Dan Schwartz

Bret Sayre (of Baseball Prospectus and owner of The Dynasty Guru) clustered three skills together and termed the cluster the ‘Holy Trinity’ of pitching. During the 2013 season, he pointed to pitchers whom you should watch. Last week, Ray Guilfoyle of Fake Teams also delved into this approach initially focusing on ground ball rates and then transitioning to K/9 and BB/9.

I was going to run z-scores for each of these categories and then rank them all by the z-sum but then I remembered why would I do that when there was already one encompassing stat that incorporated all three of these stats and only these three stats? It’s called QERA or QuickERA, which as Baseball Prospectus puts it, is based solely on strikeout rate, walk rate and GB/FB ratio.

Here’s the formula unscaled and then scaled:
Unscaled QERA: 2.69+K%*(-3.4)+BB%*3.88+GB%*(-0.66))^2
Scaled  QERA = unscaled QERA / League ERA * League QERA

Is QERA a good way to rank pitchers? Sure. Here are the year to year correlations for each factor:

GB% = .85

K% = .82

BB% = .67

However, QERA is not hitting many other important factors that affect surface ERA’s. Naturally, it’s not incorporating statistics like HR/FB which we see in xFIP, it’s not incorporating Fly Balls (which as we know falls for hits even less than Ground Balls) which is in SIERA …both generally better predictors of ERA than QERA.

And then there are other skills like SwStr% that isn’t in any known formula to date and luck/fluky statistics like LOB (left on base)% . ***I actually incorporate both of these into my expected ERA formula in my pitching projections. You can find my SP projections here. Each week starting next week again, I’ll output 25 more guys.

The expected ERA formula I’m starting with (prior to SwStr% and LOB% adjustments/additions in conjunction with park factors) comes from Stephen Staude’s BERA formula. New factors in this formula are also important: additional balls in play data like IFFB% (infield fly ball percentage) as well as ZC% (zone contact).

So back to the holy trinity. To summarize, each ‘HT’ factor is very important (as we can still see in BERA), and together they provide much of the significance associated with overall success and ERA, but there are lots of other factors like luck/flukiness (LOB% and HR/FB) that could have a significant (year to year) effect on a pitcher.

How to use the Holy Trinity?

Starting with the holy trinity is a great way to look at player skill still, and we’ll do that here quickly through QERA rather than z-sums of the three categories. But then, ensure you look at the other factors I’ve mentioned above to understand and validate performance within season – especially until stats start to stabilize.  Here’s pitchers above 90 IP ranked by QERA (I do like me some Cobb & Kluber and bolded some other relative surprises):

Name

Age

GS

IP

K%

BB%

GB%

ERA

xFIP

SIERA

QERA

(s)QERA

Matt Harvey

24

26

178.1

27.70%

4.50%

47.70%

2.27

2.63

2.71

2.5856

2.4250

Yu Darvish

26

32

209.2

32.90%

9.50%

41.00%

2.83

2.84

2.83

2.7869

2.6138

Felix Hernandez

27

31

204.1

26.30%

5.60%

51.40%

3.04

2.66

2.84

2.8017

2.6277

Cliff Lee

34

31

222.2

25.30%

3.70%

44.30%

2.87

2.78

2.9

2.8257

2.6502

Chris Sale

24

30

214.1

26.10%

5.30%

46.60%

3.07

2.95

2.96

2.8923

2.7127

Adam Wainwright

31

34

241.2

22.90%

3.70%

49.10%

2.94

2.8

3.01

2.9960

2.8099

Max Scherzer

28

32

214.1

28.70%

6.70%

36.30%

2.9

3.16

2.98

3.0088

2.8219

Clayton Kershaw

25

33

236

25.60%

5.70%

46.00%

1.83

2.88

3.06

3.0177

2.8303

Anibal Sanchez

29

29

182

27.10%

7.20%

45.40%

2.57

2.91

3.1

3.0566

2.8668

A.J. Burnett

36

30

191

26.10%

8.40%

56.50%

3.3

2.92

3.1

3.0822

2.8908

Stephen Strasburg

24

30

183

26.10%

7.70%

51.50%

3

3.15

3.17

3.1027

2.9100

Jose Fernandez

20

28

172.2

27.50%

8.50%

45.10%

2.19

3.08

3.22

3.1939

2.9955

Hisashi Iwakuma

32

33

219.2

21.40%

4.90%

48.70%

2.66

3.28

3.4

3.3529

3.1447

Alex Cobb

25

22

143.1

23.20%

7.80%

55.80%

2.76

3.02

3.26

3.3693

3.1600

Madison Bumgarner

23

31

201.1

24.80%

7.70%

46.80%

2.77

3.32

3.41

3.3734

3.1639

Corey Kluber

27

24

147.1

22.40%

5.40%

45.50%

3.85

3.1

3.32

3.3768

3.1671

Homer Bailey

27

32

209

23.40%

6.40%

46.10%

3.49

3.34

3.39

3.3799

3.1700

David Price

27

27

186.2

20.40%

3.70%

44.90%

3.33

3.27

3.43

3.3989

3.1878

Justin Masterson

28

29

193

24.30%

9.50%

58.00%

3.45

3.33

3.4

3.4210

3.2085

Tyson Ross

26

16

125

23.60%

8.70%

54.90%

3.17

3.43

3.41

3.4701

3.2546

Cole Hamels

29

33

220

22.30%

5.50%

42.70%

3.6

3.44

3.48

3.4722

3.2565

Scott Kazmir

29

29

158

24.10%

7.00%

40.90%

4.04

3.36

3.45

3.5054

3.2876

Gerrit Cole

22

19

117.1

21.30%

6.00%

49.10%

3.22

3.14

3.41

3.5139

3.2956

John Lackey

34

29

189.1

20.70%

5.10%

46.80%

3.52

3.49

3.56

3.5164

3.2980

Marco Estrada

29

21

128

23.10%

5.70%

37.60%

3.87

3.63

3.51

3.5254

3.3064

Tony Cingrani

23

18

104.2

28.60%

10.20%

34.30%

2.92

3.49

3.39

3.5607

3.3395

Rick Porcello

24

29

177

19.30%

5.70%

55.30%

4.32

3.19

3.39

3.5720

3.3501

Francisco Liriano

29

26

161

24.50%

9.50%

50.50%

3.02

3.12

3.5

3.5808

3.3584

Dan Haren

32

30

169.2

21.10%

4.30%

36.00%

4.67

3.67

3.6

3.6170

3.3923

Jeff Samardzija

28

33

213.2

23.40%

8.50%

48.20%

4.34

3.45

3.6

3.6331

3.4075

Doug Fister

29

32

208.2

18.10%

5.00%

54.30%

3.67

3.42

3.49

3.6489

3.4223

Julio Teheran

22

30

185.2

22.00%

5.80%

37.80%

3.2

3.76

3.62

3.6770

3.4486

Jordan Zimmermann

27

32

213.1

18.60%

4.60%

47.60%

3.25

3.48

3.67

3.6938

3.4643

Patrick Corbin

23

32

208.1

20.70%

6.30%

46.70%

3.41

3.48

3.64

3.6957

3.4661

Mike Minor

25

32

204.2

22.10%

5.60%

35.00%

3.21

3.64

3.63

3.7052

3.4750

Clay Buchholz

28

16

108.1

0.231

0.087

0.477

1.74

3.41

3.66

3.7146

3.4839

Mat Latos

25

32

210.2

21.20%

6.60%

45.10%

3.16

3.56

3.66

3.7157

3.4849

Hyun-Jin Ryu

26

30

192

19.70%

6.30%

50.60%

3

3.46

3.67

3.7275

3.4960

Zack Greinke

29

28

177.2

20.60%

6.40%

45.60%

2.63

3.45

3.67

3.7518

3.5188

Ricky Nolasco

30

33

199.1

19.80%

5.50%

43.00%

3.7

3.58

3.72

3.7885

3.5532

Shelby Miller

22

31

173.1

23.40%

7.90%

38.40%

3.06

3.73

3.68

3.7927

3.5571

Ubaldo Jimenez

29

32

182.2

25.00%

10.30%

43.90%

3.3

3.62

3.74

3.8021

3.5659

Justin Verlander

30

34

218.1

23.50%

8.10%

38.40%

3.46

3.67

3.68

3.8097

3.5730

Ivan Nova

26

20

139.1

19.80%

7.50%

53.50%

3.1

3.68

3.66

3.8209

3.5835

Tim Lincecum

29

32

197.2

23.00%

9.00%

45.30%

4.37

3.56

3.75

3.8346

3.5964

Kris Medlen

27

31

197

19.20%

5.70%

45.30%

3.11

3.55

3.76

3.8392

3.6007

Hiroki Kuroda

38

32

201.1

18.20%

5.20%

46.60%

3.31

3.6

3.79

3.8628

3.6229

Lance Lynn

26

33

201.2

23.10%

8.90%

43.10%

3.97

3.66

3.76

3.8630

3.6231

Gio Gonzalez

27

32

195.2

23.40%

9.30%

43.90%

3.36

3.51

3.76

3.8632

3.6232

Roberto Hernandez

32

24

151

17.60%

5.90%

53.20%

4.89

3.6

3.66

3.8785

3.6376

Matt Garza

29

24

155.1

20.90%

6.40%

38.60%

3.82

3.73

3.79

3.8926

3.6508

Charlie Morton

29

20

116

17.20%

7.30%

62.90%

3.26

3.69

3.52

3.8939

3.6520

Tim Hudson

37

21

131.1

17.80%

6.70%

55.80%

3.97

3.56

3.75

3.9065

3.6638

Ervin Santana

30

32

211

18.70%

5.90%

46.20%

3.24

3.69

3.85

3.9133

3.6702

Derek Holland

26

33

213

21.10%

7.20%

40.80%

3.42

3.68

3.81

3.9310

3.6868

Chris Capuano

34

20

105.2

17.70%

5.30%

46.40%

4.26

3.67

3.87

3.9506

3.7052

Andrew Cashner

26

26

175

18.10%

6.70%

52.50%

3.09

3.62

3.8

3.9524

3.7069

James Shields

31

34

228.2

20.70%

7.20%

41.60%

3.15

3.72

3.85

3.9641

3.7179

Nick Tepesch

24

17

93

18.70%

6.60%

47.30%

4.84

3.82

3.86

3.9924

3.7444

Jose Quintana

24

33

200

19.70%

6.70%

42.50%

3.51

3.86

3.92

3.9986

3.7503

Dallas Keuchel

25

22

153.2

18.00%

7.60%

55.80%

5.15

3.58

3.71

4.0184

3.7688

Anthony Swarzak

27

0

96

17.80%

5.70%

45.20%

2.91

3.81

3.61

4.0306

3.7803

Chris Archer

24

23

128.2

19.20%

7.20%

46.80%

3.22

3.91

3.95

4.0308

3.7804

Joe Blanton

32

20

132.2

17.70%

5.60%

44.30%

6.04

3.84

3.92

4.0526

3.8008

Matt Cain

28

30

184.1

20.80%

7.20%

37.70%

4

3.88

3.91

4.0535

3.8018

Jon Lester

29

33

213.1

19.60%

7.40%

45.00%

3.75

3.9

3.97

4.0551

3.8032

Jake Peavy

32

23

144.2

20.50%

6.10%

32.70%

4.17

4.03

3.95

4.0556

3.8037

CC Sabathia

32

32

211

19.30%

7.20%

44.70%

4.78

3.76

3.95

4.0729

3.8199

Kevin Slowey

29

14

92

19.20%

4.60%

28.70%

4.11

4.01

3.92

4.1057

3.8507

A.J. Griffin

25

32

200

20.80%

6.60%

32.10%

3.83

4.18

4.01

4.1088

3.8536

Chris Tillman

25

33

206.1

21.20%

8.10%

38.60%

3.71

3.88

3.95

4.1157

3.8601

Garrett Richards

25

17

145

16.30%

7.10%

57.90%

4.16

3.58

3.7

4.1174

3.8617

Randall Delgado

23

19

116.1

16.70%

4.90%

42.10%

4.26

3.94

4.09

4.1390

3.8819

Bronson Arroyo

36

32

202

15.10%

4.10%

44.40%

3.79

3.97

4.15

4.1724

3.9132

Tommy Milone

26

26

156.1

18.90%

5.90%

35.20%

4.14

4.15

4.06

4.1779

3.9184

Bartolo Colon

40

30

190.1

15.20%

3.80%

41.50%

2.65

3.95

4.17

4.1891

3.9289

Jeremy Hefner

27

23

130.2

17.80%

6.70%

44.60%

4.34

4.05

4.11

4.2041

3.9430

Carlos Villanueva

29

15

128.2

19.70%

7.60%

40.00%

4.06

3.97

3.96

4.2069

3.9456

Dillon Gee

27

32

199

16.90%

5.60%

42.60%

3.62

4.07

4.14

4.2087

3.9473

Edwin Jackson

29

31

175.1

17.40%

7.60%

51.30%

4.98

3.86

4.04

4.2218

3.9596

Josh Collmenter

27

0

92

22.10%

8.60%

32.70%

3.13

4.06

3.64

4.2290

3.9663

C.J. Wilson

32

33

212.1

20.60%

9.30%

44.40%

3.39

3.93

4.08

4.2329

3.9700

Wade Miley

26

33

202.2

17.40%

7.80%

52.00%

3.55

3.77

4.03

4.2347

3.9717

Scott Feldman

30

30

181.2

17.40%

7.40%

49.60%

3.86

3.96

4.1

4.2360

3.9729

Yovani Gallardo

27

31

180.2

18.60%

8.50%

49.20%

4.18

3.74

4.05

4.2546

3.9904

Brandon McCarthy

29

22

135

13.20%

3.60%

48.20%

4.53

3.77

4.08

4.2550

3.9907

Andy Pettitte

41

30

185.1

16.30%

6.10%

46.00%

3.74

3.88

4.13

4.2803

4.0144

Jeremy Hellickson

26

31

174

18.30%

6.80%

39.60%

5.17

4.15

4.15

4.2861

4.0198

Kyle Lohse

34

32

198.2

15.50%

4.50%

40.20%

3.35

4.03

4.23

4.2943

4.0276

Jon Niese

26

24

143

16.90%

7.70%

51.50%

3.71

3.84

4.08

4.3026

4.0353

John Danks

28

22

138.1

15.30%

4.60%

41.40%

4.75

4.08

4.24

4.3058

4.0383

Mark Buehrle

34

33

203.2

15.90%

5.80%

45.30%

4.15

4.09

4.21

4.3075

4.0400

R.A. Dickey

38

34

224.2

18.80%

7.50%

40.30%

4.21

4.23

4.18

4.3090

4.0414

Jered Weaver

30

24

154.1

18.50%

5.80%

30.80%

3.27

4.31

4.22

4.3379

4.0684

Chad Gaudin

30

12

97

21.70%

9.90%

38.40%

3.06

4

3.99

4.3384

4.0689

Mike Leake

25

31

192.1

15.20%

6.00%

48.70%

3.37

3.91

4.17

4.3455

4.0756

Aaron Harang

35

26

143.1

18.10%

6.40%

36.00%

5.4

4.38

4.22

4.3486

4.0784

Eric Stults

33

33

203.2

15.30%

4.70%

40.40%

3.93

4.13

4.28

4.3494

4.0792

Paul Maholm

31

26

153

15.70%

7.00%

51.30%

4.41

3.89

4.09

4.3648

4.0937

Phil Hughes

27

29

145.2

18.90%

6.50%

30.80%

5.19

4.39

4.23

4.3946

4.1216

Ian Kennedy

28

31

181.1

20.50%

9.20%

38.20%

4.91

4.19

4.16

4.4009

4.1276

Martin Perez

22

20

124.1

15.90%

7.00%

48.10%

3.62

4.04

4.25

4.4249

4.1500

Wei-Yin Chen

27

23

137

18.20%

6.80%

34.40%

4.07

4.14

4.23

4.4437

4.1676

Felix Doubront

25

27

162.1

19.70%

10.10%

45.60%

4.32

4.14

4.26

4.4568

4.1800

Bud Norris

28

30

176.2

19.00%

8.70%

40.20%

4.18

4.22

4.26

4.4785

4.2003

Esmil Rogers

27

20

137.2

16.10%

7.40%

47.20%

4.77

4.06

4.18

4.4868

4.2081

Justin Grimm

24

17

98

17.20%

7.70%

42.90%

5.97

4.22

4.29

4.4979

4.2185

Ryan Dempster

36

29

171.1

20.80%

10.50%

40.80%

4.57

4.21

4.26

4.4983

4.2189

Kyle Kendrick

28

30

182

13.80%

5.90%

49.40%

4.7

4.15

4.34

4.5100

4.2299

Jason Vargas

30

24

150

16.90%

7.10%

40.20%

4.02

4.29

4.35

4.5180

4.2374

Henderson Alvarez

23

17

102.2

13.60%

6.50%

53.50%

3.59

3.97

4.18

4.5229

4.2419

Matt Moore

24

27

150.1

22.30%

11.80%

39.40%

3.29

4.32

4.31

4.5352

4.2535

Dan Straily

24

27

152.1

19.40%

8.90%

36.40%

3.96

4.42

4.32

4.5603

4.2770

Edinson Volquez

29

32

170.1

18.30%

9.90%

47.60%

5.71

4.07

4.34

4.5700

4.2862

Miguel Gonzalez

29

28

171.1

16.90%

7.40%

38.90%

3.78

4.31

4.41

4.6044

4.3184

Samuel Deduno

29

18

108

14.50%

8.90%

59.70%

3.83

4.06

4.13

4.6152

4.3285

Jhoulys Chacin

25

31

197.1

15.40%

7.50%

46.80%

3.47

3.97

4.34

4.6161

4.3294

Zack Wheeler

23

17

100

19.50%

10.70%

43.20%

3.42

4.21

4.4

4.6528

4.3638

Nathan Eovaldi

23

18

106.1

17.30%

8.90%

43.80%

3.39

4.15

4.43

4.6571

4.3679

Wade Davis

27

24

135.1

18.50%

9.40%

40.50%

5.32

4.15

4.34

4.6588

4.3694

Wily Peralta

24

32

183.1

16.10%

9.10%

51.00%

4.37

4.13

4.39

4.6616

4.3721

Jarrod Parker

24

32

197

16.40%

7.70%

41.20%

3.97

4.41

4.48

4.6623

4.3727

Tyler Chatwood

23

20

111.1

13.90%

8.60%

58.50%

3.15

4

4.2

4.6871

4.3960

Hector Santiago

25

23

149

20.90%

11.00%

36.40%

3.56

4.65

4.34

4.6914

4.4000

Joe Kelly

25

15

124

14.90%

8.30%

51.10%

2.69

4.19

4.31

4.7010

4.4090

Juan Nicasio

26

31

157.2

16.90%

9.10%

45.10%

5.14

4.32

4.48

4.7125

4.4197

Trevor Cahill

25

25

146.2

16.00%

10.20%

56.20%

3.99

4.11

4.37

4.7125

4.4198

Jordan Lyles

22

25

141.2

14.50%

7.60%

48.40%

5.59

4.41

4.47

4.7195

4.4263

Jerome Williams

31

25

169.1

14.70%

7.60%

47.10%

4.57

4.24

4.42

4.7272

4.4336

Erik Bedard

34

26

151

20.80%

11.30%

36.40%

4.59

4.61

4.42

4.7568

4.4613

Jorge de la Rosa

32

30

167.2

15.70%

8.70%

47.30%

3.49

4.08

4.45

4.7593

4.4637

Zach McAllister

25

24

134.1

17.40%

8.50%

37.10%

3.75

4.53

4.49

4.7670

4.4709

Kevin Correia

32

31

185.1

12.80%

5.70%

44.00%

4.18

4.24

4.57

4.7767

4.4800

Travis Wood

26

32

200

17.50%

8.00%

33.20%

3.11

4.5

4.5

4.7798

4.4829

Jonathan Pettibone

22

18

100.1

15.10%

8.70%

49.40%

4.04

4.3

4.53

4.7879

4.4905

Joe Saunders

32

32

183

13.10%

7.40%

51.20%

5.26

4.23

4.49

4.8128

4.5138

Jason Hammel

30

23

139.1

15.70%

7.90%

40.10%

4.97

4.56

4.56

4.8315

4.5314

Jeff Locke

25

30

166.1

17.60%

11.80%

53.20%

3.52

4.19

4.54

4.8326

4.5324

Mike Pelfrey

29

29

152.2

14.90%

7.80%

43.20%

5.19

4.54

4.63

4.8440

4.5432

Tom Koehler

27

23

143

15.30%

9.00%

47.60%

4.41

4.28

4.54

4.8613

4.5594

Alexi Ogando

29

18

104.1

16.80%

9.60%

40.70%

3.11

4.64

4.65

4.9402

4.6334

J.A. Happ

30

18

92.2

18.60%

10.80%

36.50%

4.56

4.82

4.69

4.9985

4.6881

Jeremy Guthrie

34

33

211.2

12.30%

6.50%

42.90%

4.04

4.55

4.79

5.0215

4.7095

Ryan Vogelsong

35

19

103.2

14.40%

8.10%

40.90%

5.73

4.5

4.71

5.0389

4.7259

Bruce Chen

36

15

121

15.70%

7.20%

27.70%

3.27

4.93

4.81

5.0748

4.7596

Dylan Axelrod

27

20

128.1

12.50%

7.30%

40.30%

5.68

4.91

4.89

5.2087

4.8852

Jacob Turner

22

20

118

15.00%

10.50%

45.70%

3.74

4.71

4.92

5.2248

4.9003

Barry Zito

35

25

133.1

14.10%

8.90%

36.30%

5.74

4.81

4.93

5.3654

5.0322

Scott Diamond

26

24

131

9.00%

6.30%

46.90%

5.43

4.71

5.04

5.3773

5.0433

Luis Mendoza

29

15

94

12.90%

10.30%

50.00%

5.36

4.73

4.93

5.3872

5.0526

Jason Marquis

34

20

117.2

13.90%

13.10%

52.30%

4.05

4.81

5.21

5.6668

5.3148

Jake Westbrook

35

19

116.2

8.40%

9.60%

56.30%

4.63

4.95

5.1

5.7855

5.4261

Lucas Harrell

28

22

153.2

12.60%

12.50%

51.50%

5.86

4.97

5.19

5.7922

5.4324

 

If you’re interested in further delving, send us a request through our Custom Fantasy Posts link, and we can look into all of these other factors we’re talking about for a specific (group of) pitchers you may want to compare.

 

 

 

 

2 comments on ““Holy Trinity” of Pitching = QERA… but then there’s BERA

  1. Dan SchwartzDan Schwartz on said:

    remember while extreme GB-pitchers are great to look at, a pitcher with an elevated FB% could be as good…if their infield flyball% is high (almost never fall for hits) and they’re in friendly confines with excellent HR-related pitching park factors, at time’s it might be preferential to lean toward the FB pitcher here (especially if they had a crappy infield behind them).

  2. Dan SchwartzDan Schwartz on said:

    The reason BERA as the base for our expected ERA’s works so well…right from Stephen’s post on FanGraphs:

    “So, as a final note, to sum up: 1) BERA is supposed to show the “true” ERA based on one season’s worth of data”

    With the incorporation of other very relevant stats, i think we have an overall excellent expected ERA.

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