SP Plate Discipline Update – a Look Back So Far

a look back at predicted K rates from April, and how things played out in May.

(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.

NameActual K%Predicted K%K% Difference
Gerrit Cole39.40%30.25%-9.15%
Mike Foltynewicz27.10%20.40%-6.70%
Caleb Smith29.90%23.95%-5.95%
Justin Verlander32.30%27.05%-5.25%
James Paxton32.70%27.65%-5.05%
Nick Pivetta28.80%23.80%-5.00%
Francisco Liriano19.20%24.25%5.05%
Aaron Sanchez16.70%21.85%5.15%
Masahiro Tanaka22.80%28.05%5.25%
Lucas Giolito11.60%18.45%6.85%
Luis Castillo22.50%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:

NameOriginal DiscrepancyCurrent DiscrepancyK% Direction PredictedDirection MovedK% Actual ChangeGap Closure from K%
Steven Matz-13.60%-3.60%DownDown-9.70%99.90%
Derek Holland-11.10%-3.30%DownDown-3.70%44.89%
Gerrit Cole-7.40%-9.15%DownUp1.40%N/A (no closure)
Mike Foltynewicz-7.10%-6.70%DownDown-2.60%58.28%
Marco Gonzales-6.90%-3.85%DownDown-6.40%78.49%
Carlos Martinez-6.90%-2.45%DownDown-5.40%97.00%
Jose Berrios-6.70%-0.60%DownDown-5.30%97.77%
Hyun-Jin Ryu-6.20%-6.50%DownUp0.60%N/A (no closure)
Rich Hill-5.90%-5.45%DownDown-2.50%59.79%
Jaime Garcia-5.80%-0.60%DownDown-4.20%94.64%
Rick Porcello-5.70%-2.55%DownDown-2.30%87.98%
Jake Arrieta-5.50%-2.75%DownDown-6.00%77.32%
Caleb Smith-5.50%-5.95%DownDown-2.80%42.60%
Jameson Taillon-5.50%-2.30%DownDown-2.00%73.53%
Junior Guerra-5.00%-2.10%DownDown-1.40%46.56%
Carlos Carrasco5.20%2.60%UpUp2.90%98.94%
Masahiro Tanaka5.30%5.25%UpDown-0.50%N/A (no closure)
Tyler Anderson5.30%3.75%UpDown-3.90%N/A (no closure)
CC Sabathia5.50%2.85%UpUp2.30%97.74%
Aaron Sanchez7.00%5.15%UpUp2.90%88.41%
James Shields7.20%4.45%UpUp5.10%82.49%
Matt Boyd7.80%2.35%UpUp4.10%90.22%
Lucas Giolito8.00%6.85%UpUp2.50%77.42%
Mike Fiers8.90%2.95%UpUp2.00%20.41%
Luis Castillo9.80%8.35%UpUp2.50%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:


Table A – Qualified Starters

RankNameERAPitcher ScorePrevious ScoreScore ChangePD ScorePredicted K%Actual K%K% DifferenceBB%xSLGSLG – xSLG
1Jacob deGrom1.52104.3%105.5%-1.1%99.6%30.6%32.8%-2.2%7.3%0.273-0.013
2Max Scherzer2.13101.4%103.7%-2.3%105.1%33.7%38.2%-4.5%6.4%0.349-0.016
3Chris Sale2.76100.3%101.8%-1.5%104.3%32.9%34.8%-1.9%6.7%0.358-0.010
4Justin Verlander1.1199.2%98.9%0.3%91.9%27.1%32.3%-5.3%5.0%0.290-0.034
5Noah Syndergaard3.0698.8%98.5%0.3%99.1%30.6%28.3%2.3%4.8%0.3430.021
6Patrick Corbin2.4798.1%99.5%-1.3%99.8%30.7%32.7%-2.0%7.1%0.357-0.051
7Gerrit Cole2.0596.3%98.7%-2.4%95.4%30.3%39.4%-9.2%6.9%0.352-0.057
8Charlie Morton2.2694.9%99.2%-4.3%87.9%27.3%31.6%-4.3%8.2%0.3210.016
9Aaron Nola2.2793.4%91.7%1.7%86.3%24.7%24.6%0.1%6.3%0.330-0.046
10Lance McCullers Jr.3.9892.0%95.8%-3.8%91.1%27.3%26.4%0.9%9.7%0.380-0.025
11Sean Newcomb2.7592.0%92.5%-0.5%78.0%22.6%26.6%-4.1%12.0%0.2930.007
12James Paxton3.1091.1%89.6%1.5%91.8%27.7%32.7%-5.1%7.3%0.398-0.071
13Blake Snell2.5691.0%89.2%1.8%90.1%27.4%27.3%0.1%8.3%0.387-0.046
14Masahiro Tanaka4.6290.3%90.1%0.3%95.9%28.1%22.8%5.3%6.7%0.435-0.017
15Trevor Bauer2.6189.8%90.3%-0.5%89.5%26.7%28.6%-2.0%8.3%0.399-0.084
16Kyle Gibson3.5789.6%88.5%1.1%88.0%26.6%24.8%1.8%10.5%0.392-0.044
17Carlos Carrasco3.9889.1%89.6%-0.5%93.2%27.0%24.4%2.6%5.5%0.434-0.044
18Jose Berrios3.6789.1%87.8%1.3%87.4%24.1%24.7%-0.6%4.8%0.395-0.041
19Jon Gray5.4088.8%89.0%-0.1%86.9%26.6%26.3%0.3%5.9%0.3950.055
20Luis Castillo5.4988.4%89.2%-0.8%97.5%30.9%22.5%8.4%9.2%0.4710.004
21Vince Velasquez4.0888.2%87.1%1.2%79.4%24.2%28.5%-4.3%8.5%0.3530.081
22Luis Severino2.2887.8%89.5%-1.7%87.2%25.5%29.2%-3.8%7.2%0.410-0.115
23Alex Wood3.7587.8%89.5%-1.7%86.5%23.2%23.1%0.1%4.0%0.406-0.030
24Caleb Smith3.5187.6%89.0%-1.4%83.1%24.0%29.9%-6.0%11.3%0.386-0.066
25Dylan Bundy4.4686.8%85.6%1.2%100.1%29.9%28.3%1.6%6.8%0.510-0.016
26Tyler Anderson4.7286.5%89.3%-2.8%83.8%24.7%20.9%3.8%9.4%0.4050.069
27Mike Clevinger3.1486.5%86.5%0.0%83.2%23.5%21.5%2.0%8.1%0.401-0.039
28J.A. Happ3.8486.4%85.8%0.6%83.5%24.7%29.5%-4.9%6.7%0.405-0.042
29Matt Boyd3.0086.3%86.4%-0.1%76.5%22.0%19.6%2.4%8.7%0.360-0.071
30Zack Greinke3.6586.2%87.0%-0.7%91.9%26.7%27.1%-0.5%3.8%0.463-0.038
31Gio Gonzalez2.1086.0%86.7%-0.8%75.7%21.5%23.2%-1.7%10.3%0.358-0.022
32Corey Kluber2.1785.6%84.4%1.2%83.7%23.1%26.1%-3.1%3.3%0.417-0.077
33Rick Porcello3.6585.4%86.2%-0.8%77.4%20.2%22.7%-2.6%4.9%0.377-0.017
34Tyson Ross3.2985.2%84.2%1.0%80.2%22.4%24.9%-2.5%8.8%0.399-0.068
35Tyler Skaggs3.6085.1%85.8%-0.7%82.8%24.3%25.1%-0.8%7.5%0.417-0.026
36Jameson Taillon4.5384.8%83.7%1.2%77.4%19.8%22.1%-2.3%7.0%0.3850.008
37Julio Teheran4.2084.3%85.9%-1.6%83.0%23.9%20.6%3.3%10.9%0.4290.009
38Kevin Gausman4.3184.1%86.3%-2.3%87.6%25.7%21.0%4.7%6.1%0.463-0.009
39Nick Pivetta3.2683.6%83.0%0.6%83.0%23.8%28.8%-5.0%6.0%0.438-0.095
40Chris Archer4.2983.4%81.4%2.0%89.1%27.3%23.5%3.8%8.1%0.482-0.061
41Tanner Roark3.1782.9%81.3%1.6%77.6%21.1%22.5%-1.4%7.4%0.412-0.076
42Dallas Keuchel3.3982.8%82.8%0.0%74.0%19.0%18.6%0.3%6.3%0.3890.020
43Stephen Strasburg3.1382.8%83.4%-0.5%83.7%24.2%28.6%-4.4%6.6%0.454-0.075
44Luke Weaver4.6382.1%83.0%-0.9%75.9%21.7%21.5%0.2%8.1%0.411-0.038
45Kyle Freeland3.4382.0%84.6%-2.5%74.5%19.9%20.4%-0.6%7.8%0.403-0.010
46Cole Hamels3.7482.0%84.0%-1.9%85.5%24.7%23.2%1.5%9.2%0.476-0.045
47Michael Fulmer4.6081.6%84.9%-3.3%83.5%22.8%20.1%2.7%9.3%0.469-0.047
48Clayton Richard4.9781.4%82.7%-1.3%77.3%21.6%19.8%1.8%8.5%0.4300.008
49Mike Foltynewicz2.5581.4%81.4%-0.1%70.0%20.4%27.1%-6.7%11.4%0.382-0.026
50Miles Mikolas2.5881.1%80.3%0.8%76.2%18.5%19.5%-1.1%2.7%0.427-0.087
51German Marquez4.2180.6%80.0%0.7%69.9%19.5%21.0%-1.5%9.3%0.3910.022
52Jake Arrieta2.1680.1%79.0%1.1%64.3%14.5%17.2%-2.8%8.2%0.360-0.049
53Zack Godley4.3879.5%82.3%-2.8%80.8%24.3%21.4%2.9%11.2%0.478-0.060
54Jake Odorizzi3.3479.5%80.6%-1.1%83.1%24.0%23.5%0.5%9.7%0.494-0.035
55Jakob Junis3.6179.3%76.6%2.7%78.1%20.6%22.1%-1.6%6.1%0.464-0.038
56Chad Bettis3.6878.4%77.3%1.1%73.5%19.9%15.2%4.7%8.2%0.445-0.047
57Jhoulys Chacin3.6978.1%77.9%0.2%69.9%19.3%16.7%2.6%10.4%0.425-0.057
58Lance Lynn5.9478.1%78.9%-0.8%74.4%20.5%21.5%-1.0%14.2%0.4550.005
59David Price4.0477.8%77.2%0.6%71.8%18.9%22.8%-3.9%10.0%0.441-0.063
60Jose Urena4.6977.7%78.9%-1.2%72.3%18.4%19.6%-1.2%5.7%0.446-0.036
61James Shields4.4677.3%77.6%-0.3%72.7%20.3%15.8%4.5%9.8%0.454-0.138
62Michael Wacha2.7176.9%76.8%0.1%72.9%21.7%20.4%1.3%9.6%0.461-0.158
63Jose Quintana4.7876.8%74.9%1.9%68.8%18.8%21.9%-3.1%11.6%0.4350.013
64Kyle Hendricks3.1676.8%75.3%1.5%75.3%20.3%20.3%-0.1%5.2%0.478-0.075
65Marco Gonzales3.6076.7%73.0%3.6%71.6%17.4%21.2%-3.9%6.0%0.455-0.046
66Sean Manaea3.3476.6%78.6%-2.0%78.4%21.0%18.9%2.1%4.6%0.502-0.143
67Jon Lester2.7176.4%78.1%-1.6%75.8%21.6%20.7%0.9%8.4%0.486-0.098
68Aaron Sanchez4.7775.9%78.7%-2.8%75.8%21.9%16.7%5.2%12.7%0.494-0.080
69Tyler Mahle4.7675.7%76.1%-0.5%73.7%20.7%22.1%-1.4%8.7%0.4820.014
70Francisco Liriano3.9075.7%72.0%3.7%80.7%24.3%19.2%5.1%12.4%0.529-0.156
71Reynaldo Lopez2.9375.6%74.3%1.3%69.7%20.1%16.7%3.4%10.3%0.457-0.089
72Daniel Mengden2.8573.4%71.7%1.7%72.8%18.1%16.2%1.9%2.3%0.507-0.133
73Brandon McCarthy5.0272.8%73.5%-0.7%62.4%14.3%17.7%-3.5%7.5%0.4450.033
74Jason Hammel5.2372.6%69.7%2.9%77.2%19.9%15.0%4.9%6.3%0.546-0.099
75Trevor Williams3.4372.6%73.0%-0.4%65.4%16.0%17.1%-1.1%7.9%0.468-0.089
76Marco Estrada5.6871.2%74.6%-3.5%72.8%19.1%16.5%2.6%6.7%0.5360.023
77Ivan Nova4.9671.0%69.9%1.1%73.7%19.2%17.6%1.6%3.8%0.545-0.059
78Ty Blach4.9070.9%71.7%-0.8%60.7%14.7%11.1%3.6%8.5%0.460-0.047
79Felix Hernandez5.8370.5%71.7%-1.2%66.5%16.6%18.8%-2.3%9.6%0.503-0.054
80Chad Kuhl3.9469.6%67.4%2.1%69.5%20.2%22.2%-2.0%8.8%0.536-0.071
81Ian Kennedy5.1569.3%69.9%-0.6%71.9%17.5%20.8%-3.3%7.7%0.555-0.061
82Danny Duffy5.7169.3%65.0%4.3%73.6%19.8%18.6%1.2%10.7%0.567-0.065
83Chris Stratton4.9769.1%67.5%1.7%65.5%17.8%19.2%-1.5%10.8%0.515-0.060
84Lucas Giolito7.5368.5%73.6%-5.1%63.4%18.5%11.6%6.9%14.3%0.510-0.041
85Mike Leake4.9367.3%66.4%0.9%68.5%15.4%13.9%1.5%5.7%0.560-0.093
86Bartolo Colon3.7066.7%63.8%2.9%62.2%12.9%14.7%-1.9%3.0%0.526-0.069
87Andrew Cashner5.0765.8%64.5%1.3%64.6%16.5%19.7%-3.3%11.3%0.553-0.030
88Homer Bailey6.6862.1%61.1%1.0%64.2%15.8%13.0%2.8%8.2%0.600-0.036

Table B – 10 IP Minimum

NameIPERAPitcher ScorePD ScorePredicted K%Actual K%K% DifferenceBB%xSLGSLG – xSLG
Shohei Ohtani40.13.3596.7%97.6%33.1%32.3%0.8%8.7%0.361-0.037
Kenta Maeda51.13.6895.0%94.9%28.7%30.3%-1.7%8.3%0.3660.037
Domingo German20.16.6491.7%92.2%28.6%26.7%1.9%11.6%0.392-0.014
Trevor Cahill442.2590.9%92.5%28.7%24.6%4.1%5.4%0.405-0.089
Ross Stripling262.4290.4%74.6%18.5%31.1%-12.6%2.8%0.2920.081
Walker Buehler412.2089.8%72.3%19.7%30.0%-10.4%5.6%0.285-0.028
Johnny Cueto320.8489.8%76.2%22.3%22.2%0.0%5.1%0.311-0.105
Eduardo Rodriguez53.24.0289.3%85.0%25.0%28.0%-3.1%8.2%0.376-0.008
John Gant156.0089.2%90.3%26.8%29.2%-2.4%6.2%0.413-0.057
Carlos Martinez501.6288.9%73.2%20.0%22.4%-2.5%10.5%0.302-0.062
Robbie Ray27.24.8888.7%92.1%30.1%36.3%-6.3%13.7%0.432-0.013
Andrew Heaney46.23.0987.7%85.9%25.7%26.7%-1.1%8.9%0.403-0.038
Jordan Montgomery27.13.6287.5%79.9%21.5%19.8%1.7%10.3%0.366-0.010
Hyun-Jin Ryu29.22.1287.1%79.3%24.8%31.3%-6.5%8.7%0.367-0.059
Fernando Romero28.21.8886.5%80.1%24.8%25.0%-0.2%11.2%0.380-0.115
Jack Flaherty29.12.1585.8%77.5%24.2%27.7%-3.5%8.0%0.373-0.062
Jordan Lyles22.23.9785.7%82.5%22.9%25.5%-2.7%8.5%0.4070.033
Blaine Hardy16.12.7685.4%75.7%20.1%18.8%1.3%2.9%0.3660.028
Nick Kingham243.7585.4%82.1%24.6%26.0%-1.5%4.2%0.409-0.002
Jaime Barria30.12.9784.8%88.1%23.5%19.4%4.1%6.5%0.457-0.112
Clayton Kershaw442.8684.3%83.6%23.9%26.5%-2.7%5.5%0.433-0.024
Garrett Richards543.6784.3%79.5%24.3%25.6%-1.4%12.0%0.406-0.040
Wade LeBlanc26.11.7184.2%79.7%17.6%18.6%-1.0%3.9%0.408-0.102
CC Sabathia50.23.7383.7%77.4%20.2%17.3%2.9%6.4%0.4000.024
Austin Bibens-Dirkx116.5583.5%93.1%25.6%20.8%4.8%1.9%0.508-0.018
Sam Gaviglio11.12.3883.4%88.8%22.9%26.1%-3.2%6.5%0.480-0.061
Anibal Sanchez153.6083.1%84.4%21.9%21.9%-0.1%7.8%0.4550.019
Yu Darvish404.9582.7%78.2%24.1%27.2%-3.2%11.7%0.4180.007
Michael Soroka14.23.6881.6%87.8%25.4%21.7%3.7%5.8%0.498-0.054
Wei-Yin Chen29.15.2281.1%71.4%19.0%15.6%3.4%10.9%0.3950.123
Zack Wheeler505.4080.1%78.3%21.4%23.6%-2.3%8.4%0.454-0.036
Joey Lucchesi47.13.2380.0%78.4%23.6%24.9%-1.4%7.8%0.456-0.033
Hector Velazquez10.22.5380.0%91.4%23.4%22.7%0.6%4.6%0.543-0.091
Jeremy Hellickson432.3079.9%77.6%20.6%21.0%-0.4%3.7%0.452-0.086
Nick Tropeano453.8079.8%82.9%25.5%20.4%5.1%8.8%0.489-0.041
Zach Eflin223.2779.1%70.2%20.8%26.1%-5.3%7.6%0.414-0.033
Yonny Chirinos22.24.3778.3%75.9%20.1%21.0%-0.9%8.0%0.462-0.043
Ryan Yarbrough14.24.9177.9%60.9%15.1%20.3%-5.3%7.8%0.3670.026
Jason Vargas20.110.6277.3%86.8%24.9%20.2%4.7%9.6%0.5480.104
Sonny Gray49.25.9877.0%72.0%20.6%18.6%2.0%11.9%0.453-0.020
Jaime Garcia45.25.5276.5%72.3%20.5%21.1%-0.6%10.3%0.4620.044
David Hess17.14.1576.3%78.3%19.3%13.9%5.4%6.9%0.5040.003
Jordan Zimmermann31.14.8876.3%78.0%20.6%23.7%-3.2%6.7%0.503-0.047
Tyler Chatwood48.14.1075.9%60.7%17.1%19.8%-2.7%20.3%0.392-0.065
Trevor Richards23.24.9475.7%70.9%19.7%22.4%-2.7%13.1%0.463-0.019
Dylan Covey17.13.6375.6%59.7%14.9%18.4%-3.6%9.2%0.390-0.086
Chase Anderson554.4275.5%69.9%18.7%16.6%2.1%9.6%0.460-0.005
Brent Suter51.24.8875.4%75.7%19.2%18.8%0.4%4.9%0.500-0.014
Matt Moore40.28.1975.3%77.9%21.3%15.9%5.4%10.1%0.5150.029
Junior Guerra48.12.9874.8%69.3%19.9%22.0%-2.1%10.0%0.465-0.118
Luis Perdomo148.3674.4%68.5%21.2%22.1%-0.9%11.7%0.4650.058
Mike Minor54.15.6373.1%79.5%22.2%21.2%0.9%5.1%0.556-0.021
Andrew Triggs41.15.2373.0%75.9%21.4%23.6%-2.3%9.9%0.533-0.120
Adam Wainwright184.0072.9%55.9%14.0%17.1%-3.1%15.9%0.4000.035
Steven Matz45.23.5572.7%59.4%17.4%21.0%-3.6%10.8%0.427-0.006
Zach Davies435.2372.3%69.1%18.7%16.3%2.4%9.0%0.497-0.003
Mike Fiers494.7872.0%71.4%18.2%15.2%3.0%4.7%0.516-0.003
Elieser Hernandez152.4071.8%70.0%20.4%14.3%6.1%1.8%0.510-0.037
Marcus Stroman37.17.7171.2%72.9%21.2%18.2%3.0%10.2%0.537-0.082
Clay Buchholz111.6471.0%70.7%18.3%13.2%5.1%2.6%0.525-0.201
Matt Harvey40.14.9170.5%65.6%16.9%18.8%-2.0%4.6%0.4970.009
Jake Faria47.25.4870.4%68.1%19.0%18.2%0.7%10.8%0.516-0.093
Derek Holland53.14.7370.4%66.9%18.0%21.3%-3.3%9.8%0.508-0.056
Dillon Peters24.25.8470.0%62.0%16.3%14.4%1.9%11.7%0.480-0.011
Jeff Samardzija35.26.5669.9%68.8%17.7%15.7%2.0%13.9%0.527-0.084
Drew Pomeranz326.7569.4%63.9%16.6%20.4%-3.9%12.5%0.5000.045
Eric Skoglund49.26.7069.1%70.3%17.3%18.0%-0.7%6.5%0.548-0.018
Adam Plutko18.13.9368.9%70.6%16.9%16.7%0.2%6.9%0.552-0.014
Brandon Woodruff11.28.4968.7%68.9%21.2%17.5%3.7%12.3%0.543-0.064
Matt Wisler17.13.6368.7%73.6%20.7%18.6%2.1%7.1%0.575-0.160
Andrew Suarez36.25.6568.2%64.2%15.2%23.9%-8.7%5.2%0.519-0.005
Brandon Finnegan20.27.4068.0%63.0%16.0%13.6%2.4%14.6%0.5130.040
Dan Straily31.23.6967.6%73.3%20.2%16.9%3.3%12.5%0.587-0.156
Ben Lively23.26.8567.1%62.8%17.9%19.1%-1.3%8.7%0.5240.021
Doug Fister554.0967.1%57.2%12.7%15.0%-2.3%6.9%0.487-0.028
Kendall Graveman34.17.6067.0%67.6%17.1%17.1%0.0%8.2%0.557-0.015
Sal Romano555.8966.9%55.4%13.0%15.5%-2.6%11.5%0.4770.005
Eric Lauer29.17.6766.2%57.0%14.7%19.1%-4.4%10.2%0.4970.073
Brett Anderson15.17.6364.9%67.1%18.0%11.1%6.9%8.3%0.5820.009
Joe Biagini18.27.7164.9%65.9%16.7%14.4%2.3%10.0%0.574-0.080
Steven Brault265.5463.8%69.2%20.0%13.4%6.6%12.5%0.611-0.213
Taijuan Walker133.4662.7%57.6%15.1%16.1%-1.1%8.9%0.548-0.156
Alex Cobb46.16.8062.2%54.4%11.7%11.5%0.2%5.5%0.5340.075
Daniel Gossett19.16.0561.7%64.6%16.0%13.1%2.9%7.1%0.608-0.089
Hector Santiago246.3861.4%67.1%19.2%19.3%-0.2%13.8%0.628-0.058
Jarlin Garcia333.5561.0%65.1%18.0%16.7%1.3%9.9%0.620-0.222
Bryan Mitchell326.4759.3%47.1%11.8%10.4%1.4%16.9%0.523-0.027
Rich Hill24.26.2058.1%59.4%16.3%21.7%-5.5%11.3%0.621-0.055
Miguel Gonzalez12.112.4156.8%66.3%15.5%7.6%7.9%9.1%0.6840.099
Josh Tomlin308.1055.9%73.8%19.1%12.9%6.2%3.6%0.746-0.019
Matt Koch464.5055.8%58.2%12.8%11.3%1.5%6.7%0.644-0.138
Carson Fulmer318.1355.3%56.5%14.6%16.6%-2.1%15.3%0.640-0.136
Martin Perez22.19.6755.1%51.8%11.6%10.9%0.6%10.1%0.6100.060
Chris Tillman26.210.4651.6%49.4%11.9%9.5%2.4%12.4%0.6410.011

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

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

  • Avatar Bbboston says:


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

    • 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.

      • Avatar Bbboston says:

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

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