Game Scores and Won-Lost Records

June 9, 2013

                After the article about Pitcher Consistency I had a question about the relationship between Game Scores and Won-Lost records.    I’ve written that up before, of course, but I can research the issue better now than I could before.    Let’s eliminate the levels of Game Scores where we have less than 100 Games to work with.

                In the data from 1952 through 2011 we have 108 games with Game Scores of 93.   In those 108 games the won-lost record of the starting pitchers (who had Game Scores of 93) is 96-1, and their ERA is 0.06.     The won-lost record of their teams is 98-10, a .907 percentage:

SP Game Score

Count

SP Win

SP Loss

SP WPct

Tm Win

Tm Loss

SP WPct

ERA

93

108

96

1

.990

98

10

.907

0.06

92

162

143

3

.979

151

11

.932

0.11

91

218

198

4

.980

204

14

.936

0.14

90

279

261

2

.992

267

12

.957

0.08

 

                The ERA and the Winning Percentage of the Starting Pitcher change only a little with Game Scores in the eighties:

SP Game Score

Count

SP Win

SP Loss

SP WPct

Tm Win

Tm Loss

SP WPct

ERA

89

375

346

5

.986

358

17

.955

0.12

88

418

383

4

.990

399

19

.955

0.12

87

559

519

7

.987

533

25

.955

0.15

86

701

645

7

.989

668

33

.953

0.15

85

895

834

13

.985

855

40

.955

0.17

84

964

885

20

.978

912

51

.947

0.22

83

1150

1065

24

.978

1094

56

.951

0.30

82

1365

1226

34

.973

1280

85

.938

0.33

81

1446

1308

47

.965

1352

93

.936

0.41

80

1563

1391

51

.965

1446

117

.925

0.44

 

                In the 70s, starting pitcher winning percentage goes down by about 10 points for each point on the Game Score.    A Game Score of 73 is more or less equivalent to an ERA of 1.00; a Game Score of 74 is more or less equivalent to a .900 winning percentage.

SP Game Score

Count

SP Win

SP Loss

SP WPct

Tm Win

Tm Loss

SP WPct

ERA

79

1718

1503

66

.958

1557

161

.906

0.52

78

1953

1678

99

.944

1754

198

.899

0.57

77

2053

1733

98

.946

1828

223

.891

0.67

76

2248

1879

127

.937

1983

265

.882

0.76

75

2403

1949

191

.911

2079

323

.866

0.84

74

2552

1967

228

.896

2134

418

.836

0.91

73

2692

2052

272

.883

2236

456

.831

0.99

72

3045

2244

321

.875

2448

594

.805

1.07

71

3083

2231

335

.869

2451

631

.795

1.14

70

3248

2258

384

.855

2558

690

.788

1.22

 

                Going from 70 to 60 adds a run a game to the ERA.   A Game Score of 61 is good enough to win half of your starts; a Game Score of 60 isn’t.   You’ll win most of your decisions at 60, but a little less than half of your starts:

 

SP Game Score

Count

SP Win

SP Loss

SP WPct

Tm Win

Tm Loss

SP WPct

ERA

69

3502

2379

461

.838

2662

839

.760

1.33

68

3626

2342

559

.807

2658

964

.734

1.43

67

3867

2482

579

.811

2868

996

.742

1.53

66

3973

2477

613

.802

2898

1074

.730

1.63

65

4181

2498

736

.772

2925

1254

.700

1.75

64

4300

2429

846

.742

2884

1412

.671

1.86

63

4495

2557

845

.752

3074

1419

.684

1.98

62

4498

2319

955

.708

2867

1630

.638

2.05

61

4629

2335

1038

.692

2912

1712

.630

2.22

60

4646

2281

1074

.680

2891

1751

.623

2.35

 

                If you ask the question "What is the most common Game Score for the Winning Pitcher in a Game?", the answer is "65", but the graph is very flat.     There are 2000+ wins (in the data) for every Game Score from 56 to 73, but with a peak of less than 2500.   And, as we go from 60 to 50, we go from good to average:

SP Game Score

Count

SP Win

SP Loss

SP WPct

Tm Win

Tm Loss

SP WPct

ERA

59

4896

2295

1169

.663

2998

1895

.613

2.49

58

4794

2148

1168

.648

2856

1937

.596

2.62

57

4999

2158

1335

.618

2885

2109

.578

2.76

56

5007

2067

1382

.599

2807

2198

.561

2.93

55

5050

1947

1477

.569

2717

2324

.539

3.04

54

5004

1899

1453

.567

2689

2312

.538

3.19

53

4889

1757

1503

.539

2583

2300

.529

3.40

52

5128

1761

1663

.514

2626

2496

.513

3.53

51

5035

1646

1645

.500

2566

2469

.510

3.75

50

4868

1484

1609

.480

2407

2455

.495

3.91

 

                A Game Score of 51 will actually get the starting pitcher a winning percentage of .500.   A Game Score of 50 will get the team a winning percentage of .500.   It would be cool if a Game Score of 56 led to a .560 winning percentage, etc., but it doesn’t quite work that way.   The winning percentages diverge away from .500 a little more quickly than the Game Scores move away from 50, and then the winning percentages flatten out, above 70/.700, so that the Game Scores can catch up.   A Game Score of 50 is a 3.91 ERA.    Below 50, the starting pitcher’s winning percentage drops like a bucket of paint:

 

SP Game Score

Count

SP Win

SP Loss

SP WPct

Tm Win

Tm Loss

SP WPct

ERA

49

4771

1369

1734

.441

2211

2557

.464

4.10

48

4668

1281

1769

.420

2128

2536

.456

4.32

47

4546

1105

1839

.375

1941

2602

.427

4.51

46

4503

1082

1823

.372

1918

2581

.426

4.72

45

4278

959

1819

.345

1779

2495

.416

4.95

44

4355

918

1929

.322

1722

2630

.396

5.15

43

4267

831

1927

.301

1643

2624

.385

5.41

42

4142

747

1912

.281

1508

2634

.364

5.67

41

4110

633

2035

.237

1456

2653

.354

5.89

40

3940

557

1953

.222

1327

2611

.337

6.22

 

                So the starting pitcher’s ERA goes from 2.35 to 6.22 between 40 and 60.    A Game Score of 45, you lose two-thirds of your games and have a 5.00 ERA.      The most common Game Score for a losing pitcher is 34:

 

SP Game Score

Count

SP Win

SP Loss

SP WPct

Tm Win

Tm Loss

SP WPct

ERA

39

3921

530

1987

.211

1284

2634

.328

6.49

38

3900

468

2026

.188

1265

2631

.325

6.78

37

3810

359

2101

.146

1140

2668

.299

7.15

36

3585

319

2046

.135

1015

2565

.284

7.47

35

3697

266

2126

.111

1022

2675

.276

7.94

34

3554

210

2184

.088

890

2663

.250

8.27

33

3348

186

2038

.084

801

2545

.239

8.87

32

3339

148

2107

.066

763

2576

.229

9.28

31

3317

156

2168

.067

747

2567

.225

9.70

30

3168

111

2064

.051

670

2496

.212

10.28

 

                At 30, your ERA is 10.00 and your winning percentage is .050.    You wouldn’t think those numbers could get worse, but they do:

SP Game Score

Count

SP Win

SP Loss

SP WPct

Tm Win

Tm Loss

SP WPct

ERA

29

2952

101

2005

.048

585

2367

.198

10.83

28

2869

94

1979

.045

553

2315

.193

11.41

27

2701

57

1898

.029

502

2197

.186

11.93

26

2630

49

1890

.025

473

2155

.180

12.60

25

2339

26

1732

.015

374

1965

.160

13.25

24

2095

36

1547

.023

331

1764

.158

13.63

23

1994

25

1520

.016

279

1714

.140

14.50

22

1775

17

1336

.013

281

1494

.158

15.23

21

1616

13

1295

.010

199

1416

.123

15.65

20

1439

14

1164

.012

171

1267

.119

16.38

 

                At this point, the wins are just flukes—games in which both starting pitchers are hammered—so the data is unstable.  The one pitcher who was credited with a Win with a Game Score of 11 was Russ Meyer of the Dodgers on September 9, 1955.   Meyer pitched 7 innings against the Cubs, gave up 16 hits, 8 runs all earned, walked 2 and struck out no one, but was credited with a win in a 16-9 victory, matched up against Toothpick Sam in the second game of a double header.  Below 11, we have no wins at all for Starting Pitchers:

 

SP Game Score

Count

SP Win

SP Loss

SP WPct

Tm Win

Tm Loss

SP WPct

ERA

19

1184

8

954

.008

147

1037

.124

16.47

18

1100

4

922

.004

113

987

.103

17.09

17

910

7

756

.009

86

824

.095

18.20

16

808

3

692

.004

66

742

.082

18.77

15

696

4

596

.007

57

639

.082

19.79

14

607

3

520

.006

54

553

.089

19.83

13

507

1

440

.002

36

471

.071

20.38

12

443

2

390

.005

28

415

.063

21.51

11

375

1

334

.003

29

346

.077

22.38

10

288

0

255

.000

19

269

.066

22.96

 

                And we run out of sufficient data at a Game Score of 5:

 

SP Game Score

Count

SP Win

SP Loss

SP WPct

Tm Win

Tm Loss

SP WPct

ERA

9

231

0

201

.000

17

214

.074

23.90

8

203

0

186

.000

12

191

.059

23.04

7

181

0

154

.000

13

168

.072

24.45

6

133

0

124

.000

3

130

.023

26.16

5

103

0

95

.000

6

97

.058

26.49

 

           

                This chart puts all the winning percentages in one place, so you can print it out and carry it in your wallet, make a wall chart out of it or something:

 

Starting Pitcher Winning Percentage, By Game Score

 

0

1

2

3

4

5

6

7

8

9

0--

.000

.000

.000

.000

.000

.000

.000

.000

.000

.000

1--

.001

.002

.003

.004

.005

.006

.007

.008

.009

.010

2--

.012

.011

.013

.017

.020

.022

.027

.031

.040

.048

3--

.053

.059

.070

.081

.098

.115

.136

.151

.180

.208

4--

.224

.242

.279

.300

.322

.345

.371

.381

.420

.442

5--

.476

.497

.514

.538

.564

.572

.599

.618

.645

.661

6--

.679

.694

.711

.731

.751

.772

.790

.808

.822

.836

7--

.852

.867

.875

.885

.898

.911

.924

.936

.944

.956

8--

.963

.965

.972

.977

.978

.983

.984

.985

.986

.987

9--

.988

.989

.990

.991

.992

.994

.995

.997

.998

.999

10--

1.000

 

 

 

 

 

 

 

 

 

 

                I grouped the data to even out the gaps a little bit. … .a "58" has yielded a .648 winning percentage in the actual data, but is shown at .645 in this chart.    And here’s the same chart, for ERA:

ERA, By Game Scores

 

0

1

2

3

4

5

6

7

8

9

0--

27.92

27.55

26.40

26.17

25.93

25.60

25.18

24.50

23.82

23.46

1--

22.86

22.25

21.52

20.52

20.03

19.65

18.73

18.16

17.22

16.59

2--

16.33

15.62

15.16

14.49

13.70

13.24

12.60

11.95

11.42

10.84

3--

10.30

9.74

9.29

8.86

8.31

7.95

7.50

7.16

6.80

6.50

4--

6.22

5.91

5.67

5.42

5.16

4.95

4.73

4.52

4.32

4.11

5--

3.92

3.75

3.54

3.40

3.21

3.05

2.93

2.76

2.62

2.49

6--

2.35

2.22

2.07

1.98

1.86

1.75

1.64

1.53

1.43

1.33

7--

1.23

1.15

1.07

0.99

0.91

0.84

0.76

0.67

0.58

0.52

8--

0.45

0.41

0.36

0.31

0.26

0.23

0.19

0.15

0.13

0.11

9--

0.10

0.09

0.08

0.07

0.06

0.05

0.04

0.03

0.02

0.01

10--

0.01

 

 

 

 

 

 

 

 

 

 

 

 

On Doubles Becoming Homers

                There is an old theory.. ..I think I heard it when I was a kid. ..that if a young player hits doubles, then, as he ages, those may become home runs or some of them may become home runs.   I think I’ve probably studied this before, but our ability to study these kind of issues gets stronger all the time, with better computers and better data bases and better methods, so thought I’d take a look at it again.

                I started with a matched-set study:   Find two players who are identical in all of the other relevant respects, but one of whom hits more doubles than the other one, then look at the rest of their careers.   We’re looking to see whether the one who hits more doubles as a young player will, later on, hit more home runs.

                Here’s what I did.   First of all, I took a spreadsheet which has batting records for every hitter in history (through 2011; haven’t updated this one, either.)   From that file, I eliminated all players older than 27 years of age, because we are studying young players, which left me with 27,086 lines of data to work with.   Then I eliminated all players with less than 500 career plate appearances, to help deal with small-sample distractions; this left me with 11,729 lines of data.   I eliminated all players from before 1900 or since 2000—before 1900, because the game was so different and wasn’t actually major league baseball, and since 2000, since many of those players (post-2000) would still be in mid-career.    That left me with 8,973 lines of data.    Then I eliminated players who had less than 200 plate appearances in their most recent season.   This left me with 7,840 lines of data.

                For those 7,840 player/seasons, I "coded" each player with an 8-element code.

The first element was his age. . .23 for a 23-year-old player, etc.

The second element was his plate appearances in the most recent season, divided by 150, rounded down to the nearest integer, so that a player with 200-299 plate appearances would be "1", 300-449 would be "2", 450-599 would be "3", and 600 or more would be "4".

                The third element was  int(OPS*30) …that is, his OPS for the season, times 30, rounded down to the last integer.    A player with a .700 OPS would be a 21, .800 would be 24, .900 would be 27, etc.

                The fourth element was int(RC27*2). .. .that is, his runs created per 27 outs, times 2, rounded down to the last integer.

                The fifth element was int(HR/3). . .that is, one-third of his home runs, rounded down, so that a player hitting 30 home runs would be at "10", a player hitting 40 home runs would be at "13", etc.

                The sixth element was career games played divided by 100, rounded down.

                The seventh element was the career percentage of his hits which were home runs, rounded down to the nearest whole integer.  .. .7.29% would be "7", 7.94% would still be "7", etc.

                The eighth element was int(OPS*30) again, but with the career OPS, whereas earlier it was with the season’s OPS. 

                I then sorted the data to identify players who had identical codes.     There were 132 sets of identical codes in the data. ..for example, Todd Zeile after the 1992 season and Ray Lankford after the 1993 season were both coded 26-3-21-8-2-4-8-22—"26", because they were 26 years old, "3", because they had 450 to 599 plate appearances, "21" because both had OPS between .700 and .733, "8" because each player created between 4.00 and 4.50 runs per 27 outs, "2" because each player had 6 to 8 home runs, "4" because each player had played 400 to 499 games in his career, "8" because each player had hit home runs with 8 to 8.99% of his career home runs, and "22" because each player had a career OPS (at that time) between .734 and .766.  

                Unfortunately, while the two young Cardinals were a perfect match on these 8 elements, this fact was useless to us because they also had hit almost exactly the same number of doubles in their careers, as a percentage of their hits.    What we are looking for is players who are alike in terms of playing time, home run rate, age and overall offensive ability, but different in the number of doubles that they hit.  

                I love doing matched-set studies because it is always fun to see who matches up against whom.    Zeile and Lankford are kind of logical because they were teammates, so they’re linked in time and place.    Most players who wind up paired in a matched-set study seem like reasonable matches when you look at it, but 90% of the time they are players you would never think to put together, like Sam Mele (1950) and Felix Jose (1992), or Zack Wheat (1910) with Harvey Kuenn (1953), or Dave Revering (1978) with Ron Cey (1973), or Fran Healy (1973) with Lee Tinsley (1995).

                OK, I had 132 sets of players with identical codes, and I had to trim that down to pairs of one player who hit doubles and one player who didn’t.   I decided on a minimum separation of .05 doubles as a percentage of hits. .. in other words, if one player had hit doubles with 17% of his career hits, the other player had to be under 12% or over 22% in order for the pair to be usable.   Of the 132 sets of players, 40 were usable pairs.

                To argue for the proposition that doubles become homers as the player ages. ..Eddie Joost and Freddie Patek.    Both players were coded 27-3-16-5-0-5-2-18:

First

Last

YEAR

G

AB

R

H

2B

3B

HR

RBI

BB

SO

Avg

PA

RC

RC 27

AGE

OPS

Freddie

Patek

1972

136

518

59

110

25

4

0

32

47

64

.212

577

44

2.74

27

.556

Eddie

Joost

1943

124

421

34

78

16

3

2

20

68

80

.185

496

35

2.64

27

.550

 

                Patek at this point had played 575 games in his career; Joost, 567.   Patek had a career OPS of .629; Joost, of .612.     Patek had hit 14 homers in his career; Joost had hit 13.

                There was, however, this difference:  that Patek in his career had hit only 69 doubles among 489 career hits, whereas Joost, with only 441 career hits, had hit 85 doubles.     Joost had hit doubles on 19.3% of his career hits, Patek on 14.1%, so. . .it’s a qualifying match.

                And, in fact, Joost did go on to develop much more power than Patek.    Joost would hit 134 home runs in his career, hitting 13 to 23 homers every season from 1947 to 1952.   Patek would never hit more than 6 homers in a season, 41 in his career, but would wind up his career with 8 million fly balls just short of the warning track.    Joost’ doubles did, in fact, develop into home runs as he got older and stronger.

                But it doesn’t happen 100% of the time.    There are three players in the data who are coded 23-4-22-9-6-1-12-22.  Two of them are Willie Jones, 1949, and Ernie Banks, 1953.    Both players were 23-year-old rookies with a few prior at bats.   Both players hit 19 homers, had similar enough stats that they qualify as an exact match.    The one who hit 34 doubles was Willie Jones.   The one who didn’t hit doubles was Ernie Banks.

                Actually, there were three players in that dance; the third player who was coded a 23-4-22-9-6-1-12-22, another 23-year-old rookie with 19 homers, was Ron Gant in 1988.     He hit more doubles (as a rookie) than Banks, but less than Jones.    In his career, he hit more homers than Jones, but less than Banks.   So the "doubles become homers" theory, in that case, is exactly backward; the fewer doubles you hit as a young player, the more home runs.

Let me jump to the results of the first matched-set study.     The study suggests that it is more likely than not that there is some validity in the belief that "young players’ doubles" will become homers as the player ages.    In this study the two groups of players were nearly identical in terms of career games played, at bats, triples, homers, runs created per 27 outs, OPS, career OPS, career home runs as a percentage of hits, etc.   The only difference between them was that "Group A" had hit an average of 22 doubles in the season and 37 doubles in their careers, whereas Group B had had hit an average of 14 doubles in the season, 24 in their careers, and the hitters in Group B had offset this with slightly higher numbers of singles and walks, so that their overall offensive productivity was neither greater nor less.

                In the rest of their careers, the hitters in Group A would hit an average of 31 more home runs, whereas the players in Group B would hit an average of only 27 more home runs.     This, however, is despite the inclusion of Ernie Banks in the "Group B" portion of the study, balanced against Willie Jones in the "Group A" portion.    All 80 players in the study hit a total of only 2,337 home runs in the rest of their careers.   Ernie Banks himself hit 491 of those.   Banks is an elephant in a roomful of kittens.   The fact that Group A wins the home-run hitting contest despite the fact that Group B got the elephant suggests that there may be something there.

 

                OK, that study has terrible flaws.    The study suggests that that the theory is more likely to be true than false, but the study is so badly flawed that it isn’t really worth very much.   What did I do wrong here?

                I controlled too many "extraneous" factors.    I set up too many controlled parameters, which limited the number of "exact matches" to 132, of which only 40 were usable matches.   That gave us only 40 sets of players, which is too few. ..that was one thing that I did wrong.     But I made a second and equally egregious mistake.    I didn’t control for minimum hitting competence.     I allowed players into the study if they "matched", even if neither one of them could hit worth a damn.    Since zeroes are more likely to "match" than any other number, this created a set of 40 matches—MOST of which were between two terrible hitters.     Sandy Alomar Sr., 1968, .578 OPS, is matched with Angel Salazar, 1986, .592 OPS.    Gabby Street, 1908, .568 OPS, is matched up against Bill Shipke, 1908, .573 OPS.   John Sullivan, 1943, .548 OPS, is matched up against Al Bridwell, 1906, .548 OPS.    Fritz Mollwitz, 1915, .597 OPS, is matched up with Gus Getz, 1915, .587 OPS.      Cesar Gutierrez, 1970, .573 OPS, is matched up with Carson Bigbee, 1970, .573 OPS.    Most of the study is guys like this, who have very short and very unimpressive futures because they really can’t hit.

                And I made a made a third mistake, less serious than the other two.   In the first study I allowed Zack Wheat, 1910, to be matched up with Harvey Kuenn, 1953.    1953 is not like 1910.   There is no power in the game in 1910, so nobody from 1910 is going to develop home-run power over the next five years.

                The reason that you control a lot of different inputs, in a matched set study, is to prevent extraneous elements from leaking into the study.    You’re kind of guessing what will work, guessing how many inputs you need to control and how many you can get by with controlling and still get good data.    You very often guess wrong, and then you just have to back up and start over.   So I backed up and started over.

                In the second study, I made four changes:

                1)  I cut the number of controlled parameters from 8 to 3, controlling only for age, career OPS, and the percentage of career hits which were home runs.   I did, however, change the formula for the OPS code from int(30*OPS) to int(50*OPS), which means that matched players have to fall into the same 20-point OPS range, rather than the same 33-point OPS range.

                2)  I eliminated all players from the study who created less than 4.00 runs per 27 outs, so that we would be less likely to be studying the futures of players who have no futures.

                3)   I put in a rule that a player from before 1920 (the Dead Ball era) could not be matched with a player from after 1920.

                4)  I cut the maximum age from 27 to 26 (although this was done, actually, after the fact, when it became apparent that it could be done without damaging the study.)   I just got tired of marking the matches, and stopped before I got to the 27-year-olds.  

                In the second study I wound up with 550 matched sets of players.    Despite cutting the number of controlled parameters from 8 to 3, I see no evidence of any extraneous factors leaking into the study.   The players in the aggregate seem as well matched as before.  

                To cut to the chase. . .The theory that doubles by young players are predictive of future home runs appears to be clearly true, but of limited or no value in assessing the overall worth of the players.    This chart compares the group averages of the two sets of players, 550 players in each group, in the base season of the study:

 

G

AB

R

H

2B

3B

HR

RBI

BB

SO

SB

CS

Avg.

Group A

129

460

65

129

27

5

10

60

44

57

10

4

.279

Group B

132

474

69

136

18

6

10

58

47

53

15

5

.286

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

                The players in Group A had a .768 OPS (.279/.345/.423), whereas the players in Group B had a .769 OPS (.286/.353/.416).    They were identical in age, the average player in each group being 24.658 years of age.   This chart compares their career batting totals at that time. 

 

G

AB

R

H

2B

3B

HR

RBI

BB

SO

SB

CS

Avg

Group A

357

1240

170

341

70

13

25

158

113

152

28

11

.273

Group B

368

1280

184

361

48

17

25

151

121

147

41

14

.280

 

                The players in each group had a career OPS of .745 at that time--.273/.337/.408 for Group A, .280/.344/.401 for Group B.

                In the rest of their careers, the players in Group A hit an average of 91 more home runs.   The players in Group B hit an average of only 76.    Since there are 550 players in each group, the difference between them is more 8,000 career home runs.   This cannot reasonably be suspected of being a random outcome.    The players who hit doubles as young players did, in fact, go on to hit more home runs later on.  

                But here’s the thing.   It does not appear that the players who gain power derive any significant career advantage from the increase in power.    This chart summarizes the "rest of career" performance for the two groups of players:

 

G

AB

R

H

2B

3B

HR

RBI

BB

SO

SB

CS

Avg

Group A

962

3334

477

929

175

27

91

466

362

424

58

25

.267

Group B

986

3359

479

935

153

33

76

419

368

388

85

32

.267

 

                The players who hit more doubles did have an 11-point edge in OPS for the remainder of their careers, .739 to .728   (.267/.338/.401 for Group A, .267/.338/.390 for Group B).    However, they played no more games in the rest of their careers, had no more plate appearances, scored no more runs.

                The players who hit more doubles as young players were a little stronger; the other players were a little bit faster.   It doesn’t appear that one is meaningfully better than the other, in terms of projecting a player’s future—different, but not better.

 
 

COMMENTS (20 Comments, most recent shown first)

Arrojo
Speaking of doubles by young players, 20-year old Machado is on pace to break Earl Webb's record.
8:09 PM Jun 17th
 
astilley
those - Yes, I should have included the caveat that my search only included the years searchable by Baseball Reference's Play Index, 1916-present.
8:46 AM Jun 17th
 
rgregory1956
But Bill, McCormick obviously pitched better in the clutch. And I believe he also led the league in Team Spirit. And Intangibles. And don't forget that the Giants won 9 more games than did the Phillies. You really wanna give the CYA to someone who lost 15 games?
9:55 PM Jun 14th
 
bjames
Mikeclaw actually understated the Big Unit's 5-game non-winning streak by just a tiny bit. His Game Score in the 4th game of that stretch was actually 72, not 70.

I looked to see whether there was any other pitcher in the data who had five straight Game Scores of 70 or better without a win. There is not; Randy is the only one.

Jim Bunning in his last five starts of 1967, however, came very close to that. Bunning had Game Scores of 78, 72, 78, 78 and 75, but lost games 1-0, 1-0, 1-0 and 2-1. But he did pitch a shutout, and win the other start, 4-0.

But Bunning has a special pleading, in that his run of bad luck cost him perhaps his best shot at a Cy Young Award. Koufax had retired; Gibson and Marichal both had injuries that took them out of the race, and Drysdale was 13-16. They gave the Cy Young to Mike McCormick. There is no way in hell that Mike McCormick pitched better than Bunning did, but at the time, won-lost records were everything.
8:45 PM Jun 14th
 
mikeclaw
Re: Game Scores

At least once a year I go to Retrosheet and revisit Randy Johnson, June 25-July 15, 1999. In that stretch he made five starts and put up Game Scores of 83, 73, 76, 70 and 76. He pitched 40 innings, gave up 25 hits and 6 runs (5 earned), and he struck out 62.

And he went 0-4 with one no-decision.

The first loss (9 innings, 5 hits, 1 run, 2 walks, 14 K) was the Jose Jimenez no-hitter, and the third was another 1-0 loss to Jimenez. What on Earth are the odds of Jose Jimenez (career record: 24-44, 4.92) beating Randy Johnson (303-166, 3.29, 5 Cy Youngs) by a 1-0 score twice in a week?
4:44 AM Jun 11th
 
chuck
Regarding Hunter and Blyleven, Hunter's run context (using bb-reference's multi-year pitching park factors, and pro-rating year by year to his batters faced)... his run context was 3.83 runs a game. Blyleven's was 4.34 runs a game.
Their game score averages are very close, as Bill noted.
But if one looks at league average game scores for leagues with those run contexts above- for Hunter take the AL in 1966 (3.89) or in 1971 (3.87)- the average game score in both those seasons was 54. For Blyleven's 4.34, look at AL 1973 (4.28), or AL 1975 (4.30). Both leagues had the average game score of 51.
Put on a game score to league ratio:
Hunter 56.58/54 = 1.05
Blyleven 56.85/51 = 1.11
Hunter's game score average was 4.8% better than league, Blyleven's was 11.5%​
5:19 PM Jun 10th
 
tigerlily
Thanks Bill. Speaking of Game Scores and such, who are the top 10 pitchers in baseball right now?
4:03 PM Jun 10th
 
bjames
1) Catfish Hunter's career average Game Score was 56.58. Blyleven's was 56.85.

2) A pitcher who had a Game Score of 60 in every start would have an ERA around 2.35, but a pitcher whose AVERAGE Game Score was 60 would have a higher ERA than 2.35. If you take one start of 50 and one start of 70, they won't average out to 2.35; they'll average out higher than that. If you take one start of 40 and one start of 80, they'll average out to a higher ERA than 2.35.
3:54 PM Jun 10th
 
3for3
I have an idea to make the extra inning effect go away. The idea of a game score is (well maybe it isn't) but my impression is that game scores are a percentage of 100. 100 Being perfect. If a pitcher pitches more than 9 innings, his score can be normalized, by multiplying GS*IP/100.
2:06 PM Jun 10th
 
rgregory1956
Hey Bill, I'm just wondering if the opposite is true, that if a pitcher had a career ERA of 3.00, would his average GS be around 55-56, or if he had a career ERA of 4.00 would his average GS be around 49-50? Mel Stottlemyre and Don Drysdale had ERAs around 3.00. If they had significantly different career GS averages, would that tell us something worth knowing? Catfish Hunter and Bert Blyleven have nearly identical ERAs. One's a sabermetric whipping boy, the other likely got into the Hall because of sabermetrics. I wonder if their GS averages are significantly different.
11:09 AM Jun 10th
 
doncoffin
I'm very visual with data, so I imported all that into a spreadsheet and graphed various things.

First, the distribution of game scores is indistinguishable from a normal distribution.

Second, the distribution of wins (both SP and team) are negatively-skewed (there's a long tail to the left) & the distribution of losses is positively skewed (there's a long tail to the right). In both cases, that's what you would expect--a few wins straggling out as game scores fall and a few losses straggling out as game scores rise.

Third, the SP ERA is a very nice downward-sloping nearly hyperbola--falling rapidly at first as game scores rise, then falling more and more slowly (again, just as one would expect).

Finally, the winning percentage curves (both team and SP) are more or less sigmoid--rising slowly at first, then more rapidly, then more slowly. This suggests (again unsurprisingly) diminishing returns to increasing game scores. In fact, the "marginal value" of an increasing GS hits its maximum at about a GS of 66 (using 5-point increments in GS to do the calculation); the change from a GS of 60 to a GS of 65 (or 61 to 66, or 62 to 67) is an increase in about 0.02 of team WPCT.
10:22 AM Jun 10th
 
those
astilley -- Wouldn't Joe Harris on Sept. 1, 1906 get a higher score? By the box score that appeared in the paper the next day, he'd have a game score of 126 (24-16-4-4-2-14).
9:34 AM Jun 10th
 
Robinsong
There is an interesting comparison between Bill's data and Trailblzr's in the Reader Posts. Trailblzr's covered only the period from 2000-2012, whereas Bill's goes back to 1952. In the more recent period, starter's winning percentage was higher at Game Scores of 40, 50, and 60. (241 vs. 222 at 40, 501 vs 480 at 50, and 737 vs 680 at 60). The team winning percentage is not as dramatic, with the only real difference at 60, 653 vs 623. Part of the difference is due to different levels of offense - a 2.35 ERA is more likely to win in the 2000s than the 1960s. Another part may be different usage of the bullpen - managers work starters less and are more likely to pull them before they give up a lead.

Thanks Bill and Trailblzr!
8:36 AM Jun 10th
 
raincheck
The home runs to doubles theory was thrown around a lot in LA as to James Loney. The idea was he would keep hitting for average and then add a lot of HRs as he matured. It was an ideal case expectation that set a high bar for Loney. When instead his average dropped and the HRs never materialized he became twice the disappointment that he really was.
12:20 AM Jun 10th
 
bjames
The one pitcher who had a Game Score of 93 and lost was Sandy Koufax against the Cubs on May 28, 1960; he pitched 13 innings, gave up only 3 hits and struck out 15, which gave him a Game Score of 93 even though he walked 9 men in the game, which is pretty incredible. The game was 3-3 through 9 innings, still 3-3 going into the bottom of the 14th. Koufax walked the first two hitters in the 14th and left the game, was charged with the defeat when the reliever gave up a one-out single.
Harvey Haddix (107) is the highest Game Score for a losing pitcher in the last 60 years. There is an odd thing that happens, though; extra-inning pitching performances create unusual game scores, so after your game score goes above about 98, your chance of getting a loss actually INCREASES substantially. The reason that is true is that the most common path to Game Scores over 100 is by pitching 11, 12, 13 innings in a game—which was fairly common until about 1963—and when you do that, by definition it is a game in which the OTHER pitcher (or pitchers) are ALSO pitching extremely well, so. ..you often lose. The Haddix game is a prototype of what is actually a pretty common syndrome in that era.
A game that is almost a match for Haddix. . .Jim Maloney on June 15, 1965 pitched ten innings of no-hit baseball against the Mets, striking out 15 through 9 innings, two more in the 10th, so through 10 innings he had a no-hitter and 17 strikeouts. He lost the game on a home run in the 11th inning, gave up another hit and had another strikeout, so he pitching an 11-inning 2-hitter with 18 strikeouts, Game Score 106—and lost.
Juan Marichal on August 19, 1969 against the Mets shut out the Mets for 13 innings, four hits, one walk, 13 strikeouts. Tommie Agee homered with one out in the bottom of the 14th, and Marichal lost 1-0 (Game Score 104). There was a game a little less than a month later in which Steve Carlton struck out 19 men in 9 innings, which was a major league record at the time, but gave up 2 homers to Ron Swoboda and lost 4-3. Swoboda drove in all four runs. Carlton’s game is much more famous than Marichal’s, because
1) Carlton did set the 9-inning strikeout record,
2) The pennant race was near the climax at that time, and
3) Tim McCarver, who was the catcher in that game, has talked about it on air once in a while for the last 40 years.
But Carlton gave up 9 hits and 4 runs, so his Game Score was just 72. . . .nothing like as impressive a game as Marichal’s, which was a 13-inning shutout with 13 strikeouts.

Those are the three highest Game Scores in my data for losing pitchers. Fourth is a tie. Warren Spahn on June 14, 1952, pitched 15 innings against the Cubs, striking out 18, lost the game 3-1. Spahn gave up a solo homer to Bill Serena in the 6th inning. Willie Ramsdell pitched 7 innings and Johnny Klippstein 8 innings of 2-hit shutout relief, so it was 1-1 in the 15th. Spahn gave up 2 runs in the 15th and lost. That game was in Boston; the Braves hadn’t moved yet. Spahn started that season 6-3, finished the season 14-19—but pitched as well as he ever did. He didn’t suffer any ill effects from the 15-inning outing; he was just in the middle of a long string of games in which the Braves didn’t get him any runs. But there is a second 101, which is one of my favorite games ever. On August 13, 1954, Al Aber of the Tigers and Jack Harshman of the White Sox both pitched 15 shutout innings. Aber lost the game with one out in the 16th, so his Game Score also was 101. ..the game is a dead ringer for the much more famous Marichal/Spahn contest 9 years later. But here’s what I love about it. That game was on a Friday, Friday the 13th—and both Aber and Harshman game in to pitch relief on Sunday! Harshman, having pitched a 16-inning shutout on Friday night, then pitched 2 innings of shutout relief in the second game of the double header on Sunday.
Those are the only five games in which a pitcher with a Game Score over 100 took a loss, and all of them were long games. On August 20, 1974, Nolan Ryan pitched 11 innings, struck out 19, Game Score of 99. ..lost the game 1-0. Mickey Lolich beat him with an 11-inning shutout.
On May 24, 1972, Dick Drago pitched 12 innings, struck out 13. . .which is unusual, because Drago wasn’t a strikeout pitcher. .. .6 hits, 1 run, Game Score of 98. ..lost the game 1-0. Jim Kaat also pitched 11 innings of shutout ball in that one; a reliever pitched the 12th.
Warren Spahn, in the famous 1-0, 16-inning contest against Juan Marichal in 1963, had a Game Score of 97, lost the game. Pedro Ramos on August 23, 1963, pitched 13 innings against the Red Sox, struck out 14 games, Game Score of 97….lost the game.


10:00 PM Jun 9th
 
astilley
And as for the highest game score when taking the loss, looks like Art Nehf takes the cake with a 118 game score in 1918. The Haddix game is the post-deadball record.
9:46 PM Jun 9th
 
astilley
mpiafsky,

Looks like Sanford Koufax took the loss in a game in which he had a game score of 93 on May 28, 1960.

www.baseball-reference.com/boxes/CHN/CHN196005280.shtml
9:28 PM Jun 9th
 
Paul
mpiafsky --

My first thought was Harvey Haddix's famous game in 1959 where he was perfect through 12 innings but lost the game in the 13th, but his game score for that game was actually 107. His game doesn't appear in the data presumably because there aren't 100 games during the years of the study where a pitcher got a game score of 107.

I guess what I'm saying is, don't assume that that one 93 was the best pitched losing game from 1952 to 2011.
8:03 PM Jun 9th
 
mpiafsky
Which starting pitcher had a 93 game score yet lost? Anyone know?
7:23 PM Jun 9th
 
sayhey
That's funny: I was thinking about the doubles-to-homers thing earlier today when I looked at Mike Trout's box for this year. He's on pace for 50+ doubles (he's got 20 after two this afternoon; last year he hit 27), while his home-run rate is down about 25% from last year. I would have expected movement in the opposite direction (conceding that it's still early).
5:38 PM Jun 9th
 
 
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