |
0.359 |
0.343 |
0.033 |
Michael Cuddyer |
0.346 |
0.382 |
0.306 |
-0.036 |
Josh Rutledge |
0.250 |
0.331 |
0.297 |
-0.081 |
xBABIP = expected BABIP, cBABIP = Career BABIP, dBABIP =BABIP - xBABIP
So let's first look at a few players who are absolutely destroying the ball, Cuddyer and Rosario. These two Rockies are carrying the highest xBABIP. This generally means they are hitting the ball the hardest. Now, they are both carrying a negative dBABIP which would normally say they're being unlucky, however chances are better their xBABIP will normalize to their cBABIP as they cool off from a hot April, then they'll maintain an xBABIP that high long enough for a normalization of BABIP to xBABIP. So the verdict on these two is: expect a regression, not because of luck but just because they will cool off, however they'll still have respectable numbers.
Next is Tulowitzki and Fowler, two Rockies who are performing about as you'd expect, based on their dBABIP being very close to 0 while also performing near their career numbers. It is tough to predict much here for these two, they could stay steady or go on a hot streak or cold streak as baseball players are prone to do before normalizing again.
Carlos Gonzalez and Jordan Pacheco have the highest positive dBABIP's suggesting they're being the luckiest right now. For Pacheco, I'd expect a regression from because his BABIp is not just astronomically high but his xBABIP and cBABIP and both fairly close together suggesting that's about what to expect from him. Carlos, on the other hand, is a little more interesting of a case study. His xBABIP is a lot lower then his actual BABIP which suggests he's been pretty lucky so far this spring. However, Carlos' BABIP is pretty close to his cBABIP which is normally a very good predictive tool for season totals. I'd predict that we're going to see him starting to hit the ball harder and raising up his xBABIP but the results won't be a lot different then what we are seeing right now.
This only leaves 1 player that I want to discuss today, Josh Rutledge. Rutledge is by far the unluckiest Rockies player carrying a -0.081 dBABIP, his cBABIP is low but with a young player it's not quite as useful of a tool, mostly because his low BABIP this spring has a big hit on that career total because of his low number of career late appearances. So instead let's look at some of his season totals to get an idea for what his cBABIP might be.
In 2011 Rutledge had a .414 in Modesto, 2012 .345 in Tulsa and .315 in his MLB debut season. Now minor league BABIP is inflated because of poorer pitching defense but still it gives us a picture that he should be a moderately high BABIP hitter at least. This also passes the eye test with his blazing speed to first base. Overall this paints a picture of Rutledge being my candidate for a breakout month of May. He's maintained good approach, hitting the ball well and increasing is BB rate dramatically. If he keeps this up while BABIP normalizes out expect a May similar to Fowler's May last year.
I've already been quite wordy in this post so I'm just going to leave you with one last table. This one show the players current splits and next to the splits they'd have if they were hitting their xBABIP number instead of their actual BABIP.
Name |
xBA |
xOBP% |
xSLG% |
xOPS |
BA |
OBP% |
SLG% |
OPS |
Reid Brignac |
0.225 |
0.285 |
0.281 |
0.566 |
0.250 |
0.308 |
0.306 |
0.613 |
Chris Nelson |
0.228 |
0.269 |
0.304 |
0.573 |
0.242 |
0.282 |
0.318 |
0.600 |
Wilin Rosario |
0.342 |
0.364 |
0.624 |
0.988 |
0.329 |
0.352 |
0.612 |
0.964 |
Carlos Gonzalez |
0.277 |
0.373 |
0.534 |
0.907 |
0.317 |
0.407 |
0.574 |
0.981 |
Eric Young |
0.290 |
0.325 |
0.461 |
0.786 |
0.303 |
0.338 |
0.474 |
0.811 |
Todd Helton |
0.286 |
0.350 |
0.432 |
0.782 |
0.268 |
0.333 |
0.415 |
0.748 |
Dexter Fowler |
0.304 |
0.411 |
0.607 |
1.019 |
0.303 |
0.410 |
0.606 |
1.016 |
Troy Tulowitzki |
0.312 |
0.400 |
0.605 |
1.005 |
0.317 |
0.404 |
0.610 |
1.014 |
Jordan Pacheco |
0.311 |
0.365 |
0.362 |
0.726 |
0.339 |
0.391 |
0.390 |
0.780 |
Michael Cuddyer |
0.348 |
0.409 |
0.598 |
1.007 |
0.320 |
0.384 |
0.570 |
0.954 |
Josh Rutledge |
0.301 |
0.362 |
0.423 |
0.785 |
0.235 |
0.303 |
0.357 |
0.660 |