Compared to the rest of the MLB, are the St. Louis Cardinals suffering from BABIP woes?

Scott Kane-USA TODAY Sports

How do the Cardinals stack up against the rest of the league when it comes to the BABIP monster?

In last night's disappointing 4-1 loss to the Phillies, I once again noticed that the Cardinals seemed to suffer from some tough luck on balls in play. It had finally reached the point where I felt like I had to investigate. My mind was very clearly convincing me of one thing, but would the statistics back up this thought process?

Thus, as an admittedly rough frame of reference, I used Ben's chart he created for a post back in February. Though using batted ball league averages from just one season is not ideal, it was the best I could easily come up with for this post, and because I used the same conversions for all 30 teams, I figured it was fair. If you haven't already clicked over to get a refresher on Ben's post, his chart noted that the 2013 league batting averages were .690, .232, and .218 for line drives, ground balls, and fly balls, respectively. Using this information, I was able to calculate each team's "predicted BABIP" based on their 2014 batted ball percentages (excluding last night) and then found the difference between that value and their "actual BABIP."

Team Statistics:

Team

Predicted BABIP

Actual BABIP

Difference

Cardinals

.325

.299

- .026

Rockies

.322

.321

- .001

Marlins

.317

.320

+ .003

White Sox

.322

.314

- .008

Rangers

.321

.312

- .009

Pirates

.321

.311

- .010

Tigers

.328

.309

- .019

Dodgers

.323

.306

- .017

Angels

.318

.302

- .016

Brewers

.327

.302

- .025

Orioles

.315

.302

- .013

Diamondbacks

.320

.301

- .019

Giants

.318

.300

- .018

Nationals

.317

.300

- .017

Twins

.325

.298

- .027

Braves

.321

.298

- .023

Red Sox

.321

.298

- .023

Royals

.312

.297

- .015

Yankees

.327

.297

- .030

Phillies

.326

.294

- .032

Indians

.326

.294

- .032

Reds

.324

.292

- .032

Blue Jays

.311

.291

- .020

Mariners

.320

.289

- .031

Astros

.314

.289

- .025

Cubs

.322

.287

- .035

Athletics

.317

.287

- .030

Rays

.317

.285

- .032

Mets

.325

.285

- .040

Padres

.306

.261

- .045

Despite having the sixth highest "predicted BABIP" in the league, the Cardinals find themselves in the middle of the pack (14th) when looking at "actual BABIP" at .299. On the surface, this appears quite unfortunate, especially as a fan of the hometown team, but based on the differences calculated for all 30 teams (the far right column), there appears to be eleven teams experiencing more trouble with BABIP woes than the Cardinals. Thus, things could probably be worse. We could all be Padres or Mets fans. The dropoff in "actual BABIP" can be seen as a positive because it shows that the offense has room to improve going forward, just like Craig discussed in his post titled "Cardinals' struggling offense will improve."

Individual Statistics:

Player

BABIP via 2013

Actual BABIP

Difference

Matt Adams

.337

.381

+ .044

Adam Wainwright

.272

.381

+ .109

Jon Jay

.357

.368

+ .011

Matt Carpenter

.352

.344

- .008

Tony Cruz

.350

.324

- .026

Yadier Molina

.336

.304

- .032

Matt Holliday

.306

.301

- .005

Allen Craig

.320

.301

- .019

Peter Bourjos

.335

.289

- .046

Kolten Wong

.303

.263

- .040

Jhonny Peralta

.324

.261

- .063

Daniel Descalso

.329

.240

- .089

Mark Ellis

.318

.218

- .100

Oscar Taveras

.301

.200

- .101

Shane Robinson

.298

.192

- .106

Randal Grichuk

.257

.179

- .078

In the chart, I included the 16 players who have had at least 30 plate appearances in 2014. Not surprisingly, both Adams and Jay are outperforming their predicted BABIPs. Though no one has fully proven that a high BABIP can be classified as a skill, both Adams and Jay have been known to have high BABIPs in their relatively young careers. Wainwright is outperforming as well, and again, but for different reasons, this really is no surprise. However, the remaining 13 are all underperforming their predicted BABIP.

The differences from Descalso, Ellis, Taveras, Robinson, and Grichuk are clearly the largest, but they have significantly smaller sample sizes than the rest of the group. Given more plate appearances (all but one [OT] don't deserve them), all five would likely normalize. However, in regards to Peralta, he has had 278 plate appearances, which, in my opinion, is enough to draw some preliminary conclusions. With a career BABIP of .312, I confidently expect a rise from him as the season progresses. Will he make it all the way back to .312? Maybe not, but an increase from .261 is likely in the cards. Since I already said that we will likely see some improvement in team average BABIP, this subsequently means that there must be an improvement in the individual player's BABIP as well.

Limitations:

As with just about any form of statistical analysis, I admit that limitations do, in fact, exist. Home runs are included in batted ball percentage (as likely a fly ball), but they are not included in batting average on balls in play (BABIP). Thus, this has a negative impact on certain hitters, especially the team-leader in home runs, Jhonny Peralta. As addressed earlier in this post, using just one year of batted ball league averages is not the best either. I am positive there are more limitations, but I cannot think of them at this time. Regardless of limitations, I still think it is a unique thing to keep an eye on, and I hope you were able to enjoy it as much as I did while putting it together.

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