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C the Z: what happens after the most important count?

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If the 1-1 count is the hinge upon which an at-bat either succeeds or fails, what does the data tell us about the Mariners performances in these situations?

We’ve talked at length about the Mariners’ new emphasis on clear and open communication within the organization and with the public. They’re articulating their vision and goals like no other Mariner front office has before, and it’s hard not to get excited about the things they’re saying. Colin did an excellent job of laying out why this is so important. Clearly communicated goals and philosophies are critical to actualizing them in reality, after all, if you know where your target is, it’s much easier to hit it.

Clear communication also makes evaluating your goals much easier too. That’s where I’m picking up the discussion. There was a specific piece of information shared in the video the Mariners published on Monday that I want to examine. Here’s the video again, see if you can figure out where I’m going with this:

The Mariners’ new manager, Scott Servais, highlights the 1-1 count as the most important count in an at-bat. Here we see our first tangible metric for what "Control the Zone" might mean to the Mariners. The 1-1 count is the threshold for success or failure in an at-bat. Getting to a 2-1 count greatly increases a batter’s chances for success and dramatically lowers the pitcher’s ability to generate an out. In a 1-2 count, the odds swing towards the pitcher and the batter is now at a great disadvantage.

The numbers bear this out too. In a 2-1 count, the average MLB player hits like Buster Posey or Matt Carpenter, slashing .335/.334/.537 with a .372 wOBA. In a 1-2 count, they’re hitting like a pitcher or Pete Kozma, slashing .167/.174/.248 with a .181 wOBA.

But I’m not just concerned with what happens in two specific counts, I’m interested to know what happens after a batter gets to one of those counts. Are batters and pitchers able to take advantage of favorable situations? Do they show resilience when facing long odds of success?

Thankfully, Baseball-Reference keeps very detailed split data, including count splits through all 11 count states. Here’s are some league average rates through the two counts we’re concerned with:

K%

BB%

AVG

OBP

SLG

wOBA

After 2-1 Count

20.1%

17.3%

0.250

0.382

0.405

0.351

After 1-2 Count

41.4%

5.3%

0.177

0.226

0.272

0.222

Neither of these MLB averages should be very surprising. A 2-1 count is very favorable for batters and leads to success later in the at-bat. Some batters are able to battle in an at-bat after reaching a 1-2 count, but by and large, that count is a death knell for batters.

So we’ve established the average baseline for performance after these two counts, now let’s see how have the Mariners performed in these counts. I’m specifically interested in the players Dipoto has acquired to see if they have a particular penchant for taking advantage of a favorable count or battling in an at-bat.

I’ve curated three-year splits for each player to ensure the sample size is large enough to get significant results. If a player hasn’t played three years in the majors, I’ve indicated that with a number of asterisks. Here’s the split data after a 2-1 count:

After 2-1 Count

K%

BB%

AVG

OBP

SLG

wOBA

Chris Iannetta

20.4%

26.3%

0.273

0.463

0.439

0.404

Adam Lind

16.7%

17.7%

0.288

0.413

0.477

0.390

Robinson Cano

11.7%

17.7%

0.332

0.449

0.483

0.405

Ketel Marte*

11.4%

15.9%

0.297

0.409

0.459

0.382

Kyle Seager

15.4%

17.6%

0.268

0.398

0.439

0.370

Nori Aoki

6.5%

15.5%

0.272

0.393

0.364

0.343

Seth Smith

19.8%

20.4%

0.272

0.422

0.438

0.381

Leonys Martin

18.8%

15.2%

0.220

0.344

0.344

0.313

Nelson Cruz

22.6%

17.0%

0.291

0.412

0.553

0.412

Felix Hernandez

22.0%

15.0%

0.232

0.348

0.387

0.328

Hisashi Iwakuma

26.6%

10.3%

0.186

0.269

0.305

0.259

Wade Miley

19.5%

19.1%

0.269

0.407

0.434

0.372

Taijuan Walker**

18.6%

19.9%

0.230

0.385

0.361

0.339

Nate Karns*

22.1%

17.1%

0.198

0.336

0.284

0.290

James Paxton**

17.7%

20.8%

0.238

0.392

0.396

0.352

Both Chris Iannetta and Adam Lind perform well above average after reaching a 2-1 count. And even though it’s just one year of data, Nate Karns showed an ability to keep batters from producing if he falls behind in the count. The most impressive data point has to be Hisashi Iwakuma though. He’s limited batters to a .259 wOBA after reaching a 2-1 count, showing an impressive ability to maintain control of an at-bat even while at a great disadvantage.

Here’s the split data after a 1-2 count:

After 1-2 Count

K%

BB%

AVG

OBP

SLG

wOBA

Chris Iannetta

51.5%

14.4%

0.104

0.238

0.160

0.199

Adam Lind

33.6%

4.9%

0.254

0.290

0.435

0.313

Robinson Cano

34.1%

3.4%

0.215

0.248

0.355

0.262

Ketel Marte*

41.6%

7.8%

0.214

0.273

0.257

0.240

Kyle Seager

35.4%

4.3%

0.184

0.226

0.259

0.217

Nori Aoki

18.0%

6.0%

0.243

0.303

0.279

0.264

Seth Smith

39.0%

7.3%

0.193

0.254

0.277

0.238

Leonys Martin

42.2%

4.5%

0.194

0.242

0.282

0.234

Nelson Cruz

43.6%

6.9%

0.177

0.241

0.314

0.247

Felix Hernandez

52.2%

5.2%

0.138

0.186

0.212

0.180

Hisashi Iwakuma

42.4%

3.4%

0.151

0.184

0.221

0.181

Wade Miley

40.0%

4.3%

0.190

0.226

0.286

0.226

Taijuan Walker**

42.7%

5.1%

0.194

0.239

0.329

0.248

Nate Karns*

47.6%

6.5%

0.185

0.243

0.268

0.230

James Paxton**

44.7%

6.7%

0.143

0.200

0.157

0.168

Adam Lind’s name immediately stands out as an outlier again. When he’s at a disadvantage, his ability to produce is barely below his regular rates. Nori Aoki also shows a decent ability to find success in at-bats that reach a 1-2 count. On the pitching side, James Paxton has been able to put batters away when he gets ahead in the count. Of the 150 batters he’s faced that have reached a 1-2 count, he’s allowed just 30 of them to reach base and given up just two extra base hits.

These splits should not be taken as a bellwether for success or failure. The dynamics of the pitcher/batter matchup are complex and can’t be boiled down to one number. But this data gives us some evidence for a player’s ability to react to favorable or unfavorable situations. I’m sure "Control the Zone" goes beyond this simple measure, but as a proxy for this philosophy, it shows us who is already set up for success. Adam Lind has shown an ability to take advantage of favorable counts while showing resilience when behind in the count. I’m sure Jerry Dipoto would like to see that from all of his players, throughout the organization.