The Pitcher Plate Discipline Rankings 5/25

Welcome to this week’s edition of the Plate Discipline Leaderboard. If you’re new to this post, the calculations of each metric are outlined below:

Plate Discipline A
O-Swing%
Inverse Contact % = (100% – Contact %)
2SwStr+1=(2*SwStr%+1%)
F-Strike %

The standard deviations above/below average for everything aside from F-Strike% is multiplied by three. Then, those three numbers are added to the standard deviation above/below average for F-Strike%, and that total is divided by four.

Plate Discipline B
Inverse xSLG = (100% – xSLG %)

The standard deviation above/below average is multiplied by one and one half.

Final Plate Discipline
The average of Plate Discipline A and Plate Discipline B.

Please note that since statcast data isn’t available yet, all data used today will be up-to-date through 5/22.

 

NameO-Swing%Inv Contact%F-Strike%SwStr%PD Grade AxSLGPD Grade BFinal PD GradeLetter Grade
Blake Snell1.0771.1880.6861.1821.033250.3011.04851.040875A+
Luis Castillo0.9691.0530.5020.9480.8680.2331.15051.00925A+
Stephen Strasburg1.1280.990.6680.9420.9320.2981.0530.9925A+
Gerrit Cole1.050.9360.6170.990.898250.3331.00050.949375A
Caleb Smith1.1280.9540.6051.0260.928250.3650.95250.940375A
Jacob deGrom1.1010.8520.6550.9480.8890.3480.9780.9335A -
Kenta Maeda0.990.90.7080.8880.87150.3470.97950.9255A -
Justin Verlander1.1670.90.6390.9660.9180.3810.92850.92325A -
Tyler Glasnow0.9120.7920.6230.750.769250.2891.06650.917875A -
Chris Paddack1.0410.6840.7390.7440.8020.3151.02750.91475A -
Charlie Morton0.9990.8220.5750.7680.7910.3161.0260.9085A -
Max Scherzer0.9960.930.6940.990.90250.3930.91050.9065A -
Chris Sale1.0140.9330.6550.9180.880.390.9150.8975B+
Domingo German1.1010.8280.6010.840.84250.380.930.88625B+
Matthew Boyd1.0230.8070.6480.8340.8280.3740.9390.8835B+
Martin Perez0.9990.720.6680.7140.775250.340.990.882625B+
German Marquez1.0320.8280.6790.8580.849250.3970.90450.876875B+
Hyun-Jin Ryu1.0350.7320.6330.7380.78450.3540.9690.87675B+
Mike Minor0.9540.7740.6290.7320.772250.3460.9810.876625B+
Eduardo Rodriguez1.0590.8430.6350.810.836750.4010.89850.867625B
Noah Syndergaard0.8880.6960.6540.6960.73350.3350.99750.8655B
Frankie Montas1.080.6720.6260.7140.7730.3660.9510.862B
Robbie Ray0.9450.8730.5510.7740.785750.3810.92850.857125B
Joe Musgrove1.0080.6930.6140.6960.752750.360.960.856375B
Sonny Gray0.8160.7620.5630.6360.694250.3311.00350.848875B
Jake Odorizzi0.9210.7380.5830.7080.73750.3610.95850.848B
Jack Flaherty0.8580.8220.6130.7620.763750.3790.93150.847625B
Yu Darvish0.840.9210.5530.7860.7750.3880.9180.8465B
Max Fried0.870.690.6630.6780.725250.360.960.842625B
Patrick Corbin0.990.8640.60.8160.81750.4230.86550.8415B
Sandy Alcantara1.050.7650.5640.7620.785250.4090.88650.835875B -
Zack Greinke1.0890.5880.6630.570.72750.3730.94050.834B -
Pablo Lopez0.9330.6870.570.6960.72150.3690.94650.834B -
Zack Wheeler0.90.660.6410.6720.718250.3690.94650.832375B -
Marcus Stroman0.8850.7230.5850.6660.714750.3680.9480.831375B -
Masahiro Tanaka0.9540.6660.6750.660.738750.3920.9120.825375B -
Trevor Richards0.9150.8370.5590.8040.778750.4210.86850.823625B -
Spencer Turnbull0.9120.7470.6130.7320.7510.4070.88950.82025B -
Brandon Woodruff0.8910.7230.5880.7320.73350.3970.90450.819B -
Jose Quintana0.9030.6870.6650.630.721250.3890.91650.818875B -
Carlos Carrasco1.0590.8250.6640.8580.85150.4790.78150.8165B -
Cole Hamels0.9180.6870.6040.660.717250.3920.9120.814625B -
Shane Bieber1.0080.9270.6170.8520.8510.4830.77550.81325B -
Madison Bumgarner1.0260.6750.6990.7140.77850.4370.84450.8115B -
Jordan Lyles0.90.6450.6460.6180.702250.3990.90150.801875B -
Kyle Gibson0.9510.8550.6830.7680.814250.4740.7890.801625B -
Zach Eflin0.8970.630.6980.6060.707750.4030.89550.801625B -
Luke Weaver0.9270.6960.6180.6720.728250.4170.87450.801375B -
Trevor Williams1.0920.5910.6910.6360.75250.4340.8490.80075B -
Julio Teheran0.7890.780.5910.7020.71550.410.8850.80025B -
Jon Gray0.8940.7950.6410.7680.77450.4560.8160.79525C+
Trent Thornton0.9390.7350.5390.6660.719750.4210.86850.794125C+
Lance Lynn0.8430.6150.580.6060.6610.3850.92250.79175C+
Trevor Bauer0.8010.7920.5510.720.7160.4220.8670.7915C+
Jose Berrios1.050.6240.6540.6360.7410.4390.84150.79125C+
Yusei Kikuchi0.8040.5790.6010.5760.640.3730.94050.79025C+
Walker Buehler0.8310.6660.6090.6660.6930.410.8850.789C+
Marco Gonzales0.9420.5310.660.5340.666750.3940.9090.787875C+
Wade Miley0.9030.5820.5670.5640.6540.3880.9180.786C+
Zach Davies0.8670.5310.5730.4980.617250.3790.93150.774375C+
Tyler Mahle0.8340.6150.6730.5880.67750.4320.8520.76475C
Yonny Chirinos0.9960.5280.6480.5760.6870.4440.8340.7605C
Aaron Nola0.9030.6570.5670.5580.671250.4340.8490.760125C
Kyle Hendricks0.9150.5460.6650.5340.6650.4320.8520.7585C
Jose Urena0.9990.5250.5460.5220.6480.4350.84750.74775C
Aaron Sanchez0.8430.6690.5730.6480.683250.4610.80850.745875C
Jhoulys Chacin0.7530.540.5810.4860.590.40.90.745C
Matt Strahm0.9240.5580.6460.5820.67750.4640.8040.74075C
Kyle Freeland0.9540.6510.6250.6540.7210.4950.75750.73925C -
Ivan Nova1.0680.5640.5960.5940.70550.4860.7710.73825C -
Tanner Roark0.8250.5610.5670.5220.618750.4320.8520.735375C -
Brett Anderson0.8340.5370.5610.5160.6120.4290.85650.73425C -
Reynaldo Lopez0.7560.7110.5830.6780.6820.4760.7860.734C -
Jakob Junis0.8910.6540.6260.6060.694250.4860.7710.732625C -
Mike Fiers0.8910.4620.6360.4680.614250.4350.84750.730875C -
J.A. Happ0.9090.6180.6140.6180.689750.4870.76950.729625C -
Michael Pineda0.9930.690.6270.7380.7620.5370.69450.72825C -
Rick Porcello0.8340.5160.6730.510.633250.4530.82050.726875C -
Brad Keller0.8220.5970.5470.5340.6250.4520.8220.7235C -
Jake Arrieta0.8310.5340.6310.4920.6220.4560.8160.719C -
Miles Mikolas0.8820.4770.6710.480.62750.4620.8070.71725C -
Adam Wainwright0.6450.510.5570.4260.53450.4170.87450.7045C -
Merrill Kelly0.8310.6030.5340.5760.6360.50.750.693D+
Mike Leake1.0170.4890.6720.5160.67350.5420.6870.68025D+
Andrew Cashner0.7620.630.580.570.63550.5240.7140.67475D+
Jorge Lopez0.8550.630.5640.5820.657750.5530.67050.664125D
Trevor Cahill0.7470.6240.5940.6120.644250.5550.66750.655875D

 

Stephen Strasburg

PD Rank: #3

Year K% eK% BB% O-Swing% Contact% SwStr% F-Strike% xSLG
2017 29.1% 26.6% 6.7% 33.4% 73.7% 12.9% 63.5% .345
2018 28.7% 25.3% 7.0% 32.4% 74.2% 11.9% 61.0% .401
2019 30.6% 30.75% 6.3% 38.0% 68.7% 14.6% 66.2% .294

Normally I try to write about a new player every week, but this week I wanted to revisit Stephen Strasburg as I think that we can use his case as a prime chance to learn more about what the Plate Discipline Rankings are trying to tell us. Back on 4/24, we took a look at Strasburg’s peculiar situation as he was top 5 in our rankings yet the results had not shown thrown through.  Just for reference, here were his plate discipline numbers and results at the time:

Year K% eK% BB% O-Swing% Contact% SwStr% F-Strike% xSLG
2019 as 4/24 31.2% 29.75% 7.2% 37.7% 69.1% 13.8% 63.2% .339
Year ERA IP FIP BABIP SIERA
2019 as of 4/24 4.11 30.1 3.57 .264 3.18

If you go back and read the original piece, Strasburg was putting up career highs in nearly all the relevant plate discipline categories but something was holding him back. After some investigation, I concluded that Strasburg was throwing his two-seamer/sinker far too often as it was really the pitch that found way too much of the plate for a sinker and was getting hit HARD, while his four-seamer and curveball were excelling. At the time, I prescribed that he needed to cut down on his sinker usage and throw his best pitch more often (especially for strikes) which meant more curveballs. Did Strasburg manage to make me look like I knew what I was talking about? Here are his usage numbers from 2018 and 2019 before 4/25:

Pitch Usage% K% SwStr% BB% Zone% AVG
Four-Seamer 2018 45.0% 20.8% 8.4% 7.9% 60.7% .280
Four-Seamer 2019 before 4/25 29.1% 32.3% 10.0% 6.5% 48.5% .097
Sinker 2018 7.0% 7.1% 7.9% 2.4% 40.1% .361
Sinker 2019 before 4/25 21.3% 10.0% 4.3% 16.7% 49.1% .292
Changeup 2018 19.9% 48.0% 21.5% 6.9% 23.3% .160
Changeup 2019 before 4/25 21.1% 40.0% 26.2% 3.3% 34.0% .207
Curveball 2018 19.5% 40.0% 12.1% 5.5% 47.2% .175
Curveball 2019 before 4/25 26.0% 48.0% 18.1% 0.0% 40.9% .125
Slider 2018 8.6% 15.6% 11.8% 6.7% 44.6% .286
Slider 2019 before 4/25 2.5% 0.0% 8.3% 0.0% 41.7% 1.000

You can see a difficulty throwing for strikes and the sinker getting hit really well which can cause a lot of problems. Now, what about his usage/results AFTER 4/24?

Pitch Usage% K% SwStr% BB% Zone% AVG
Four-Seamer 2019 after 4/25 36.1% 32.0% 12.1% 5.9% 52.2% .277
Sinker 2019 After 4/25 12.8% 5.5% 15.5% 5.5% 68.9% .235
Changeup 2019 after 4/25 16.1% 46.7% 28.3% 13.3% 28.4% .200
Curveball 2019 after 4/25 34.9% 45.5% 17.6% 4.5% 47.7% .098
Slider 2019 after 4/25 0.0% 0.0% 0.0% 0.0% 0.0% .000

Now that’s more like it. We actually see a couple of really interesting things going on here. For one thing, even with a near 10.0% increase in usage, Strasburg’s curveball retains its effectiveness. In addition, notice that 6.8% increase in Zone% for the curveball. I mentioned in the previous article that I’m a big believer in being able to throw your breaking pitches in the zone to establish that you can throw the pitch for strikes. This keeps the hitter from being able to lay off the pitch simply because they pick up on the spin or see the break.  Often times, this ability is what takes an average pitcher with an elite breaker and turns them into a stud. To further emphasize my point, think of it this way.  Before April 24th, Strasburg threw a mere 85 curveballs in counts where there were less than two strikes with 44 of them (51.8%) being thrown for strikes. After April 24th, Strasburg threw 116 curveballs on those same counts with 62 of them (or 53.4%) of them in the zone. All of that while maintaining its strikeout rate and swinging strike rate!

The other interesting effect we see here is that, in a smaller dose, the sinker suddenly became much more effective, dropping its AVG against all the way down to .235.  What was most concerning about Strasburg’s sinker was where he was throwing it, as it seemed to end up either up in the zone or right down the pipe, both of which are exactly where you should throw a sinker if you want it to end up in the upper deck.  Just for reference here were his sinker locations previous to 4/25:

And here’s his sinker locations since that date:

Glory, Glory Hallelujah! Now that’s more like it!  Obviously, there’s still more pitches ending up in the middle of the plate than desired, but look at all those sinkers down and painting the black!  Don’t miss the three glorious red squares on the other side that shows he’s able to use the pitch successfully against hitters on both sides of the plate.  Now it’s hard to know if the sinker’s newfound location cluster and success is rooted in its reduced usage, but it is awfully coincidental. As it will be the entire rest of the season, we’ll have to continue to monitor Strasburg’s sinker usage/success and see if this new reality holds up for him. Oh? I haven’t told his overall numbers since 4/25?  Feast your eyes on some tasty fantasy SP goodness:

Time Period IP ERA WHIP SO BB
2019 After 4/25 41.1 2.63 0.92 48 9

That’s Cy Young level results and it can’t be random that it matches up with the same time period as when Strasburg began throwing his curveball more and his sinker less/better. It will be interesting to monitor his pitch mix moving forward and see if the trend continues, returns to its previous numbers, or evens out somewhere in between. Either way, Strasburg’s ERA is down to 3.25 for the season and it’s reasonable to wonder if we might be seeing the best Strasburg we’ve seen yet. This is what these rankings are all about. We saw a discrepancy between his skill based plate discipline ranking and the results he was getting so far. This caused us to dig deeper into his pitch locations and pitch mix and locate the problem. Strasburg corrected the problem and since then, he has been pitching exactly like the skills indicated he should have been all along. It doesn’t always end up working out quite so cleanly but it is a great example of these rankings value none the less. Here’s hoping in the coming weeks we’re able to find more of these cases and see what we can discover about the relationship between skill and results.

Recommendation: Revel in every moment of Strasburg’s awesomeness this season!

 

Hyun Jin Ryu

PD Rank: 19th

Year K% eK% BB% O-Swing% Contact% SwStr% F-Strike% xSLG
2017 21.4% 23.8% 8.3% 31.6% 75.3% 10.9% 60.3% .444
2018 27.5% 24.7% 4.6% 29.1% 74.8% 11.6% 58.6% .365
2019 27.4% 24.6% 1.9% 33.6% 75.5% 11.8% 63.3% .354

For 82.1 IP last season, Hyun-Jin Ryu pitched like a man possessed putting together an astonishing 1.97 ERA with 89 strikeouts and a 1.01 WHIP. Then, as happens for so very many of Ryu’s seasons, it was abruptly ended by injury. At this point in his career, it’s pretty much like clockwork and so owning him comes with the understanding that it’s not a full season solutionbut look at those numbers.  That’s freaking video game numbers over roughly the same IP workload as elite closers and that’s not nothing.  All that being said, there was no way coming into this season that we could reasonably expect Ryu to repeat those numbers and it turns out we were right. So far he’s actually been better than he was last year.  Check it out:

Year IP ERA WHIP K% BB% FIP SIERA
2018 82.1 1.97 1.01 27.5% 4.6% 3.00 3.18
2019 59.1 1.52 0.74 27.4% 1.9% 2.61 3.02

As I like to say, peep that WHIP and BB% (I’ve never said that before in my life but still that WHIP and BB% are nutsie cuckoo). Obviously, there is some regression coming for Ryu at some point based on the FIP and SIERA (and common sense) but before we bring reality back into the picture take a moment to simply bask in the excellence of Ryu’s last 141.2 IP.  Now, of course, this is the Pitcher Plate Discipline Ranks (The P.P.D.R.!) so we have to ask ourselves do the skills back up the results? As you can see above, yes and no.  So far in 2019, Ryu has demonstrated elite ability, which has earned him an 87.7 Final PD Grade and a number 19 ranking on the board. There’s no arguing that, so far, Ryu has the underlying stats to back up excellent production but likely not this excellent. Honestly, just at a glance, the skills seem much more appropriate for output similar to his FIP and SIERA, which as you can see is pretty elite in and of itself. Yet with that being said, we’re still looking at 3/4 a full season’s worth of elite production so how has he been able to defy the PD and predictive numbers?

My first thought has to do with the forgotten PD number, F-Strike%. So far in 2019, Ryu has managed an astonishing 63.3% F-Strike%, which means that he is throwing a strike on the first pitch of 63.3% of his at-bats. That already goes a long way to limit walks and therefore keep runners off base and therefore helps explain the elite WHIP and indirectly helps explain some of the lower ERA. It goes further than that though.  One of the main reasons we talk about F-Strike% is the effect that it has on suppressing runs and hits. Back in 2005, Craig Burley at the Hardball Times put together a seminal work on the effects that a ball or a strike has on the outcomes of a particular situation. About ten years later, Dan Meyer took it a step further and used that information to examine the same effect on wOBA in an AB. Check out the difference between a first-pitch ball and a first-pitch strike:

Count AVG OBP SLG LWR/PA Benefit wOBA
0-0 Strike .261 .296 .411 -.029 -.069 .262
0-0 Ball .280 .385 .459 .040 .069 .355

That’s a huge effect. We’re talking about 19 points of AVG, 85 points of OBP, 48 points of SLG, nearly .07 runs per PA! Simply Ryu is having that effect on opposing offenses 63.3% of the time!  The real important part of F-Strike% comes into play though once you factor in its effect on the pitch that comes after it. Here’s the same numbers for the results each possible pitch has on the outcomes of AB with a 0-1 count:

Count AVG OBP SLG LWR/PA Benefit wOBA
0-1 Strike .231 .256 .349 -.085 -.058 .196
0-1 Ball .248 .319 .396 -0.27 .058 .293

Regardless of whether the followup pitch is a ball or a strike, you can see that both results have a negative effect on LWR/PA and tend to result in a below average wOBA.  Consistently throwing a first-pitch strike sets a pitcher up for success in so many different ways and to see that Ryu is putting up a career-high F-Strike% this year gives us some insight into how he is outperforming his overall PD ranking and his FIP and SIERA numbers.  The thing is…it’s not the whole story.  Ryu ranks just 38th in the league in F-Strike%, so while he is one of the best at starting an at-bat with a strike it’s not enough, in my opinion, to simply explain his success.  His pitch mix hasn’t radically changed from last year either. This leads me to wonder if the rest of our solution lies in the effectiveness of his pitches changing as opposed to how often he throws them.  Ryu’s pVAL/100 history over the last three years makes me think I might be on to something. Note that Ryu’s arsenal according to Baseball Savant includes a four-seamer (his main pitch), a Cutter (his best pitch), a sinker, a changeup (his most effective pitch), a curveball (more than likely a slurve as it often gets classified as a slider as well):

Year FA/100 FC/100 SI/100 CH/100 CU/100
2017 -2.79 1.06 N/A 1.04 1.24
2018 1.78 -0.38 -0.05 3.11 1.37
2019 2.93 2.59 2.63 5.69 -3.74

Outside of his curveball, several pitches have made huge leaps this season so far in terms of effectiveness and it might be worth taking a look to see why. Let’s start with the four-seamer. Velocity has remained consistent as has movement the last two yearsso it can’t be that.  Honestly, the biggest change I can find right off the bat is that he is walking way fewer batters this year with the pitch (down from 6.7% all the way to 1.6%!) than ever before but that can only be part of the equation.  In fact, several specific PD numbers for the pitch are significantly down (or up depending on the stat) this year contrary to what you’d expect for a pitcher having Ryu’s success.

Year FA SwStr% FA Contact% FA Z-Contact%
2017 7.5% 81.1% 87.2%
2018 12.1% 74.7% 79.9%
2019 7.8% 83.2% 90.3%

That 90.3 Z-Contact% is a bit troubling as hitters are making a ton of contact on his four-seamer in the zone, but when you consider the Dodgers elite defense, this might not be a completely bad way to go, especially when you consider it has only been barreled up once this season according to Statcast. How is that even possible with that much contact?  Check out the heatmap for the pitch so far in 2019:

Man, if you were to ask me to find the perfect example of the current pitching Meta on four-seamers this might be it. That is all up in the zone and into right-handers. Those are pitches that are both hard to hit and nearly impossible to square up without elite bat speed. Combine this with the Dodgers defense and honestly, I’m seeing all I need to in order to understand the success of Ryu’s fastball.  Now, how about that cutter?

Year FC SwStr% FC Contact% FC Z-Contact%
2017 6.3% 86.4% 90.3%
2018 6.8% 84.7% 87.5%
2019 14.2% 70.6% 84.2%

Here we see the opposite of the four-seamer which makes sense.  That SwStr% makes it a truly deadly weapon. This article over at Fangraphs by the incredible Jay Jaffe from a week or two ago does a really great job profiling the changes in his cutter and changeup and I don’t want to step too much on his toes as he has forgotten more about baseball than I’ll ever know, but check it out as it really sums up how he has utilized the difference between command and control to become a much better pitcher. The cutter is running in on right-handed batters and away from left-handed batters while the changeup is largely falling down and in to left-handed batters and diving down and away from right-handed batters. It doesn’t get better than that.  It’s not just that he isn’t walking hitters or throwing more strikes, it’s that he’s throwing better strikes and seemingly doing anything he wants with all the pitches in his arsenal. This may seem like a common comparison, but it reminds me a lot of Greg Maddux even though they had different styles and arsenals, the beauty of Maddux was that he was always in control of the at-bat. He put his pitches where he wanted, when he wanted in order to get you to do what he wanted you to.  It seems like Ryu is doing something very similar this year and is looking like the best pitcher in baseball right now because of it.  It’s unfortunate that based on his health record we won’t be getting a full season Ryu, but based on how he is pitching so far this yearwhile I do believe there has to be some regression comingI think we are likely seeing a pitcher who will outperform his underlying metrics a bit because of his process and command and that is invaluable.

Recommendation: If you find someone who wants him don’t wait too long to sell high before he gets hurt or Dodgeritis’d but if you’d rather hold on to him enjoy the awesome innings he does give youbecause a lot of this is for realwe just don’t know for how long.

Daniel Port

Daniel is a Fantasy Baseball writer, Brewer, and Theatrical Technician, located in Denver, Colorado. A lifelong fan of baseball and the Cleveland Indians since before Albert Belle tried to murder Fernando Vina, he used to tell his Mom he loved her using Sammy Sosa's home run salute, has a perfectly reasonable amount of love for Joey Votto and believes everything in life should be announced using bat flips. If you want to talk baseball, beer, or really anything at all you can find him on twitter at @DanielJPort !

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Comments


Daniel Port

Hi! Thanks so much for reading! I believe at the point where the data was pulled for this week, Giolito hadn’t met the qualified innings threshold yet but should pass it this upcoming week so you should see him then!

theKraken

Here is the update of my crude analysis. This is the top 11 – Verlander and Cole are way ahead of the rest.
Cole
Verlander
Snell
C Smith
Paddack
Strasburg
Boyd
Bieber
Scherzer
Ryu
Morton

Not a bad list… especially for your fantasy team. My point has always been that identifying the top and bottom is super easy and it has never been a problem with all but the worst of analysis. I like to draw attention to the idea that new analysis is not necessarily better. I am not sure my method is not a simplified version of a game score – it has clear problems. Most sabermetrics are very crude attempts to quantify what everybody in the know already understands and in some cases it is even a step backward if not a re-branding of something pre-existing. For example, pretty much everything about modern defense. You are watching generations of knowledge and experience swirl the drain when you turn on any broadcast… but it is new and progressive… so it has that going for it.

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