2016 NL Starting-Pitcher Contact Management: Non-Qualifiers

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February 17, 2017

Pitchers and catchers are in the house, we unfortunately have our first major spring training injury, and our offseason series of contact management/quality articles rolls toward its conclusion. Earlier this week, we examined American League pitching non-qualifiers; today, our eyes turn to the senior circuit.

Some weeks back, we looked at pitchers who’d recorded 162 innings or more. That wasn’t all that many hurlers. In order to bring the respective sample sizes of pitchers and hitters into some sort of equilibrium — i.e. close to the overall MLB player population breakdown — we need an awful lot more pitchers. Hence, these two articles and the mammoth tables of additional pitchers they contain. Here goes:

NL Non-Qual P BIP Profiles
NAME AVG MPH FB MPH LD MPH GB MPH POP% FLY% LD% GB% ADJ C K% BB% ERA- FIP- TRU-
T.Anderson 85.1 85.7 88.0 83.9 2.4% 26.3% 20.4% 50.9% 76 20.7% 5.9% 72 81 71
Kershaw 87.3 89.4 91.0 84.9 4.5% 25.6% 20.5% 49.4% 80 31.6% 2.0% 43 45 50
C.Richard 89.3 88.8 92.2 88.6 0.4% 17.5% 17.0% 65.1% 83 13.4% 10.1% 82 102 101
Boyer 86.2 82.9 90.7 87.2 3.0% 25.9% 22.1% 48.9% 83 9.2% 6.0% 92 92 100
Lyles 87.4 87.6 93.4 84.0 1.0% 24.0% 23.5% 51.5% 83 11.7% 10.3% 119 103 105
Rusin 89.7 89.3 94.0 88.3 0.4% 20.2% 21.0% 58.4% 84 19.7% 6.6% 76 72 82
Friedrich 89.2 89.0 94.4 88.7 5.9% 30.4% 18.8% 44.9% 86 17.6% 9.2% 120 102 93
Stripling 89.4 90.6 93.3 86.7 1.0% 28.1% 20.1% 50.8% 87 17.7% 7.2% 100 96 90
Vogelsong 89.0 90.5 91.2 87.7 4.8% 33.3% 21.4% 40.5% 89 16.7% 10.9% 120 124 101
Urena 88.3 87.8 93.3 86.0 3.2% 27.1% 22.0% 47.7% 90 15.6% 7.8% 149 118 98
Urias 87.2 86.2 91.7 86.2 3.8% 25.8% 26.8% 43.7% 90 25.0% 9.2% 86 78 81
Chatwood 87.9 87.7 89.7 87.8 1.5% 24.1% 17.2% 57.2% 91 17.5% 10.5% 79 97 101
Conley 88.5 89.6 92.9 85.5 4.0% 37.1% 20.7% 38.2% 91 21.2% 10.6% 94 105 93
Suarez 89.0 90.4 93.7 86.6 1.5% 29.8% 20.8% 47.9% 91 15.2% 7.3% 109 118 99
Matz 89.2 89.3 94.0 87.3 2.7% 25.2% 21.0% 51.1% 92 23.6% 5.7% 86 82 79
G.Cole 88.5 88.1 91.8 87.2 3.4% 25.6% 25.4% 45.6% 93 19.4% 7.1% 96 83 91
Locke 89.5 88.0 93.6 89.0 2.3% 30.1% 20.4% 47.2% 94 12.9% 7.8% 135 120 109
Garza 90.1 90.1 95.5 88.4 4.1% 23.9% 17.2% 54.8% 94 15.2% 7.8% 105 101 103
Guerra 88.9 90.0 94.2 86.9 4.2% 31.6% 18.9% 45.3% 95 20.3% 8.7% 65 86 94
Nicasio 89.1 89.3 93.7 87.7 3.8% 30.9% 21.8% 43.5% 95 26.9% 8.8% 112 94 80
Nola 87.6 88.7 92.3 85.2 0.9% 23.9% 20.0% 55.2% 96 25.1% 6.0% 116 73 79
Taillon 89.4 89.9 95.0 87.1 2.6% 24.8% 20.2% 52.4% 98 20.3% 4.1% 84 92 87
Pomeranz 88.6 90.6 92.7 86.2 4.4% 32.8% 16.6% 46.2% 99 26.5% 9.3% 79 92 85
Greinke 88.6 90.3 91.9 86.4 3.7% 30.8% 19.5% 45.9% 99 20.1% 6.2% 100 99 93
Kazmir 86.3 87.5 89.8 83.5 5.1% 30.8% 23.3% 40.8% 99 22.7% 8.8% 115 110 92
Rea 90.8 90.3 95.3 89.4 3.5% 28.8% 22.7% 45.0% 100 17.6% 9.7% 120 113 106
Liriano 88.9 91.0 94.4 85.6 1.3% 29.0% 17.8% 52.0% 101 23.0% 11.6% 115 119 98
DeSclafani 89.5 87.4 95.2 88.6 3.1% 32.4% 22.6% 41.9% 101 20.7% 5.9% 78 93 92
J.Gomez 87.9 88.4 91.9 85.7 1.3% 24.9% 21.8% 52.0% 102 15.8% 7.4% 118 94 108
Wacha 88.5 89.8 92.1 86.2 1.8% 27.8% 23.9% 46.6% 103 18.8% 7.4% 124 95 101
J.Ross 89.4 89.9 92.1 87.6 2.9% 27.4% 27.1% 42.6% 103 20.8% 6.5% 82 85 95
Strasburg 88.2 89.0 92.8 84.4 3.0% 36.2% 21.3% 39.5% 103 30.6% 7.4% 86 72 75
C.Torres 86.5 86.8 88.9 85.8 1.8% 32.9% 20.7% 44.6% 103 23.0% 8.9% 64 87 94
Adleman 89.1 90.5 89.1 88.3 1.9% 43.6% 18.2% 36.4% 104 16.4% 7.0% 95 124 108
Nicolino 91.2 91.4 95.6 88.2 2.1% 29.8% 21.5% 46.6% 104 10.7% 5.8% 122 110 120
Eflin 89.5 89.5 95.3 87.0 4.5% 35.8% 23.5% 36.2% 104 11.4% 6.3% 134 129 118
Foltynewicz 89.3 89.9 92.5 87.7 4.1% 33.3% 21.4% 41.2% 105 21.1% 6.7% 104 106 96
Godley 90.7 92.0 94.3 88.1 1.7% 26.5% 18.1% 53.8% 106 17.9% 7.5% 146 119 107
deGrom 88.7 89.2 93.4 85.7 2.4% 29.3% 22.7% 45.6% 107 23.7% 6.0% 77 80 90
W.Peralta 89.6 91.1 93.1 87.9 1.2% 26.3% 22.5% 50.0% 107 16.8% 7.8% 113 109 111
De La Cruz 90.0 92.7 95.5 85.3 1.9% 32.5% 22.5% 43.1% 108 13.4% 8.0% 118 129 122
Harvey 88.7 88.9 92.3 86.8 3.8% 30.1% 25.3% 40.8% 110 18.9% 6.2% 122 84 105
C.Anderson 88.6 90.1 91.5 85.6 2.6% 38.5% 22.8% 36.1% 111 18.6% 8.2% 102 118 111
Velasquez 89.0 89.7 92.6 86.8 5.0% 36.2% 24.0% 34.8% 111 27.6% 8.2% 100 94 89
Blair 90.0 92.4 94.7 85.2 5.5% 32.5% 22.2% 39.7% 111 14.2% 10.5% 183 152 128
Perdomo 90.4 91.2 94.2 88.6 0.8% 20.5% 19.7% 59.0% 113 15.9% 7.0% 143 118 117
Corbin 90.7 90.9 94.2 89.5 1.2% 25.6% 19.4% 53.8% 114 18.7% 9.4% 118 116 116
De La Rosa 89.5 89.8 92.7 88.2 3.3% 28.4% 21.0% 47.3% 114 17.6% 10.3% 112 120 120
Lamb 87.2 88.7 92.7 83.6 4.2% 31.9% 21.8% 42.1% 114 18.2% 9.8% 152 128 118
Wisler 89.9 88.3 94.7 89.0 2.4% 36.0% 21.4% 40.2% 115 17.1% 7.3% 120 121 116
A.Bradley 90.1 90.4 93.0 88.0 1.7% 28.1% 25.1% 45.1% 115 22.4% 10.5% 115 98 109
Cain 89.8 89.1 95.7 88.0 6.5% 32.3% 23.9% 37.3% 115 18.1% 8.1% 143 131 115
Peavy 90.5 92.4 93.7 88.2 4.3% 40.2% 19.1% 36.4% 117 19.6% 6.9% 140 111 110
P.Clemens 91.6 92.9 96.9 88.2 4.1% 38.4% 17.6% 39.8% 117 16.9% 9.9% 100 139 125
Norris 90.7 90.3 93.3 90.0 1.2% 29.6% 21.6% 47.6% 118 20.6% 9.9% 126 107 115
Cashner 90.9 91.0 95.2 89.6 1.8% 31.3% 20.5% 46.5% 119 19.1% 10.2% 130 119 121
Niese 89.8 91.0 92.4 88.0 1.5% 27.0% 20.4% 51.1% 119 16.1% 8.6% 137 139 125
E.Jackson 89.3 91.1 93.0 87.5 3.0% 35.7% 21.1% 40.2% 119 16.4% 11.0% 147 131 131
Butler 91.3 90.6 93.8 90.7 2.3% 27.3% 24.5% 45.8% 123 16.0% 7.2% 146 122 125
Oberholtzer 90.7 91.9 94.9 88.9 2.1% 34.2% 21.4% 42.3% 124 16.8% 9.0% 144 148 129
Chen 90.2 90.9 93.6 88.2 2.6% 35.5% 21.4% 40.5% 125 19.2% 4.6% 121 112 114
Verrett 90.5 92.9 94.5 86.9 1.1% 32.1% 22.7% 44.1% 127 16.3% 10.6% 131 133 137
Villanueva 90.0 91.8 93.1 86.8 4.2% 29.6% 24.2% 42.1% 129 19.0% 4.4% 149 126 117
S.Miller 90.6 89.3 93.4 91.6 3.2% 32.3% 22.6% 41.9% 131 15.2% 9.1% 141 117 142
Simon 89.3 93.4 91.0 86.1 1.8% 28.9% 19.7% 49.5% 137 13.1% 10.4% 221 167 156
Morgan 90.1 89.9 94.7 87.6 3.5% 34.4% 25.0% 37.1% 141 18.8% 5.7% 146 117 130

Most of the column headers are self-explanatory, including average BIP speed (overall and by BIP type), BIP type frequency, K and BB rates, and traditional ERA-, FIP-, and “tru” ERA-, which incorporates the exit-speed and -angle data. Each pitcher’s Adjusted Contact Score (ADJ C) is also listed. Adjusted Contact Score applies league-average production to each pitcher’s individual actual BIP type and velocity mix, and compares it to league average of 100. The pitchers are listed in overall Adjusted Contact Score order.

Cells are also color-coded. If a pitcher’s value is two standard deviations or more higher than average, the field is shaded red. If it’s one to two STD higher than average, it’s shaded orange. If it’s one-half to one STD higher than average, it’s shaded dark yellow. If it’s one-half to one STD less than average, it’s shaded blue. If it’s over one STD less than average, it’s shaded black. Ran out of colors at that point. On the rare occasions that a value is over two STD lower than average, we’ll mention it if necessary in the text.

Before we get to the pitchers, a couple words regarding year-to-year correlation of pitchers’ plate-appearance frequencies and BIP authority allowed. From 2013 to -15, ERA qualifiers’ K and BB rates and all BIP frequencies except for liner rate (.14 correlation coefficient) correlated very closely from year to year. The correlation coefficients for K% (.81), BB% (.66), and pop-up (.53), fly-ball (.76) and grounder (.86) rates are extremely high. While BIP authority correlates somewhat from year to year — FLY/LD authority is .37, grounder authority is .25 — it doesn’t correlate nearly as closely as frequency. Keep these relationships in mind as we move on to some player comments.

Obviously, we’re not going to discuss each and every pitcher on this list; that wouldn’t make for a very readable article. There are some relievers listed, and I’m going to steer clear of all of them. We’ll stick to the most notable starters among the group, many of whom can be expected to rank among 2017’s ERA qualifiers.

Hello, Tyler Anderson. This is exactly the type of pitcher whom the Colorado Rockies need. A respectable K/BB foundation and a solid grounder rate, to be sure, but the highlight is his ability to suppress contact authority across all BIP types (Fly Ball, Liner, Grounder and Overall Adjusted Contact Scores of 70, 87, 79 and 76, respectively). The color spectrum doesn’t fully do him justice: his overall, fly ball and liner authority allowed were all over two full standard deviations lower than the NL average. This guy is for real and is part of an organizational effort to put strong contact managers in place. Jordan Lyles, Chris Rusin and Tyler Chatwood are also well placed on this list, but none of them are Tyler Anderson.

Clayton Kershaw didn’t qualify for the ERA title last season due to injury, and that is absolutely the only negative thing that can be said about his campaign. His 2016 K-BB spread was truly historic, and he was an elite contact manager to boot. This part of his game is largely unappreciated; he’s the ultra-rare hurler who simultaneously runs high pop-up and grounder rates, and muffles contact authority across the board (Fly Ball, Liner, Grounder and Overall Adjusted Contact Scores of 66, 99, 82 and 80, respectively) — if not quite to the extent Anderson did in his rookie campaign. He is an all-time great at his absolute best; appreciate him while you can.

I’m not quite done with the superlatives yet: here comes Julio Urias. How good was he in 2016? Good enough to be an above-average contact manager despite a stratospheric liner-rate allowed, way up in the 97th percentile. He too strangles contact authority, particularly on fly balls (68 Adjusted Contact Score) — i.e. the BIP type that can produce damage. His K rate was exceptional and his BB rate acceptable considering his developmental stage. Did I mention that he did this as a teenager? Once he’s stretched out, watch out. A healthy Julio Urias is the next truly great starting pitcher.

It took Steven Matz a long time to get healthy and establish himself in the big leagues, and he still hasn’t been able to throw the 162 innings in a season necessary to qualify for an ERA title. His line above is largely devoid of color, but I’d argue that this indicates a lack of weaknesses more than anything else. His K/BB foundation is strong, and his grounder tendency solid. His authority allowed is basically in the average range across the board, but he was quite unlucky on grounders last season (.280 AVG-.286 SLG, 132 Unadjusted Contact Score, vs. 99 adjusted mark supported by the granular data). An above-average K/BB guy with above-average contact-management skill is a potential All-Star, if he can deliver the innings bulk.

Gerrit Cole was obviously hampered by injury last season, affecting his K/BB foundation more than his contact-management skills. He walks a fine line with regard to the latter. He’s exhibited a solid grounder tendency and muted fly-ball authority (62 Adjusted Contact Score) quite well last season; he is, however, one of a relatively small group of pitchers who rather consistently allows high liner rates. His liner rate allowed was way up in the 95th percentile last season and has never been below league average. The pros have outweighed the cons to date, though I don’t see him ever evolving into an exceptional contact manager, which I think he needs to do to become a true ace.

Hats off to Junior Guerra for a surprising breakthrough rookie season, but it must be stipulated that there was an awful lot of good fortune involved. Part of it was due to a low liner-rate allowed (in the 12th percentile) that’s likely to regress upward, and much more was due to good luck on ground balls. Hitters batted a puny .149 AVG-.167 SLG (40 Unadjusted Contact Score) despite underlying fundamentals supporting a much higher, but still solid, 90 mark. Adjusted for context, Guerra was much closer to a league-average pitcher than his ERA- or FIP- indicated. Still, a nice find by the Brew Crew.

Aaron Nola appeared to be on the fast track to stardom before his mid-2016 injury. He posted an exceptional profile across the board, from his very strong K/BB foundation to his solid grounder tendency to his squelching of authority. The only minor quibble: even for a grounder pitcher, his pop-up rate was exceedingly low. His ERA- was misleadingly high, due to poor event sequencing. Like Matz, his combination of K/BB and contact-management strengths spells future All-Star berths if he can remain healthy.

Not to sound like a broken record, but Jameson Taillon belongs to the same family of pitchers as Matz and Nola. His low BB rate rivals theirs, though Taillon hasn’t shown comparable bat-missing ability. In terms of contact management, Taillon and Matz might lack Nola’s eventual upside. The young Phil combines a more optimal BIP mix with better suppression of authority. One advantage for Taillon over Nola: his pop-up rate sits in the average range despite a low fly-ball rate. All three have bright futures, though I would consider Taillon’s qualitative upside to be marginally lower than the others. He could potentially offset that with superior durability, if he has put his injuries behind him.

Drew Pomeranz takes his act to the AL and Fenway Park this season. It cannot be argued that the Padres, who have been deservedly panned for many of their recent moves, sold Pomeranz at the absolute apex of his value. He battled injuries before and after last summer’s deal, but still netted one of Boston’s top pitching prospects, Anderson Espinoza. The big lefty has always been a high K and BB guy, but his 2016 numbers were bolstered by the lowest liner rate allowed in the NL. That number is certain to regress upward in 2017. There is a huge gulf between Pomeranz’ ceiling and floor; he’s not guaranteed a rotation spot as spring training begins, but could provide a huge boost if everything clicks.

The Arizona Diamondbacks’ rotation posted as bad a collective contact-management effort as one could possibly imagine. Their only starter who was anywhere near average in this discipline was Zack Greinke, whom they paid to be a lot better than average. Greinke’s huge 2015 campaign had a whole lot of good fortune baked into it; readers of my work know how I felt about his massive contract. As one might expect, Greinke’s K rate is trending downward in his early 30s, down from the 92nd to the 73rd to the 57th percentile from 2014 to -16. His Adjusted Fly Ball Contact Score (112) was higher than the league average, and his low liner rate (21st percentile) suggests he might even have been a bit lucky last season.

The glass-half-full approach might suggest that new, more analytically oriented management will help in Arizona, and that the poor contact-management performance of Archie Bradley, Patrick Corbin, Shelby Miller, and Robbie Ray actually presents a huge opportunity. That may, in fact, prove to be the case. As for Greinke, specifically, I still believe his best days are behind him. He’s got some upside above his 2016 performance level, but he’s no longer elite.

With Alex Reyes, our first injured spring trainer, set to undergo Tommy John surgery, the Cards need Michael Wacha even more today than they did yesterday. One might look at that elevated liner rate (up in the 85th percentile) and think, hey, there’s some regression ahead that should make Wacha a materially better-than-average starter. Not so fast. He has now posted well above-average liner rates allowed in all three of his MLB seasons, with the 72nd percentile representing the low-water mark. His stuff is relatively pedestrian, his K and BB rates have stagnated, and the pop-up tendency he displayed as a youngster seems to be slipping away from him. When he was in the draft, I loved him as a quick return but relatively modest ceiling guy, and that appears to be how it’s playing out.

Ah, liner-rate regression… Joe Ross‘ contact-management profile was little changed from 2015 to 2016, except for one big difference: his liner rate surged from the 2nd to the 98th percentile. The young righty’s 2016 “tru” ERA- was higher than both his ERA- and FIP-, but once that liner rate settles in around the average range, the three could be aligned quite well in the 85-90 range. There’s nothing in his profile that predicts a quantum leap in performance, but there aren’t material weaknesses either. A pitcher-friendly home park coupled with an 83 Adjusted Fly Ball Contact Score suggests that Ross is in position to fully reach his potential in Washington.

Once upon a time, Stephen Strasburg simply reared back and fired with little regard for contact management, allowing well harder-than-average authority as recently as 2014. The 2016 season was the first in which he held overall authority below the average range, and his liner-rate allowed declined by quite a bit. He’s evolved into a fairly extreme fly-ball guy, which carries with it some degree of risk, but he controlled authority in the air (97 Adjusted Contact Score) and, especially, on the ground (78) last season. His K/BB spread is exceptional, and an average contact-management effort over 200 innings equals instant Cy Young candidate. At this point, it’s quantity, not quality, that remains concerning, as Strasburg has only qualified for two ERA titles, and none since 2014.

Speaking of the quality/quantity conundrum, we turn to Jacob deGrom. Some readers challenged my concerns regarding his ability to handle a workload when I expressed them last offseason. He’ll pitch 2017 at age 29, has qualified for a single MLB ERA title, and pitched all of 323 innings in parts of five minor-league seasons. His strong K/BB foundation affords him plenty of margin for error with regard to contact management. In 2016, he needed it. His liner-rate allowed (74th percentile) was elevated and should regress downward moving forward, but I still see him as no better than an average contact manager on balance. There’s no go-to grounder or pop-up tendency, and he yielded fairly hard fly-ball authority (107 Adjusted Contact Score) last season. I like him, but wouldn’t bet heavily on him moving forward.

We can file Vince Velasquez alongside Drew Pomeranz in the huge difference between floor and ceiling department. Injury history? Check. High fly-ball rate, which increases overall risk? Check. Outlier liner rate? Check. (Pomeranz’ was outlier-low; Velazquez, outlier-high.) Big K rate, with signs of BB rate improvement? Check. I’d argue that the Phils’ righty was in fact quite unlucky in 2016. Hitters batted an extreme .741 AVG-.988 SLG (130 Unadjusted Contact Score) on liners, while the underlying granular data supports a much lower 98 mark. Health remains a big question mark here. When healthy and “on,” he’s exceptional. He has a long to way to go to become an average contact manager, but is good enough to be a mid-rotation starter even if he doesn’t.





 

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Mattabattacolamember
7 years ago

So it seems like your dislike of deGrom is mostly related to health, but on an inning by inning basis do you still see him as elite?

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Mattabattacolamember
7 years ago
Reply to  Mattabattacola

Also your articles are awesome. SO much information on so many players, thank you.

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