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.

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


Bbboston

Chaz,

Great article! Can you tell me how Gossett’s ranking would change if you only looked at his last two games? Thanks!

Chaz Steinberg

thanks! Gossett has pitched slightly better in the last couple games on the PD side, mainly by making substantial improvements in his O-Swing% which should help a bit. But the main strikeout predictors (Contact% and SwStr%) are very close to his overall numbers. The StatCast side is also in line with overall production, and not very good (xSLG of .626 for those 2 games). So overall the last two games probably don’t change a whole lot.

Bbboston

Interesting. I asked because he’s had a noticeable mph spike and I wondered about the performance impact? Thanks!

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