Do Clutch Pitchers Exist?

 

by PETE PALMER

 

(this was originally published in the spring 1985 issue of The National Pastime, published by SABR, the Society for American Baseball Research-Special thanks for Gerald Wachs for copying and sending me the article)

 

Over a career, a good pitcher wins more games than an average pitcher by allowing fewer runs than average and by receiving good batting support from his teammates. A clutch pitcher wins more games than expected-based on the number of runs scored and allowed-because he performs better in close games. That is, provided he exists.

The number of runs allowed by each pitcher is available from normal season data. Runs scored for each pitcher can be estimated by taking the runs his team scored times his innings pitched, all over nine, times the games the team played. Pitcher batting can be included by taking 80 percent of the number of runs produced compared to the average pitcher; 20 percent is assumed to be reflected already in the overall team scoring. This is done by using Linear Weights, which credit: 0.47 runs for a single; 0.31 runs for each extra base, walk, or hit by pitch; and a value for each hitless at bat which makes the league average come to zero (this value is generally -0.25 to -0.27 runs). For pitchers, the league average for runs produced is found by examining pitcher batting only.

The relationship between runs and wins was described in my previous National Pastime article ("Runs and Wins," 1982) as well as in The Hidden Game of Baseball (1984). The num ber of runs ,needed to produce an extra win over the course of a season is equal to ten times the square root of the number of runs scored per inning by both teams. Since the number of runs per inning is usually around one, the number of runs per extra win is about ten, and almost always between nine and eleven.

A pitcher would not be expected to win exactly the number of games predicted by the formula. The meas ured error can be expressed in terms of the standard deviation of the dis- tribution of differences. The standard deviation is calculated by taking the square root of the average of the squares of the differences between expected and actual wins. If the distribution is normal, two-thirds of the differences should fall within one standard deviation and 95 percent within two. The anticipated standard deviation can be compared to the value actually found, and if they are about the same, the conclusion would be that the variation between a pitcher's runs allowed and runs scored and his wins is due only to chance and that there is no such thing as a clutch pitcher .

This anticipated standard deviation is not easy to find. If all teams were evenly matched and runs scored and allowed were not known, the standard deviation could be expressed exactly based on a binomial distribution, which represents the outcomes of many coin flips-no heads out of five, one head, etc. This number is equal to the square root of the probability of success times the probability offailure times the number of trials or games. For 162 de- cisions by a .500 pitcher, this would be the square root of one-half times one-halftimes 162, or 6.36. However, if runs scored and allowed are known, the number would be smaller. Based on the minimum value found from the study cited above, this is about 4.1, or two-thirds of the original value. In the present study, though, runs scored are only estimated by overall team figures, introducing an error which will cause the anticipated standard deviation employed to be set at five-sixths of the binomial one. Previous investigations have revealed th~t the variation in run scoring due to chance is equal to the square root of twice the number of runs involved, so if a pitcher went 162 innings and expected to have 81 runs scored for him, the standard deviation due to chance alone would be equal to the square root of two times 81, or 12.7.

The data analyzed consisted of all pitchers from 1900 through 1983 who had at least 150 decisions-ofwhom there were 529. Twenty-six men would have been expected to exceed two standard deviations; only twenty were found. Of these twenty, six were modern ace relief pitchers. All six had a much lower winning percentage than expected, a fact probably due to the score situations when they entered the game. They were likely to have been brought in when the score was tied, but more likely still to have entered when their team was ahead, thus making it easier to have picked up a loss than a win.

To illustrate this, a separate study was performed, checking the lowest ratio of runs allowed to losses for all pitchers since 1900. The top eight were all modern reliefpitchers, led by Skip Lockwood, who in 1979 allowed only seven runs for the Mets yet lost five games. The data (five losses minimum) are presented in Table A.

           

 

 

 

 

 

 

 

 

 

 

TABLE A

 

 

 

Player

Club

Lg

Year

Runs

Losses

Runs/Loss

Skip Lockwood

NY

N

1979

7

5

1.40

Diomedes Olivo

Stl

N

1963

9

5

1.80

Steve Howe

LA

N

1983

15

7

2.14

Jim Brewer

LA

N

1972

16

7

2.29

Lee Smith

Chi

N

1983

23

10

2.30

Rollie Fingers

SD

N

1978

33

13

2.54

Darold Knowles

Was

A

1970

36

14

2.57

Al Worthingtion

Min

A

1964

18

7

2.57

 

 

 

The records of the six reliefpitchers whose won-lost records failed of prediction by more than two standard deviations are presented in Table B.

 

 

 

TABLE B

 

 

 

 

Runs

W-L

W-L

 

No. of

Player

for-agst

actual

expected

Diff.

std. dev.

Skip Lockwood

512-539

57-97

74-80

-17

3.30

Rollie Fingers

762-569

112-110

132-90

-20

3.24

Mike Marshall

647-548

97-112

115-94

-18

3.02

Stu Miller

846-697

105-103

120-88

-15

2.44

Hoyt Wilhelm

1007-773

143-122

159-106

-16

2.33

Goose Gossage

581-440

81-73

93-61

-12

2.23

 

 

In a sample of 529, it would be expected that one pitcher would diverge from the norm by more than three standard deviations. Two were found, both of whom lost considerably more games than predicted. Their records are presented in Table C.

 

 

 

TABLE C

 

 

 

 

Runs

W-L

W-L

 

No. of

Player

for-agst

actual

expected

Diff.

std. dev.

Bert Blyleven

1499-1191

176-160

202-134

-26.00

3.34

Red Ruffmg

2688-2117

273-225

303-195

-30.00

3.24

 

 

No other pitcher was more than 2.6 standard deviations from expected in either direction. Unfortunately, of the twelve remaining pitchers who were over two standard deviations from expected, four played in the early part of the century when runs-allowed data was incomplete and thus had to be estimated from known data on earned runs allowed, as shown in the Macmillan encyclopedia. These are indicated with asterisks. Thus more research is needed to find the exact figures. Table D presents these twelve.

 

 

 

TABLE D

 

 

 

 

Runs

W-L

W-L

 

No. of

Player

for-agst

actual

expected

Diff.

std. dev.

Tom Seaton

621-614

93-65

80-78

13

2.52

Joe Coleman, Jr.

1082-1202

142-135

126-151

16

2.34

Togie Pittinger

869-982

115-112

101-126

14

2.22

Casey Patten

822-1069

105-128

91-142

14

2.18

Wes Ferrell

1565-1382

193-128

178-143

15

2.05

Mike Torrez

1458-1469

184-155

168-171

16

2.05

Dave Koslo

820-740

92-107

107-92

-15

2.60

Harry Howell

1186-1103

131-145

147-129

-16

2.48

Eddie Smith

752-816

73-113

87-99

-14

2.39

Dizzy Trout

1385-1166

170-161

188-143

-18

2.38

Denny Lemaster

835-778

90-105

103-92

-13

2.32

Otto Hess

660-644

70-90

82-78

-12

2.20

 

 

            Looking at the top winners in history (Table E) , most come pretty close to what would be expected.

 

 

 

TABLE E

 

 

 

 

Runs

W-L

W-L

 

No. of

Player

for-agst

actual

expected

Diff.

std. dev.

Walter Johnson

2663-1902

417-279

435-261

-18

1.66

Pete Alexander

2534-1851

373-208

365-216

8

0.79

Christy Mathewson

2357-1613

372-189

366-195

6

0.64

Warren Spahn

2684-2016

363-245

375-233

-12

1.16

Eddie Plank

2207-1570

326-193

328-191

-2

0.21

Gaylord Perry

2434-2128

314-265

323-256

-9

0.86

Lefty Grove

2396-1594

300-141

301-140

-1

0.11

Steve Carlton

2188-1733

300-200

299-201

1

0.10

Early Wynn

2285-2037

300-244

298-246

2

0.24

Robin Roberts

2192-1962

286-245

290-241

4

0.41

Fergie Jenkins

2186-1853

284-226

290-220

-6

0.66

 

 

            The conclusion is that although good pitchers allow fewer runs than average, clutch pitchers do not exist.


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