Starting Pitcher Metrics Update – now with StatCast!

(Photo by Juan Salas/Icon Sportswire)

Welcome back to my “Plate Discipline” series. This week, I’m trying something new and exciting – broadening the scope of my analysis by incorporating StatCast data.  Previously, contact management was a completely missing element in my ranking system, which led to some strange results and a lot of double-checking other sites to figure out why.  StatCast is the best thing we’ve got right now to measure quality of contact, so my goal with this is to make my rankings more comprehensive of a pitcher’s full skill set, and help identify other types of luck beyond just strikeout rates.  I’m open to feedback, so if you have any thoughts about the new ranking system please let me know in the comments!

The specific metric from StatCast that I’ve chosen to incorporate into my rankings is xSLG. why xSLG? The answer is simple, but twofold:
1) I wanted this to capture “power luck”. This should cover luck factors like ballpark dimensions, weather conditions, and outfield defense. If a pitcher has given up many “cheap” homers relative to league average, they would be considered unlucky, and vice versa.
2) I wanted this to be completely independent of strikeout rates, which are covered already by the PD metrics. xwOBA would be the other option here, but that does include strikeouts (and walks) so I went with xSLG.

The method I used to update my scoring system is as follows:
1) Take the inverse of the xSLG (1-xSLG) because for SLG, a higher number is bad for the pitcher. We want a higher number to be good.
2) Multiply this by 1.5. This brings it to roughly the same level as PD scores, with almost all pitchers falling between 50% and 100%. Just a handful of the top pitchers can barely top 100%.
3) Add the PD score to this number, and divide by two to bring the number back to the 100 scale.

You may be asking – “Why should plate discipline and xSLG have equal weight?” and that’s a great question. For starters, in the MLB in 2018, strikeouts and walks compose just 31% of plate appearances. The other 69% end with a batted ball, so it’s easy to see that contact management is very important. I considered giving xSLG the full 69% weight, but I wanted to apply a significant discount for two reasons:
1) Going back to the premise of why we care so much about plate discipline metrics in the first place, strikeouts are just the most reliable method of getting outs. Managing contact is great, but depending on this can be a bit more risky for a pitcher. I want to make sure my rankings reflect that.
2) This is for fantasy purposes, and strikeouts are still a fantasy category. A strikeout is just worth more than other outs for fantasy purposes, so I didn’t want to make all outs completely equal.

With that out the way, here is the new-look data table.  Data is through Tuesday May 22.

 

RANKINGS

Ranking Name Team ERA IP PD Score Predicted K% Actual K% K% Difference xSLG SLG-xSLG Pitcher Score
1 Jacob deGrom Mets 1.75 51.1 101.01% 31.15% 34.30% -3.15% 0.267 -0.001 105.48%
2 Max Scherzer Nationals 1.78 65.2 107.74% 34.85% 40.90% -6.05% 0.336 -0.017 103.67%
3 Chris Sale Red Sox 2.17 70.2 104.61% 32.70% 34.50% -1.80% 0.340 -0.007 101.81%
4 Patrick Corbin Diamondbacks 2.6 62.1 100.80% 31.25% 33.30% -2.05% 0.346 -0.041 99.45%
5 Charlie Morton Astros 1.94 55.2 90.90% 28.80% 32.10% -3.30% 0.284 0.022 99.15%
6 Justin Verlander Astros 1.05 68.2 93.41% 27.65% 32.80% -5.15% 0.304 -0.059 98.91%
7 Gerrit Cole Astros 1.86 67.2 98.43% 31.70% 40.20% -8.50% 0.340 -0.050 98.72%
8 Noah Syndergaard Mets 2.91 58.2 97.92% 29.90% 27.80% 2.10% 0.339 0.024 98.53%
9 Shohei Ohtani Angels 3.35 40.1 97.60% 33.05% 32.30% 0.75% 0.361 -0.037 96.72%
10 Lance McCullers Jr. Astros 3.2 59 93.88% 28.50% 28.00% 0.50% 0.348 -0.040 95.84%
11 Kenta Maeda Dodgers 3.98 43 93.10% 28.20% 28.30% -0.10% 0.373 0.040 93.58%
12 Sean Newcomb Braves 2.39 52.2 80.08% 23.25% 27.10% -3.85% 0.300 -0.018 92.54%
13 Domingo German Yankees 7.36 14.2 87.90% 27.95% 25.80% 2.15% 0.362 0.003 91.80%
14 Aaron Nola Phillies 2.37 64.2 85.55% 24.10% 23.00% 1.10% 0.348 -0.042 91.68%
15 Trevor Cahill Athletics 2.75 36 96.12% 30.50% 25.20% 5.30% 0.423 -0.084 91.33%
16 Anibal Sanchez Braves 1.64 11 88.63% 23.55% 26.70% -3.15% 0.375 -0.034 91.19%
17 Jordan Lyles Padres 2.5 18 86.29% 23.75% 28.60% -4.85% 0.369 -0.010 90.47%
18 Trevor Bauer Indians 2.35 65 89.05% 26.15% 26.80% -0.65% 0.390 -0.081 90.28%
19 Masahiro Tanaka Yankees 4.95 56.1 96.77% 28.25% 21.70% 6.55% 0.444 -0.015 90.08%
20 Johnny Cueto Giants 0.84 32 76.17% 22.25% 22.20% 0.05% 0.311 -0.105 89.76%
21 James Paxton Mariners 3.3 62.2 91.76% 28.05% 31.50% -3.45% 0.417 -0.082 89.61%
22 Carlos Carrasco Indians 3.65 66.2 93.80% 27.20% 23.90% 3.30% 0.431 -0.060 89.57%
23 Luis Severino Yankees 2.35 65 89.80% 26.00% 30.00% -4.00% 0.405 -0.114 89.53%
24 Alex Wood Dodgers 3.32 57 86.41% 22.95% 22.30% 0.65% 0.383 -0.047 89.48%
25 Tyler Anderson Rockies 4.74 49.1 86.52% 25.90% 22.40% 3.50% 0.386 0.079 89.31%
26 Blake Snell Rays 3.07 58.2 91.15% 28.05% 26.20% 1.85% 0.418 -0.039 89.22%
27 Luis Castillo Reds 5.61 51.1 100.26% 31.95% 22.10% 9.85% 0.479 0.014 89.21%
28 Eduardo Rodriguez Red Sox 4.13 48 85.39% 25.20% 28.20% -3.00% 0.381 -0.005 89.12%
29 Caleb Smith Marlins 3.83 49.1 85.23% 24.45% 31.70% -7.25% 0.381 -0.061 89.04%
30 Jon Gray Rockies 5.34 55.2 88.24% 27.20% 26.00% 1.20% 0.402 0.052 88.97%
31 Carlos Martinez Cardinals 1.62 50 73.18% 19.95% 22.40% -2.45% 0.302 -0.062 88.94%
32 Jaime Barria Angels 2.13 25.1 88.38% 22.85% 19.40% 3.45% 0.407 -0.149 88.67%
33 Robbie Ray Diamondbacks 4.88 27.2 92.10% 30.05% 36.30% -6.25% 0.432 -0.013 88.65%
34 Kyle Gibson Twins 3.96 50 87.48% 26.85% 25.60% 1.25% 0.403 -0.057 88.51%
35 Walker Buehler Dodgers 2.38 34 68.46% 18.25% 29.60% -11.35% 0.284 -0.034 87.93%
36 Jose Berrios Twins 3.82 61.1 84.63% 23.15% 24.50% -1.35% 0.394 -0.043 87.77%
37 Jordan Montgomery Yankees 3.62 27.1 79.93% 21.50% 19.80% 1.70% 0.366 -0.010 87.52%
38 Hyun-Jin Ryu Dodgers 2.12 29.2 79.34% 24.80% 31.30% -6.50% 0.367 -0.059 87.14%
39 Vince Velasquez Phillies 4.18 51.2 79.45% 24.15% 28.40% -4.25% 0.369 0.086 87.05%
40 Zack Greinke Diamondbacks 3.71 60.2 93.81% 26.75% 27.80% -1.05% 0.466 -0.040 86.95%
41 CC Sabathia Yankees 2.4 41.1 82.54% 22.00% 19.10% 2.90% 0.391 -0.021 86.94%
42 Gio Gonzalez Nationals 2.38 56.2 77.78% 22.50% 24.60% -2.10% 0.362 -0.018 86.74%
43 Mike Clevinger Indians 2.87 59.2 85.18% 24.45% 21.30% 3.15% 0.415 -0.065 86.46%
44 Matt Boyd Tigers 3.29 52 78.25% 22.50% 19.60% 2.90% 0.370 -0.066 86.37%
45 Kevin Gausman Orioles 3.48 62 88.22% 25.80% 22.00% 3.80% 0.437 0.000 86.34%
46 Rick Porcello Red Sox 3.39 63.2 78.88% 20.90% 24.40% -3.50% 0.376 -0.023 86.24%
47 Jack Flaherty Cardinals 2.31 23.1 77.25% 24.95% 30.30% -5.35% 0.368 -0.075 86.03%
48 Julio Teheran Braves 4.17 54 83.50% 23.80% 21.00% 2.80% 0.411 0.010 85.93%
49 J.A. Happ Blue Jays 3.97 59 85.62% 25.45% 29.60% -4.15% 0.427 -0.053 85.79%
50 Tyler Skaggs Angels 2.88 50 80.68% 23.40% 25.40% -2.00% 0.394 -0.045 85.79%
51 Dylan Bundy Orioles 4.7 53.2 99.83% 30.00% 26.80% 3.20% 0.524 -0.003 85.62%
52 Fernando Romero Twins 1.66 21.2 84.21% 25.85% 24.40% 1.45% 0.423 -0.139 85.38%
53 Andrew Heaney Angels 3.35 40.1 82.37% 24.00% 27.70% -3.70% 0.413 -0.028 85.21%
54 Michael Fulmer Tigers 4.35 51.2 85.17% 23.25% 21.10% 2.15% 0.436 -0.038 84.88%
55 Kyle Freeland Rockies 3.17 54 77.14% 21.30% 22.40% -1.10% 0.387 -0.025 84.55%
56 Corey Kluber Indians 2.36 72.1 82.10% 22.55% 26.00% -3.45% 0.422 -0.078 84.40%
57 Clayton Kershaw Dodgers 2.86 44 83.57% 23.85% 26.50% -2.65% 0.433 -0.024 84.31%
58 Tyson Ross Padres 3.35 53.2 78.59% 22.20% 24.30% -2.10% 0.401 -0.081 84.22%
59 Garrett Richards Angels 3.31 51.2 79.92% 24.15% 26.00% -1.85% 0.410 -0.041 84.21%
60 Zach Eflin Phillies 1.56 17.1 71.67% 20.85% 25.00% -4.15% 0.356 -0.094 84.14%
61 Cole Hamels Rangers 3.38 58.2 88.27% 25.75% 25.10% 0.65% 0.469 -0.050 83.96%
62 Jameson Taillon Pirates 4.56 51.1 75.81% 19.40% 21.90% -2.50% 0.390 0.015 83.66%
63 Nick Kingham Pirates 3.44 18.1 80.63% 24.00% 30.40% -6.40% 0.423 -0.080 83.59%
64 Stephen Strasburg Nationals 3.36 67 85.09% 24.85% 27.90% -3.05% 0.456 -0.062 83.35%
65 Nick Pivetta Phillies 3.23 53 83.36% 23.55% 28.00% -4.45% 0.449 -0.102 83.00%
66 Luke Weaver Cardinals 4.31 54.1 75.36% 21.50% 21.90% -0.40% 0.396 -0.043 82.98%
67 Dallas Keuchel Astros 3.43 63 73.52% 18.70% 18.60% 0.10% 0.386 0.022 82.81%
68 Yu Darvish Cubs 4.95 40 78.17% 24.05% 27.20% -3.15% 0.418 0.007 82.73%
69 Clayton Richard Padres 4.87 61 78.42% 21.95% 20.20% 1.75% 0.420 0.016 82.71%
70 Ross Stripling Dodgers 3.26 19.1 65.11% 14.95% 28.40% -13.45% 0.336 0.054 82.36%
71 Zack Godley Diamondbacks 3.78 52.1 79.62% 23.55% 21.70% 1.85% 0.433 -0.041 82.34%
72 Wade LeBlanc Mariners 1.33 20.1 78.27% 16.70% 19.20% -2.50% 0.434 -0.118 81.58%
73 Michael Soroka Braves 3.68 14.2 87.82% 25.40% 21.70% 3.70% 0.498 -0.054 81.56%
74 Mike Foltynewicz Braves 2.72 53 70.33% 20.75% 27.10% -6.35% 0.383 -0.021 81.44%
75 Chris Archer Rays 5.01 59.1 89.43% 27.30% 22.40% 4.90% 0.511 -0.063 81.39%
76 Tanner Roark Nationals 3.39 58.1 77.99% 21.45% 22.80% -1.35% 0.436 -0.087 81.30%
77 Jake Odorizzi Twins 3.17 54 83.25% 24.30% 24.40% -0.10% 0.481 -0.039 80.55%
78 Jeremy Hellickson Nationals 2.13 38 78.25% 20.70% 21.80% -1.10% 0.450 -0.109 80.38%
79 Miles Mikolas Cardinals 2.24 60.1 76.66% 18.95% 19.40% -0.45% 0.441 -0.099 80.26%
80 Joey Lucchesi Padres 3.23 47.1 78.36% 23.55% 24.90% -1.35% 0.456 -0.033 79.98%
81 German Marquez Rockies 4.62 50.2 70.85% 19.65% 20.90% -1.25% 0.406 0.024 79.98%
82 Hector Velazquez Red Sox 2.53 10.2 91.36% 23.35% 22.70% 0.65% 0.543 -0.091 79.96%
83 Elieser Hernandez Marlins 1.8 10 75.98% 22.60% 13.50% 9.10% 0.443 -0.110 79.76%
84 Zack Wheeler Mets 5.32 44 79.08% 21.30% 23.60% -2.30% 0.470 -0.046 79.29%
85 Jake Arrieta Phillies 2.82 44.2 60.99% 13.35% 15.60% -2.25% 0.353 -0.039 79.02%
86 Jose Urena Marlins 4.55 57.1 73.27% 18.25% 18.70% -0.45% 0.436 -0.040 78.93%
87 Lance Lynn Twins 6.34 44 76.50% 21.40% 21.80% -0.40% 0.458 0.013 78.90%
88 Aaron Sanchez Blue Jays 4.47 50.1 78.05% 23.30% 17.60% 5.70% 0.471 -0.063 78.70%
89 Sean Manaea Athletics 2.71 66.1 79.13% 21.45% 19.80% 1.65% 0.480 -0.154 78.57%
90 Yonny Chirinos Rays 4.37 22.2 75.93% 20.10% 21.00% -0.90% 0.462 -0.043 78.31%
91 Jon Lester Cubs 2.52 50 78.25% 22.40% 21.20% 1.20% 0.481 -0.115 78.05%
92 Ryan Yarbrough Rays 4.91 14.2 60.90% 15.05% 20.30% -5.25% 0.367 0.026 77.92%
93 Jhoulys Chacin Brewers 3.32 57 69.11% 19.10% 17.30% 1.80% 0.422 -0.054 77.91%
94 Sonny Gray Yankees 5.48 46 70.56% 19.95% 17.20% 2.75% 0.433 -0.027 77.81%
95 James Shields White Sox 4.52 61.2 74.39% 21.15% 16.00% 5.15% 0.461 -0.136 77.62%
96 Chad Bettis Rockies 3.3 60 72.60% 19.90% 16.10% 3.80% 0.454 -0.074 77.25%
97 David Price Red Sox 4.38 51.1 72.71% 19.15% 21.60% -2.45% 0.455 -0.054 77.23%
98 Jason Vargas Mets 9.87 17.1 85.48% 23.70% 18.40% 5.30% 0.542 0.094 77.09%
99 Michael Wacha Cardinals 3.08 49.2 71.61% 21.60% 21.00% 0.60% 0.454 -0.130 76.76%
100 Jakob Junis Royals 3.51 56.1 76.99% 19.80% 20.80% -1.00% 0.492 -0.064 76.60%
101 Jordan Zimmermann Tigers 4.88 31.1 77.97% 20.55% 23.70% -3.15% 0.503 -0.047 76.26%
102 Tyler Mahle Reds 4.53 53.2 75.01% 21.15% 22.60% -1.45% 0.485 0.010 76.13%
103 Nick Tropeano Angels 4.45 32.1 79.23% 24.40% 19.40% 5.00% 0.514 -0.078 76.07%
104 Tyler Chatwood Cubs 3.74 45.2 61.04% 17.45% 20.20% -2.75% 0.394 -0.076 75.97%
105 Trevor Richards Marlins 4.94 23.2 70.89% 19.70% 22.40% -2.70% 0.463 -0.019 75.72%
106 Mike Minor Rangers 5.59 48.1 79.54% 22.65% 21.90% 0.75% 0.523 0.003 75.54%
107 Junior Guerra Brewers 2.98 42.1 68.01% 19.90% 23.00% -3.10% 0.448 -0.123 75.41%
108 Wei-Yin Chen Marlins 6.55 22 66.45% 17.55% 13.90% 3.65% 0.438 0.150 75.38%
109 Kyle Hendricks Cubs 3.4 55.2 74.01% 19.85% 19.50% 0.35% 0.489 -0.066 75.33%
110 Matt Moore Rangers 8.19 40.2 77.85% 21.25% 15.90% 5.35% 0.515 0.029 75.30%
111 Jose Quintana Cubs 4.47 48.1 66.62% 17.95% 21.10% -3.15% 0.445 -0.013 74.94%
112 Marco Estrada Blue Jays 5.15 50.2 76.08% 20.25% 18.60% 1.65% 0.512 0.033 74.64%
113 Luis Perdomo Padres 8.36 14 68.53% 21.20% 22.10% -0.90% 0.465 0.058 74.39%
114 Reynaldo Lopez White Sox 2.98 54.1 69.51% 19.80% 17.20% 2.60% 0.473 -0.094 74.28%
115 Chase Anderson Brewers 3.86 51.1 67.05% 17.60% 15.90% 1.70% 0.460 -0.031 74.03%
116 Jeff Samardzija Giants 6.3 30 73.11% 19.40% 16.70% 2.70% 0.504 -0.085 73.75%
117 Lucas Giolito White Sox 6.42 47.2 65.80% 19.85% 12.30% 7.55% 0.458 -0.061 73.55%
118 Brandon McCarthy Braves 4.67 52 62.35% 15.10% 18.80% -3.70% 0.436 0.037 73.48%
119 Brent Suter Brewers 5.05 41 69.94% 16.70% 16.60% 0.10% 0.487 -0.008 73.44%
120 Jaime Garcia Blue Jays 6.28 38.2 70.22% 20.00% 21.40% -1.40% 0.494 0.045 73.06%
121 Zach Davies Brewers 4.24 34 73.21% 21.20% 17.20% 4.00% 0.514 -0.073 73.05%
122 Marco Gonzales Mariners 4.66 46.1 70.91% 17.05% 21.40% -4.35% 0.499 -0.028 73.03%
123 Trevor Williams Pirates 3.05 59 65.59% 16.25% 17.10% -0.85% 0.464 -0.115 73.00%
124 Andrew Triggs Athletics 5.23 41.1 75.87% 21.35% 23.60% -2.25% 0.533 -0.120 72.96%
125 Adam Wainwright Cardinals 4 18 55.88% 14.00% 17.10% -3.10% 0.400 0.035 72.94%
126 Mike Fiers Tigers 4.57 43.1 71.83% 18.60% 15.30% 3.30% 0.512 -0.006 72.51%
127 Francisco Liriano Tigers 3.42 52.2 73.83% 21.55% 17.60% 3.95% 0.532 -0.204 72.01%
128 Felix Hernandez Mariners 5.53 55.1 69.51% 17.95% 20.00% -2.05% 0.507 -0.047 71.73%
129 Ty Blach Giants 4.37 57.2 61.20% 14.75% 10.80% 3.95% 0.452 -0.051 71.70%
130 Daniel Mengden Athletics 3.3 57.1 71.65% 18.00% 16.10% 1.90% 0.522 -0.109 71.68%
131 Marcus Stroman Blue Jays 7.71 37.1 72.91% 21.20% 18.20% 3.00% 0.537 -0.082 71.18%
132 Matt Harvey – – – 4.63 35 63.62% 16.35% 19.50% -3.15% 0.477 0.001 71.04%
133 Drew Pomeranz Red Sox 5.97 28.2 67.10% 17.45% 21.10% -3.65% 0.505 0.012 70.68%
134 Jake Faria Rays 5.48 47.2 68.14% 18.95% 18.20% 0.75% 0.516 -0.093 70.37%
135 Dillon Peters Marlins 5.84 24.2 62.02% 16.30% 14.40% 1.90% 0.480 -0.011 70.01%
136 Ian Kennedy Royals 5.3 52.2 72.94% 17.80% 21.00% -3.20% 0.554 -0.056 69.92%
137 Ivan Nova Pirates 4.79 56.1 73.39% 19.30% 17.80% 1.50% 0.558 -0.080 69.85%
138 Jason Hammel Royals 5.7 60 75.80% 19.00% 12.50% 6.50% 0.576 -0.121 69.70%
139 Eric Skoglund Royals 6.15 45.1 73.69% 18.00% 18.00% 0.00% 0.566 -0.086 69.40%
140 Steven Matz Mets 4.42 36.2 58.85% 17.65% 22.90% -5.25% 0.474 0.007 68.88%
141 Brandon Woodruff Brewers 8.49 11.2 68.86% 21.20% 17.50% 3.70% 0.543 -0.064 68.71%
142 Matt Wisler Braves 3.63 17.1 73.56% 20.65% 18.60% 2.05% 0.575 -0.160 68.65%
143 Brandon Finnegan Reds 7.4 20.2 63.05% 15.95% 13.60% 2.35% 0.513 0.040 68.05%
144 Derek Holland Giants 4.94 47.1 64.99% 17.45% 21.00% -3.55% 0.529 -0.049 67.82%
145 Chris Stratton Giants 4.92 53 62.45% 16.55% 18.30% -1.75% 0.517 -0.071 67.45%
146 Eric Lauer Padres 6.67 27 58.97% 15.55% 18.90% -3.35% 0.494 0.054 67.43%
147 Chad Kuhl Pirates 4.53 49.2 69.72% 20.90% 22.20% -1.30% 0.566 -0.074 67.41%
148 Ben Lively Phillies 6.85 23.2 62.80% 17.85% 19.10% -1.25% 0.524 0.021 67.10%
149 Kendall Graveman Athletics 7.6 34.1 67.59% 17.05% 17.10% -0.05% 0.557 -0.015 67.02%
150 Sal Romano Reds 5.62 49.2 54.44% 12.25% 15.10% -2.85% 0.471 -0.004 66.89%
151 Doug Fister Rangers 3.43 44.2 58.31% 14.10% 17.10% -3.00% 0.497 -0.067 66.88%
152 Mike Leake Mariners 5.46 57.2 70.01% 16.30% 14.60% 1.70% 0.582 -0.086 66.36%
153 Andrew Suarez Giants 5.68 31.2 62.90% 14.20% 22.70% -8.50% 0.548 -0.015 65.35%
154 Danny Duffy Royals 6.88 51 74.08% 20.05% 18.70% 1.35% 0.627 -0.064 65.01%
155 Hector Santiago White Sox 7.11 19 61.63% 18.05% 18.80% -0.75% 0.545 -0.016 64.94%
156 Brett Anderson Athletics 7.63 15.1 67.11% 17.95% 11.10% 6.85% 0.582 0.009 64.90%
157 Joe Biagini Blue Jays 7.71 18.2 65.88% 16.70% 14.40% 2.30% 0.574 -0.080 64.89%
158 Andrew Cashner Orioles 4.72 55.1 63.33% 15.70% 20.20% -4.50% 0.562 -0.057 64.52%
159 Dan Straily Marlins 3.6 20 76.72% 21.50% 15.90% 5.60% 0.653 -0.209 64.39%
160 Bartolo Colon Rangers 3.68 51.1 59.79% 12.00% 14.80% -2.80% 0.548 -0.066 63.80%
161 Steven Brault Pirates 5.54 26 69.17% 19.95% 13.40% 6.55% 0.611 -0.213 63.76%
162 Taijuan Walker Diamondbacks 3.46 13 57.58% 15.05% 16.10% -1.05% 0.548 -0.156 62.69%
163 David Hess Orioles 6.75 10.2 70.31% 15.45% 14.60% 0.85% 0.636 -0.006 62.45%
164 Alex Cobb Orioles 6.56 35.2 58.25% 13.25% 11.30% 1.95% 0.558 0.088 62.28%
165 Homer Bailey Reds 6.11 53 63.59% 15.25% 13.10% 2.15% 0.609 -0.056 61.12%
166 Jarlin Garcia Marlins 3.55 33 65.05% 18.00% 16.70% 1.30% 0.620 -0.222 61.03%
167 Bryan Mitchell Padres 6.47 32 47.09% 11.80% 10.40% 1.40% 0.523 -0.027 59.32%
168 Rich Hill Dodgers 6.2 24.2 59.36% 16.25% 21.70% -5.45% 0.621 -0.055 58.10%
169 Matt Koch Diamondbacks 3.95 41 59.79% 13.45% 13.00% 0.45% 0.635 -0.141 57.27%
170 Miguel Gonzalez White Sox 12.41 12.1 66.26% 15.45% 7.60% 7.85% 0.684 0.099 56.83%
171 Josh Tomlin Indians 8.1 30 73.79% 19.05% 12.90% 6.15% 0.746 -0.019 55.94%
172 Carson Fulmer White Sox 8.13 31 56.52% 14.55% 16.60% -2.05% 0.640 -0.136 55.26%
173 Martin Perez Rangers 9.67 22.1 51.79% 11.55% 10.90% 0.65% 0.610 0.060 55.15%
174 Chris Tillman Orioles 10.46 26.2 49.38% 11.85% 9.50% 2.35% 0.641 0.011 51.61%

 

Some new columns have been added this week:
1) ERA, just to help provide context and tie everything back to the real world results
2) xSLG, as explained above
3) SLG – xSLG. This is included for reference to save everyone a trip to the Savant website. It should be used similar to the “K% difference” column, where a positive number indicates bad luck so far (i.e. likely to regress positively), while a negative number indicates good luck so far (likely to regress negatively).
3) “Pitcher Score”. For lack of a better name, this is the new combined ranking metric with a 50/50 weight between PD Score and xSLG. If you have a better name, please let me know in the comments!

A few immediate observations:
1) Jacob DeGrom has been killing it on the contact-management side, enough for him to supplant Scherzer & Sale for the overall top spot in the ranking. He’s been super-elite at both components, ranking 3rd in PD score and 1st in xSLG. He’s just pitching incredibly well this year.
2) How about that Astros rotation? Four pitchers in the top 10 is pretty nuts.
3) Some of my previous darlings, like Castillo and Bundy, have taken significant hits now that contact-management is included. They are both still top 50 pitchers, and they should be owned. But these new rankings should be a much better reflection of their overall value.
4) Likewise, some good pitchers who graded out very poorly in my previous rankings have jumped way up. Carlos Martinez and Johnny Cueto come to mind, who have been excellent contact managers in 2018 but are not striking out a lot of guys.
5) Chris Tillman is dead last, so I must be doing something right.

 

PLAYER UPDATES

Nick Pivetta (SP, Philadelphia Phillies)

Something seems to have clicked for Pivetta, as demonstrated by the drop in his ERA from six last year down to just 3.2 so far this year. Strikeouts are up, walks are down, plus hard contact and home runs are down, so at a glance it seems like good news all around. But let’s see if we can break it down further.  For starters, to break out the components of his Pitcher Score, basically PD metrics and xSLG have contributed equally to his ranking. He is currently above average, but not elite, in both areas.

On the plate discipline side, he’s posting solid improvements across the board from last year. All four of the metrics I track are up significantly, supporting both the rise in his strikeout rate and fall in walk rate relative to last year’s numbers. So that’s certainly great news and a big part of his success. He’s over-performing his K% slightly, but not so much to be considered a red flag.

When it comes to contact management though, things look pretty different. His xSLG figure is essentially unchanged from last year’s number. It looks like what happened here is that he was unlucky last year, and this year his luck has swung over to the positive side. His 2017 SLG-xSLG was +50, but this year’s it’s sitting at -102. So in total, from last year to this year he has reduced his real SLG by 150 points while allowing the same overall quality of batted balls.

Conclusion: His improvement in strikeout and walk rates is real, but the improved contact management is more a mirage based on good fortune. Overall he’s certainly improved over last year, but not to the extent his drop in ERA would suggest.

 

Trevor Cahill (SP, Oakland Ahtletics)

Cahill has stood out in the rankings so far early in the season. In last week’s update, he had the third highest PD score of all pitchers. Granted, he had less innings than most pitchers. But it’s hard to look at the 36 innings he’s compiled this year and not be encouraged.  He was also very good for a short stretch last year before getting injured, so it’s not completely out of nowhere.

On the plate discipline side, Cahill continues to put up VERY solid metrics. His Contact% and SwStr% are both among the top 10 in MLB, so his strikeout stuff is very real. He’s under-performing a bit on his K rate, as the metrics point towards 30% rather than 25%.

Looking at his contact management, he appears to have benefited from some good luck this year with a SLG – xSLG of -84. His xSLG mark of .423 is still slightly above average (average = .460).

Conclusion: Like Pivetta, it seems to be a mixed bag of real and less-real. Their contact management skills rate similarly, But Cahill has demonstrated significantly better strikeout skills, explaining the gap of 50 pitchers between them in the rankings.

 

Walker Buehler (SP, Los Angeles Dodgers)

Buehler has been one of the biggest mismatches between my rankings and real performance so far this year. Last week he received a D grade in plate discipline…with an ERA of two point something. Now that StatCast data has been added, we can see why. He’s jumped over 100 spots as a result, due to his elite contact management thus far. By xSLG, he’s been the 3rd best pitcher in MLB, which is pretty impressive stuff for a rookie.

On the plate discipline side, he continues his pattern of completely smashing expectations for his K%. That over-performance of 11% is still 2nd largest in MLB behind his teammate Ross Stripling. But actually, he’s started closing the gap already – it was 14% just a week ago.  His actual K% has dropped two percent in that week, and should continue to fall.

Buehler’s SLG – xSLG does indicate a little bit of good luck, but 30 points is not really enough to worry about. After all, he’s still been the 3rd best contact-manager so far even with the 30 points removed.

Conclusion: The new ranking system should help you feel a lot better about owning Buehler. The K% should continue to drop, but the elite contact management means he is still someone you want to own.

 

Anibal Sanchez (SP, Atlanta Braves)

Just a quick note here, yes the stats look good but it’s 11 innings. This is a 34-year-old Anibal Sanchez we’re talking about, and I’m not buying it. He just gave up 8 runs in a rehab start in AAA and I think that’s all you really need to know.  Maybe he might be worth keeping an eye on for very, very deep leagues.

 

Miles Mikolas (SP, St. Louis Cardinals)

Mikolas is a guy I was expecting to jump way up in the rankings once quality of contact was included. After all, how else could a pitcher with just average strikeout ability put up such a good stat line? However, it just didn’t happen. By PD metrics, he is the #84 ranked pitcher, and by the combined metric he jumped all the way to…#79. In contrast to Buehler, who jumped over 100 spots, this should be concerning to Mikolas owners.

Mikolas is not a big strikeout guy; his K% is just around league average (20%).  The PD metrics are perfectly in line with that rate, so no red flags there.  He’s got an elite walk rate of just 2.5%.  Obviously that’s a good thing, but with those low strikeout and walk rates, almost four out of every five of Mikolas’ plate appearances will end in a batted ball. So you could say that he depends on managing quality of contact even more than most pitchers.

And that brings me to the reason he didn’t jump up the rankings: Statcast says there’s a fair amount of luck involved on his batted balls. His xSLG value of .441 is just barely better than average, but his actual SLG is 100 points lower.  With that in mind, we have a pitcher who looks just average in both strikeout ability and contact management, and the only area he is elite is preventing walks.

Conclusion: Mikolas has been depending on weak contact so far this year and it may not be fully sustainable.  His profile leaves him even more vulnerable to fluctuations in BABIP than a typical pitcher, which is concerning given the large discrepancy in his xSLG vs actual results. Mikolas’ saving grace is an extremely low walk rate. This should help him limit the damage if and when it arrives, but I’d still consider selling here.

 

Stephen Strasburg (SP, Washington Nationals)

Strasburg, unlike the previous two guys, is known for his strikeout stuff and not as a contact manager. So it’s not a shock that he took a bit of a hit once the StatCast data was added. However I’m still surprised at his overall low score and ranking just the 64th best pitcher overall. His ERA of 3.36 is still very solid, but I think it’s fair to ask – what’s going on with Stras?

Even on the plate discipline side, he hasn’t been elite this year. He finished 2017 with the 11th best PD score of all qualified starters, but this year all his metrics have dipped slightly in the wrong direction, and is currently ranked just 43rd with a “B” grade. He’s still striking out 28% of batters though, so it’s hasn’t really affected him all that much.

On the xSLG side, Strasburg rates almost exactly average with his mark of .456.  This is over a hundred points worse than he was last year.  He’s been a bit lucky so far this year, with a -60 “SLG-xSLG” which has helped masked the issue.  But the spike in his HR rate (career high) does not appear to be a fluke – he’s been getting hit hard.

Looking at Strasburg’s pitch usage, not much has really changed. However when you look at the individual pitch results, his 4-seamer really stands out as having problems this year. He’s allowing an wRC+ of 192 on that pitch, compared to 113 for his career and 102 last year.  Considering he throws the 4-seamer over 40% of the time, that’s a real concern.  Looking at the PitchFX movement numbers, these are also down so that could explain it.  Hopefully he figures that out soon, otherwise he’s looking like just an above average pitcher, not the elite arm you probably paid for.

Conclusion: Strasburg has seemed to take a bit of downturn this year, in both areas.  There seems to be a problem with his fastball, his key pitch, and I might consider selling for the right deal, while his ERA is still nice looking.

 

Hyun-Jin Ryu (SP, Los Angeles Dodgers) and Vince Velazquez (SP, Philadelphia Phillies)

I’m writing about these two completely unrelated pitchers together, because I noticed going down the list that they have extremely similar stat lines at this point in the season, when it comes to the underlying metrics.  Take a look at this:

Ranking Name Team O-Swing% Contact% F-Strike% SwStr% BB% xSLG
38 Hyun-Jin Ryu Dodgers 25.40% 73.20% 56.50% 10.90% 8.70% 0.367
39 Vince Velasquez Phillies 26.90% 75.30% 57.80% 11.30% 8.40% 0.369

They are essentially the same pitcher…but Ryu’s ERA is just half of Velasquez (2.1 vs 4.2).  The difference seems entirely due to batted ball luck.  Ryu’s “SLG – xSLG” is -60.  Meanwhile Velasquez’s number is +80.  So despite the fact these two pitchers have allowed the same overall quality of batted balls, there is a swing of 140 points in real SLG between their results.  Some of this is likely to be park-related, as Ryu does play in a more pitcher-friendly park.  But I think it really serves to illustrate just how much of an impact this “power luck” can have on a pitcher.

Chaz Steinberg http://reddit.com/u/chazzy_cat

Third generation Giants fan, begrudging Kershaw admirer, and lover of Taco Bell

sdf

Comments


Harper Wallbanger

Any reason for xSLG over xISO? Would expect xSLG to be less reliable because xStats still has trouble with BABIP (doesn’t account for shifting, doesn’t seem to fully capture footspeed). I guess I’m assuming you’re using Perpetua’s xStats and not Baseball Savant’s – is that right?

Barrett

Kluber is #56? I get that some things are being challenged here, but his numbers don’t make much sense for mere mortals and he’s all the way down to #56? How does this work exactly?

Chaz Steinberg

So basically – Kluber has this reputation for being bad in the early goings, but then picking up steam over the season. He’s done that this year too, only he got luckier than usual, so it didn’t really affect his ERA. But both sides of the metrics (plate discipline and batted ball quality) are significantly down from last year, and that’s why he’s ranked lower. However he’s pretty much the one guy I wouldn’t really worry about a low rank in May. He’s got a history of doing this, and seems to be getting back on track already, with his last start (against Houston no less) his best start of the year.

Mallex P. Keaton

I loved the pure PD list, the xSLG addition is even better. One question I have is do you think it would be worth it to put BB% or WHIP information into the calculation? Or at least add it to the chart? Maybe even using FIP or xFIP instead of ERA on the chart? Thanks for all the work you’ve done here.

Chaz Steinberg

Thanks for the feedback! Walks are included in the calculation already, via O-Swing% and F-Strike% metrics. Overall, walks are roughly 1/4th of the PD score so about 1/8th of the Pitcher Score. I have been considering changes in this area to give walks more prominence, and also incorporating some other feedback I’ve received about the general weightings. Stay tuned for updates

Rusty

Luckily or maybe not so luckily, I have 7 of your top 31. I have two questions…

1. Does Patrick Corbin’s recent drop in velo scare you regardless of these numbers? (He’s mine)
2. Domingo German is on our wire. Considering your rankings it seems like a good idea to go get him. Shallow-ish 10 team H2H points league with DeGrom, Corbin, CMart, McCullers, Carrasco, Ray, Snell, Cahill, Soroka, Alex Reyes, Knebel, Musgrove, Rodon. Could you see adding German?

Chaz Steinberg

Sorry for the delay, but:
1) Corbin is not quite as good at 89 MPH as he was at 93 when he was completely dominant. But even sitting at 89 I think he is still someone you want on your squad. He still projects as a 25-30% strikeout guy, and a strong contact manager with a high GB rate.
2) Keep in mind the sample sizes. German only has a couple starts under his belt and so we don’t really know much about him. Your staff looks really solid and assuming Musgrove & Rodon are on your DL, there’s no one I would drop for German.

Larry

Good stuff. Solely for the sake of clarity and ease of digesting the data, I would suggest formatting all the numbers in the ERA column to two decimal places and giving all the numbers in the IP column one decimal point.

Leave a Comment


Your email address will not be published.