Fixing Brandon McCarthy May Lead to Fixing Nathan Eovaldi

Syndicated Sunday... on a Monday. This user from the Fangraphs community blog explains how the New York Yankees fixed Brandon McCarthy after coming over from Arizona and how they can do the same with Nathan Eovaldi this season. It's a long read, and it all will not be here on the blog so CLICK HERE for the entire post, but it's a great read. Especially if you like sabermetrics.

Brandon McCarthy got off to a rough start in 2014 with the Arizona Diamondbacks. Between the atrocious ERA (5.01) and the seemingly endless supply of baseballs leaving the yard (HR/FB rate of 20%), the D’Backs cut their losses and dealt McCarthy to the Yankees in early July for Vidal Nuno. What the Yankees saw was a pitcher who was terribly unlucky and just needed a little more time for variance to run its course. Well, and maybe a few things that needed adjusting.

McCarthy was among the league leaders in BABIP at the time of the trade with a .345 mark, and many seemed to think this number had to come down. BABIP after all is pretty volatile, and takes several years to stabilize, so we can expect a large amount of variance in a time period as short as a few months. The real questions are: how deserving was McCarthy of an inflated BABIP? Are there tendencies that make some pitchers more prone to higher rates than others? What can teams do to fix higher BABIPs?

First, let’s take a look at McCarthy’s zone profiles in 2014 before being traded.

No wonder hitters were teeing off against McCarthy; lefties saw plenty of offerings over the middle of the plate while righties were exclusively pitched low-and-away. Hitters could walk up to the plate with confidence knowing they’d either get a pitch in their wheelhouse or only in a few spots. This took any advantage of unpredictability out of the hands of McCarthy and subjected him to a higher than average BABIP.

Now, let’s look at how they changed after being dealt.

After coming over to New York, McCarthy looks like a completely different pitcher. The biggest changes appear to be throwing inside on right handed hitters and keeping the ball away from left handed hitters. His new found ability to mix up his locations helped keep hitters off-balance.

As for McCarthy’s pitch selection before and after the trade, that changed as well.

Left Handed Hitters

Month	Fourseam	Sinker	Cutter	Curve	Change
4/14	5.60	45.60	26.40	22.40	0.00
5/14	9.26	50.74	18.52	18.89	2.59
6/14	12.00	46.80	6.00	29.20	5.60
7/14	11.92	47.69	18.46	21.92	0.00
8/14	25.38	23.24	25.38	25.69	0.00
9/14	32.02	18.72	19.21	30.05	0.00

Right Handed Hitters

Month	Fourseam	Sinker	Cutter	Curve
4/14	2.29	66.06	6.88	23.85
5/14	5.06	61.60	3.80	29.54
6/14	8.70	55.56	3.38	32.37
7/14	21.74	46.64	15.42	15.81
8/14	24.35	43.91	13.65	18.08
9/14	24.61	45.55	10.47	19.37

Left handed hitters saw a dramatic increase in four-seamers, with the sinker, cutter, and curve all being mixed in rather evenly. Righties also saw a drift away from the sinker and a more even distribution of pitches. The result was a modest .307 BABIP from July onwards. This all makes me wonder if the Yankees have found a market inefficiency — pitchers with an excellent skill set, an inflated BABIP, and zone profiles plus pitch arsenals that were all too predictable. Alter the sequencing to fix the pitcher, and you’ll see the outcomes line up more accurately with the underlying skill set.

Well, if they did it once, can they do it again? Or at least try to?

Enter Nathan Eovaldi. Eovaldi has impressed scouts for years with a blazing mid-90′s fastball. Unfortunately, the results haven’t matched his potential. Like McCarthy, Eovaldi was marred by the same tendencies- an inflated BABIP (.323) with an ERA that’s well above his FIP (4.37 vs 3.37), but he had an impressive walk rate (1.94 BB/9). His zone profiles provide some insight as well: