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Second Thoughts Game #93: White Sox 6, Indians 2

Second Thoughts Game #93: White Sox 6, Indians 2
Zach McAllister (Photo: Cleveland Indians)
July 13, 2014
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McAllister’s Return

Zach McAllister (7 IP, 3 R/ER, 1 HR, 4 H, 2 BB, 2 K, 92 pitches, 63 strikes, 9 swinging strikes) first performance back in the big leagues was far more reminiscent of his 2.23 ERA of his AAA starts than were they of his previous starts of 2014, starts with a combined ERA of 5.89 to date. Z-Mac was far from ace-like, certainly: FIP and xFIP – based on his strikingly low strikeout rate for the game – did not look kindly on his performance, and while single-game FIP/xFIP are comically unreliable in single-game samples, they nevertheless convey the point: strikeout rates this low don’t lead to sustainable major league success.

That said, two indelible facts are working in McAllister’s favor on that front: while his career K%-BB% numbers (10.9% on his career, putting him on par with the 2014 numbers ofEdwin Jackson and Jon Niese) have been more back-of-the-rotation than middle-of-the-rotation, they’ve nevertheless been good enough, at least, to sustain a solid back-end pitcher. In other words, one does not bail on 324 career innings of fair performance based on a poor seven innings – nor, this author would posit, McAllister’s mediocre 54.1 IP thus far in 2014. The larger body of work, except in cases of cataclysmic injury or substantial velocity loss, should be preferred over the smaller body of work, even if the latter is more recent. His results at the major league level thus far in 2014 are far from encouraging; they are likewise far from defining him as a pitcher going forward, since his game ERA on Saturday was 3.86 – entirely acceptable from a back-end pitcher.

Likewise working to McAllister’s advantage is the fact that his low strikeout rate is merely superficially poor – as this author is quite fond of noting, the swinging strike rate measures bat-missing ability; the strikeout measures when he missed those bats. The stats devoid of context should, on the whole, be preferred to those dependent upon context. In that vein, McAllister’s swinging strike rate of 9.78% on the game was a number of things: above the league average swinging strike rate, better than his career average, and far better than his Correia-esque 6.6% SwStr% on the year. It is indeed true that the White Sox are the third most strikeout-prone team in the majors, but given the state of McAllister’s arsenal in mid-May, heavily-qualified victories are far preferable to outright and decisive defeat.

BABIP Watch 2014

Batting Average on Balls in Play, BABIP, is a statistic that tracks precisely what it sounds like it would. If one removes walks, sacrifices, and hit-by-pitches, one gets Batting Average – normal batting average, the one created by the prudent and just Framers of the Constitution themselves, and one whose pre-eminent wisdom is the Polaris for baseball writers’ wandering ships.

Yet Batting Average encompasses a great many skills – strikeouts and home runs, for instance, which hinge far more on the power and plate discipline abilities more than the ability to make consistently hard contact. If one removes strikeouts and home runs from batting average, one isolates the ability to simply get on base when a ball is hit in play. The primary advantage of this is to isolate those plays in which fielders actions or inactions had a large impact. Fielders have a substantial impact on a play, and the ability of a fielder is entirely independent of a batter. Given this influence, most major league players have a BABIP near .300, rarely straying higher than .350 for prolonged stretches of time and likewise rarely sustainably dropping below .250. Higher than .350 or lower than .250

Hence, what follows is a chart of Cleveland Indians batters’ BABIPs for 2014, min. 100 PA.

 

PA

HR

K%

BABIP

AVG

Lonnie Chisenhall

290

9

15.50%

0.368

0.328

Michael Bourn

309

3

22.30%

0.341

0.267

Michael Brantley

382

14

8.10%

0.326

0.327

Jason Kipnis

286

3

17.50%

0.309

0.258

Yan Gomes

302

11

23.80%

0.307

0.258

Mike Aviles

206

3

14.60%

0.298

0.266

Asdrubal Cabrera

380

8

18.20%

0.293

0.254

David Murphy

322

6

14.00%

0.264

0.243

Nick Swisher

309

8

27.20%

0.262

0.207

Ryan Raburn

161

2

24.80%

0.257

0.203

Carlos Santana

356

14

22.20%

0.236

0.209

Source: FanGraphs

Lonnie at the top of the list should surprise no one. Frequently, batter BABIP is put forth as a matter of luck - often, BABIP rates do express luck. In Lonnie’s case, there certainly was some luck on groundballs that just eluded the grasp of a defender or an occasional infield hit that would have normally been an out but for a mistake made by a defender. At the same time, however, Chisenhall was undeniably hitting incredibly well during that time, with a singles stroke that made .400 seem shockingly within reach for a brief stretch.

What BABIP’s incredible volatility reminds onlookers, however, is that both the skill and luck components, each of which powered Lonnie’s run, will regress to the mean in all but the most exceptionally talented cases (e.g.: Mike TroutMiguel Cabrera). Hitters can go on stretches within which they hit .390 or better – as did Chisenhall – but sustaining that for more than two months requires hall-of-fame-level focus, ability, and luck.

Likewise, the bottom of the list demonstrates that BABIP is not identical to luck. Santana’s .236 BABIP is reflective of his lackluster speed, his generally poor contact in the first half of the season, as well as the fact that, when batting against right-handed pitchers, Santana has a devilish pull tendency. This pull tendency leads to a great many doubles in the right-field corner, but it also leads to extremely few groundball singles to right field. That Santana’s offensive profile is a strong one in spite of this fact is a testament to his plate discipline and his power.

Simultaneously, while Santana’s offensive profile has factors that suggest that he should have a lower than average BABIP, these factors do not in turn suggest a .236 BABIP. Santana, in an average season, will likely find himself staring down the barrel of a .275 BABIP, his career rate. Any deviation from that fact is a deviation from Santana’s offensive profile. In 2013, Santana’s BABIP was .300, and this author noted that Santana’s profile did not lend itself to a high BABIP. This has proven true. Thus, when Santana has trended in the opposite direction, fans must steady their frequently frenzied emotions and remind themselves that their underachievers are frequently not as bad as they seem, in much the same sense that their overachievers are unlikely to keep up their performance.

Barring Corey Kluber, of course.

John can be reached on Twitter at @JHGrimmHe can also be reached by e-mail at john.h.grimm@hotmail.com.

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