Kyle Hendricks and Location-Based Contact Management

This month last year, Connor Kurcon of Six Man Rotation set out to quantify the location aspect of command (or “LRP”). By establishing an accounting system that credited and debited pitchers for changes in ball-strike counts based on the attack zone of and hitter’s disposition (take? swing? ball in play?) for every pitch, he effectively created an alternative to Pitch Value (PVal) that rewards optimal movement through ball-strike counts but with much more pitcher and hitter context.

His findings are as you’d expect: Jacob deGrom and Justin Verlander lead the pack, with Gerrit Cole, Max Scherzer, and Clayton Kershaw not far behind. Other budding aces like Jack Flaherty and Mike Clevinger pepper the list, and some pleasant surprises (such as Brendan McKay, Caleb Smith, and, for those still thirsting, Jake Odorizzi) are scattered throughout as well. Out of the bullpen, newly anointed relief ace Nick Anderson led the pack followed by the underrated Emilio Pagán, breakout reliever Giovanny Gallegos, and others.

Near the end of his post, Kurcon includes a subhead dedicated to Kyle Hendricks where he highlights how Hendricks, widely respected as a command artist, fares lukewarmly by measure of LRP. He then reminds us “LRP doesn’t paint the full picture of command.” True that.

Fortunately, Kurcon has left the door open for me to tie up loose ends with find Gs I’ve been meaning to write up for a couple of months now. Never fear, Hendricks is the command artist we know and love — it’s just that he relies heavily on incurring contact in optimal pitch locations. It is a needle very few pitchers can thread, but Hendricks does it masterfully.

Methodology (gross math stuff, feel free to skip)

Back in July, I wrote about launch angle and what pitchers can and cannot control. In the post, I used a nonlinear regression to establish that pitch location influences launch angle. It was cool! If not dense and confusing for many. As noted above, feel free to skip past this if you’re just lookin’ for the goods.

I use the same framework here. Launch angle directly affects weighted on-base average on contact (wOBAcon). Launch angle relative to pitch location, therefore, should directly affect wOBAcon relative to pitch location as well (in other words, the results there and here are invariably related), justifying the same fundamental model structure.

To set up everything, I calculated the average weighted on-base average on contact (wOBAcon) for every [pitch_x, pitch_z] set of pitch coordinates (horizontal location and height, respectively). I also flagged fastballs (binary variable: 1 for four-seamer/two-seamer/sinker/cutter, 0 for not), which, in my research on launch angles, were shown to have a more dramatic effect on launch angle at any given pitch location. To make the regression nonlinear, I included squared terms for pitch_x and pitch_z. Launch angle did not behave linearly; as a pitch strays from the center of the plate, launch angle tends to decrease. It stands to reason, then, that contact management bears a nonlinear relationship with pitch location as well. Lastly, I omit certain sets of pitch coordinates to which pitchers rarely throw to reduce noise.

In what I’m calling an alternative framework, instead of flagging all fastballs, I flag all pitches thrown less than 91 mph (1 for velocities up to but not including 91 mph, 0 for all else). This caters specifically to Hendricks but carries implications beyond him (possibly to other low-velocity command artists such as Zack Greinke) because Hendricks’ fastball differs greatly from other fastballs; as such, this simple binary (fastball or not) might misconstrue the (in)effectiveness of Hendricks’ sinker. I want to test this hypothesis, that his lower-than-average velocity actually benefits him because he combines it with pinpoint command.

(To be 100% clear: the 91-mph threshold is completely arbitrary and absolutely open to revision/debate. I just thought to myself, Off the top of my head, what is Hendricks’, like, max velo? Probably 91.)

The two equations are as follows…

Traditional: wOBAcon = β * pitch_x + β * pitch_z + β * pitch_x² + β * pitch_z² + β * fastball + ε

  • Adjusted r² = 0.70

Alternative: wOBAcon = β * pitch_x + β * pitch_z + β * pitch_x² + β * pitch_z² + β * less_than_91 + ε

  • Adjusted r² = 0.72 (interesting — it’s better!)

Both models are quite strong but fall short of my models for launch angle. That should be reasonably expected. You can influence launch angle (and exit velocity), but it’s out of your hands, so to speak, after that. Moreover, small nuances in a batted ball event could separate a single from a double, a double from a triple, a long fly ball out from a home run. wOBAcon, while an effective catch-all metric to measure production (hitters) or damage allowed (pitchers), fails to capture the probabilistic nuance — aka, random chance — of every batted ball that makes baseball great.

Results

No horsin’ around — here’s how Hendricks ranks in location-based “deserved” wOBAcon annually under the traditional “flag all fastballs” framework:

  • 2017: 8th of 127 (min. 300 batted ball events)
  • 2018: 5th of 126 (min. 300 BBEs)
  • 2019: 24th of 116 (min. 300 BBEs)
  • 2020: 15th of 114 (min. 100 BBEs)

And here’s how Hendricks ranks annually under the alternative “flag pitches less than 91 mph” framework:

  • 2017: 2nd of 127
  • 2018: 3rd of 126
  • 2019: 14th of 116
  • 2020: 11th of 114

By virtue of where he ranks among his contemporaries, Hendricks’ reduced velocity actually does appear to benefit him! (To be clear, I am reluctant to claim that this, a slower fastball being better at managing contact, is a universal truth. I think velocity is only as effective as location allows it to be. A well-placed 89-mph sinker from Hendricks is gold, but that same pitch grooved down the middle might get torched.)

Here’s a list of pitchers who have cracked the top-25 multiple times since the start of 2017, sorted by number of times (I relaxed the threshold to 250 BBE for this one). These results follow the traditional framework and would inevitably vary under the alternative (and possibly more compelling) framework. However, I had already compiled this list before developing the alternative framework and am too lazy to compile the list again (sorry):

  • Four times:
    • Dallas Keuchel (1st in 2017, 13th in 2018, 17th in 2019, 5th in 2020).Probably one of the first pitchers someone thinks of when they hear “contact management.”
  • Thrice:
    • Aníbal Sánchez (6th in 2018, 11th in 2019, 20th in 2020). No coincidence he experienced a resurgence in 2018-19, and it’s possible he got unlucky this year.
    • Blake Snell (22nd in 2017, 18th in 2018, 17th in 2020). More evidence that 2019’s 4.29 ERA and .343 batting average on balls in play (BABIP) were outliers.
    • Hyun-Jin Ryu (3rd in 2017, 2nd in 2019, 8th in 2020). He didn’t meet the threshold for 2018, but he would’ve ranked highly if he did (and joined Keuchel in the “four times” club). One of the best in the biz.
    • Jason Vargas (4th in 2017, 4th in 2018, 7th in 2019). Bad pitcher who thrived on elite home run prevention.
    • Kenta Maeda (15th in 2018, 6th in 2019, 2nd in 2020). Finally getting the respect he deserves.
    • Masahiro Tanaka (19th in 2017, 8th in 2018, 16th in 2019). Let’s get this man out of Yankee Stadium and see what he can do!
    • Zack Greinke (5th in 2017, 3rd in 2018, 5th in 2019). Like Hendricks and Keuchel, one of the first names to spring to mind when “contact management” is mentioned.
  • Twice:
    • Alex Wood (2nd in 2017, 20th in 2018). I admit, Wood is fantasy kryptonite for me. After this year it’s time to cut bait.
    • Eduardo Rodriguez (23rd in 2018, 4th in 2019). An interesting inclusion here. Here’s to hoping he recovers fully from COVID-19.
    • Gio González (17th in 2017, 14th in 2018). The wheels came off in 2018 but he still held it together despite horrible strikeout and walk rates. His 2017 campaign was legit!
    • Jacob deGrom (15th in 2019, 18th in 2020). I mean.
    • John Means (10th in 2019, 11th in 2020). Super weird season this year, but I think I’m buying his ERA beating his estimators. It’s just a question of if 44 innings this year supersede 155 innings last year. (His best 10-game K-BB% rate never eclipsed 14% last year… it’s 19.9% right now. I’m inclined to buy, and fade the 21.8% HR/FB.)
    • Jon Lester (8th in 2017, 25th in 2019). Fart noise.
    • Marco Gonzales (2nd in 2018, 12th in 2019). He cracks the top-25 under the “alternative” framework. There’s a lot to like here from a deep-league perspective. I’m just concerned his low-3.00s ERA this year inflates his draft price a little too much next year. He’s more likely the 4.00 ERA guy we already know.
    • Tommy Milone (3rd in 2019, 1st in 2020). Uhhhhh…
    • Trevor Richards (21st in 2018, 13th in 2019). Bad pitcher, half-decent home run prevention.
    • Wade LeBlanc (1st in 2018, 1st in 2019). This helps explain the .281 BABIP since 2014. Again, pitch to contact only gets you so far if you can’t strike anyone out.
    • Zach Davies (9th in 2019, 4th in 2020). Maybe his 3.30 ERA the last two years isn’t so lucky after all (although it probably shouldn’t be a full run lower than his 4.35 FIP.) The 39-point difference between his career ERA and FIP checks out on first glance.

It would have behooved me to include a list of bottom-feeders as well. Again, laziness overtakes me, but I will make note of one pitcher in particular: Germán Márquez has consistently underperfomed locationally, except during — you guessed it — his 2018 breakout campaign. If you pitch in Denver you should probably be spotting your pitches much more effectively. It’s wild to me to think Márquez has taken the bad hand Coors Field has dealt him and somehow made it worse. (If I wasn’t clear about this before, know now: these results are agnostic to ballpark and defense.)

Discussion

Like Kurcon’s LRP, location-based analysis is not the be-all and end-all of quantifying pitcher command. But I do think it is a solid foundation for discussing the merits of contact management. We see distinct outliers, both good and bad, that fit our understanding of those pitchers. What’s more, many of those pitchers have shown an ability to repeat their location-based success (or lack thereof) year over year.

And this model isn’t overly rigorous to begin with — it cultivates results using location alone without considering countless other ways to describe a pitch, such as its physical characteristics (movement, release point, etc.). Something more statistically complex undoubtedly would bear even more fruit.

Paul Mammino of RotoFanatic introduced an ERA estimator called Stuff-ERA earlier this year. It also relies on location as one of its foundations and uses it, among other things, to estimate wOBAcon. The models are (as just noted) complex but opaque, making them difficult to judge, but many of the “surprise” names (Vargas, Davies, Milone) are repeated here, as are some of the headliners (Maeda, Snell, Greinke).

ERA estimators like fielding independent pitching (FIP), expected FIP (xFIP), and skill-interactive ERA (SIERA) make fairly broad, often incorrect assumptions about pitcher (in)ability to manage contact. FanGraphs uses FIP is calculate wins above replacement (WAR). If we can quantify contact management more reliably, we can confidently explain why, for example, Hendricks’ career 3.12 ERA is four-tenths of a run lower than his FIP (3.53) — and why Hendricks deserves a few more WAR than he has been credited.

As for this analysis’ incompatibility with LRP: LRP does many things, but it does not specifically measure contact quality allowed. This does! That’s the simple explanation.

As these differences pertain to Hendricks, LRP relies on changing game states to attribute run values to a pitcher. LRP likely admires Hendricks’ pristine walk rates, but fundamentally he is not a strikeout pitcher. He relies prominently on pitching to contact, which is inherently less valuable than racking up strikeouts, no matter how good one is at the former.

Out of necessity, LRP oversimplifies some of its assumptions related to pitch location and the value of balls in play based on generalized attack zones and ball-strike counts. These simplifications are beneficial broadly but likely do Hendricks a disservice. He is not inducing whiffs in the chase or waste areas, nor is he overpowering hitters in the heart of the plate. He lives, literally and idiomatically, on the edge (or, using Statcast’s nomenclature, in the shadows).

Lastly, this exercise heavily condenses the range of estimated values. Since the start of 2017, single-season wOBAcon values (among pitchers who incurred at least 300 BBE) range from .285 to .483, with a mean of .381. Location-based “deserved” wOBAcon finds a similar mean (.384) but estimates a much narrower range (.356 to .405), with a variance roughly one-seventh as large. Such is the nature of these regressed values and of trying to wrangle the discrete values wOBA assigns to batter ball outcomes, which are widely varied. Maybe a probabilistic model (ordered probit?) might better capture the effect I’m looking for (i.e., increased/decreased probability of incurring damage) but for now that’s neither here nor there.

It is better, then, to proceed with the understanding that the tables below are more important in terms of rank and magnitude rather than actual value. (Because, again, location is but a small part of a much larger puzzle.) As I just alluded to, think of it as: a lower location-based “deserved” wOBAcon implies a lower probability of incurring damage, and vice versa. My recommended use: Verify uncommonly low or high BABIPs and HR/FBs, as opposed to taking the estimates at face value. This is helpful especially for players with short track records and also veterans with long track records who see an abrupt shift in their results.

Tables

Because the juiciness of the ball has fluctuated so dramatically year to year, it is difficult to compare results across seasons within a single table. (Actual and “deserved” wOBAcon values are universally higher in 2019 than, say, 2015, which may unfairly favor Player X or discredit Player Y by comparing apples to oranges.) Accordingly, I have provided separate tables for 2019 (full season) and 2020 (through Thursday, September 24).

2019:

2019 Location-Based “x”wOBAcon
rank player_name BBE wOBAcon x_wOBAcon diff
1 Wade LeBlanc 280 0.416 0.349 0.067
2 Hyun-Jin Ryu 421 0.320 0.354 -0.033
3 Tommy Milone 327 0.379 0.356 0.023
4 Eduardo Rodriguez 473 0.380 0.357 0.023
5 Zach Davies 454 0.344 0.358 -0.014
6 Zack Greinke 579 0.325 0.358 -0.033
7 Jason Vargas 444 0.367 0.360 0.007
8 Cal Quantrill 317 0.365 0.361 0.004
9 Kenta Maeda 394 0.344 0.361 -0.018
10 Anibal Sanchez 362 0.371 0.362 0.010
11 Dallas Keuchel 277 0.369 0.363 0.006
12 Marco Gonzales 491 0.358 0.363 -0.005
13 Trevor Richards 333 0.369 0.364 0.004
14 John Means 466 0.345 0.364 -0.019
15 Jordan Zimmermann 384 0.420 0.365 0.054
16 Kyle Hendricks 519 0.358 0.367 -0.009
17 Jon Lester 346 0.425 0.368 0.057
18 Kevin Gausman 288 0.423 0.369 0.054
19 Masahiro Tanaka 550 0.380 0.369 0.011
20 Stephen Strasburg 517 0.351 0.369 -0.019
21 Adam Plutko 353 0.381 0.370 0.011
22 Dylan Bundy 471 0.406 0.370 0.036
23 Mike Montgomery 252 0.443 0.370 0.073
24 Kyle Freeland 349 0.443 0.371 0.072
25 Andrew Cashner 462 0.352 0.372 -0.020
26 Justin Verlander 475 0.353 0.372 -0.019
27 Jacob deGrom 469 0.342 0.372 -0.031
28 Jhoulys Chacin 311 0.446 0.372 0.073
29 Aaron Nola 525 0.378 0.373 0.006
30 Mike Clevinger 289 0.357 0.373 -0.016
31 Chase Anderson 326 0.395 0.373 0.022
32 Jose Urena 276 0.390 0.373 0.017
33 Trevor Williams 465 0.408 0.373 0.035
34 Martin Perez 368 0.413 0.373 0.040
35 Jake Arrieta 421 0.392 0.374 0.018
36 Blake Snell 251 0.425 0.374 0.051
37 Luis Castillo 459 0.345 0.374 -0.029
38 J.A. Happ 479 0.389 0.374 0.015
39 Patrick Corbin 513 0.374 0.374 -0.001
40 Jose Berrios 579 0.378 0.375 0.003
41 Julio Teheran 486 0.349 0.375 -0.026
42 David Price 251 0.422 0.375 0.047
43 Michael Wacha 325 0.422 0.376 0.046
44 Zach Eflin 502 0.388 0.376 0.012
45 Max Scherzer 371 0.385 0.376 0.009
46 Jose Quintana 512 0.395 0.376 0.019
47 Mike Minor 581 0.361 0.376 -0.015
48 Robbie Ray 404 0.444 0.377 0.067
49 Sandy Alcantara 588 0.345 0.377 -0.032
50 Trent Thornton 376 0.385 0.377 0.008
51 Taylor Clarke 261 0.421 0.377 0.044
52 Kyle Gibson 474 0.414 0.377 0.037
53 Caleb Smith 402 0.398 0.377 0.021
54 Jordan Lyles 390 0.397 0.377 0.019
55 Rick Porcello 569 0.398 0.378 0.020
56 Ivan Nova 549 0.390 0.378 0.012
57 Jake Odorizzi 329 0.369 0.378 -0.010
58 Antonio Senzatela 437 0.402 0.378 0.024
59 Zack Wheeler 568 0.372 0.378 -0.006
60 Miles Mikolas 549 0.375 0.378 -0.003
61 Mike Leake 474 0.406 0.379 0.028
62 Chris Paddack 372 0.357 0.379 -0.021
63 Nick Pivetta 282 0.437 0.379 0.058
64 Felix Pena 265 0.369 0.379 -0.010
65 Mike Soroka 497 0.319 0.379 -0.060
66 Frankie Montas 262 0.358 0.379 -0.021
67 Domingo German 394 0.395 0.380 0.015
68 Marcus Stroman 421 0.343 0.380 -0.037
69 Vince Velasquez 325 0.444 0.381 0.064
70 Chris Sale 341 0.434 0.381 0.054
71 Mike Fiers 544 0.331 0.381 -0.050
72 Steven Brault 296 0.398 0.381 0.018
73 Brandon Woodruff 310 0.369 0.381 -0.012
74 Jeff Samardzija 381 0.352 0.381 -0.029
75 Peter Lambert 315 0.441 0.381 0.059
76 Trevor Cahill 261 0.414 0.382 0.033
77 Jack Flaherty 473 0.328 0.382 -0.054
78 Joey Lucchesi 385 0.358 0.382 -0.024
79 Cole Hamels 319 0.403 0.382 0.021
80 Aaron Sanchez 406 0.396 0.382 0.014
81 Joe Musgrove 455 0.382 0.382 -0.001
82 Shane Bieber 538 0.389 0.382 0.006
83 Jalen Beeks 306 0.394 0.382 0.012
84 Tanner Roark 475 0.411 0.383 0.028
85 Merrill Kelly 켈리 415 0.383 0.383 0.000
86 Yu Darvish 260 0.440 0.383 0.057
87 Brad Keller 503 0.330 0.383 -0.053
88 Aaron Brooks 350 0.389 0.383 0.005
89 Noah Syndergaard 557 0.379 0.383 -0.004
90 Clayton Kershaw 438 0.361 0.383 -0.023
91 Madison Bumgarner 359 0.372 0.383 -0.012
92 Jorge Lopez 385 0.428 0.384 0.044
93 Trevor Bauer 468 0.409 0.384 0.026
94 Drew Smyly 276 0.455 0.384 0.071
95 Homer Bailey 485 0.368 0.384 -0.016
96 Sonny Gray 416 0.327 0.384 -0.058
97 Adrian Sampson 413 0.448 0.384 0.063
98 Jose Suarez 255 0.464 0.385 0.080
99 Yonny Chirinos 379 0.352 0.385 -0.033
100 Ariel Jurado 410 0.413 0.385 0.028
101 Charlie Morton 432 0.358 0.385 -0.027
102 Gerrit Cole 437 0.387 0.385 0.002
103 Sam Gaviglio 277 0.366 0.385 -0.019
104 Brett Anderson 591 0.332 0.385 -0.053
105 Chris Archer 315 0.427 0.385 0.042
106 Adam Wainwright 381 0.389 0.385 0.004
107 David Hess 262 0.478 0.385 0.093
108 Dereck Rodriguez 281 0.421 0.385 0.036
109 Chris Bassitt 342 0.330 0.385 -0.056
110 Jakob Junis 535 0.406 0.385 0.020
111 Gabriel Ynoa 381 0.402 0.386 0.016
112 Matt Strahm 329 0.425 0.386 0.039
113 Matthew Boyd 478 0.447 0.386 0.061
114 Reynaldo Lopez 564 0.421 0.386 0.035
115 Tyler Mahle 348 0.402 0.386 0.016
116 Andrew Heaney 250 0.427 0.386 0.040
117 Yusei Kikuchi 528 0.426 0.387 0.039
118 Jaime Barria 257 0.451 0.387 0.064
119 Danny Duffy 383 0.379 0.387 -0.008
120 Zach Plesac 340 0.353 0.387 -0.034
121 Pablo Lopez 330 0.372 0.387 -0.016
122 James Paxton 300 0.391 0.388 0.003
123 Lance Lynn 471 0.390 0.388 0.002
124 Shaun Anderson 304 0.387 0.388 0.000
125 Glenn Sparkman 474 0.402 0.388 0.014
126 Steven Matz 468 0.400 0.389 0.012
127 Daniel Norris 435 0.398 0.389 0.009
128 Anthony DeSclafani 281 0.355 0.389 -0.034
129 Walker Buehler 390 0.363 0.389 -0.026
130 Michael Pineda 416 0.383 0.390 -0.006
131 Max Fried 465 0.404 0.390 0.014
132 Lucas Giolito 412 0.369 0.390 -0.021
133 Tyler Beede 354 0.404 0.391 0.013
134 Spencer Turnbull 431 0.383 0.391 -0.007
135 Jon Gray 419 0.401 0.391 0.009
136 Adrian Houser 298 0.382 0.392 -0.009
137 Mike Foltynewicz 338 0.381 0.392 -0.011
138 German Marquez 495 0.393 0.393 0.000
139 Eric Lauer 326 0.387 0.395 -0.008
140 Dakota Hudson 520 0.341 0.395 -0.054
Click headers to sort! (Default sort: x_wOBAcon)
Min. 250 BBE

2020:

2020 Location-Based “x”wOBAcon
rank player_name BBE wOBAcon x_wOBAcon diff
1 Tommy Milone 127 0.491 0.354 0.137
2 Zach Davies 147 0.333 0.355 -0.022
3 Tyler Anderson 141 0.343 0.356 -0.013
4 Dallas Keuchel 131 0.305 0.357 -0.052
5 Kenta Maeda 153 0.321 0.359 -0.038
6 Hyun Jin Ryu 류현진 116 0.406 0.362 0.044
7 Jordan Montgomery 128 0.429 0.362 0.066
8 Steven Brault 109 0.286 0.362 -0.076
9 Brett Anderson 133 0.399 0.363 0.035
10 Alec Mills 183 0.355 0.365 -0.010
11 Tyler Mahle 110 0.359 0.365 -0.006
12 Kyle Hendricks 238 0.335 0.366 -0.031
13 Pablo Lopez 144 0.340 0.367 -0.027
14 Anibal Sanchez 133 0.475 0.368 0.107
15 Aaron Nola 154 0.373 0.368 0.004
16 Shane Bieber 118 0.338 0.369 -0.030
17 Adam Wainwright 115 0.298 0.369 -0.071
18 Jon Lester 116 0.408 0.369 0.039
19 Erick Fedde 143 0.369 0.370 -0.001
20 John Means 110 0.389 0.370 0.019
21 Blake Snell 119 0.432 0.371 0.062
22 Marco Gonzales 136 0.330 0.371 -0.041
23 Zac Gallen 121 0.329 0.371 -0.042
24 Nick Margevicius 118 0.369 0.371 -0.003
25 Randy Dobnak 149 0.335 0.372 -0.037
26 Dylan Cease 158 0.355 0.372 -0.016
27 Kyle Gibson 185 0.430 0.372 0.058
28 Trevor Richards 107 0.451 0.372 0.079
29 Kris Bubic 145 0.402 0.373 0.029
30 J.A. Happ 121 0.332 0.373 -0.041
31 Matthew Boyd 163 0.484 0.373 0.111
32 Kyle Freeland 212 0.355 0.373 -0.018
33 Rick Porcello 171 0.405 0.374 0.031
34 Aaron Civale 146 0.418 0.375 0.044
35 Trevor Williams 174 0.445 0.375 0.070
36 David Peterson 134 0.302 0.375 -0.073
37 Kwang Hyun Kim 김광현 118 0.295 0.375 -0.080
38 Adrian Houser 173 0.411 0.375 0.036
39 Julio Teheran 105 0.468 0.375 0.093
40 Sixto Sanchez 112 0.322 0.376 -0.053
41 Mike Minor 144 0.380 0.376 0.004
42 Zack Greinke 161 0.382 0.376 0.005
43 Trevor Bauer 129 0.346 0.376 -0.030
44 Austin Voth 134 0.456 0.377 0.080
45 Ryan Weber 119 0.399 0.377 0.022
46 Danny Duffy 153 0.398 0.377 0.022
47 Johnny Cueto 169 0.383 0.377 0.006
48 Jacob deGrom 134 0.363 0.377 -0.014
49 Patrick Corbin 215 0.441 0.377 0.064
50 Max Scherzer 142 0.465 0.378 0.087
51 Asher Wojciechowski 109 0.477 0.378 0.099
52 Martin Perez 131 0.397 0.378 0.019
53 Dakota Hudson 102 0.267 0.378 -0.112
54 Gerrit Cole 169 0.402 0.378 0.024
55 Tony Gonsolin 101 0.256 0.378 -0.123
56 Andrew Heaney 170 0.355 0.379 -0.024
57 Brady Singer 157 0.341 0.379 -0.038
58 Dylan Bundy 168 0.344 0.379 -0.036
59 Jose Berrios 158 0.360 0.379 -0.019
60 Tanner Roark 135 0.510 0.379 0.130
61 Zack Wheeler 190 0.329 0.379 -0.051
62 JT Brubaker 119 0.386 0.380 0.006
63 Masahiro Tanaka 139 0.395 0.380 0.015
64 Jake Arrieta 137 0.400 0.380 0.020
65 Carlos Carrasco 153 0.376 0.381 -0.005
66 Brandon Woodruff 159 0.379 0.381 -0.002
67 Alex Young 111 0.416 0.381 0.035
68 Jesus Luzardo 163 0.395 0.381 0.014
69 Zach Plesac 139 0.322 0.382 -0.060
70 Mike Fiers 196 0.363 0.382 -0.019
71 Robbie Ray 135 0.489 0.382 0.107
72 Derek Holland 114 0.492 0.382 0.110
73 Julio Urias 157 0.304 0.383 -0.079
74 Zach Eflin 147 0.457 0.383 0.075
75 Luis Castillo 161 0.385 0.383 0.002
76 Kevin Gausman 146 0.402 0.383 0.019
77 Taijuan Walker 109 0.353 0.383 -0.030
78 Thomas Eshelman 101 0.354 0.383 -0.029
79 Sandy Alcantara 100 0.348 0.384 -0.036
80 Cristian Javier 135 0.321 0.384 -0.063
81 Chris Paddack 153 0.417 0.384 0.033
82 Dustin May 119 0.333 0.384 -0.051
83 Ross Stripling 150 0.433 0.385 0.048
84 Lucas Giolito 159 0.345 0.385 -0.040
85 Taylor Clarke 105 0.361 0.385 -0.024
86 Frankie Montas 140 0.440 0.385 0.055
87 Lance Lynn 171 0.355 0.386 -0.031
88 Alex Cobb 166 0.365 0.386 -0.022
89 Ryan Castellani 132 0.371 0.386 -0.016
90 Chris Bassitt 128 0.357 0.386 -0.029
91 Logan Webb 154 0.400 0.387 0.013
92 Griffin Canning 153 0.409 0.387 0.022
93 Anthony DeSclafani 103 0.407 0.387 0.019
94 Chad Kuhl 119 0.351 0.387 -0.036
95 Lance McCullers Jr. 140 0.359 0.388 -0.028
96 Jordan Lyles 193 0.398 0.388 0.010
97 Antonio Senzatela 220 0.323 0.388 -0.065
98 Luke Weaver 130 0.455 0.389 0.066
99 Jon Gray 139 0.381 0.390 -0.008
100 Patrick Sandoval 107 0.441 0.390 0.051
101 Justus Sheffield 143 0.309 0.391 -0.082
102 Sean Manaea 163 0.376 0.391 -0.015
103 Spencer Turnbull 140 0.313 0.391 -0.078
104 Justin Dunn 107 0.313 0.391 -0.078
105 Max Fried 146 0.319 0.391 -0.073
106 German Marquez 222 0.361 0.393 -0.032
107 Clayton Kershaw 133 0.295 0.393 -0.098
108 Jorge Lopez 115 0.364 0.394 -0.030
109 Sonny Gray 121 0.369 0.395 -0.026
110 Dinelson Lamet 140 0.293 0.395 -0.103
111 Brad Keller 143 0.236 0.399 -0.163
112 Garrett Richards 140 0.360 0.401 -0.041
113 Tyler Glasnow 122 0.431 0.401 0.030
114 Framber Valdez 188 0.352 0.404 -0.052
Click headers to sort! (Default sort: x_wOBAcon)
Min. 100 BBE





Currently investigating the relationship between pitcher effectiveness and beard density. Two-time FSWA award winner, including 2018 Baseball Writer of the Year, and 8-time award finalist. Previously featured in Lindy's Sports' Fantasy Baseball magazine (2018, 2019). Tout Wars competitor. Biased toward a nicely rolled baseball pant.

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This extremely rules.