SP Plate Discipline Update – a Look Back So Far

(Photo by George Walker/Icon Sportswire)

Welcome back to my series on plate discipline/pitching metrics.  This time, I’m going to focus on a close examination of the core principle behind my work – the so-called “strikeout rate discrepancies” predicted by plate discipline metrics compared to the actual rate.  At this point, roughly two months of baseball are complete, which is as good a time as any to pause and take a look back.  That way we can compare the data after 1 month to current data, and see how the predictions hold up on a month-to-month basis.  I’m sure there still are a lot of doubts surrounding this whole theory, and honestly I can’t blame anyone.  This is fairly new stuff, and after all, I’m just some guy on the internet.  Why should you believe me?  So let’s put the numbers to the test.

To get things started, here are the qualified starting pitchers who currently sport a greater discrepancy than +/- five points.  If you recall, 5% is the upper end of the margin for error, so under my theory any discrepancies beyond that amount should be considered red flags.  This table is sorted by the K% difference, with the over-achievers on top and under-achievers on the bottom.

Name	Actual K%	Predicted K%	K% Difference
Gerrit Cole	39.40%	30.25%	-9.15%
Mike Foltynewicz	27.10%	20.40%	-6.70%
Caleb Smith	29.90%	23.95%	-5.95%
Justin Verlander	32.30%	27.05%	-5.25%
James Paxton	32.70%	27.65%	-5.05%
Nick Pivetta	28.80%	23.80%	-5.00%
Francisco Liriano	19.20%	24.25%	5.05%
Aaron Sanchez	16.70%	21.85%	5.15%
Masahiro Tanaka	22.80%	28.05%	5.25%
Lucas Giolito	11.60%	18.45%	6.85%
Luis Castillo	22.50%	30.85%	8.35%

Right off the bat, we can easily see just how far this list has been reduced since it was first introduced towards the end of April.  At that point, there were 29 pitchers with a discrepancy larger than five points.  Now we are down to just eleven.  Already, this goes a long way towards validating my theory that these discrepancies in small samples should mostly work themselves out as more innings are compiled.  The size of the largest discrepancies are also coming down.  In that first list, the largest outlier was +13.6, and there were a handful over ten percent.  Now, there are no qualified SP over ten points.  Gerrit Cole is posting the largest discrepancy at -9.  Furthermore, of those eleven pitchers, half of them are right around the five point line.  There are really only 4-5 qualified guys left in the league who are meaningfully separated from that line.  So it seems very clear that at least part of my theory is holding up – the discrepancies are indeed shrinking and vanishing as more time goes by.

The fact that the discrepancies are shrinking doesn’t tell the full story though.  I think we also need to look at which side of things are moving.  It could be that the strikeout rates are changing to support the metrics, or vice versa.  If the strikeout rates are what’s moving, that would support my theory that the metrics are the truest reflection of the underlying strikeout skills.  But if it’s the opposite, that would make the PD metrics less useful.  If that was true, my theory would be more like the tail wagging the dog, and not predictive in the slightest.  So let’s drill down into that original set of outliers, and see how things have played out since then.

To examine this, I am going to take the “predicted K%” from a month ago, and see how it compares to changes in the actual K% from that time up to now.  My goal is to measure two things:

1) Each “predicted K%” value indicated a direction that the K% should move going forward from that point in time.  For example, if the predicted value was higher than the actual K% during that first update, the predicted direction would be “up”.   The first thing I want to measure is just what percentage of players at least got the direction right.  That should serve as a good starting point.  If this is a low percentage…well, I guess it’s back to the drawing board for me.

2) It’s not just the direction that matters.  If the model predicts a +10% change in strikeouts, but you just get +1%, well that’s not really validating the model.  Obviously some discrepancies still remain, so I’m accepting that the changes in strikeout rates are going to fall short of the predicted changes.  Extreme outliers just need more data to stabilize, that’s the nature of statistics.  But the second thing I want to measure is the predominant factor involved in the “gap closure”.  Basically, I want to know: was it the strikeouts that have changed to meet the predicted rates, or was it the metrics that changed to meet the strikeout rates?  My assumption here is that both sets of data will move a bit, but if my theory holds up, we should clearly see the strikeout rate movement as the predominant factor.

Here are the results:

Name	Original Discrepancy	Current Discrepancy	K% Direction Predicted	Direction Moved	K% Actual Change	Gap Closure from K%
Steven Matz	-13.60%	-3.60%	Down	Down	-9.70%	99.90%
Derek Holland	-11.10%	-3.30%	Down	Down	-3.70%	44.89%
Gerrit Cole	-7.40%	-9.15%	Down	Up	1.40%	N/A (no closure)
Mike Foltynewicz	-7.10%	-6.70%	Down	Down	-2.60%	58.28%
Marco Gonzales	-6.90%	-3.85%	Down	Down	-6.40%	78.49%
Carlos Martinez	-6.90%	-2.45%	Down	Down	-5.40%	97.00%
Jose Berrios	-6.70%	-0.60%	Down	Down	-5.30%	97.77%
Hyun-Jin Ryu	-6.20%	-6.50%	Down	Up	0.60%	N/A (no closure)
Rich Hill	-5.90%	-5.45%	Down	Down	-2.50%	59.79%
Jaime Garcia	-5.80%	-0.60%	Down	Down	-4.20%	94.64%
Rick Porcello	-5.70%	-2.55%	Down	Down	-2.30%	87.98%
Jake Arrieta	-5.50%	-2.75%	Down	Down	-6.00%	77.32%
Caleb Smith	-5.50%	-5.95%	Down	Down	-2.80%	42.60%
Jameson Taillon	-5.50%	-2.30%	Down	Down	-2.00%	73.53%
Junior Guerra	-5.00%	-2.10%	Down	Down	-1.40%	46.56%
Carlos Carrasco	5.20%	2.60%	Up	Up	2.90%	98.94%
Masahiro Tanaka	5.30%	5.25%	Up	Down	-0.50%	N/A (no closure)
Tyler Anderson	5.30%	3.75%	Up	Down	-3.90%	N/A (no closure)
CC Sabathia	5.50%	2.85%	Up	Up	2.30%	97.74%
Aaron Sanchez	7.00%	5.15%	Up	Up	2.90%	88.41%
James Shields	7.20%	4.45%	Up	Up	5.10%	82.49%
Matt Boyd	7.80%	2.35%	Up	Up	4.10%	90.22%
Lucas Giolito	8.00%	6.85%	Up	Up	2.50%	77.42%
Mike Fiers	8.90%	2.95%	Up	Up	2.00%	20.41%
Luis Castillo	9.80%	8.35%	Up	Up	2.50%	85.01%

 

First off, just a quick note: there were 29 pitchers in the original discrepancy list but only 25 here.  The explanation for that is four of those guys were injured or moved to the bullpen after that update.  Only starting innings are included in the data, so essentially these four pitchers were not given any chance to close their gaps, and it doesn’t make sense to include them. For the record, those pitchers are Robbie Ray, Miguel Gonzalez, Steven Brault, and Josh Tomlin.

To quantify my earlier observation that the size of discrepancies is coming down, we can take a quick look at the averages.  In the first article, the average discrepancy of all listed outliers was around seven points.  Now, that’s down to just 4 points, within the margin for error.  That is another data point in support of the idea that the discrepancies are being reduced over time.  It also gives us some clues as to the time frame it takes for these things to stabilize.  On average, the discrepancies are being reduced by almost half, over a month or so.

Moving on, let’s try to answer the two questions posed above.

Q1)  Did the model predict that strikeout rates would move in the right direction?

A1)  Yes, it would appear so.  21 of the 25 pitchers got the predicted direction correct, which is a solid 84% success rate.  Furthermore, of the four pitchers whose predictions weren’t correct, three of them only moved a tiny amount in said wrong direction.  Tyler Anderson is the sole major outlier here.

Q2)  What about the amounts?  Are the strikeouts changing, or have we been wasting our time reading this nonsense??

A2)  Again, it does look like we pass the test here. The last column on the table shows (for pitchers who did close their gap at least partially) what portion of that closure was from the strikeout side.  As you can see, most pitchers (81%) are over 50% and many are at 90+.  There is again only a single major outlier, Mike Fiers and his 20%.  The average contribution towards the gap closure from strikeout rates is 76%. In retrospect, this fits nicely with the statistical data from my original piece.  As a reminder, here are the two graphs showing the correlations between strikeouts and the two PD metrics I use to predict them:

Do those R-squared values seem familiar?  R-squared values are another way of saying “the data fits this model X percent of the time”, so the fact that the predictions are roughly 75% accurate should be pretty much exactly what we expected.

Overall I am certainly encouraged by these results.  It’s only two months of data, but 75-84% accurate predictions (depending on measurement) seems pretty solid and also in line with what we know from my previous work.  There is one VERY important caveat though, in that I am only analyzing the most extreme outliers here.  This is important to understand.  The metrics just aren’t going to be very useful for analyzing every pitcher, since the vast majority of them are operating within their normal expected range.  But the evidence does seem to support my methodology for identifying the largest outliers, and also predict what will happen to them going forward.

So this is good.  I’m happy I get to keep working on this and not throw it all out the window.  But, these results don’t come without downsides.  If a system is designed to catch outliers, but the outliers are disappearing, at some point the system stops being useful.  Going forward, I’ll need to take this account in order to stay relevant.  With that being said, and also incorporating some feedback from readers, I’m going to start by splitting out the rankings into two tables.  I’ll present the qualified SP first in one table, and then present another table for the guys with less innings.  Here you go:

RANKINGS

Table A – Qualified Starters

Rank Name ERA Pitcher Score Previous Score Score Change PD Score Predicted K% Actual K% K% Difference BB% xSLG SLG – xSLG 1 Jacob deGrom 1.52 104.3% 105.5% -1.1% 99.6% 30.6% 32.8% -2.2% 7.3% 0.273 -0.013 2 Max Scherzer 2.13 101.4% 103.7% -2.3% 105.1% 33.7% 38.2% -4.5% 6.4% 0.349 -0.016 3 Chris Sale 2.76 100.3% 101.8% -1.5% 104.3% 32.9% 34.8% -1.9% 6.7% 0.358 -0.010 4 Justin Verlander 1.11 99.2% 98.9% 0.3% 91.9% 27.1% 32.3% -5.3% 5.0% 0.290 -0.034 5 Noah Syndergaard 3.06 98.8% 98.5% 0.3% 99.1% 30.6% 28.3% 2.3% 4.8% 0.343 0.021 6 Patrick Corbin 2.47 98.1% 99.5% -1.3% 99.8% 30.7% 32.7% -2.0% 7.1% 0.357 -0.051 7 Gerrit Cole 2.05 96.3% 98.7% -2.4% 95.4% 30.3% 39.4% -9.2% 6.9% 0.352 -0.057 8 Charlie Morton 2.26 94.9% 99.2% -4.3% 87.9% 27.3% 31.6% -4.3% 8.2% 0.321 0.016 9 Aaron Nola 2.27 93.4% 91.7% 1.7% 86.3% 24.7% 24.6% 0.1% 6.3% 0.330 -0.046 10 Lance McCullers Jr. 3.98 92.0% 95.8% -3.8% 91.1% 27.3% 26.4% 0.9% 9.7% 0.380 -0.025 11 Sean Newcomb 2.75 92.0% 92.5% -0.5% 78.0% 22.6% 26.6% -4.1% 12.0% 0.293 0.007 12 James Paxton 3.10 91.1% 89.6% 1.5% 91.8% 27.7% 32.7% -5.1% 7.3% 0.398 -0.071 13 Blake Snell 2.56 91.0% 89.2% 1.8% 90.1% 27.4% 27.3% 0.1% 8.3% 0.387 -0.046 14 Masahiro Tanaka 4.62 90.3% 90.1% 0.3% 95.9% 28.1% 22.8% 5.3% 6.7% 0.435 -0.017 15 Trevor Bauer 2.61 89.8% 90.3% -0.5% 89.5% 26.7% 28.6% -2.0% 8.3% 0.399 -0.084 16 Kyle Gibson 3.57 89.6% 88.5% 1.1% 88.0% 26.6% 24.8% 1.8% 10.5% 0.392 -0.044 17 Carlos Carrasco 3.98 89.1% 89.6% -0.5% 93.2% 27.0% 24.4% 2.6% 5.5% 0.434 -0.044 18 Jose Berrios 3.67 89.1% 87.8% 1.3% 87.4% 24.1% 24.7% -0.6% 4.8% 0.395 -0.041 19 Jon Gray 5.40 88.8% 89.0% -0.1% 86.9% 26.6% 26.3% 0.3% 5.9% 0.395 0.055 20 Luis Castillo 5.49 88.4% 89.2% -0.8% 97.5% 30.9% 22.5% 8.4% 9.2% 0.471 0.004 21 Vince Velasquez 4.08 88.2% 87.1% 1.2% 79.4% 24.2% 28.5% -4.3% 8.5% 0.353 0.081 22 Luis Severino 2.28 87.8% 89.5% -1.7% 87.2% 25.5% 29.2% -3.8% 7.2% 0.410 -0.115 23 Alex Wood 3.75 87.8% 89.5% -1.7% 86.5% 23.2% 23.1% 0.1% 4.0% 0.406 -0.030 24 Caleb Smith 3.51 87.6% 89.0% -1.4% 83.1% 24.0% 29.9% -6.0% 11.3% 0.386 -0.066 25 Dylan Bundy 4.46 86.8% 85.6% 1.2% 100.1% 29.9% 28.3% 1.6% 6.8% 0.510 -0.016 26 Tyler Anderson 4.72 86.5% 89.3% -2.8% 83.8% 24.7% 20.9% 3.8% 9.4% 0.405 0.069 27 Mike Clevinger 3.14 86.5% 86.5% 0.0% 83.2% 23.5% 21.5% 2.0% 8.1% 0.401 -0.039 28 J.A. Happ 3.84 86.4% 85.8% 0.6% 83.5% 24.7% 29.5% -4.9% 6.7% 0.405 -0.042 29 Matt Boyd 3.00 86.3% 86.4% -0.1% 76.5% 22.0% 19.6% 2.4% 8.7% 0.360 -0.071 30 Zack Greinke 3.65 86.2% 87.0% -0.7% 91.9% 26.7% 27.1% -0.5% 3.8% 0.463 -0.038 31 Gio Gonzalez 2.10 86.0% 86.7% -0.8% 75.7% 21.5% 23.2% -1.7% 10.3% 0.358 -0.022 32 Corey Kluber 2.17 85.6% 84.4% 1.2% 83.7% 23.1% 26.1% -3.1% 3.3% 0.417 -0.077 33 Rick Porcello 3.65 85.4% 86.2% -0.8% 77.4% 20.2% 22.7% -2.6% 4.9% 0.377 -0.017 34 Tyson Ross 3.29 85.2% 84.2% 1.0% 80.2% 22.4% 24.9% -2.5% 8.8% 0.399 -0.068 35 Tyler Skaggs 3.60 85.1% 85.8% -0.7% 82.8% 24.3% 25.1% -0.8% 7.5% 0.417 -0.026 36 Jameson Taillon 4.53 84.8% 83.7% 1.2% 77.4% 19.8% 22.1% -2.3% 7.0% 0.385 0.008 37 Julio Teheran 4.20 84.3% 85.9% -1.6% 83.0% 23.9% 20.6% 3.3% 10.9% 0.429 0.009 38 Kevin Gausman 4.31 84.1% 86.3% -2.3% 87.6% 25.7% 21.0% 4.7% 6.1% 0.463 -0.009 39 Nick Pivetta 3.26 83.6% 83.0% 0.6% 83.0% 23.8% 28.8% -5.0% 6.0% 0.438 -0.095 40 Chris Archer 4.29 83.4% 81.4% 2.0% 89.1% 27.3% 23.5% 3.8% 8.1% 0.482 -0.061 41 Tanner Roark 3.17 82.9% 81.3% 1.6% 77.6% 21.1% 22.5% -1.4% 7.4% 0.412 -0.076 42 Dallas Keuchel 3.39 82.8% 82.8% 0.0% 74.0% 19.0% 18.6% 0.3% 6.3% 0.389 0.020 43 Stephen Strasburg 3.13 82.8% 83.4% -0.5% 83.7% 24.2% 28.6% -4.4% 6.6% 0.454 -0.075 44 Luke Weaver 4.63 82.1% 83.0% -0.9% 75.9% 21.7% 21.5% 0.2% 8.1% 0.411 -0.038 45 Kyle Freeland 3.43 82.0% 84.6% -2.5% 74.5% 19.9% 20.4% -0.6% 7.8% 0.403 -0.010 46 Cole Hamels 3.74 82.0% 84.0% -1.9% 85.5% 24.7% 23.2% 1.5% 9.2% 0.476 -0.045 47 Michael Fulmer 4.60 81.6% 84.9% -3.3% 83.5% 22.8% 20.1% 2.7% 9.3% 0.469 -0.047 48 Clayton Richard 4.97 81.4% 82.7% -1.3% 77.3% 21.6% 19.8% 1.8% 8.5% 0.430 0.008 49 Mike Foltynewicz 2.55 81.4% 81.4% -0.1% 70.0% 20.4% 27.1% -6.7% 11.4% 0.382 -0.026 50 Miles Mikolas 2.58 81.1% 80.3% 0.8% 76.2% 18.5% 19.5% -1.1% 2.7% 0.427 -0.087 51 German Marquez 4.21 80.6% 80.0% 0.7% 69.9% 19.5% 21.0% -1.5% 9.3% 0.391 0.022 52 Jake Arrieta 2.16 80.1% 79.0% 1.1% 64.3% 14.5% 17.2% -2.8% 8.2% 0.360 -0.049 53 Zack Godley 4.38 79.5% 82.3% -2.8% 80.8% 24.3% 21.4% 2.9% 11.2% 0.478 -0.060 54 Jake Odorizzi 3.34 79.5% 80.6% -1.1% 83.1% 24.0% 23.5% 0.5% 9.7% 0.494 -0.035 55 Jakob Junis 3.61 79.3% 76.6% 2.7% 78.1% 20.6% 22.1% -1.6% 6.1% 0.464 -0.038 56 Chad Bettis 3.68 78.4% 77.3% 1.1% 73.5% 19.9% 15.2% 4.7% 8.2% 0.445 -0.047 57 Jhoulys Chacin 3.69 78.1% 77.9% 0.2% 69.9% 19.3% 16.7% 2.6% 10.4% 0.425 -0.057 58 Lance Lynn 5.94 78.1% 78.9% -0.8% 74.4% 20.5% 21.5% -1.0% 14.2% 0.455 0.005 59 David Price 4.04 77.8% 77.2% 0.6% 71.8% 18.9% 22.8% -3.9% 10.0% 0.441 -0.063 60 Jose Urena 4.69 77.7% 78.9% -1.2% 72.3% 18.4% 19.6% -1.2% 5.7% 0.446 -0.036 61 James Shields 4.46 77.3% 77.6% -0.3% 72.7% 20.3% 15.8% 4.5% 9.8% 0.454 -0.138 62 Michael Wacha 2.71 76.9% 76.8% 0.1% 72.9% 21.7% 20.4% 1.3% 9.6% 0.461 -0.158 63 Jose Quintana 4.78 76.8% 74.9% 1.9% 68.8% 18.8% 21.9% -3.1% 11.6% 0.435 0.013 64 Kyle Hendricks 3.16 76.8% 75.3% 1.5% 75.3% 20.3% 20.3% -0.1% 5.2% 0.478 -0.075 65 Marco Gonzales 3.60 76.7% 73.0% 3.6% 71.6% 17.4% 21.2% -3.9% 6.0% 0.455 -0.046 66 Sean Manaea 3.34 76.6% 78.6% -2.0% 78.4% 21.0% 18.9% 2.1% 4.6% 0.502 -0.143 67 Jon Lester 2.71 76.4% 78.1% -1.6% 75.8% 21.6% 20.7% 0.9% 8.4% 0.486 -0.098 68 Aaron Sanchez 4.77 75.9% 78.7% -2.8% 75.8% 21.9% 16.7% 5.2% 12.7% 0.494 -0.080 69 Tyler Mahle 4.76 75.7% 76.1% -0.5% 73.7% 20.7% 22.1% -1.4% 8.7% 0.482 0.014 70 Francisco Liriano 3.90 75.7% 72.0% 3.7% 80.7% 24.3% 19.2% 5.1% 12.4% 0.529 -0.156 71 Reynaldo Lopez 2.93 75.6% 74.3% 1.3% 69.7% 20.1% 16.7% 3.4% 10.3% 0.457 -0.089 72 Daniel Mengden 2.85 73.4% 71.7% 1.7% 72.8% 18.1% 16.2% 1.9% 2.3% 0.507 -0.133 73 Brandon McCarthy 5.02 72.8% 73.5% -0.7% 62.4% 14.3% 17.7% -3.5% 7.5% 0.445 0.033 74 Jason Hammel 5.23 72.6% 69.7% 2.9% 77.2% 19.9% 15.0% 4.9% 6.3% 0.546 -0.099 75 Trevor Williams 3.43 72.6% 73.0% -0.4% 65.4% 16.0% 17.1% -1.1% 7.9% 0.468 -0.089 76 Marco Estrada 5.68 71.2% 74.6% -3.5% 72.8% 19.1% 16.5% 2.6% 6.7% 0.536 0.023 77 Ivan Nova 4.96 71.0% 69.9% 1.1% 73.7% 19.2% 17.6% 1.6% 3.8% 0.545 -0.059 78 Ty Blach 4.90 70.9% 71.7% -0.8% 60.7% 14.7% 11.1% 3.6% 8.5% 0.460 -0.047 79 Felix Hernandez 5.83 70.5% 71.7% -1.2% 66.5% 16.6% 18.8% -2.3% 9.6% 0.503 -0.054 80 Chad Kuhl 3.94 69.6% 67.4% 2.1% 69.5% 20.2% 22.2% -2.0% 8.8% 0.536 -0.071 81 Ian Kennedy 5.15 69.3% 69.9% -0.6% 71.9% 17.5% 20.8% -3.3% 7.7% 0.555 -0.061 82 Danny Duffy 5.71 69.3% 65.0% 4.3% 73.6% 19.8% 18.6% 1.2% 10.7% 0.567 -0.065 83 Chris Stratton 4.97 69.1% 67.5% 1.7% 65.5% 17.8% 19.2% -1.5% 10.8% 0.515 -0.060 84 Lucas Giolito 7.53 68.5% 73.6% -5.1% 63.4% 18.5% 11.6% 6.9% 14.3% 0.510 -0.041 85 Mike Leake 4.93 67.3% 66.4% 0.9% 68.5% 15.4% 13.9% 1.5% 5.7% 0.560 -0.093 86 Bartolo Colon 3.70 66.7% 63.8% 2.9% 62.2% 12.9% 14.7% -1.9% 3.0% 0.526 -0.069 87 Andrew Cashner 5.07 65.8% 64.5% 1.3% 64.6% 16.5% 19.7% -3.3% 11.3% 0.553 -0.030 88 Homer Bailey 6.68 62.1% 61.1% 1.0% 64.2% 15.8% 13.0% 2.8% 8.2% 0.600 -0.036

Table B – 10 IP Minimum

Name IP ERA Pitcher Score PD Score Predicted K% Actual K% K% Difference BB% xSLG SLG – xSLG Shohei Ohtani 40.1 3.35 96.7% 97.6% 33.1% 32.3% 0.8% 8.7% 0.361 -0.037 Kenta Maeda 51.1 3.68 95.0% 94.9% 28.7% 30.3% -1.7% 8.3% 0.366 0.037 Domingo German 20.1 6.64 91.7% 92.2% 28.6% 26.7% 1.9% 11.6% 0.392 -0.014 Trevor Cahill 44 2.25 90.9% 92.5% 28.7% 24.6% 4.1% 5.4% 0.405 -0.089 Ross Stripling 26 2.42 90.4% 74.6% 18.5% 31.1% -12.6% 2.8% 0.292 0.081 Walker Buehler 41 2.20 89.8% 72.3% 19.7% 30.0% -10.4% 5.6% 0.285 -0.028 Johnny Cueto 32 0.84 89.8% 76.2% 22.3% 22.2% 0.0% 5.1% 0.311 -0.105 Eduardo Rodriguez 53.2 4.02 89.3% 85.0% 25.0% 28.0% -3.1% 8.2% 0.376 -0.008 John Gant 15 6.00 89.2% 90.3% 26.8% 29.2% -2.4% 6.2% 0.413 -0.057 Carlos Martinez 50 1.62 88.9% 73.2% 20.0% 22.4% -2.5% 10.5% 0.302 -0.062 Robbie Ray 27.2 4.88 88.7% 92.1% 30.1% 36.3% -6.3% 13.7% 0.432 -0.013 Andrew Heaney 46.2 3.09 87.7% 85.9% 25.7% 26.7% -1.1% 8.9% 0.403 -0.038 Jordan Montgomery 27.1 3.62 87.5% 79.9% 21.5% 19.8% 1.7% 10.3% 0.366 -0.010 Hyun-Jin Ryu 29.2 2.12 87.1% 79.3% 24.8% 31.3% -6.5% 8.7% 0.367 -0.059 Fernando Romero 28.2 1.88 86.5% 80.1% 24.8% 25.0% -0.2% 11.2% 0.380 -0.115 Jack Flaherty 29.1 2.15 85.8% 77.5% 24.2% 27.7% -3.5% 8.0% 0.373 -0.062 Jordan Lyles 22.2 3.97 85.7% 82.5% 22.9% 25.5% -2.7% 8.5% 0.407 0.033 Blaine Hardy 16.1 2.76 85.4% 75.7% 20.1% 18.8% 1.3% 2.9% 0.366 0.028 Nick Kingham 24 3.75 85.4% 82.1% 24.6% 26.0% -1.5% 4.2% 0.409 -0.002 Jaime Barria 30.1 2.97 84.8% 88.1% 23.5% 19.4% 4.1% 6.5% 0.457 -0.112 Clayton Kershaw 44 2.86 84.3% 83.6% 23.9% 26.5% -2.7% 5.5% 0.433 -0.024 Garrett Richards 54 3.67 84.3% 79.5% 24.3% 25.6% -1.4% 12.0% 0.406 -0.040 Wade LeBlanc 26.1 1.71 84.2% 79.7% 17.6% 18.6% -1.0% 3.9% 0.408 -0.102 CC Sabathia 50.2 3.73 83.7% 77.4% 20.2% 17.3% 2.9% 6.4% 0.400 0.024 Austin Bibens-Dirkx 11 6.55 83.5% 93.1% 25.6% 20.8% 4.8% 1.9% 0.508 -0.018 Sam Gaviglio 11.1 2.38 83.4% 88.8% 22.9% 26.1% -3.2% 6.5% 0.480 -0.061 Anibal Sanchez 15 3.60 83.1% 84.4% 21.9% 21.9% -0.1% 7.8% 0.455 0.019 Yu Darvish 40 4.95 82.7% 78.2% 24.1% 27.2% -3.2% 11.7% 0.418 0.007 Michael Soroka 14.2 3.68 81.6% 87.8% 25.4% 21.7% 3.7% 5.8% 0.498 -0.054 Wei-Yin Chen 29.1 5.22 81.1% 71.4% 19.0% 15.6% 3.4% 10.9% 0.395 0.123 Zack Wheeler 50 5.40 80.1% 78.3% 21.4% 23.6% -2.3% 8.4% 0.454 -0.036 Joey Lucchesi 47.1 3.23 80.0% 78.4% 23.6% 24.9% -1.4% 7.8% 0.456 -0.033 Hector Velazquez 10.2 2.53 80.0% 91.4% 23.4% 22.7% 0.6% 4.6% 0.543 -0.091 Jeremy Hellickson 43 2.30 79.9% 77.6% 20.6% 21.0% -0.4% 3.7% 0.452 -0.086 Nick Tropeano 45 3.80 79.8% 82.9% 25.5% 20.4% 5.1% 8.8% 0.489 -0.041 Zach Eflin 22 3.27 79.1% 70.2% 20.8% 26.1% -5.3% 7.6% 0.414 -0.033 Yonny Chirinos 22.2 4.37 78.3% 75.9% 20.1% 21.0% -0.9% 8.0% 0.462 -0.043 Ryan Yarbrough 14.2 4.91 77.9% 60.9% 15.1% 20.3% -5.3% 7.8% 0.367 0.026 Jason Vargas 20.1 10.62 77.3% 86.8% 24.9% 20.2% 4.7% 9.6% 0.548 0.104 Sonny Gray 49.2 5.98 77.0% 72.0% 20.6% 18.6% 2.0% 11.9% 0.453 -0.020 Jaime Garcia 45.2 5.52 76.5% 72.3% 20.5% 21.1% -0.6% 10.3% 0.462 0.044 David Hess 17.1 4.15 76.3% 78.3% 19.3% 13.9% 5.4% 6.9% 0.504 0.003 Jordan Zimmermann 31.1 4.88 76.3% 78.0% 20.6% 23.7% -3.2% 6.7% 0.503 -0.047 Tyler Chatwood 48.1 4.10 75.9% 60.7% 17.1% 19.8% -2.7% 20.3% 0.392 -0.065 Trevor Richards 23.2 4.94 75.7% 70.9% 19.7% 22.4% -2.7% 13.1% 0.463 -0.019 Dylan Covey 17.1 3.63 75.6% 59.7% 14.9% 18.4% -3.6% 9.2% 0.390 -0.086 Chase Anderson 55 4.42 75.5% 69.9% 18.7% 16.6% 2.1% 9.6% 0.460 -0.005 Brent Suter 51.2 4.88 75.4% 75.7% 19.2% 18.8% 0.4% 4.9% 0.500 -0.014 Matt Moore 40.2 8.19 75.3% 77.9% 21.3% 15.9% 5.4% 10.1% 0.515 0.029 Junior Guerra 48.1 2.98 74.8% 69.3% 19.9% 22.0% -2.1% 10.0% 0.465 -0.118 Luis Perdomo 14 8.36 74.4% 68.5% 21.2% 22.1% -0.9% 11.7% 0.465 0.058 Mike Minor 54.1 5.63 73.1% 79.5% 22.2% 21.2% 0.9% 5.1% 0.556 -0.021 Andrew Triggs 41.1 5.23 73.0% 75.9% 21.4% 23.6% -2.3% 9.9% 0.533 -0.120 Adam Wainwright 18 4.00 72.9% 55.9% 14.0% 17.1% -3.1% 15.9% 0.400 0.035 Steven Matz 45.2 3.55 72.7% 59.4% 17.4% 21.0% -3.6% 10.8% 0.427 -0.006 Zach Davies 43 5.23 72.3% 69.1% 18.7% 16.3% 2.4% 9.0% 0.497 -0.003 Mike Fiers 49 4.78 72.0% 71.4% 18.2% 15.2% 3.0% 4.7% 0.516 -0.003 Elieser Hernandez 15 2.40 71.8% 70.0% 20.4% 14.3% 6.1% 1.8% 0.510 -0.037 Marcus Stroman 37.1 7.71 71.2% 72.9% 21.2% 18.2% 3.0% 10.2% 0.537 -0.082 Clay Buchholz 11 1.64 71.0% 70.7% 18.3% 13.2% 5.1% 2.6% 0.525 -0.201 Matt Harvey 40.1 4.91 70.5% 65.6% 16.9% 18.8% -2.0% 4.6% 0.497 0.009 Jake Faria 47.2 5.48 70.4% 68.1% 19.0% 18.2% 0.7% 10.8% 0.516 -0.093 Derek Holland 53.1 4.73 70.4% 66.9% 18.0% 21.3% -3.3% 9.8% 0.508 -0.056 Dillon Peters 24.2 5.84 70.0% 62.0% 16.3% 14.4% 1.9% 11.7% 0.480 -0.011 Jeff Samardzija 35.2 6.56 69.9% 68.8% 17.7% 15.7% 2.0% 13.9% 0.527 -0.084 Drew Pomeranz 32 6.75 69.4% 63.9% 16.6% 20.4% -3.9% 12.5% 0.500 0.045 Eric Skoglund 49.2 6.70 69.1% 70.3% 17.3% 18.0% -0.7% 6.5% 0.548 -0.018 Adam Plutko 18.1 3.93 68.9% 70.6% 16.9% 16.7% 0.2% 6.9% 0.552 -0.014 Brandon Woodruff 11.2 8.49 68.7% 68.9% 21.2% 17.5% 3.7% 12.3% 0.543 -0.064 Matt Wisler 17.1 3.63 68.7% 73.6% 20.7% 18.6% 2.1% 7.1% 0.575 -0.160 Andrew Suarez 36.2 5.65 68.2% 64.2% 15.2% 23.9% -8.7% 5.2% 0.519 -0.005 Brandon Finnegan 20.2 7.40 68.0% 63.0% 16.0% 13.6% 2.4% 14.6% 0.513 0.040 Dan Straily 31.2 3.69 67.6% 73.3% 20.2% 16.9% 3.3% 12.5% 0.587 -0.156 Ben Lively 23.2 6.85 67.1% 62.8% 17.9% 19.1% -1.3% 8.7% 0.524 0.021 Doug Fister 55 4.09 67.1% 57.2% 12.7% 15.0% -2.3% 6.9% 0.487 -0.028 Kendall Graveman 34.1 7.60 67.0% 67.6% 17.1% 17.1% 0.0% 8.2% 0.557 -0.015 Sal Romano 55 5.89 66.9% 55.4% 13.0% 15.5% -2.6% 11.5% 0.477 0.005 Eric Lauer 29.1 7.67 66.2% 57.0% 14.7% 19.1% -4.4% 10.2% 0.497 0.073 Brett Anderson 15.1 7.63 64.9% 67.1% 18.0% 11.1% 6.9% 8.3% 0.582 0.009 Joe Biagini 18.2 7.71 64.9% 65.9% 16.7% 14.4% 2.3% 10.0% 0.574 -0.080 Steven Brault 26 5.54 63.8% 69.2% 20.0% 13.4% 6.6% 12.5% 0.611 -0.213 Taijuan Walker 13 3.46 62.7% 57.6% 15.1% 16.1% -1.1% 8.9% 0.548 -0.156 Alex Cobb 46.1 6.80 62.2% 54.4% 11.7% 11.5% 0.2% 5.5% 0.534 0.075 Daniel Gossett 19.1 6.05 61.7% 64.6% 16.0% 13.1% 2.9% 7.1% 0.608 -0.089 Hector Santiago 24 6.38 61.4% 67.1% 19.2% 19.3% -0.2% 13.8% 0.628 -0.058 Jarlin Garcia 33 3.55 61.0% 65.1% 18.0% 16.7% 1.3% 9.9% 0.620 -0.222 Bryan Mitchell 32 6.47 59.3% 47.1% 11.8% 10.4% 1.4% 16.9% 0.523 -0.027 Rich Hill 24.2 6.20 58.1% 59.4% 16.3% 21.7% -5.5% 11.3% 0.621 -0.055 Miguel Gonzalez 12.1 12.41 56.8% 66.3% 15.5% 7.6% 7.9% 9.1% 0.684 0.099 Josh Tomlin 30 8.10 55.9% 73.8% 19.1% 12.9% 6.2% 3.6% 0.746 -0.019 Matt Koch 46 4.50 55.8% 58.2% 12.8% 11.3% 1.5% 6.7% 0.644 -0.138 Carson Fulmer 31 8.13 55.3% 56.5% 14.6% 16.6% -2.1% 15.3% 0.640 -0.136 Martin Perez 22.1 9.67 55.1% 51.8% 11.6% 10.9% 0.6% 10.1% 0.610 0.060 Chris Tillman 26.2 10.46 51.6% 49.4% 11.9% 9.5% 2.4% 12.4% 0.641 0.011

Data is current through games of Tuesday, May 29.  Methodologies for the calculations remain unchanged from last week.  Pitcher score is weighted 50/50 between PD score and xSLG.  PD score is weighted as 3 points O-Swing%, 3 points Contact%, 3 points SwStr%, 1 point F-Strike%.

Predicted K% is calculated only from Contact% and SwStr%.

For both “K% difference” and “SLG-xSLG” columns, a negative number indicates good luck that should regress negatively going forward.  A positive number indicates bad luck that should regress positively.

ERA, BB%, and SLG-xSLG are included for reference only and not included in any calculations.