Weekly SP Metrics Update – Season Mid-Point Edition

(Photo by Kevin Abele/Icon Sportswire)

 

Welcome back, everyone.  We’re now almost halfway through the MLB season, a great time to look back at some of the more surprising performances so far.  With that in mind, here are the newly updated mid-season rankings, starting with the qualified SP:

Rank Name Prev Diff Score IP ERA PD Score xK% K% xK%-K% BB% xSLG SLG-xSLG
1 Max Scherzer 1 0 103.4% 114.2 2.04 108.6% 34.3% 37.1% -2.8% 6.1% 0.345 -0.029
2 Jacob deGrom 2 0 103.0% 101.1 1.69 102.5% 30.8% 31.4% -0.7% 6.7% 0.310 -0.024
3 Chris Sale 3 0 103.0% 109.0 2.56 104.4% 32.4% 35.6% -3.2% 6.5% 0.323 -0.011
4 Patrick Corbin 5 +1 96.6% 100.0 3.24 101.5% 31.0% 32.5% -1.6% 6.8% 0.389 -0.057
5 Trevor Bauer 6 +1 95.9% 107.0 2.44 93.5% 28.3% 31.8% -3.6% 7.9% 0.345 -0.035
6 Justin Verlander 4 -2 95.5% 113.2 1.82 88.9% 25.8% 31.3% -5.5% 5.3% 0.319 -0.012
7 Charlie Morton 9 +2 94.7% 95.2 2.54 91.1% 28.3% 31.0% -2.8% 9.9% 0.345 -0.011
8 Aaron Nola 7 -1 92.9% 101.1 2.58 84.6% 23.8% 25.1% -1.4% 7.3% 0.326 -0.027
9 Jon Gray 12 +3 92.8% 88.0 5.52 92.2% 29.1% 28.6% 0.5% 6.9% 0.377 0.068
10 Gerrit Cole 8 -2 92.8% 105.2 2.56 91.6% 28.4% 36.3% -8.0% 8.2% 0.374 -0.056
12 Blake Snell 13 +1 92.1% 101.1 2.31 91.7% 29.1% 28.1% 1.0% 10.0% 0.383 -0.059
11 Luis Severino 10 -1 92.1% 111.2 2.10 90.7% 26.7% 30.4% -3.8% 6.0% 0.376 -0.083
13 Carlos Carrasco 15 +2 90.6% 91.1 4.24 94.4% 27.4% 25.6% 1.8% 5.9% 0.422 -0.031
14 Jose Berrios 19 +5 90.3% 103.0 3.15 89.2% 24.9% 26.9% -2.0% 4.9% 0.391 -0.043
15 Eduardo Rodriguez 14 -1 90.2% 81.2 3.86 85.4% 24.2% 26.1% -2.0% 7.4% 0.367 0.009
16 Lance McCullers Jr. 20 +4 90.0% 94.1 3.82 89.9% 27.2% 25.7% 1.5% 9.3% 0.399 -0.027
17 James Paxton 16 -1 89.6% 103.2 3.65 90.6% 26.7% 32.0% -5.4% 7.2% 0.410 -0.033
18 Sean Newcomb 11 -7 88.6% 87.0 2.59 77.0% 21.9% 23.9% -2.0% 10.8% 0.332 -0.027
19 Vince Velasquez 24 +5 87.9% 86.1 4.69 82.6% 25.0% 28.5% -3.5% 9.0% 0.379 0.046
20 Mike Clevinger 18 -2 87.8% 104.0 3.03 83.4% 23.6% 22.4% 1.2% 8.1% 0.386 -0.034
21 Tyler Anderson 29 +8 87.1% 85.2 4.62 85.2% 25.0% 21.1% 3.9% 8.2% 0.406 0.064
22 Tyler Skaggs 23 +1 87.1% 87.0 2.69 85.3% 24.3% 26.3% -2.0% 7.3% 0.407 -0.030
23 Kyle Gibson 22 -1 86.8% 88.2 3.25 83.3% 24.0% 23.3% 0.7% 10.6% 0.398 -0.058
24 Nick Pivetta 25 +1 86.8% 84.1 4.06 85.0% 24.9% 28.6% -3.8% 6.8% 0.410 -0.018
25 Corey Kluber 31 +6 86.8% 113.1 2.54 82.8% 22.4% 26.9% -4.5% 3.0% 0.395 -0.049
26 Dylan Bundy 28 +2 86.5% 96.0 3.75 94.5% 27.5% 27.0% 0.5% 7.3% 0.477 -0.037
27 Mike Foltynewicz 26 -1 86.4% 84.0 2.14 75.3% 21.9% 28.4% -6.6% 10.7% 0.350 -0.040
28 Alex Wood 27 -1 86.3% 80.2 4.13 86.5% 23.0% 22.3% 0.7% 4.8% 0.426 -0.037
29 Lance Lynn [UR] [N/A] 84.8% 78.2 4.81 78.5% 22.5% 22.8% -0.4% 13.2% 0.392 0.007
30 Jameson Taillon 30 0 84.7% 88.2 3.96 76.7% 20.3% 22.4% -2.1% 6.7% 0.382 0.007
31 Luis Castillo 33 +2 84.7% 83.2 5.70 92.7% 28.7% 22.4% 6.3% 8.8% 0.489 -0.004
32 Rick Porcello 35 +3 84.2% 99.1 3.44 74.0% 19.0% 22.7% -3.7% 5.3% 0.370 -0.025
33 Miles Mikolas 41 +8 84.1% 97.0 2.69 78.6% 18.7% 18.5% 0.2% 3.1% 0.402 -0.067
34 J.A. Happ 34 0 83.9% 97.0 3.62 76.5% 21.5% 27.0% -5.5% 6.9% 0.391 -0.035
35 Stephen Strasburg 40 +5 83.5% 80.2 3.46 85.1% 24.5% 29.1% -4.6% 5.8% 0.454 -0.055
36 Matt Boyd 37 +1 83.4% 82.1 4.15 74.0% 21.1% 19.9% 1.2% 9.5% 0.381 -0.023
37 Tyson Ross 36 -1 82.8% 95.0 3.32 77.0% 20.8% 22.7% -1.9% 8.8% 0.409 -0.045
38 Zack Greinke 46 +8 82.7% 96.0 3.66 86.4% 23.9% 26.0% -2.1% 4.9% 0.474 -0.039
39 Dallas Keuchel 44 +5 82.6% 97.0 3.90 74.2% 18.9% 18.5% 0.4% 6.0% 0.393 0.025
40 Clayton Richard 45 +5 82.5% 100.0 4.23 78.3% 21.9% 19.5% 2.4% 7.7% 0.422 -0.035
41 Sonny Gray 52 +11 82.2% 80.1 4.93 76.9% 22.4% 20.6% 1.8% 9.6% 0.417 -0.001
42 Zack Wheeler 47 +5 82.0% 81.2 4.85 77.9% 21.1% 22.3% -1.2% 8.8% 0.426 -0.051
43 Cole Hamels 48 +5 81.9% 97.1 3.61 85.8% 24.8% 23.3% 1.5% 8.9% 0.480 -0.033
44 Kevin Gausman 32 -12 81.9% 94.1 4.20 84.4% 24.1% 21.7% 2.4% 6.2% 0.471 0.004
45 German Marquez 43 -2 81.8% 83.0 5.53 72.0% 20.5% 22.2% -1.8% 9.3% 0.389 0.091
46 Gio Gonzalez 39 -7 81.5% 85.2 3.68 75.9% 21.8% 22.0% -0.3% 10.5% 0.420 -0.015
47 Michael Fulmer 49 +2 81.4% 86.1 4.17 80.2% 21.6% 20.9% 0.7% 8.4% 0.449 -0.047
48 Zack Godley 51 +3 81.1% 88.1 4.58 81.3% 24.5% 22.0% 2.5% 11.4% 0.461 -0.039
49 Kyle Freeland 50 +1 80.7% 91.1 3.55 73.6% 19.3% 20.4% -1.1% 8.2% 0.415 -0.014
50 Jose Urena 56 +6 80.0% 94.0 4.40 74.3% 18.7% 19.4% -0.7% 5.4% 0.429 -0.040
51 Julio Teheran 53 +2 79.9% 81.2 4.52 78.4% 22.9% 21.4% 1.5% 11.4% 0.457 -0.021
52 Jake Odorizzi 57 +5 79.8% 79.2 4.97 82.9% 24.1% 22.7% 1.4% 10.2% 0.489 -0.004
53 David Price 58 +5 78.9% 93.1 3.66 70.5% 18.7% 23.3% -4.7% 8.6% 0.418 -0.034
54 Jake Arrieta 55 +1 78.8% 84.0 3.54 66.8% 15.5% 16.8% -1.3% 7.7% 0.394 -0.005
55 Tyler Mahle 70 +15 78.5% 86.0 3.98 75.9% 21.3% 22.0% -0.8% 9.8% 0.460 -0.005
56 Marco Gonzales 63 +7 78.4% 91.1 4.04 75.8% 18.7% 21.1% -2.5% 5.3% 0.460 -0.037
57 Jhoulys Chacin 54 -3 78.1% 92.0 3.82 70.9% 19.7% 18.1% 1.6% 10.2% 0.431 -0.059
58 Kyle Hendricks 65 +7 78.1% 89.1 3.73 73.6% 19.4% 18.9% 0.4% 7.4% 0.449 -0.045
59 Tanner Roark 60 +1 78.1% 92.1 4.09 73.4% 19.6% 20.5% -0.9% 8.6% 0.448 -0.051
60 Aaron Sanchez 62 +2 77.9% 79.2 4.52 76.6% 22.5% 18.7% 3.8% 12.6% 0.472 -0.085
61 Jose Quintana 59 -2 77.7% 80.1 4.26 69.8% 18.8% 22.0% -3.2% 10.7% 0.430 -0.015
62 Luke Weaver 64 +2 77.4% 84.1 4.59 72.6% 20.5% 21.0% -0.5% 8.7% 0.452 -0.052
63 Jakob Junis 61 -2 77.4% 96.1 4.67 76.9% 20.3% 21.8% -1.6% 6.5% 0.481 -0.009
64 Sean Manaea 68 +4 77.3% 100.2 3.40 78.5% 20.7% 18.5% 2.2% 4.7% 0.493 -0.127
65 James Shields 67 +2 77.0% 99.1 4.53 74.5% 20.5% 16.3% 4.2% 8.9% 0.470 -0.088
66 Marco Estrada 71 +5 77.0% 84.1 4.48 76.8% 21.0% 19.4% 1.6% 6.3% 0.486 -0.001
67 Chad Bettis 69 +2 76.6% 92.1 5.07 71.8% 19.6% 17.0% 2.6% 9.1% 0.457 0.003
68 Michael Wacha 72 +4 76.3% 84.1 3.20 72.9% 21.0% 20.0% 0.9% 10.1% 0.468 -0.124
69 Reynaldo Lopez 73 +4 75.8% 94.0 3.73 70.0% 19.2% 16.8% 2.4% 9.8% 0.456 -0.061
70 Ivan Nova 76 +6 75.7% 81.1 3.98 74.2% 19.1% 19.2% -0.1% 4.4% 0.486 -0.051
71 Steven Matz [UR] [N/A] 75.7% 78.0 3.69 61.8% 17.6% 21.4% -3.9% 9.2% 0.403 0.016
72 Trevor Williams 74 +2 74.8% 87.0 4.03 67.3% 16.9% 17.8% -0.9% 7.8% 0.451 -0.055
73 Felix Hernandez 77 +4 74.6% 95.1 5.10 70.7% 18.1% 19.2% -1.2% 8.4% 0.477 -0.046
74 Jon Lester 66 -8 74.5% 95.0 2.18 71.1% 19.6% 19.4% 0.1% 8.4% 0.481 -0.139
75 Derek Holland [UR] [N/A] 74.4% 85.0 4.24 71.8% 20.4% 22.1% -1.8% 9.2% 0.486 -0.043
76 Chase Anderson 75 -1 74.2% 82.1 4.37 68.0% 18.4% 18.8% -0.4% 10.1% 0.464 -0.056
77 Danny Duffy 79 +2 72.7% 88.2 5.18 73.9% 20.7% 19.3% 1.4% 11.2% 0.524 -0.059
78 Brandon McCarthy 78 0 72.6% 78.2 4.92 65.9% 15.7% 19.2% -3.5% 6.2% 0.471 0.027
79 Ian Kennedy 81 +2 71.1% 86.2 5.09 70.3% 17.7% 20.9% -3.2% 8.4% 0.521 -0.044
80 Chris Stratton 82 +2 70.1% 87.0 4.14 65.7% 17.4% 18.2% -0.8% 9.2% 0.503 -0.094
81 Sal Romano 84 +3 70.0% 85.0 5.40 63.2% 15.6% 16.5% -0.9% 9.6% 0.488 -0.021
82 Lucas Giolito [UR] [N/A] 69.7% 78.1 7.01 64.2% 18.3% 13.3% 5.0% 13.1% 0.498 -0.041
83 Jason Hammel 83 0 69.7% 94.1 5.34 72.9% 18.6% 14.6% 4.0% 7.1% 0.556 -0.107
84 Chad Kuhl 80 -4 69.0% 85.0 4.55 69.8% 20.5% 21.7% -1.2% 8.9% 0.546 -0.083
85 Andrew Cashner 85 0 68.8% 82.1 4.70 64.0% 16.5% 18.3% -1.9% 10.4% 0.509 0.002
86 Daniel Mengden 86 0 68.8% 90.2 4.47 68.5% 16.7% 14.5% 2.2% 5.0% 0.540 -0.097
87 Mike Leake 87 0 68.3% 100.2 4.11 68.5% 15.5% 14.4% 1.1% 5.5% 0.546 -0.113
88 Bartolo Colon 88 0 61.0% 82.2 4.90 58.5% 11.5% 13.9% -2.5% 3.8% 0.576 -0.062

A couple minor changes have been made in the table since last time:

  1.  Added IP, by request
  2.  Changed “Predicted K%” to “xK%” (to save space)
  3.  Dropped the word “Pitcher” from Pitcher Score, now it’s just “Score” (also to save space)

For Reference:

  1.  “PD Score” is short for plate discipline score, a combined metric of strikeout ability weighted as:  3 points O-Swing%, 3 points Contact%, 3 points SwStr%, 1 point F-Strike%.
  2.  “Score” is a combined metric weighted equally between PD Score and xSLG.
  3.  For both luck indication columns (xK%-K% and SLG-xSLG) positive numbers indicate bad luck so far (likely to regress positively) while negative numbers indicate good luck so far (likely to regress negatively).  A higher SLG value is bad for pitchers, and I wanted it to be easy to remember: Positive Numbers = Good Outlook.
  4.  IP, ERA, K%, BB%, and SLG are not included in the calculations, these are only included in the table for reference.
  5.  For any newcomers, to understand the concepts behind these numbers I would recommend reading my introduction piece.

 

Halfway through the season, three pitchers have definitely separated themselves from the pack.  Max Scherzer, Jacob DeGrom and Chris Sale have been head & shoulders above the rest of MLB, their scores tightly bunched up around 103, while the 4th best pitcher is all the way down at 96.  Some other pitchers have better ERAs, but my early picks for the Cy Young as of right now would definitely be two of those three guys.  All three of them have been elite in both major components – strikeout ability and contact management.  These guys are well known as some of the best pitchers in MLB, so there really shouldn’t be any surprises.  But outside the top three, there are plenty to talk about.  Let’s start with one of the most difficult:

 

#9 Jon Gray (SP, Colorado Rockies)

Can we nickname him “the Enigma” already?  Most of the pitchers in my top 10 have pristine ERAs, because they are good pitchers.  And then, there’s Jon Gray.  His ERA currently sits at 5.52, which is not an ERA that one typically associates with good pitchers.  I honestly wouldn’t blame anyone for thinking I’m crazy to have him up so high on the list.  But it’s important to remember that these metrics are not subjective at all.  If a pitcher’s rank is way off from expected, we can usually learn something by trying to figure out WHY, rather than dismiss the model.  In Gray’s case, the explanation for why he is high on the list is an easy one.  He is simply missing the most bats in his career, by far.  He has made huge strides in his bat-missing ability, as demonstrated by his Contact% falling by ten percent since last year.  But the explanation for his ERA is not as simple.  One might assume it’s just his home park – he plays in Colorado, after all, widely recognized as the least pitcher-friendly park in MLB.

Unfortunately for that narrative, it’s simply not the case.  His home/road splits, in terms of both ERA and FIP, are essentially identical.  Taking a closer look, it seems to me that Gray has been extremely unlucky in 2018.  His start against Texas on June 16th is a great example.  In that game, in five innings he allowed seven baserunners, including one home run.  He gave up six runs in that game (five earned) while striking out nine (40%).  That’s textbook sequencing luck, with the walk, multiple hits and the home run all coming in the same inning.  Three of the hits that led directly to the runs were groundballs.  Oh, and another groundball went for an error that also led to a run.  It’s pretty hard to blame Gray for that one – his mistakes were essentially one walk and one homer.  With good sequencing luck that’s just one earned run instead of five – a huge difference.  And there are several numbers that suggest this is a microcosm of his season so far, not an aberration.  For starters, he’s got positive values in both of my luck indication columns, which is more rare than you might think.  His BABIP is 40 points above his career number.  Lastly, his bullpen has been atrocious at preventing inherited runners from scoring.  His overall LOB% of 63% is low – but if you look at the rate limited to his 3rd time through the order, i.e. the times more likely to be the bullpen, that drops all the way to 47%, meaning those runners are scoring more often than not.  Terrible.

 

#14 Jose Berrios (SP, Minnesota Twins)

While it’s not a complete shock for someone as talented and hyped as Berrios to shoot up the rankings, at just 24 years old, it’s still surprising and impressive to me what he’s done so far this year.  While he did improve his ERA vastly between 2016 and 2017, his improvements in plate discipline metrics were much more modest, grading out roughly as average last season.  Don’t get me wrong, an “average” pitcher at age 23 is still impressive.  But this year, he’s truly broken out and taken things to the next level.  His metrics are improved across the board, suggesting a rise in his strikeout rate of about five points, which is exactly how much his strikeout rate did rise.  No red flags there.  On the flip side, he’s also improved his walk rate dramatically for two years running.  Those changes are backed up by significant improvements in his O-Swing% and F-Strike%, and so I also believe his now-excellent walk rate.

In 2017, Berrios’ main weakness was lefties.  His splits last year were fairly extreme, with a FIP of three against righties, but five against lefties.  This year he has basically no splits, putting up similarly great numbers against both righties and lefties.  But let’s not get too far ahead of ourselves – it’s too small of a sample size to conclude that his problems with lefties are a thing of the past.  I think it’s actually more likely that he will struggle against lefties again in the second half.  The reason I say that, is that his BABIP vs. lefties has dropped 70 points from last year, but the batted ball profile didn’t change barely at all.  So I think some good fortune has masked his weakness against lefties this year, rather than him figuring everything out.  In the end, the strikeout and walk improvements are very believable, but I suspect his contact management is likely to regress in the second half.  The future is very bright for Jose, but he’s not a capital-A Ace, quite yet.

 

#25 Corey Kluber (SP, Cleveland Indians)

Kluber has easily been the most contentious pitcher in my rankings all year.  Every week, someone asks – “Why is Kluber so low?  He’s easily top 3”.  And over the past couple years overall, that is true.  He is a fantastic pitcher.  But the simple fact is that he just hasn’t pitched the same this year as he did last year.  Specifically, he has not been missing bats at the same rate.  In the full season 2017 rankings (which can be found at the bottom of my introduction piece here) he ranked #1 overall.  So I want to be absolutely clear – the model is not biased against him whatsoever, he is just pitching differently.  And it’s not just a matter of early-season struggles either.  Kluber is known to start off the season poorly, and in 2017 he lived up to that reputation.  But by the end of June, those struggles were long in the rear view mirror.  Here is a snapshot of his plate discipline metrics, 1st half of 2017 vs 1st half of 2018:

Time Period Sw-Str% Contact% O-Swing%
1st Half 2017 15.1% 68.1% 35.8%
1st Half 2018 10.7% 77.3% 35.3%

His O-Swing% has remained elite, which makes sense because his walk rate has also remained elite this year.  But the other metrics point towards a drop in his strikeout rate of roughly ten points, which is a huge, huge difference.  And in fact, Kluber’s strikeout rate is down seven points this year.  The numbers say it’s likely to drop even further if he doesn’t change anything.  This is something that can go unnoticed when a pitcher is still performing very well.  But to me the data is pretty clear in that Kluber has been lucky this year to have such a pristine ERA.  If he keeps pitching like this, his second half could be very disappointing.  That BABIP is currently 55 points under his career, and his LOB of 85% is also high, to go along with negative numbers in both luck columns above.

There is one easily-identifiable thing Kluber could do to help regain his mojo.  Last year, a huge part of his success was increasing the slider usage from 20% up to 27%.  This is a story we’ve seen time and again – identify your best pitch, and throw it more often.  This year, his slider usage is back down closer to 20%.  That slider is still allowing an wRC+ of negative three, so I have to wonder – why not throw it more often?  It worked incredibly well for him last year, and as the saying goes – “If it ain’t broke, don’t fix it”.

 

#38 Zack Greinke (SP, Arizona D-Backs)

Greinke is another guy who generates a fair amount of discussion due to his low placement in the rankings.  He was in my top 10 last year, so #38 is a very noticeable dropoff.  Is it possible Greinke’s age is starting to catch up with him?  He is 34 after all.  His velocity is down a tick this year as well.  At first glance, maybe that could explain his declining metrics.  All three of the main metrics are trending the wrong direction for him this year, although they are more incremental changes than huge drops, which is something that would fit a small decline in velocity.  The metrics would suggest a drop in his strikeout rate of about 3% from last year.  His actual K% has dropped less than 1 percent, so he’s been a tiny bit fortunate in that area.  Overall though, we are still working within the margin of error; it’s not a big enough difference to mean much.  His actual strikeout rate of 26% is within the ballpark of what his metrics would suggest.  So what gives?

It seems that contact management has been Greinke’s main weakness in 2018.  His xSLG this year is nowhere near elite at .474, which is 40 points worse than league average, and 76 points worse than he did last year.  He went from above average in that respect, to well below average.  Looking a bit closer, it seems to me that something is off with his slider this year.  It doesn’t seem related to velocity, as the slider hasn’t really budged velocity-wise.  But hitters seem to be squaring it up MUCH better than previously in Greinke’s career.  His slider has a negative overall pitch value in 2018, something that has never happened in his career.  Actually it’s been the opposite – the slider has the best value of all his pitches, both overall and on a per-pitch basis.  For his career it has allowed an wRC+ of just 38.  This year, 105.  Having looked at quite a few slider pitch-splits this year, this definitely stands out to me as an anomaly.  I thought maybe he was tipping his pitches, but a glance at his release points doesn’t show anything out of the ordinary.  So my best theory is this:

Greinke’s slider location, 2017:

Greinke’s slider location, 2018:

He’s simply getting more of the plate than you want for a slider, with that entire dark red portion squarely within the zone.  That could definitely explain why hitters are able to square it up much better.  It’s the best theory I’ve got, at least.  The bright side is that pitch location is fixable.

Moving on, here is the data for the non-qualified SP (10 IP minimum):

Name Team Score IP ERA PD Score xK% K% xK%-K% BB% xSLG SLG-xSLG
Freddy Peralta Brewers 106.7% 22.2 1.59 100.3% 31.9% 41.7% -9.8% 10.7% 0.246 -0.086
Matt Strahm Padres 104.8% 13.1 1.35 103.1% 31.1% 35.3% -4.2% 5.9% 0.290 -0.061
Noah Syndergaard Mets 98.8% 64.2 3.06 99.1% 30.6% 28.3% 2.3% 4.8% 0.343 0.021
Jonathan Loaisiga Yankees 98.6% 14.0 1.93 92.6% 26.9% 31.6% -4.7% 14.0% 0.303 -0.079
Shohei Ohtani Angels 96.9% 49.1 3.10 99.1% 34.0% 30.5% 3.5% 10.0% 0.368 -0.048
Domingo German Yankees 94.9% 49.0 5.88 102.1% 31.8% 27.1% 4.7% 6.8% 0.416 0.044
Ross Stripling Dodgers 92.6% 62.0 2.32 83.9% 22.4% 29.3% -6.9% 2.4% 0.325 0.066
Kenta Maeda Dodgers 91.2% 67.0 3.49 91.3% 27.5% 27.3% 0.2% 9.4% 0.392 -0.018
Jack Flaherty Cardinals 90.6% 57.2 2.50 85.1% 26.9% 29.4% -2.6% 6.9% 0.359 -0.034
John Gant Cardinals 90.0% 22.0 4.09 84.8% 24.9% 25.3% -0.4% 9.9% 0.365 -0.090
Johnny Cueto Giants 89.8% 32.0 0.84 76.2% 22.3% 22.2% 0.0% 5.1% 0.311 -0.105
Masahiro Tanaka Yankees 89.5% 72.2 4.58 98.1% 28.8% 24.8% 4.0% 6.5% 0.460 -0.019
Trevor Cahill Athletics 89.2% 48.2 2.77 92.2% 28.5% 25.0% 3.5% 5.9% 0.425 -0.072
Walker Buehler Dodgers 88.8% 51.1 2.63 72.4% 19.1% 26.7% -7.6% 5.5% 0.298 -0.025
Robbie Ray Diamondbacks 88.7% 27.2 4.88 92.1% 30.1% 36.3% -6.3% 13.7% 0.432 -0.013
Carlos Martinez Cardinals 88.2% 72.2 3.22 75.5% 20.9% 22.4% -1.6% 13.2% 0.328 -0.022
Brad Keller Royals 88.0% 25.2 2.45 76.9% 21.8% 16.0% 5.8% 10.4% 0.340 -0.063
CC Sabathia Yankees 87.7% 76.1 3.18 81.5% 22.1% 18.6% 3.5% 6.1% 0.374 0.025
Austin Bibens-Dirkx Rangers 87.5% 22.2 3.57 87.0% 22.7% 17.5% 5.2% 6.2% 0.413 -0.038
Jordan Montgomery Yankees 87.5% 27.1 3.62 79.9% 21.5% 19.8% 1.7% 10.3% 0.366 -0.010
Hyun-Jin Ryu Dodgers 87.1% 29.2 2.12 79.3% 24.8% 31.3% -6.5% 8.7% 0.367 -0.059
Shane Bieber Indians 87.0% 18.1 2.45 86.8% 26.5% 29.0% -2.5% 4.0% 0.419 0.033
Garrett Richards Angels 86.3% 68.1 3.42 84.0% 25.8% 26.8% -1.0% 11.0% 0.410 -0.050
Mike Montgomery Cubs 86.0% 35.2 2.02 83.7% 21.6% 18.0% 3.6% 6.5% 0.411 -0.130
Nick Kingham Pirates 85.4% 35.1 3.82 85.6% 25.0% 24.3% 0.7% 4.9% 0.432 -0.066
Andrew Heaney Angels 84.8% 78.2 3.43 82.3% 23.3% 22.5% 0.7% 6.6% 0.418 -0.037
Clayton Kershaw Dodgers 83.9% 52.0 2.94 81.2% 22.5% 26.6% -4.1% 5.6% 0.422 -0.021
Seth Lugo Mets 83.9% 23.0 3.52 77.3% 21.6% 26.5% -4.9% 5.9% 0.397 -0.003
Sam Gaviglio Blue Jays 83.6% 36.1 4.21 79.9% 20.8% 22.1% -1.3% 6.5% 0.418 0.019
Joe Musgrove Pirates 83.5% 33.1 4.59 76.5% 20.4% 21.0% -0.7% 6.1% 0.396 0.026
Ryne Stanek Rays 83.5% 10.0 1.80 87.0% 26.6% 26.3% 0.3% 10.5% 0.466 -0.290
Caleb Smith Marlins 83.3% 77.1 4.19 82.9% 24.3% 27.0% -2.7% 10.1% 0.442 -0.054
Anibal Sanchez Braves 83.1% 44.0 2.86 76.8% 19.9% 23.7% -3.8% 7.3% 0.404 -0.008
Chris Archer Rays 83.0% 76.1 4.24 86.4% 26.4% 23.7% 2.7% 8.1% 0.469 -0.050
Zach Eflin Phillies 83.0% 49.2 3.44 76.1% 21.4% 24.6% -3.3% 5.8% 0.400 -0.012
Yu Darvish Cubs 82.7% 40.0 4.95 78.2% 24.1% 27.2% -3.2% 11.7% 0.418 0.007
Dereck Rodriguez Giants 81.2% 27.1 3.95 76.7% 20.9% 20.2% 0.6% 5.0% 0.429 -0.016
Fernando Romero Twins 81.0% 51.1 4.38 79.0% 22.8% 19.6% 3.2% 8.5% 0.446 -0.037
Brent Suter Brewers 81.0% 75.2 4.28 79.7% 21.2% 19.4% 1.8% 5.1% 0.451 -0.018
Caleb Ferguson Dodgers 80.9% 10.2 7.59 78.9% 26.7% 25.0% 1.7% 12.5% 0.447 -0.047
Junior Guerra Brewers 80.6% 76.2 2.82 75.5% 21.8% 23.3% -1.6% 9.5% 0.428 -0.081
Wilmer Font Rays 80.6% 16.0 1.69 67.9% 19.8% 23.4% -3.7% 9.4% 0.378 -0.068
Carlos Rodon White Sox 80.3% 24.1 3.70 75.5% 19.7% 18.5% 1.2% 6.8% 0.433 -0.063
Jeremy Hellickson Nationals 80.0% 43.1 2.28 77.3% 20.5% 20.7% -0.3% 3.7% 0.448 -0.087
Hector Velazquez Red Sox 80.0% 10.2 2.53 91.4% 23.4% 22.7% 0.6% 4.6% 0.543 -0.091
Nick Tropeano Angels 79.8% 54.0 4.83 83.7% 25.5% 19.6% 5.9% 8.9% 0.494 -0.019
Michael Soroka Braves 79.8% 25.2 3.51 78.7% 22.0% 18.6% 3.4% 6.2% 0.461 -0.048
Wade LeBlanc Mariners 79.0% 52.2 2.91 79.5% 19.3% 19.7% -0.4% 5.6% 0.476 -0.088
Jordan Lyles Padres 78.8% 47.0 4.79 72.6% 17.9% 19.9% -2.1% 5.5% 0.433 0.051
Yonny Chirinos Rays 78.3% 22.2 4.37 75.9% 20.1% 21.0% -0.9% 8.0% 0.462 -0.043
Ryan Yarbrough Rays 78.3% 20.2 5.23 70.7% 18.4% 19.1% -0.7% 6.7% 0.427 -0.002
Trevor Richards Marlins 78.3% 44.0 4.91 71.4% 19.2% 20.9% -1.8% 10.5% 0.432 -0.014
Tyler Chatwood Cubs 78.3% 68.1 3.95 65.8% 19.1% 20.3% -1.3% 20.0% 0.395 -0.064
Blaine Hardy Tigers 78.2% 43.2 3.71 70.2% 17.1% 16.6% 0.5% 5.5% 0.426 0.005
Jason Vargas Mets 77.9% 37.2 8.60 82.2% 22.9% 17.8% 5.1% 7.8% 0.509 0.104
Dylan Covey White Sox 77.9% 44.1 3.45 64.9% 16.3% 18.7% -2.4% 10.4% 0.394 -0.072
Joey Lucchesi Padres 77.6% 53.0 3.57 75.9% 22.8% 25.1% -2.3% 8.7% 0.471 -0.037
Paul Blackburn Athletics 77.1% 17.1 8.83 72.3% 17.4% 14.3% 3.1% 5.2% 0.454 0.053
Chris Bassitt Athletics 77.1% 16.1 3.86 69.3% 14.6% 17.8% -3.3% 4.1% 0.434 -0.125
Jordan Zimmermann Tigers 77.0% 41.1 4.35 76.5% 19.9% 22.5% -2.7% 5.8% 0.483 -0.073
Madison Bumgarner Giants 76.8% 25.1 3.20 67.2% 16.2% 16.2% 0.0% 6.7% 0.424 0.008
Jaime Barria Angels 76.7% 50.1 3.40 84.6% 24.2% 20.1% 4.1% 6.7% 0.541 -0.112
Nathan Eovaldi Rays 76.6% 35.1 4.08 74.5% 17.9% 22.4% -4.6% 3.7% 0.476 -0.051
Wei-Yin Chen Marlins 75.9% 49.2 6.70 67.9% 17.0% 15.6% 1.4% 10.4% 0.440 0.095
Francisco Liriano Tigers 75.5% 61.2 3.94 79.4% 23.8% 19.1% 4.7% 12.7% 0.523 -0.157
Frankie Montas Athletics 75.4% 36.2 3.68 62.0% 15.5% 13.9% 1.6% 8.2% 0.408 0.000
Clay Buchholz Diamondbacks 74.9% 38.2 2.56 74.6% 18.9% 20.3% -1.4% 4.6% 0.499 -0.126
Marcus Stroman Blue Jays 74.8% 42.1 6.80 73.1% 21.0% 18.8% 2.2% 9.6% 0.490 -0.053
Yovani Gallardo Rangers 74.5% 10.1 7.84 58.9% 12.6% 14.9% -2.4% 10.6% 0.399 0.125
Mike Fiers Tigers 74.4% 77.2 4.29 71.3% 18.3% 17.3% 1.0% 5.4% 0.483 0.015
Luis Perdomo Padres 74.4% 14.0 8.36 68.5% 21.2% 22.1% -0.9% 11.7% 0.465 0.058
Matt Moore Rangers 74.1% 55.0 8.02 75.2% 20.1% 14.7% 5.4% 9.3% 0.513 0.056
Erick Fedde Nationals 73.6% 22.0 5.32 72.9% 19.6% 17.4% 2.2% 6.5% 0.505 -0.011
Andrew Triggs Athletics 73.0% 41.1 5.23 75.9% 21.4% 23.6% -2.3% 9.9% 0.533 -0.120
Adam Wainwright Cardinals 72.9% 18.0 4.00 55.9% 14.0% 17.1% -3.1% 15.9% 0.400 0.035
Steven Wright Red Sox 72.7% 24.0 4.13 67.0% 19.0% 16.8% 2.2% 9.9% 0.478 -0.122
Zach Davies Brewers 72.3% 43.0 5.23 69.1% 18.7% 16.3% 2.4% 9.0% 0.497 -0.003
Brandon Woodruff Brewers 72.2% 15.2 6.32 65.8% 19.2% 18.1% 1.1% 12.5% 0.476 -0.083
Mike Minor Rangers 72.2% 78.1 5.06 75.6% 20.4% 19.6% 0.8% 5.7% 0.542 -0.045
Eric Lauer Padres 72.2% 57.0 5.05 61.8% 15.8% 19.3% -3.6% 10.2% 0.450 0.041
Drew Pomeranz Red Sox 72.1% 37.0 6.81 65.2% 16.7% 20.8% -4.2% 12.1% 0.473 0.070
Andrew Suarez Giants 71.7% 65.0 4.43 65.8% 15.7% 23.0% -7.3% 4.9% 0.482 -0.037
David Hess Orioles 71.3% 41.1 5.44 68.6% 16.4% 13.3% 3.1% 9.4% 0.507 -0.007
Matt Harvey – – – 71.3% 69.0 4.83 64.5% 16.5% 17.4% -0.9% 5.5% 0.480 -0.031
Jaime Garcia Blue Jays 71.0% 61.1 6.16 68.1% 19.0% 19.9% -1.0% 11.0% 0.508 0.004
Ty Blach Giants 70.9% 60.2 4.90 60.7% 14.7% 11.1% 3.6% 8.5% 0.460 -0.047
Jake Faria Rays 70.4% 47.2 5.48 68.1% 19.0% 18.2% 0.7% 10.8% 0.516 -0.093
Dillon Peters Marlins 70.0% 24.2 5.84 62.0% 16.3% 14.4% 1.9% 11.7% 0.480 -0.011
Jeff Samardzija Giants 69.9% 35.2 6.56 68.8% 17.7% 15.7% 2.0% 13.9% 0.527 -0.084
Elieser Hernandez Marlins 69.6% 22.0 4.50 71.6% 21.2% 18.7% 2.5% 5.5% 0.549 0.011
Anthony DeSclafani Reds 69.5% 22.0 4.09 59.6% 14.5% 19.6% -5.2% 8.7% 0.470 0.030
Eric Skoglund Royals 69.1% 49.2 6.70 70.3% 17.3% 18.0% -0.7% 6.5% 0.548 -0.018
Alex Cobb Orioles 68.8% 70.0 6.56 63.9% 15.2% 15.5% -0.3% 5.7% 0.508 0.055
Matt Wisler Braves 68.7% 17.1 3.63 73.6% 20.7% 18.6% 2.1% 7.1% 0.575 -0.160
Dan Straily Marlins 68.6% 52.1 4.82 76.3% 21.8% 19.6% 2.2% 10.9% 0.594 -0.122
Brandon Finnegan Reds 68.0% 20.2 7.40 63.0% 16.0% 13.6% 2.4% 14.6% 0.513 0.040
Ben Lively Phillies 67.2% 23.2 6.85 62.8% 17.9% 19.1% -1.3% 8.7% 0.522 0.023
Kendall Graveman Athletics 67.0% 34.1 7.60 67.6% 17.1% 17.1% 0.0% 8.2% 0.557 -0.015
Adam Plutko Indians 66.8% 29.0 4.66 70.3% 16.6% 17.5% -0.9% 6.7% 0.578 -0.042
John Lamb Angels 66.7% 10.0 7.20 72.3% 17.8% 22.0% -4.3% 8.0% 0.593 0.124
Doug Fister Rangers 66.4% 66.0 4.50 55.9% 11.8% 13.8% -2.0% 6.6% 0.487 -0.031
Rich Hill Dodgers 65.0% 35.2 5.30 61.8% 15.6% 22.2% -6.6% 9.9% 0.546 -0.046
Daniel Gossett Athletics 64.9% 24.1 5.18 66.2% 15.5% 11.8% 3.7% 7.8% 0.576 -0.081
Brett Anderson Athletics 64.9% 15.1 7.63 67.1% 18.0% 11.1% 6.9% 8.3% 0.582 0.009
Joe Biagini Blue Jays 64.9% 18.2 7.71 65.9% 16.7% 14.4% 2.3% 10.0% 0.574 -0.080
Steven Brault Pirates 63.8% 26.0 5.54 69.2% 20.0% 13.4% 6.6% 12.5% 0.611 -0.213
Taijuan Walker Diamondbacks 62.7% 13.0 3.46 57.6% 15.1% 16.1% -1.1% 8.9% 0.548 -0.156
Homer Bailey Reds 62.1% 62.0 6.68 64.2% 15.8% 13.0% 2.8% 8.2% 0.600 -0.036
Hector Santiago White Sox 61.1% 32.1 6.12 62.0% 16.5% 15.9% 0.6% 15.2% 0.598 -0.027
Jarlin Garcia Marlins 61.0% 33.0 3.55 65.1% 18.0% 16.7% 1.3% 9.9% 0.620 -0.222
Bryan Mitchell Padres 59.3% 32.0 6.47 47.1% 11.8% 10.4% 1.4% 16.9% 0.523 -0.027
Ryan Carpenter Tigers 57.4% 12.0 6.75 58.5% 14.9% 10.7% 4.2% 3.6% 0.624 -0.047
Matt Koch Diamondbacks 56.9% 69.2 4.52 60.7% 13.5% 12.1% 1.4% 5.9% 0.646 -0.140
Miguel Gonzalez White Sox 56.8% 12.1 12.41 66.3% 15.5% 7.6% 7.9% 9.1% 0.684 0.099
Josh Tomlin Indians 55.9% 30.0 8.10 73.8% 19.1% 12.9% 6.2% 3.6% 0.746 -0.019
Carson Fulmer White Sox 55.3% 31.0 8.13 56.5% 14.6% 16.6% -2.1% 15.3% 0.640 -0.136
Martin Perez Rangers 55.1% 22.1 9.67 51.8% 11.6% 10.9% 0.6% 10.1% 0.610 0.060
Chris Tillman Orioles 51.6% 26.2 10.46 49.4% 11.9% 9.5% 2.4% 12.4% 0.641 0.011

 

Madison Bumgarner (SP, San Francisco Giants)

For the second season in a row, injuries to the Giants’ ace have put a damper on their playoff hopes.  Now that he’s returned and pitched a handful of games, there are a lot of eyes on him.  Everyone wants to know – is he the same pitcher?  Last year he wasn’t, and then he got injured again.  And at first glance, the metrics are certainly not great.  That 16% expected K-rate is way, way down from his career rate of 24%.  But there are also reasons to be hopeful.  For starters, he pitched yesterday which is not included in the data.  In that game he struck out eight, with a Contact% of 77%.  This was against the Rockies, one of the worst offense away from Coors, so take it with a grain of salt – but at least his metrics as well as strikeout rate are moving in the right direction.  His actual K% rose by three percent from that one game.

Looking at his pitch-splits, something seems off with his curveball.  Bumgarner’s curve has long been his best pitch, generating a 16% whiff rate and allowing a wRC+ of just 43 for his career.  This year, the results are still outstanding with an wRC+ of negative 24.  But instead of missing bats, it’s generating weak contact.  The whiff rate is down to just 8.5%, but the LD% is also super low at just 7%.  Actually, batters have only got a single hit off Bumgarner’s 82 curveballs thrown.  This was a home run, leaving his BABIP for curveballs at precisely .000.  And overall his BABIP of .226 is certainly a red flag.  But at the same time, there doesn’t seem to be anything wrong with his movement numbers, velocity, or release points for the curveball.  So I want to believe the whiffs and strikeouts are coming back, and could help counteract any potential regression coming from BABIP.  His velocity is also trending upward so far, an indication that he’s rebuilding his strength after the injury.  I’ll be keeping a close eye on him, the jury is still out – but there are some hopeful signs pointing towards a return to form.

 

Walker Buehler (SP, Los Angeles Dodgers)

For the first couple months of the season, Buehler stood out as having the largest difference between expected K% and actual K% of anyone in the league.  The difference was about 15% (15 vs. 30) as of mid-May, where I rated him a solid “SELL”.  Since then, the results have still been pretty positive for the most part.  Overall he’s continued to pitch well, with his ERA as well as FIP/SIERA looking very nice.  He has also missed more bats since then, increasing his expected K% from about 16 up to the current level of 19%, which is at least average-ish.  At the same time, his actual K% has dropped about 3.5%.  So his gap is certainly closing, as we should expect, but in his case it’s coming about equally from both sides, rather than predominantly the K% moving.  In past analysis, we found the gap closure is typically attributed about 75% to the strikeout rate changing, not the metrics, so he is bucking the trend a bit.

Since that update, he has undoubtedly done better than I expected, as reflected by his overall score rising to 88.  But I think I still have to rate him as a “SELL”.  Even with the significant gap closure, he still sports one of the largest K% discrepancies.  But mainly his schedule has just been incredibly fortunate.  Six of his nine starts have come against the Padres, Marlins and Rockies, which are three of the most inept offenses in the game.  His tougher matchups, the Giants and Reds, have been at home.  Truly he’s only had one bad matchup all season – against the Rockies in Coors, where he only struck out two and gave up 4 ER.  Between that, the K% discrepancy, and potential innings limits, I think that is enough to take advantage of his current stat line and cash in the chips.

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

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

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Comments


theKraken

I don’t think the sell tag is going to work out great for Buehler. I think his DL stint means that he will be taking regular turns for the remainder of the fantasy season, which is great. His command and GB rate mean that he doesn’t have to get everyone out with Ks. That is absent from this analysis, no? It isn’t super common to limit walks, throw GBs and have some K ability. I am not sure that he will ever be a workhorse, but I think he has some of the best stuff in MLB. I also get a feeling that he is figuring things out when I watch him pitch. Most rookies are working unsustainable stuff, but I think there is more in the tank with WB. I think this is the last opportunity to buy before he comes back… unless he has a rough first game back or something, but I would bet that most owners are worried about his health… which should be a non-issue – this is just limiting his innings masked as a DL stint. I don’t have confidence that he won’t get hurt, but he currently is not and I don’t think many people know that. I was watching the game where he was removed with injury – there was no injury, no trainers – he went and sat on the bench with a bewildered look on his face…

Chaz Steinberg

That’s completely fair! I wouldn’t say command and GB rate are completely absent from the analysis, but yes it is tilted a bit in favor of strikeout pitchers for sure. That’s mainly just because this is for fantasy, where a strikeout is worth more than a regular out. But I do agree in general that he has a very promising career in front of him and should be a productive MLB pitcher.

Johnny Ryall

Jon Gray has been beat up by SF this year. You would sit him tonight @SF, right?

Also, who would you say has a better year ROS: Berrios or Snell?

Chaz Steinberg

No, I am throwing him out there in the league I own him. Pitcher vs. individual team numbers don’t tend to hold up over time, and AT&T is a great place to pitch. If you can’t start a guy in SF, he’s probably not worth owning, even with the Giants offensive improvement over the previous years.

Tom

Awesome stuff Chaz. This is legitimately the best objective measure of pitching performance you will find anywhere on the internet. Keep these coming. Fantastic work!

firsttimer

Thanks for putting this together!

Can we talk about Tyler Anderson? I’m curious to know how his ranking is so high when his actual stats are just mediocre and similar to the last season stat.

Lion

watching this list throughout the season, you would expect that, though there may be a few rare outliers, most the rankings would eventually align with player performance. At one point is the amount of rare outliers significant enough to question the model itself? I understand researching into why someone like J Gray is ranked so high and explain it, but when you are having to explain 1/3 of the list because their real life production does not seem to mirror their ranking on this list, then I would think maybe the model needs tweaked to try and more accurately represent what we see happening, or to better predict future production. If the model doesn’t do either of those two objectives, then whats the point of ranking them? what value do I get out this list? I cant use it for determining trades. The eye test and 3 months worth of production say you are crazy to go with luis castillo over mikolas and stras and price, or gray and velazques over corey kluber, and many more examples. Don’t want to be a party pooper on this list, just want to understand what I can or should be using this list for when it comes to making fantasy decisions.

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