Archive for Starting Pitchers

Pitcher Injury Factors: Literature Review & Rankings Update

Note: About 95% of this article was finished before the news that MLB is going forward with a 60 game season.  I finished it knowing that more imporant work needs to be done. This series now comes to an abrupt end and I will return to the series once the season is over one way or the other.

I’m continuing my quest to predict pitcher injuries and their effects as best as possible. I started grinding through the process last week and found through some additional work that injuries from just the past two seasons drag down production. Today, I’m going to go over some other possible other injury causes and provide updated injury ranks.

While I’ve done quite a bit of my own work on pitcher injuries, I decided to scour the web come up with some new ideas. Here are some possible ideas ranked by how I’d like to investigate them.
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SP Handedness Breakdown by Division

If we do in fact get some sort of 50-ish game season, finding any small edge could be the difference between winning and losing your league. One potential edge is platoons. Let’s take a look at the starting pitching handedness breakdown on the assumed rotations for each division. This assumes they stick to this mega-division plan by combining the East, Central, and West division from both leagues.

We’re also going to grade the pitchers using the FIP from their BAT projection. My arbitrary scale is as such: sub-4.00 is good, 4.01-4.70 is solid, and 4.71+ is bad.

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Workshop: Pitcher Injury Factors

Projecting pitcher injuries and their effect seems like the Holy Grail for fantasy analysis. From years of research on the subject, I find it’s just a frustration filled enterprise with no firm resolution. Until a start to the 2020 season has been agreed upon (or I eventually find an acceptable answer), I plan to continuously grind for a workable understanding of pitcher injuries.

First, this article will be a work in progress as I try to find answers to various questions. I can’t fill the RotoGraphs article list with a new article every time I make a change or add more information (Ed. note: Sure you can, Jeff, we’ll post all of ’em!). Every few days or so, I’ll summarize the findings from the previous article’s work and keep moving forward. The series will come to an abrupt end if the framework exists for a start to the season since other analysis will then take priority.
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Weighted Plate Discipline Index (wPDI): A Refresher

Introduction

Last year, I introduced a new (yet simple) pitcher metric. Weighted Plate Discipline Index (wPDI) arises from the core ingredients of plate discipline from the point of view of the pitcher – control, deception, and contact.

wPDI looks at the following basic binary events:

  • Was the ball thrown in the strike zone?
  • Was the ball swung on?
  • Did the batter make contact with the ball?

That’s all.

Weighted Plate Discipline Index (wPDI) does not look at generated bat speed, exit velocity, pitch speed, or quality of contact, etc. wPDI doesn’t even focus on walk rates or strikeout rates, or any other plate appearance result. wPDI focuses solely on the pure components of a pitch. Is the pitch in the zone? Is the batter swinging at pitches in the zone? Is the batter swinging at pitches outside of the zone? Is the hitter contacting the pitch?

That’s all.

In this series of articles, I will be refining and expanding upon what I had started last year. I will look at wPDI’s effectiveness and predictability. Along the way, I shall highlight both pitchers and hitters who catch our eye based on great (and poor) plate discipline performance.

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Starter Injury Accumulation Leads To Accelerated Aging

In the final part of my latest injury investigation, I trying to see if pitchers who have accumulated a certain amount of injuries age worse than those who have stayed healthy. And if so, are there any rules-of-thumb that can be used. I’ve already examined chronically injured hitters and if accumulated days missed for pitchers lead to more Injured List (IL) trips. The results aren’t groundbreaking findings that constantly hurt pitchers declining faster at certain thresholds but now some numbers can be added to the narratives.

My overall goal is to see the accumulative effect of various injuries can lead to a pitcher aging faster. The factors I had never accounted for are accumulated IL days and times of the IL for elbow, shoulder, or arm injury. Also, I’m only diving into starters (GS/G >= .5) from 2010 to 2018 because relievers, especially closers, are already tough to project. I divided the starters by their age based on a steep production decline starting at age 29. For the pitcher’s talent level, I used ERA- that increase, like ERA, as a pitcher performs worse. The results were a little mixed.

One item to keep in mind, pitchers have historically, maintained their production, or declined as they age. While there is always the exception of a pitcher improving, most are headed down with the average change of 2.8 points of ERA- a year.

The Difference in Production Based on Total Accumulated IL Days
Total IL days Median ERA- Diff Median IP Diff Avg IL Days
28 and Younger
0 1.9 18.1 20.2
1 to 90 2.2 -18.8 29.9
91 to 180 7.3 5.7 37.6
181 to 270 5.1 13.4 35.9
>270 0.6 58.0 38.3
29 and Older
0 0.6 -24.2 15.5
1 to 90 2.9 -17.2 25.8
91 to 180 0.4 -13.0 27.4
181 to 270 5.7 -23.6 39.8
>270 4.5 -4.1 40.4

What I found from this data run and others not listed, age doesn’t matter. Also, while the average IL days steadily climb, the ERA- value jump seems to increase except when it doesn’t. I simplified the results and settled on a 120-day threshold with the following results.

The Difference in Production Based on Total Accumulated IL Days
Career IL Days Median ERA- Diff Median IP Diff Avg IL Days IL Chance
<= 120 days 1.8 -10.5 24.0 40.0%
> 120 IL days 5.2 -8.0 37.8 54.8%

Once a pitcher goes over the 120-day threshold (i.e. a full MLB season), their career declines at a faster than expected rate. Again, there are always going to expectations but rostering too many of these hurt starters increases the downside potential.

Now for one more angle that I had jotted in my to-do book. Most major pitcher injuries center around the arm, so I decided to focus just on them. I bucketed the information by the number of times a pitcher was on the IL for a general arm (e.g. forearm), elbow, or shoulder injury (no wrist or hand). With more than 99% of all instances in the single digits, a simple workable division was easy to find at 2 IL stints.

The Difference in Production Based on Arm-Related IL Trips
IL Stints for Arm ERA- Change Total IL Days IL Days IL Chances
Two or Fewer 2.3 252.6 26.5 42.0%
Three or More 5.5 358.0 39.6 59.0%

Even pitchers who had two or few IL stints weren’t in great shape with them averaging over 200 days on the IL. That third arm related IL trip can be a deal-breaker because the pitcher will, on average, will see their skill degrade about twice as fast as those with two or fewer trips.

And finally here are the pitchers who have over 120 days on the IL and three or more arm related IL stints (min 10 IP last season).

Starters Who Should Age Faster Than Expected
Name Age Days Arm IL Stints FBv
Brett Anderson 32 918 6 90.8
Clay Buchholz 35 717 4 89.5
Rich Hill 40 667 6 90.3
Jason Vargas 37 657 3 84.3
Homer Bailey 34 626 8 93.0
Adam Wainwright 38 618 4 89.9
Hyun-Jin Ryu 류현진 33 558 4 90.6
Charlie Morton 36 551 4 94.4
Michael Pineda 31 511 5 92.6
Yu Darvish 33 492 5 94.2
Nathan Eovaldi 30 470 5 97.5
Zack Wheeler 30 460 5 96.7
Matt Harvey 31 433 4 93.2
Carlos Carrasco 33 432 5 93.5
Anibal Sanchez 36 427 5 90.5
Stephen Strasburg 31 421 6 93.9
Andrew Heaney 29 419 4 92.5
Danny Duffy 31 414 7 92.4
Mike Minor 32 397 3 92.6
Martin Perez 29 377 5 94.1
Andrew Cashner 33 375 4 93.9
Drew Smyly 31 355 3 91.2
Felix Hernandez 34 313 4 89.6
Jhoulys Chacin 32 300 3 90.0
Ivan Nova 33 299 4 92.4
Jordan Zimmermann 34 289 3 90.5
Steven Matz 29 244 5 93.4
Clayton Kershaw 32 217 3 90.4
David Price 34 199 4 92.0
Cole Hamels 36 182 4 91.4
Vince Velasquez 28 144 4 94.1
Gerrit Cole 29 143 4 97.2
Jake Arrieta 34 140 3 92.5
Chris Sale 31 121 4 93.2

Several high ADP names fill the list like Morton, Darvish, Strasburg, Kershaw, and Cole. Does this information mean I won’t roster the pitcher? Maybe. With a fantasy team only starting nine pitchers, I’d like those pitchers to be as rock-solid as possible with little chance of decline. The downside means that I will need them to come with a discount and is an easy tiebreaker. That’s not helpful since that’s everyone’s injury take.

My stance on the information is wishy-washy because I am unsure. I haven’t fathomed a way to weight the information especially if I want to use it to create auction prices. I need to find a way to change ERA- to fantasy-relevant stats. It wouldn’t be that hard but I’m looking at two different inputs (total days and arm related trips). Also, I should incorporate possible time missed from previous IL trips. I’m going to let the information stew for a bit and unless I come to some divine revelation, I’ll perform a forced dive once this season is over or canceled.


Pitcher IL Chances … Again

I had no plans to write or investigate pitcher injuries again. I’ve done it several times in the past with similar results. Since I needed the same information to investigate if an often injured pitcher ages faster as I did with hitters, I had the data available so why not take another stab at projecting pitcher injury risks with a few different inputs.

For a refresher, here are some of my previous findings:

The small data difference is that instead of limiting the IL days to the previous one to three seasons, I’m just using the accumulated days. Also, I tracking the number of times the pitcher went on the IL for an arm, elbow, or shoulder injury. The two different factors are joined by fastball velocity, Zone%, and age to see what leads to injuries the next season.

I took all the data from all the pitchers and ran it through a Trees analysis and got the following chart.

This image sums up pitcher injuries perfectly. The best predictor of future injuries is past injuries. And just because a pitcher had never been on the IL, on average, they will still spend ~18 days on it. It’s just re-enforcing common sense backed up by study after study.

From my previous work, the injury rates between starters and relievers (i.e. starters who can’t stay healthy) are drastically different. For that reason, I ran the analysis splitting out starters (GS/G >= .5) from relievers (GS/G < .5). Here are the two decision trees.

Starters

Relievers

We have some further division taking place fastball velocity on both, a further out 460-day threshold for starters, and age requirement. I’m not a huge fan of the multi-branched tree with many variables. I’m all for keeping it simple. Using the above variables, I cut and diced the data into several possible combinations and came up with the following divisions.

Pitcher IL Chances
Starters Relievers
Category Avg IL Days IL Chances Count Avg IL Days IL Chances Count
0 IL, <= 93 mph 17 28% 463 13 22% 728
0 IL, > 93 mph 28 45% 141 20 32% 628
> 0 IL, <= 93 mph 31 49% 857 23 37% 832
> 0 IL, > 93 mph 34 57% 268 27 43% 668
> 460 days 55 63% 59

Again, the rates are similar to my previous findings. The only changes are the pitch velocity groupings and that rough over-460 IL day group who average two months on the IL a year. For me, I focus on starters and will designate the starters into three risk groups:

  • Low: No IL, low velo.
  • Medium: No IL, high velo, and some IL, low velo.
  • High: Some IL, high velo, and high IL.

Not all injuries can be avoided, but the injury downside is just another factor to consider when setting each pitcher’s fantasy value.

And what’s a study without the players to consider for the upcoming season. Here are the historic IL days and fastball velocity for any pitcher with 10 starts last season.

2020 Starters Group by Historic IL Days & Fastball Velocity
Name Age 2019 IP Combined IL Days FBv
Ryan Yarbrough 28 141 0 88.2
Nick Margevicius 24 57 0 88.3
Alex Young 26 83 0 89.3
Trevor Richards 27 135 0 90.9
Adam Plutko 28 109 0 91.1
Dillon Peters 27 72 0 91.1
Dario Agrazal 25 73 0 91.2
Jose Quintana 31 171 0 91.4
Asher Wojciechowski 31 82 0 91.6
Jaime Barria 23 82 0 91.7
Jose Suarez 22 81 0 91.8
Merrill Kelly 켈리 31 183 0 91.9
Matthew Boyd 29 185 0 92.0
Tanner Roark 33 165 0 92.1
Ariel Jurado 24 122 0 92.4
Yusei Kikuchi 29 161 0 92.5
Aaron Civale 25 57 0 92.6
Peter Lambert 23 89 0 92.7
Jose Berrios 26 200 0 92.8
Zac Gallen 24 80 0 92.9
David Hess 26 80 0 93.0
Shane Bieber 25 214 0 93.1
Brad Keller 24 165 0 93.4
Miles Mikolas 31 184 0 93.6
Brendan McKay 24 49 0 93.7
Dakota Hudson 25 174 0 93.7
Chris Paddack 24 140 0 93.9
Jack Flaherty 24 196 0 93.9
Zach Plesac 25 115 0 94.0
Tyler Beede 27 117 0 94.3
Adrian Houser 27 111 0 94.4
Cal Quantrill 25 103 0 94.5
Mitch Keller 24 48 0 95.4
Sandy Alcantara 24 197 0 95.6
Luis Castillo 27 190 0 96.5
Dylan Cease 24 73 0 96.5
Kyle Hendricks 30 177 66 86.9
Mike Leake 32 197 51 88.4
Dallas Keuchel 32 112 63 88.4
Zach Davies 27 159 120 88.5
Marco Gonzales 28 203 15 88.9
CC Sabathia 39 107 364 89.2
Gio Gonzalez 34 87 80 89.3
Jerad Eickhoff 29 58 284 89.5
Felix Hernandez 34 71 313 89.6
Julio Teheran 29 174 28 89.7
Zack Greinke 36 208 188 90.0
Jhoulys Chacin 32 103 300 90.0
Joey Lucchesi 27 163 36 90.2
Jon Lester 36 171 162 90.3
Mike Fiers 35 184 11 90.4
Clayton Kershaw 32 178 217 90.4
Clayton Richard 36 45 370 90.4
Rick Porcello 31 174 27 90.5
Wade Miley 33 167 83 90.5
Jordan Zimmermann 34 112 289 90.5
Anibal Sanchez 36 166 427 90.5
Dereck Rodriguez 28 99 7 90.6
Elieser Hernandez 25 82 53 90.6
Daniel Norris 27 144 357 90.8
Dylan Bundy 27 161 30 91.2
Drew Smyly 31 114 355 91.2
Trevor Williams 28 145 33 91.3
J.A. Happ 37 161 263 91.3
Madison Bumgarner 30 207 153 91.4
Ross Detwiler 34 69 165 91.4
Cole Hamels 36 141 182 91.4
Tyler Skaggs 28 79 430 91.4
Jakob Junis 27 175 13 91.5
Jordan Yamamoto 24 78 27 91.5
Masahiro Tanaka 31 182 157 91.5
Jacob Waguespack 26 78 35 91.6
Caleb Smith 28 153 117 91.6
John Means 27 155 24 91.8
Eric Lauer 25 149 30 91.9
Kyle Freeland 27 104 51 91.9
Jeff Samardzija 35 181 139 91.9
Patrick Corbin 30 202 272 91.9
Steven Brault 28 113 31 92.0
Aaron Brooks 30 110 183 92.0
David Price 34 107 199 92.0
Kenta Maeda 32 153 37 92.1
Chi Chi Gonzalez 28 63 182 92.2
Brad Peacock 32 91 257 92.2
Erick Fedde 27 78 88 92.3
Joe Musgrove 27 170 76 92.4
Robbie Ray 28 174 94 92.4
Ivan Nova 33 187 299 92.4
Danny Duffy 31 130 414 92.4
Mike Soroka 22 174 129 92.5
Jake Arrieta 34 135 140 92.5
Marcus Stroman 29 184 217 92.5
Andrew Heaney 29 95 419 92.5
Shaun Anderson 25 96 16 92.6
Jordan Lyles 29 141 252 92.6
Mike Minor 32 208 397 92.6
Trent Thornton 26 154 11 92.9
Jake Odorizzi 30 159 75 92.9
Aaron Nola 27 202 143 92.9
Eduardo Rodriguez 27 203 162 93.1
Michael Wacha 28 126 227 93.1
Chris Sale 31 147 121 93.2
Matt Harvey 31 59 433 93.2
Tyler Mahle 25 129 33 93.3
Kyle Gibson 32 160 57 93.3
Sonny Gray 30 175 96 93.3
Chase Anderson 32 139 96 93.4
Edwin Jackson 36 67 148 93.4
Steven Matz 29 160 244 93.4
Glenn Sparkman 28 136 89 93.5
Chris Bassitt 31 144 290 93.5
Carlos Carrasco 33 80 432 93.5
Domingo German 27 143 25 93.6
Pablo Lopez 24 111 108 93.6
Zach Eflin 26 163 122 93.6
Aaron Sanchez 27 131 299 93.6
Taylor Clarke 27 84 16 93.7
Antonio Senzatela 25 124 29 93.7
Jeff Hoffman 27 70 31 93.7
Spencer Turnbull 27 148 32 93.8
Max Fried 26 165 52 93.8
Griffin Canning 24 90 53 93.9
Yonny Chirinos 26 133 81 93.9
Luke Weaver 26 64 117 93.9
Andrew Cashner 33 150 375 93.9
Stephen Strasburg 31 209 421 93.9
Kevin Gausman 29 102 117 94.0
Chris Archer 31 119 92 94.1
Vince Velasquez 28 117 144 94.1
Martin Perez 29 165 377 94.1
Lance Lynn 33 208 249 94.2
Lucas Giolito 25 176 30 94.3
Dylan Covey 28 58 131 94.4
Trevor Bauer 29 213 38 94.6
Justin Verlander 37 223 69 94.7
Anthony DeSclafani 30 166 318 94.7
Max Scherzer 35 172 64 94.9
Mike Foltynewicz 28 117 84 94.9
Reynaldo Lopez 26 184 14 95.5
German Marquez 25 174 38 95.5
Mike Clevinger 29 126 79 95.5
James Paxton 31 150 361 95.5
Blake Snell 27 107 79 95.6
Jose Urena 28 84 124 95.9
Jon Gray 28 150 136 96.1
Dinelson Lamet 27 73 289 96.1
Brandon Woodruff 27 121 57 96.3
Walker Buehler 25 182 16 96.6
Frankie Montas 27 96 183 96.6
Jacob deGrom 32 204 32 96.9
Tyler Glasnow 26 60 155 97.0
Gerrit Cole 29 212 143 97.2
Noah Syndergaard 27 197 213 97.7
Zack Wheeler 30 195 460 96.7
Nathan Eovaldi 30 67 470 97.5
Yu Darvish 33 178 492 94.2
Michael Pineda 31 146 511 92.6
Charlie Morton 36 194 551 94.4
Hyun-Jin Ryu 류현진 33 182 558 90.6
Adam Wainwright 38 171 618 89.9
Homer Bailey 34 163 626 93.0
Jason Vargas 37 149 657 84.3
Rich Hill 40 58 667 90.3
Clay Buchholz 35 59 717 89.5
Brett Anderson 32 176 918 90.8

Notes

  • The pitcher with the highest velocity and IL experience is Noah Syndergaard. That 2020 IL stint didn’t take long.
  • The oldest starter to never have been on the IL is Tanner Roark at 33.
  • Darvish and Morton are going as the 17th and 18th pitchers even though they’ve broken the 460-day threshold.
  • Just go and scroll through the starters who have been on the IL and throw over 93-mph, especially over 95. Lots of them have spent considerable time on the IL over their careers. I’m thinking to target “safer” but elite starters if given the opportunity like Corbin, Bieber, Kershaw, Castillo, and Flaherty. There is no way to completely stay away from the injury risk but why not add a pitcher with a 28% chance (Boyd) than someone with a 63% chance (Ryu)

These conclusions were about 80% in line with what I expected with fastball velocity nudging itself in. Next up will be taking this information and seeing if a higher injury rate ages pitchers more than projected.


National League Pitchers Value Down With the DH

One of my Launch Angle Podcast partners, Rob Silver, brought up how if there is a universal DH, the NL pitcher will no longer face ineffective bat-wielding pitchers. Simply, pitchers can’t hit. Last season in 4789 PA, National League pitchers hit for a combined .126/.157/.160. Our own Dan Szymborski continued the discussion to see if dominating pitchers hitting was a repeatable trait. I’m going to go a different route to investigate, using Dan’s information, how a pitcher’s projection would change going to an American League team (effectively including a DH) and this number affects a pitcher ranking.

I’m going to start off saying to not take any of the following information as the gospel truth. I’m trying to achieve a better projection that’ll be closer to the final outcome. Each stat and step in the process can be nitpicked along the way. I’m not even sure if the following method is the best way but it’s a way. I’m trying to move the discussion from “The DH will be a try breaker for me when drafting” (quote from a podcast I heard) to actually putting some number behind the possible changes.

Also, I’m not here to argue on why Jacob deGrom started out as the 8th ranked starter and he’s now 9th. I just collect a projection set. Anyone who uses stats to generate their projections will have their own secret sauce. I have my own. I just need a projection framework and live with it. Here is how I set it up.

I downloaded the 2020 ZiPS projections from here at FanGraphs. I used the ZiPS values since Dan created them and I’ll be using the inter-league adjustments provide in his article.

Then I changed each pitcher’s stats using Dan’s 2019 variables.

Variable: Change

  • BB%: +4%
  • K%: -5%
  • AVG: +.007
  • RC/G: +13%

I just adjusted the pitcher’s projected stats by the above values and created a hits estimate from the AVG with few assumptions.

NL pitcher innings projections have two offsetting values that could also be in play. The worse results could lead to fewer innings thrown (i.e. early hook) and the pitcher’s value could drop. On the other hand, the times a pitcher nearing his pitch limit will be replaced by a pinch batter will disappear. One of the two factors will likely dominate

Next, I used the 2019 12-team SGP (Standings Gain Points) formula from The Process to create pitcher valuations. The SGP value is the expected jump in the standings if that pitcher’s stats are added to a team’s stats. Here are results from the final top-40 starting pitcher using the SGP formula and ZiPS projections (I’m not sure why the TJS pitchers are still included but I don’t get paid the big bucks to know such things).

NL Starting Pitcher Adjustments
Initial Adjusted
Rank Name IP W K ERA WHIP SGP Rank W K ERA WHIP SGP Difference
1 Gerrit Cole 200 16 280 3.11 1.01 18.5 1 18.5 0
2 Justin Verlander 190.3 16 243 3.22 0.98 17.5 2 17.5 0
4 Lucas Giolito 176 14 235 3.22 1.07 15.4 3 15.4 1
3 Max Scherzer 174 13 236 3.00 0.98 16.0 4 13 224 3.39 1.02 13.8 -1
7 Chris Sale 164.7 13 216 3.12 1.01 15.0 5 15.0 2
5 Jack Flaherty 189.7 13 236 3.13 1.05 15.4 6 13 224 3.54 1.09 13.2 -1
6 Stephen Strasburg 184.7 15 221 3.22 1.09 15.2 7 15 210 3.63 1.13 13.0 -1
9 Shane Bieber 195.7 13 213 3.63 1.11 14.1 8 14.1 1
8 Jacob deGrom 184.3 12 223 2.88 1.04 14.7 9 12 212 3.26 1.08 12.5 -1
10 Luis Severino 166.3 14 201 3.52 1.12 13.9 10 13.9 0
11 Walker Buehler 167.7 11 201 3.27 1.07 13.3 11 13.3 0
12 Clayton Kershaw 166.7 12 176 3.24 1.04 13.1 12 13.1 0
15 Zack Greinke 179.7 13 172 3.91 1.12 12.7 13 12.7 2
13 Trevor Bauer 190.3 13 222 3.74 1.25 13.0 14 13 211 4.22 1.29 10.7 -1
14 Aaron Nola 194 12 213 3.57 1.2 11.4 15 12 202 4.04 1.24 10.7 -1
20 Charlie Morton 159 12 185 3.34 1.18 10.6 16 10.6 4
21 Jose Berrios 190 13 193 4.17 1.25 10.6 17 10.6 4
16 Luis Castillo 175.3 12 198 3.59 1.19 11.0 18 12 188 4.06 1.23 10.3 -2
24 Lance Lynn 173.3 14 193 4.05 1.33 10.2 19 10.2 5
17 German Marquez 180 12 190 4.00 1.18 10.8 20 12 181 4.52 1.21 10.2 -3
18 Noah Syndergaard 186.7 11 197 3.33 1.17 10.8 21 11 187 3.76 1.21 10.1 -3
19 Patrick Corbin 182.3 12 205 3.80 1.24 10.7 22 12 195 4.30 1.28 10.0 -3
26 Mike Clevinger 146.7 11 175 3.62 1.19 9.8 23 9.8 3
28 Matthew Boyd 173 10 193 4.37 1.24 9.6 24 9.6 4
29 Blake Snell 135.3 11 173 3.33 1.2 9.6 25 9.6 4
23 Zac Gallen 159 12 185 3.62 1.22 10.3 26 12 176 4.09 1.26 9.6 -3
22 Robbie Ray 164.3 11 222 4.00 1.3 10.3 27 11 211 4.52 1.35 9.6 -5
25 Chris Paddack 159 10 174 3.68 1.11 10.1 28 10 165 4.16 1.15 9.5 -3
31 James Paxton 143.7 11 169 3.82 1.21 9.5 29 9.5 2
34 Jake Odorizzi 149.7 12 158 4.09 1.26 9.1 30 9.1 4
27 Yu Darvish 154.3 8 190 3.56 1.13 9.7 31 8 181 4.02 1.17 9.1 -4
30 Sonny Gray 158 11 171 3.82 1.21 9.6 32 11 162 4.31 1.25 8.9 -2
37 Eduardo Rodriguez 174.3 12 177 4.28 1.34 8.9 33 8.9 4
38 Carlos Carrasco 131.3 10 152 3.97 1.16 8.9 34 8.9 4
39 Mike Minor 172.7 12 161 4.48 1.29 8.9 35 8.9 4
32 Mike Soroka 176 11 154 3.32 1.16 9.5 36 11 146 3.76 1.20 8.9 -4
33 Kyle Hendricks 169.3 12 143 3.67 1.18 9.4 37 12 136 4.14 1.21 8.8 -4
41 Corey Kluber 144.7 11 145 3.98 1.2 8.8 38 8.8 3
42 Masahiro Tanaka 168 11 150 4.34 1.23 8.8 39 8.8 3
43 Tyler Glasnow 119.7 9 162 3.53 1.19 8.7 40 8.7 3

The changes are significant once all three factors (WHIP, ERA, strikeouts) are factored in. While the rank changes by just one or two with the top-10 or so arms, the difference becomes significant around pick 20 with moves of four spots. Maybe this change is a tie-breaker for some owners, but if an owner gains an extra ~1 SGP from all nine pitchers, it becomes nine spots in the standings. I think every owner would take those extra spots.

Just eyeballing the differences, it’s ~0.40 increase in ERA and 0.04 bump in WHIP to go with the 5% drop in strikeouts. The near half run increase in ERA will scare off quite a few owners by itself. Other owners will get blow off the possible changes, but in my current opinion, they will be playing catch up if they ignore them.

Again, don’t take my word for it … I’m still coming to grips with Lance Lynn possibly jumping Patrick Corbin. I could be wrong with these calculations but hopefully, some other analysts will step up and perform the calculations. The possible change in production is likely the biggest valuation change with half the pitchers facing legit MLB hitters instead of the irrelevant pitcher.


Innings Per Start Analysis: Fool’s Gold

For my last few articles, I’ve been focusing on how some pitcher value changes based on the innings thrown per start. Today I’m going to examine the pitchers who go long into starts but could be landmines for their owners.

Just for reminder, I’m highlighting the pitchers who go longer into games because I expect the second Spring Training to be shorter than normal. Pitchers won’t be stretched out to start the season. Also, the games will be condensed with some starters in piggy-back situations as managers need to pull out all the stops to win games. While most of the pitchers who go long into games will be helpful, I found a few starters who I can’t recommend.

The following starters are in the top-300 in NFBC ADP and the stats are combined from the past three seasons.

Length of Starts
Name G GS IP IP/G Threw 90 Pitches 100 Pitches Reached 5 IP 5 IP/G 5 IP/GS 6 IP/GS Reached 6 IP ADP
Gerrit Cole 98 98 616 6.3 93 63 95 97% 97% 79% 77 6
Jacob deGrom 95 95 622 6.6 86 70 88 93% 93% 83% 79 7
Walker Buehler 62 53 329 5.3 41 16 48 77% 91% 60% 32 12
Max Scherzer 91 91 594 6.5 84 68 86 95% 95% 85% 77 16
Justin Verlander 101 101 643 6.4 95 72 96 95% 95% 81% 82 20
Jack Flaherty 67 66 369 5.5 41 23 53 79% 80% 48% 32 22
Shane Bieber 54 52 329 6.1 40 28 48 89% 92% 71% 37 26
Mike Clevinger 80 74 448 5.6 62 41 64 80% 86% 66% 49 27
Stephen Strasburg 83 83 514 6.2 75 52 75 90% 90% 76% 63 28
Chris Sale 84 84 520 6.2 73 54 73 87% 87% 70% 59 35
Clayton Kershaw 82 81 515 6.3 59 25 75 91% 93% 86% 70 37
Luis Castillo 78 78 450 5.8 54 35 68 87% 87% 56% 44 40
Blake Snell 78 78 417 5.3 50 34 60 77% 77% 50% 39 44
Patrick Corbin 99 98 592 6.0 81 41 88 89% 90% 70% 69 45
Chris Paddack 26 26 141 5.4 11 0 20 77% 77% 38% 10 49
Lucas Giolito 68 68 395 5.8 54 35 58 85% 85% 65% 44 50
Yu Darvish 70 70 405 5.8 49 24 57 81% 81% 59% 41 53
Charlie Morton 88 88 508 5.8 63 33 78 89% 89% 60% 53 54
Aaron Nola 94 94 583 6.2 78 47 87 93% 93% 71% 67 57
Zack Greinke 98 98 619 6.3 83 39 91 93% 93% 78% 76 61
Tyler Glasnow 72 36 234 3.3 13 4 23 32% 64% 36% 13 61
Jose Berrios 90 89 538 6.0 71 36 75 83% 84% 63% 56 75
Brandon Woodruff 49 34 207 4.2 24 6 24 49% 71% 44% 15 78
Trevor Bauer 94 92 565 6.0 82 75 78 83% 85% 68% 63 80
Sonny Gray 88 81 468 5.3 51 29 64 73% 79% 56% 45 95
Frankie Montas 52 27 193 3.7 16 4 23 44% 85% 67% 18 100
Corey Kluber 69 69 454 6.6 55 36 62 90% 90% 80% 55 101
Mike Soroka 34 34 200 5.9 16 5 28 82% 82% 65% 22 104
James Paxton 81 81 447 5.5 59 39 61 75% 75% 54% 44 119
Zack Wheeler 77 77 464 6.0 64 41 67 87% 87% 69% 53 120
Lance Lynn 97 95 551 5.7 80 58 83 86% 87% 58% 55 121
Dinelson Lamet 35 35 187 5.4 17 6 29 83% 83% 40% 14 122
Zac Gallen 15 15 80 5.3 12 5 13 87% 87% 33% 5 123
Julio Urias 45 13 107 2.4 3 0 6 13% 46% 15% 2 126
Madison Bumgarner 72 72 448 6.2 59 34 69 96% 96% 79% 57 130
Max Fried 56 39 225 4.0 16 4 31 55% 79% 38% 15 135
Eduardo Rodriguez 86 81 470 5.5 69 49 71 83% 88% 53% 43 137
Carlos Carrasco 87 74 472 5.4 55 33 63 72% 85% 66% 49 139
David Price 68 63 358 5.3 45 21 52 76% 83% 59% 37 139
Hyun-Jin Ryu 69 68 392 5.7 36 14 52 75% 76% 57% 39 146
Kyle Hendricks 87 87 516 5.9 56 22 75 86% 86% 57% 50 153
Robbie Ray 85 85 460 5.4 71 41 69 81% 81% 46% 39 158
Matthew Boyd 89 88 491 5.5 67 30 69 78% 78% 59% 52 158
Kenta Maeda 105 71 413 3.9 26 9 51 49% 72% 32% 23 162
Carlos Martinez 113 50 372 3.3 41 20 44 39% 88% 64% 32 167
Lance McCullers Jr. 47 44 247 5.3 34 10 32 68% 73% 50% 22 172
German Marquez 90 90 532 5.9 65 26 76 84% 84% 64% 58 176
Mike Minor 125 60 443 3.5 50 28 54 43% 90% 62% 37 177
Ian Kennedy 115 52 337 2.9 35 17 38 33% 73% 46% 24 182
Sean Manaea 61 61 349 5.7 34 13 52 85% 85% 57% 35 183
Jake Odorizzi 90 90 467 5.2 70 36 66 73% 73% 37% 33 185
Luis Severino 66 66 397 6.0 52 34 58 88% 88% 61% 40 194
Jose Urquidy 9 7 41 4.6 1 0 4 44% 57% 43% 3 195
Luke Weaver 55 47 261 4.7 30 10 33 60% 70% 38% 18 197
Andrew Heaney 53 53 297 5.6 35 14 43 81% 81% 51% 27 202
Mike Foltynewicz 81 80 454 5.6 57 36 64 79% 80% 51% 41 204
Marcus Stroman 84 84 488 5.8 64 29 66 79% 79% 57% 48 207
Masahiro Tanaka 89 88 516 5.8 47 23 71 80% 81% 63% 55 207
Dylan Bundy 89 89 503 5.7 70 30 73 82% 82% 54% 48 213
Joe Musgrove 89 65 395 4.4 28 9 51 57% 78% 55% 36 216
Joshua James 55 4 84 1.5 1 0 3 5% 75% 0% 0 221
Mitch Keller 11 11 48 4.4 8 0 6 55% 55% 9% 1 228
Adrian Houser 42 18 125 3.0 4 0 9 21% 50% 17% 3 229
Ryan Yarbrough 66 20 289 4.4 18 4 30 45% 80% 55% 16 234
Caleb Smith 53 46 249 4.7 27 17 34 64% 74% 39% 18 234
Anthony DeSclafani 52 52 282 5.4 19 5 39 75% 75% 38% 20 247
Joey Lucchesi 56 56 294 5.2 22 7 43 77% 77% 32% 18 248
Jon Gray 77 76 433 5.6 52 21 61 79% 80% 55% 42 248
Garrett Richards 25 25 113 4.5 8 4 14 56% 56% 20% 5 248
Chris Archer 84 84 469 5.6 67 40 70 83% 83% 58% 49 251
Aaron Civale 10 10 58 5.8 4 0 9 90% 90% 60% 6 252
Alex Wood 67 59 340 5.1 27 5 49 73% 83% 56% 33 252
Sandy Alcantara 46 38 240 5.2 32 10 34 74% 89% 58% 22 266
Michael Kopech 4 4 14 3.6 0 0 1 25% 25% 25% 1 270
Yonny Chirinos 44 25 223 5.1 12 5 30 68% 80% 44% 14 274
Rich Hill 63 62 327 5.2 33 7 49 78% 79% 45% 28 277
Dylan Cease 14 14 73 5.2 12 7 12 86% 86% 43% 6 278
Steven Matz 75 73 381 5.1 45 25 54 72% 74% 47% 34 283
Dallas Keuchel 76 76 463 6.1 62 33 70 92% 92% 70% 53 286
Miles Mikolas 64 64 385 6.0 44 13 58 91% 91% 66% 42 287
Dustin May 14 4 35 2.5 2 0 4 29% 100% 0% 0 295
Jordan Montgomery 37 36 187 5.0 17 5 25 68% 69% 39% 14 296
Cole Hamels 83 83 480 5.8 66 29 69 83% 83% 60% 50 297

Here are some pitchers whose innings per start won’t be enough to help their value.

Marcus Stroman and Masahiro Tanaka: I believe both pitchers are perfectly fine to roster, but Dallas Keuchel and Miles Mikolas are going about 100 picks later and have similar projections.

Veteran Starters Comparison
Name K/9 ERA WHIP ADP
Mikolas 7.2 4.12 1.24 287
Keuchel 7.2 4.30 1.37 286
Stroman 7.3 3.80 1.31 207
Tanaka 8.0 4.45 1.26 207
SOURCE: Depth Chart Projections

If an owner needs a steady boring vet, don’t pay up and grab the last available of these four.

Sandy Alcantara: The 24-year-old righty was 17th in innings pitched last season. Even with all those innings, he had barely any value. He won just six games. He did have 151 strikeouts which were good for 57th overall. He didn’t get hit around too bad keeping his ERA under 4.00. His projections have him taking a major step backward (4.60 ERA, 1.44 WHIP) and 190 innings of his projected ratios could be devastating. I’ll let someone else handle that time bomb.

Sean Manaea: I’m a little surprised to see Manaea so high. I’ll continue to doubt him until he finds some way to stay healthy. His five September starts provided some helium, but career stats (7.3 K/9, 1.20 WHIP, and 3.77 ERA) are in line with the four aging vets profiled earlier.

Carlos Martinez: Whenever Martinez has started over the past few seasons, he’s gone long into games. The problem is that he’s just started 50 games with more games as a reliever. I have no faith he can stay healthy through a shortened 2020 season and will head to the bullpen … again. Since his role will change, it’s tough for me to invest an 11th round pick on an unknown role. At that point in the draft, I need to know if I’m adding a starter or reliever.

Corey Kluber: I just think he may be done. His Spring Training velocities have him at or under his 2019 fastball speeds. I could not find any other information from Spring Training so I decided to compare his NFBC ADP to see his draft position jumped. Maybe others knew more than I did. In February’s online championships, he averaging pick 102. It jumped to 90 in March. Owners seem to believe in as Spring Training progressed. I’m not one of them but there will be a second chance to evaluate him to see who’s right.

David Price: Part of the allure around Price is that he goes long into games to accumulate bulk strikeouts. Since he’s now on the Dodgers, I’m not sure. The Dodgers have curtailed Kershaw’s innings so they’ll do the same with Price.


Innings Per Start Analysis: Late Bargains

On Monday, I started a dive into which pitchers owners might want to stay away from because the pitchers don’t go far into games, limiting their chances for that all-important Win. Today, I’m going to focus on those late picks who are talented and could immediately take on a full-inning workload.

Just for reminder, I’m targeting these pitchers who go longer into games because I expect the second Spring Training to be shorter than normal. Pitchers won’t be stretched out to start the season. Also, the games will be condensed with some starters in piggy-back situations as managers need to pull out all the stops to win games.
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Innings Per Start Analysis: Intro and Suspect Arms

I’m annoyed hearing and reading about how all pitchers will throw just as many innings as the aces in a shortened season. I could see the starts possibly being the same since the younger pitchers won’t wear down and need a phantom IL stint or start skipped. The deal is, if Justin Verlander and Julio Urias make the same number of starts, Verlander is going to throw a ton more innings, accumulating more Wins, and his elite rate stats will be better.

In 101 starts over the past three seasons, Verlander has thrown over 101 pitches 72 times and made it to 5 IP for to be eligible for a Win 95% of the time. Urias has never thrown over 100 pitches and in his starts and only reaches 5 IP in only 46% of them. The extra innings are going to be huge and help boost Verlander’s chance for a Win and boost the impact of his elite rate stats. I’m going to highlight the starters who can be counted on for a few more innings for an advantage in this shortened season.

I’ve backed off guessing what type of season is going to be attempted. Each week there seems to be a new plan that gets “leaked”. I don’t want my valuations to be influenced by some Florida-Arizona rankings but teams end up playing in their home parks. There is one known item, the season will be condensed and some pitchers won’t be on season-long innings limits. Also, a short second Spring Training may have some pitchers building up their arms into the season. For these reasons, I’m planning on targeting pitchers who have historically gone deep into games.
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