mcgowanforlorn

In this guest post from Kyle Matte, he looks at how some of the peripherals suggest that the outstanding results the Jays have gotten from Dustin McGowan since he moved to the bullpen aren’t maybe as outstanding as they seem. Follow Kyle on Twitter at @KyleMatte.

Dustin McGowan has been a wildly enigmatic pitcher through the first three months of the 2014 season. He set the Grapefruit League ablaze en route to being named to a rotation that ranked 29th and 28th in ERA and FIP respectively last year, with the hope he could help steer it in a more positive direction. Despite his outstanding numbers in an admittedly small sample the decision wasn’t an easy one, and the organization publicly went back and forth regarding their plans for the right hander. McGowan was a stud out of the bullpen in 2013 and has a well-documented injury background, yet the alternatives proved so inept in March that there was no other reasonable choice for the club to make but to try him in the rotation.

He made his season debut against the New York Yankees on April 4th — his first in over thirty months — and it probably went as well as could be honestly imagined, given the rust; 4 runs allowed on 9 base runners while recording just 8 outs. The poor results were accepted by most, but what caused a stir amongst savvy fans were his swinging strikes – or the lack there of. Chops McGowan induced just three whiffs across his 72 pitches (4.2%), as the Yankees made contact with or fouled off pitch after pitch. It was suggested on the Twitter machine that Dustin may have been tipping his pitches, a rumor that was later substantiated by Pitching Coach Pete Walker.

The pitch-tipping story quickly became a thing of the past, but the elusive swinging strikes remained an ongoing battle for McGowan. Over the eight starts he would make through April and May, McGowan reached the 10% swinging strike rate plateau just three times, with his high water mark coming at 12.9% on April 23rd against the Baltimore Orioles. These struggles were a new thing for McGowan. While working as a reliever in 2013, he maintained an 11.5% swinging strike rate for the season. Among 273 relievers with at least 20 innings pitched (via Fangraphs)that rate ranked 76th – tied with Brett Cecil in the 72nd percentile; among very good company.

Manager John Gibbons seemed openly relieved when it was announced McGowan would be returning to the bullpen, where he had proven to be a valuable and reliable piece. High leverage relief pitchers enter games in the late innings, usually with a narrow lead, and often with runners on base. In terms of run expectancy, two of the best ways to get out of such scenarios with minimal damage is through strikeouts and groundballs, and Dustin had proved proficient at coaxing both. 22.8% of batters he faced walked back to the dugout with their head hung in shame, and 46.6% of balls put in play burned the hypothetical worms in the Rogers Centre turf. Those figures ranked 108th(60th percentile) and 102nd(63rd percentile) respectively among the 273 relievers with at least 20 innings pitched – neither elite, but both well above average.

Unfortunately, Dustin McGowan simply hasn’t been the same pitcher since his return to the pen. Sure, on the surface he’s been outstanding – just two earned runs allowed 16.2 innings pitched (1.08 ERA) through June 25th – but the underlying numbers suggest that unless he can rediscover his old self, a tidal wave of regression might be heading his way. His bullpen strikeout rate has sunk to just 18.6%, while his overall groundball rate is a Todd Redmond-esque 37.5%. McGowan’s success can almost entirely be tied to two completely unsustainable numbers; a .180 BABIP, and an 84.8% strand rate in his relief appearances. I know Toronto’s fielding is much improved, but there’s not a defense in existence that will turn 82.0% of balls in play into outs once the sample size starts to grow beyond a few handfuls of innings.

This leads to the obvious question: what happened? While we can further analyze particular aspects of the results – which I’ll do below – there’s a limitless deluge of possible reasons for the variance. It could be a change within McGowan, mentally or physically. Perhaps he’s still wearing his insulin pump on the mound which he didn’t last season, or maybe there’s been an immeasurable alteration in the kinetic motion of his delivery. There could be an adjustment on the side of the hitters, too. After years in baseball purgatory, McGowan has returned to a landscape where scouting reports are more advanced than ever before. It’s entirely possible teams have formulated a book on him, are significantly more aware of his tendencies, and are able to game plan to take away his strengths.

Pitch
Type

Usage

Avg.
Velocity

Hor.
Mov.

Ver.
Mov.

Whiff % per swing

2 Strike
Usage

2 Strike
Whiff % per swing

Four Seam

43.18%

95.31

-4.00

9.25

25.32%

37.10%

28.00%

Sinker

25.95%

95.07

-8.01

6.21

18.37%

16.13%

15.38%

Changeup

7.61%

87.73

-7.77

2.40

35.29%

9.68%

28.57%

Slider

23.27%

86.85

3.39

0.41

48.89%

37.10%

57.89%

The table above is McGowan’s PitchFX data from the 2013 season, all of which was spent in the bullpen. The table below is McGowan’s PitchFX data from May 15th 2014 through June 25th, encompassing his 15 relief appearances at the time of this writing. All statistics are from BrooksBaseball.net.

Pitch
Type

Usage

Avg.
Velocity

Hor.
Mov.

Ver.
Mov.

Whiff % per swing

2 Strike
Usage

2 Strike
Whiff % per swing

Four Seam

58.24%

95.42

-3.54

9.79

18.52%

57.95%

20.59%

Sinker

8.42%

95.76

-7.99

6.97

0.00%

3.41%

0.00%

Changeup

7.33%

87.35

-8.61

2.94

61.54%

6.82%

60.00%

Slider

25.27%

88.14

2.79

1.84

44.83%

30.68%

37.50%

Curveball

0.73%

86.28

3.12

-2.31

0.00%

1.14%

0.00%

While the curveball appears in 2014, it’s used so infrequently that it’s rather pointless to try to pursue any conclusions regarding its impact on McGowan’s overall performance.

The overall pitch usage shares carry a similar trend in terms of hard versus off-speed versus breaking, as the four-seam fastball and sinker total is in the 65-70% range in both years, with the slider around 25% and the changeup in the 6-8% range. Continuing to focus on the hard stuff for a moment, the velocity and movement have remained relatively consistent between the two relief seasons. The differences begin to emerge in Whiff %, 2 Strike Usage, and 2 Strike Whiff %. We’ve already identified an overall decline in swinging strikes for McGowan so this result is unsurprising. However, given that we can readily observe no discernible decline in velocity or movement, the sharp drop in whiffs is likely a result of location.

Below are four heat maps indicating pitch location frequency, generated by Fangraphs. The two on the left are from the 2013 season (with the top and bottom representing all counts and 2 strike counts, respectively), while the two on the right are from the 2014 season (through 06/26) with the same top and bottom representation. It must be noted that the only timeframe allowable is full seasons, so the 2014 heat maps include McGowan’s time in the bullpen as well as the rotation.

You can draw some pretty vivid conclusions just looking at the coloration.

In 2013, McGowan pounded down and away to right handed batters, while similarly throwing his fastball and slider at the back foot of lefties. The down and away trend became even more emphatic with 2 strikes, and McGowan added a few additional wrinkles to his approach: up, and up and away.

In 2014, not so much. The pounding with McGowan has gone to the opposite end of the spectrum, as he’s pumped pitch after pitch over the meaty part of the plate. Things improve slightly with 2 strikes, but his heat map still looks more like buckshot than any kind of well planned and executed attack. Shockingly, the high pitches have almost vanished, as he’s finding most up and up/away zones with just one third of the frequency he had in his successful 2013 season.

Returning to the PitchFX tables above, another noteworthy point is the drop in 2 Strike Whiff % with his slider. In 2013, 57.89% of swing attempts against his slider ended in nothing but air, but in 2014 that rate has dropped a full twenty points to 37.50%. Unlike the fastball, there are notable differences in both slider velocity and movement from year to year, so with the command issues already detailed above, this could be a three-pronged struggle.

His slider has jumped over a mile per hour, increasing from 86.85 mph up to 88.14 mph. While I’m usually first in line to applaud augmented velocity, in the case of breaking balls and off-speed pitches it’s not always a good thing. In throwing it harder, McGowan has taken away from the natural movement his grip is creating on the pitch. The horizontal movement has declined by 0.60 inches, while the vertical movement has decreased by 1.43 inches. Intentionally or otherwise the pitch is beginning to evolve into more of a cutter than a true slider, and with an already high octane arsenal at his disposal, that’s not the direction we want McGowan going.

In addition to the decrease in swinging strikes, this new, pseudo-cutter has had another unfortunate consequence: a significantly worse batted ball profile, which has been summarized in the table below.

Year

Count

LD%

GB%

FB%

PU%

2013

13

0.00%

61.54%

7.69%

30.77%

2014

13

38.46%

38.46%

23.08%

0.00%

In 2013, 13 plate appearances against Dustin McGowan ended with a slider being put into play. 12 of those 13 (92%) were of the weak contact variety – groundballs or popups. In his 18 relief appearances between May 15th and July 1st, 13 plate appearances ended with a slider being put into play, and just 5 (45%) were of the weak variety. If a slider is being hit in the air with any regularity, either it was located poorly or it didn’t break. In McGowan’s case, it appears to be a bit of both.

Dustin McGowan has been playing with fire and living dangerously over the last six weeks since he transitioned back to the bullpen. As mentioned, I can’t speak much to the why, but the downward trends I have noted need to be addressed, and preferably before the regression train rolls into town. Step one appears to be easing off the gas ever so slightly with the slider, while step two has a few more complications. McGowan needs to start locating his pitches better; with the game being played the way it is today, hitters can turn around 95 like it’s nothing if it’s center-cut. Get back to pounding the outside corner and elevate the heat with 2 strikes. Get those swinging strikes and those weak batted balls. That’s how you survive the high leverage innings over the long haul, not with the smoke and mirrors and horseshoe-up-the-ass luck McGowan has counted on.

Comments (68)

  1. Looks like in 2014 he’s been throwing the “stinker”, not the “sinker”.

  2. Great stuff here. Good work guys.

  3. Jason Frasor is only a few weeks away from solidifying the Jays’ bullpen.

  4. Good work Kyle.
    I knew his peripherals were poor, mostly via the eye test as I am an old shit, but you have illuminated the stats very very well. After seeing my suspicions confirmed that the gopher ball he gave up on Sat was indeed a piss ass slider, he to me just does not have a very good command ( yes he has great stuff per see but so did Jeffress) and of course the old HR doesn’t mess with the illusionary BABIP either.
    Neither Mcgowan nor Santos have shown they should have the full trust of the manager on the way to a Janssen inning.
    I have a feeling the Jays are trying to find a better BP piece especially a RHP. We shall see

  5. This article was informative, insightful and the product of time and diligence. It left me more knowledge than when I started – thank you.

  6. 16.2 IP! Too small a sample set from which to draw any conclusions.

    • How bout this conclusion?

      In 16.2 IP, McGowan’s relief innings this year, he’s been terrible with his location and has been very lucky to have the results he’s had

      • Hahaha I’d completely agree with that. My only hope is that he’ll regress to his outstanding 2013 self once he logs more hours out of the bullpen.

      • 16.2 innings….is hardly enough of a sample size to try and draw conclusions from, let alone write an article about.

    • The 929 and 450 pitches in the top 2 heat maps are more than a sufficient enough sample.

      • My point is that the main criticism of the article is in regards to his performance out of the bullpen. The above two plots include pitches while starting – that raises the total number of pitches included in the analysis but the analysis to no longer examine his relief performance.

        I’d counter that a pitcher approaches pitching much differently when starting or when coming into a situational relief appearance and the analysis should reflect that.

  7. Interesting stuff. Thanks, Kyle. It makes me wonder about the coaching and approach discussions that they could be having. I wouldn’t be surprised to learn that some old school coaches look at his results and commend McGowan for moving to more of a cutter than a slider, and for inducing weak contact (weak enough, anyway, to lead to a low BABIP overall). It takes some real digging to tell a story that says “McGowan has looked good, but this might not last”, as you have, and I would imagine that many coaches would prefer to watch this with blinders and advise him to keep doin’ what he’s doin’, because it’s been working for him.

    Oh, also, if you’re going to be talking about the outcomes of thirteen sliders put into play, use raw numbers, not percentages. The difference between contact that induces a hard ground ball hit to short (and a routine out), and a line drive that goes over his head and to the gap for extra bases is a fraction of an inch. If we’re going to try to read into the outcomes of something so tiny, you need a larger sample size. Showing the raw numbers allows the reader to take into consideration that we’re talking about a small sample that could look different with a few balls getting categorized differently. The reader will be able to say ‘well I need to take this with a grain of salt, but it does tell me something’. Not complaining, here – great piece, for sure (probably the best you’ve put on the site) – but it is more honest to the reader.

    • I expressed them in percentages because that’s how Brooks Baseball expressed them. I included the BIP count so the reader could understand the small sample size at play.

      If you want the raw counts by BIP type, just multiply the sample by the percentages. For example, for 2014 line drives, 13 x 0.3846 = 5. I trust the readers to handle that if they’re curious lol

  8. Thanks for the rare morning offering!

    Your analysis was greatly appreciated!

  9. Isn’t it just as likely that the regression over a larger sample size will see his pitches look more like they did last year?

  10. McGowan has been fairly lucky. I still like him coming in in a higher leverage situation than a lot of the others down there. One thing is for certain – the Jays need to add a piece down there to help for a stretch run. Korecky could conceivably help but you have to have more than a couple relievers that you can count on when a game gets tight late in the season.

  11. There is a shit ton of luck involved in baseball and Dusty has it right now. Let’s hope hangers like he threw Viciedo are fouled back instead of pounded 430ft the rest of the way.

    Very nice post, great info.

  12. it’s cool when your team is in first and you dont have to give two shits about who’s outperforming their peripherals

  13. great insight. only thing is, relievers are by nature fickle. so the jays aren’t really playing with fire.

    if he starts to struggle as the data suggests is inevitable, he will just be moved out of high leverage situations.

  14. “The two on the left are from the 2013 season (with the top and bottom representing all counts and 2 strike counts, respectively),”

    The left heat maps are labeled 2014 and on the right 2013.

    Or am I missing something?

    • Yeah, it’s the opposite of what’s said in the article. It confused the hell out of me as well (for about 5 minutes).

    • Yeah, I think something got flipped between when I sent the article to Stoeten and when he published it. I’m sure he’ll fix it when he has the chance.

      • Thx for the clarification Kyle.
        I enjoy reading the indepth stuff ,don’t always agree, but it provides a viewpoint that needs to be considered and talked about..

  15. Yep, great article. Noticed this before, even last year his peripherals suggested just an ok reliever.

    Doesn’t mean he sucks, but gibber probably has a higher estimation of him than he should. Gibber talks like he’s an ace reliever but he really isn’t.

    The three highest leverage guys in the ‘pen have to be janssen, cecil, and loup. They are the ones with very good numbers right across the board.

    None of the power righties (santos, delabar, mcgowan) can be relied upon consistently, though they can all be dominant if used correctly.

    Redmond might actually be the best righty reliever, even if his numbers regress a bit when the HR rate normalizes. Then again, with his sinking stuff, the low HR rate might be legit.

    • Here’s the numbers for our relievers for the last 3yrs, btw, with some half assed classifications:

      Awesome (i.e. Legit 2.5ish era going forward)

      RP C.Janssen (32): 134.3ip, 2.34era, 2.78fip, 3.06xfip, 2.69siera

      Good (i.e. Legit 3.0ish era going forward)

      RP A.Loup (26): 138.3ip, 2.73era, 3.00fip, 3.49xfip, 3.08siera
      RP B.Cecil (27): 98.0ip, 3.40era, 2.75fip, 3.18xfip, 2.94siera

      Decent (kinda all over the place, butbhard to really depend on a significantly sub-4 era going forward)

      RP S.Delabar (30): 150.3ip, 3.77era, 3.80fip, 3.60xfip, 2.99siera
      RP S.Santos (31): 49.0ip, 4.41era, 3.42fip, 3.27xfip, 3.00siera
      RP D.McGowan (32): 44.7ip, 2.22era, 3.99fip, 4.02xfip, 3.54siera
      RP T.Redmond (29): 50.0ip, 3.06era, 3.39fip, 4.45xfip, 3.88siera

      Poor (lucky to approach even 4era going forward)

      RP E.Rogers (28): 130.3ip, 4.97era, 4.12fip, 4.01xfip, 3.60siera
      RP C.Jenkins (26): 52.0ip, 3.81era, 4.56fip, 4.46xfip, 4.26siera

    • Very poor article.

      Understanding/use of regression seems to be way off base

      • If you’re referring to my response, then maybe you prefer the heavily regressed zips rest of season projections, which say the same thing:

        Janssen: 2.85era, 2.87fip

        Cecil: 3.13era, 3.11fip
        Loup: 3.64era, 3.55fip

        Santos: 3.98era, 3.60fip
        Delabar: 4.29era, 4.24fip

        These four still projected as mostly starters rest of season, unfortunately making the projections kinda useless:

        McGowan: 4.69era, 4.64fip
        Redmond: 5.09era, 4.96fip
        Rogers: 5.00era, 4.67fip
        Jenkins: 5.98era, 5.82fip

  16. DrunkJaysFangraphs.com has been registered and you can buy it for the low low price of one billion zimbabwe dollars.

    One thing I wished MLBAM was more open about (may be but I don’t know) is the year to year changes on how they classify pitches. Is it possible more sinkers are being classified as 4 seamers due to less movement?

    • That I couldn’t tell you. There’s definitely still a ton of gray area in pitch classification, particularly if the pitchers themselves are evolving and trying new things.

      Marcus Stroman’s slider/curveball is a great example. They’re very similar and I honestly think it’s more of a hybrid breaking ball with two slightly different motions as opposed to two distinctly separate breaking balls.

  17. Great post.

  18. wow another ridiculously stupid post by kyle. Amazing how kyle has hours to spend crunching all these numbers which tell you absolutely nothing. What have you told us kyle, that it’s all luck. babip doesnt measure luck. Try learning math. His low ks lowers his babip. He might regress he might not. ok got it. That means you are just guessing. Great work. I guess dellin betances is getting lucky too right. Any pitcher who has great numbers will eventually regress. Kyle will be waiting to say see I predicted it!!! I am genius!!!

    • “His low ks lowers his babip”

      Strikeouts have nothing to do with BABIP. It’s batting average on balls in play, which a strikeout, you know, isn’t.

      • babip is a made up term. Its a formula. Try reading the formula. More ks lowers babip. If you subtract a number from the denominator the ratio goes up. basic math here

        If I subtract sb from ba, the new ratio goes up and down based on how big sb is right.
        Does that mean sb affects ba. No of course not, but according to you it does.

        • BABIp is BALLS IN PLAY. what does that have to do with strikeouts.

          overtime this number gives you an idea to how squarely hitters are sizing up a pitcher.

          Chapman has a 17.49 k/9 ratio right now. with a .314 BABip

          so you’re saying if his k/9 dips that his BABip is going to go up?

          You’re pretty fuckin stupid.

          • Forgive my ignorance but humour me for a second. If a guy strikes out fewer people because his balls don’t have quite as much break (or other movement) then wouldn’t one expect that those balls that didn’t quite miss the bats will still induce weak contact. So effectively the weak contact will indirectly lead to a lower BABIP assuming everything else stays equal (that is, assuming the number of squarely hit balls doesn’t increase). Not sure if that’s what the math major was saying but I don’t think he’s completely out to lunch.

        • It’s fun to watch people expose their own ignorance. Saves others the trouble.

        • Hey, Mr Math wizard:
          Perhaps you should take a few hours and write a column so we can act like jerks to you.
          Maybe you’re math challenged ratio brain can get this simple example of BABIP.
          The batter flies out to CF his first time up, then lines a single his 2nd time up. then strikes out 20 times in a row. His BAPIP is 500. If he hits HRs or SO the next 10 times up his BAPIP is still 500.
          Please learn basic math

          • Yep. That boy is a purebred mathalete.

          • Hey fukstik, are you saying that guy would have a 500 batting average? Cause if so we should probably sign whoever it is you’re talking about ;) But seriously, good example, myb he can grasp babpip now. But I doubt it.

            • Yeah, iknow. I got enuf trouble trying to fend off or understand some of the dumbass comments that come on here from some folks, who I swear just started following ball in the last year or something. It’s bad enuf watching a game with my wife who figures because I say Bautista is one of the best players they have, that he should hit a hR whenever she watches or “why didn’t he hit it to the left of the SS?
              I expect at least some common sense from people who write in to discuss BB intelligently FFS not what they here some drunk yelp about in a bar.
              My wife gets a pass, always, because, well, she is my wife and holds a certain amount of leverage, you know

            • It was really considerate for math 101 to bring enough rope along from which to hang himself.

          • I have zero understanding of anything but the most basic of baseball stats, simply because I don’t care about that part of the game. I just like to watch the game and cheer for my team.
            But I’m not stupid enough to argue with people who understand the metrics.

            I use a lot of psychometrics in my job, so pretty sure I would get it if I actually put in some effort. Maybe there’s hope for me yet?

        • “Based on the research of Voros McCracken and others, BABIP is mostly a function of a pitcher’s defense and luck, rather than persistent skill. Thus, pitchers with abnormally high or low BABIPs are good bets to see their performances regress to the mean. The league average for modern pitcher BABIP is around .300.”

          BABIP = (H – HR) / (AB – K – HR + SF + SH)

          http://www.baseballprospectus.com/glossary/index.php?search=BABIP

          Math 101 you are right that if a pitchers allows the same number of hits and strikes out fewer batters, BABIP will decrease. But for the most part once the ball is put into play the outcome is no longer controllable by the pitcher. Over time, with fewer strikeouts more of those balls in play will likely land for hits.

          Sure you can argue about whether BABIP is really “luck” or not since balls in play are complicated but regardless, as Kyle stated, the Jays defense (or any other) continuing to turn 82% of balls hit into the field of play into outs is a random variance that likely can’t be sustained. And I think Kyle did an excellent job breaking down why McGowan’s performances, measured by more traditional numbers (ERA, WHIP), might get worse before they become better.

    • did you just read the title orrrrrrrr

  19. Hopefully Wagner get’s healthy so we’ll have another high leverage option.

    Now about second base ……….

  20. Great article, Kyle. Just some notes. He doesn’t use the insulin pump anymore and he hasnt used his curveball since being in the pen.

    • That’s good to know on the insulin pump. I wasn’t sure so mostly posed it as a hypothetical.

      And I didn’t think he was throwing his curve either, but Brooks had 2 in there so I included them for the sake of completeness. More than likely just mis-labelled sliders. That’s why I didn’t bother with any analysis of them.

  21. gotta say, not nearly as confident in Eddy’s LF adventures tonight as i was yesterday…

  22. […] stuff at Drunk Jays Fans, where Kyle Matte looks at Dustin McGowan’s sudden loss of effectiveness. If you look […]

  23. […] could really use some bullpen help. Dustin McGowan hasn’t been really spot on lately and as Kyle Matte at Drunk Jays Fans pointed out last week were the Jays playing with fire when it came to McGowan. […]

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