Converting Win Shares to Winning Percentages
Almost all of sabermetrics, and I would guess most of science in general, is based on finding an obvious question which you have somehow overlooked until now. Tom Tango’s request to know what the distribution of talent was, if you converted Win Shares into winning percentages, provoked me to ask an obvious question which I had somehow overlooked until now: How do you translate Win Shares into winning percentages?
There are several problems and limitations to the approach, which I will cuss and discuss later, but once you reach the point of asking that question, it’s a relatively simple question which opens up many potential avenues of research. OK, five topics there:
1) How do you convert Win Shares into Winning Percentages?
2) Inherent flaws and limitations to the process,
3) The distribution of talent—what Tom was trying to get to,
4) Potential avenues of research opened up by this, and
5) A few issues that we can get to
1) How do you convert Win Shares into Winning Percentages?
Conceptually simple. This is only dealing with position players; not dealing with pitchers right now.
a) Take the player’s Win Shares and his playing time,
b) Establish the ratio of Win Shares to playing time,
c) Establish the NORMAL or average ratio of Win Shares to playing time, and
d) Compare the player to the average.
How do you establish playing time? I made three estimates of playing time:
E1) Games Played
E2) Plate Appearances, divided by 4.50
E3) Outs Made, Divided by 3
Then I took the average of those three, but with E3 weighted twice as heavily as the others, so that the "Playing time equivalent" was (E1 + E2 + 2*E3) / 4.
Let’s use Henry Aaron, 1963, as our illustrative example. Aaron in 1963 played in 161 games, so E1 is 161.
He had 714 plate appearances; 714 divided by 4.50 is 158.7, so E2 is 158.7.
He made 451 outs; 451 divided by 3 is 150.3, so E3 is 150.3.
His "playing time equivalent", then, is 155.1  (161 + 158.7 + 2 *(150.3)) / 4.
Aaron in 1963 is credited with 41 Win Shares, his career high. We establish the relationship of his Win Shares to his playing time, which is 41/155.1, or .264 (.264 374).
Next we establish the normal or average relationship between Win Shares and Playing time equivalents, by totaling up the Win Shares for all players in my data base, and dividing by the playing time equivalents for all players in the data base. The result is .11775. An average player, given Aaron’s playing time, would be credited with 18 Win Shares.
We can convert Aaron’s number into an Equivalent Winning Percentage, then, in either of two ways, which we will call the straightline approach and the Pythagorean approach. The straightline approach is to assume that, if a .500 player produces .11775 Win Shares per "game", a player producing twice that number of Win Shares (.2355 per game) would have a winning percentage of 1.000. Using that approach, Henry Aaron in 1963 would have a winning percentage of 1.123.
The straightline approach is actually the more useful and more representative approach, but that winningpercentagegreaterthan1.000 thing is kind of bothersome. The Pythagorean approach would be to take THAT number—1.123, for Henry Aaron in 1963—square it (1.260), take the value for the average player (.500), square that (.250), and treat those as wins and losses. That gives Bad Henry a winning percentage, for 1963, of .834.
The straightline approach is more practical for more purposes, for this reason. On the team level, the relationship of runs scored to runs allowed is a relationship of squares, or something very close to that. If a team scores 10% more runs than their opponents (1.10 to 1), they won’t win 10% more games than they lose, but 21% more. They won’t have a .524 winning percentage, but .548. But as the team gets further from .500, each run has less impact than the run before it, on average.
But when the runs created by the INDIVIDUAL are applied to the TEAM level, this effect is meaningless. Comparing a player who creates 20 runs more than an average hitter to a player who creates 10 runs more than an average hitter, the second ten runs have the same impact as the first ten runs, or virtually the same, because the runs created by an individual will never push the team into the area of diminishing returns. The practical impact of this is that the Pythagoreanderived individual winning percentage loses the ability to distinguish between good and great. If Henry Aaron created ten more runs than he did, it would have hardly any impact on his Pythagorean winning percentage, because we would be implicitly assuming that his team’s winning percentage was already near to 1.000, so scoring extra runs wouldn’t have any impact. This is not true; in fact, creating 10 more runs WOULD have impact on his value. It’s just that to represent that, you have to allow his implied winning percentage to reach 1.000, and to continue to ascend after it reaches 1.000.
2) Inherent Flaws and Limitations of the process.
There are many, I suppose, but there are four I would focus on.
First, Win Shares have already been rounded off into units before they are used in this process, which causes a loss of information when attempting to move forward from that point. A player has 16.4 Win Shares; we round it off to 16, he has lost 2½ percentage of his value. I don’t figure tenths or hundredths of a Win Share.
I don’t regret that decision, with Win Shares, because the lost information is not meaningful data. We can’t actually measure things that accurately. This is part of my issue with WAR, that it pretends to measure player’s value with more accuracy than can actually be maintained in the process of the calculation. A tenth of a Win Share is about a third of a run. Our stats are just not that accurate; it doesn’t mean anything. It was the right decision to round it off, but it makes Win Shares less accurate if you then use Win Shares to calculate another value.
Second, there were necessary choices in the construction of Win Shares which make it less than perfect for the purpose it is now being used. I was trying to measure two distinct things—win contributions, and loss contributions—in one number. Suppose that you look at four players. One contributes 15 wins to his team but also 15 losses (1515). One player is 1413, one player is 1311, and one player is 129. Wins Shares—and WAR—would represent them as being the same or nearly the same. A lower win percentage is equally valuable with more playing time. Representing value in one number, it is necessary to adjust for this somehow. Both Win Shares and WAR do this.
But the WAY that I did it makes it not ideal for our present purposes. Now, essentially, I am taking that "percentage advantage" back OUT of Win Shares, and showing two players with 15 Win Shares to be no longer equal. This is not a rational way to approach the problem. It’s basically like adding dirt to your drinking water, and then using a Brita filter to get the dirt back out before you drink the water.
Third, my data file does not have Win Shares for all players. It has data for all of the Hall of Famers and for many other players, recent players particularly. It has actual Win Shares for about onethird of the players, and for the rest, I use ESTIMATED Win Shares.
Not a catastrophic failing, since Win Shares themselves are a process of estimating a player’s value. It becomes an estimate of an estimate. This doesn’t damage the concept; it damages the execution of the concept.
And fourth, I’m not dealing with pitchers. This is not a big issue, except in dealing with position players who also pitch. In the 19^{th} century there are a lot of position players who also pitched—Monte Ward, Kid Gleason, Bobby Wallace, Elmer Smith, Perry Werden, Bob Caruthers, and many others. There are a significant number of them until about 1925. These players have to be excluded from our current calculations, since their Win Share number reflects playing time—innings—that I can’t calculate with the data that I have in this file. Would have to go in a different direction to get to Shohei Ohtani.
3) The Distribution of Talent
In 1962 a giant first baseman named Walt Bond played 12 games with the Cleveland Indians, hitting .380 with 6 homers, 17 RBI in those 12 games. He was the greatest lateseason callup of all time—or not, but let’s say that he was. We credit him with 4 Win Shares in those 12 games, giving him a winning percentage of 1.873 by the straightline method, or .934 by the Pythagorean Approach. This is the highest winning percentage in my data for a player who had (a) 100 plate appearances or (b) earned 4 Win Shares, or (c) had 3.5 estimated Win Shares if lacking actual Win Shares.
Using the straightline calculation, I have data for 38,274 players. This is the breakdown of their Linear Winning Percentages:
At Least

But Less than

Count

1.600

Or higher

13

1.550

1.600

6

1.500

1.550

10

1.450

1.500

6

1.400

1.450

18

1.350

1.400

14

1.300

1.350

15

1.250

1.300

53

1.200

1.250

77

1.150

1.200

111

1.100

1.150

154

1.050

1.100

183

1.000

1.050

257

.950

1.000

369

.900

.950

497

.850

.900

675

.800

.850

891

.750

.800

1198

.700

.750

1724

.650

.700

2066

.600

.650

2549

.550

.600

2938

.500

.550

3141

.450

.500

3150

.400

.450

3020

.350

.400

2703

.300

.350

2394

.250

.300

5339

.200

.250

3479

.150

.200

814

.100

.150

275

.050

.100

96

.000

.050

39

It thus appears as if the distribution of players in the major leagues represents the righthand slice of the bellshaped distribution curve—that is, with the greatest number of players being belowaverage—but with some elements of a normal or bellshaped distribution.
The Standard Deviation of Winning Percentages, in this study, was .198. That would change somewhat if we weighted each winning percentage by the playing time at that level, but offhand I wouldn’t know how to do that. I’m sure I could figure it out, but I also tracked the games played and plate appearances in each of these sectors of the performance spectrum. That shows that, with the exception of a little anomaly in the range of 1.150 to 1.300, where there are a total of only 251 players in three groups, there is no real difference in the playing time, except that playing time per player drops if the player’s winning percentage is below .400:
At Least

But Less than

Average Games

Average PA

1.600

Or higher

112

489

1.550

1.600

103

449

1.500

1.550

122

525

1.450

1.500

123

521

1.400

1.450

112

491

1.350

1.400

130

572

1.300

1.350

123

528

1.250

1.300

81

332

1.200

1.250

86

361

1.150

1.200

95

401

1.100

1.150

104

438

1.050

1.100

106

446

1.000

1.050

103

431

.950

1.000

112

468

.900

.950

111

462

.850

.900

112

462

.800

.850

116

481

.750

.800

115

472

.700

.750

116

470

.650

.700

116

465

.600

.650

117

466

.550

.600

116

455

.500

.550

114

439

.450

.500

112

424

.400

.450

112

418

.350

.400

109

399

.300

.350

103

371

.250

.300

79

272

.200

.250

82

235

.150

.200

104

335

.100

.150

95

324

.050

.100

78

236

.000

.050

64

184

That’s for the straightline Winning Percentage approach. For the Pythagorean approach, I’m going to include 54,735 players in my data. The difference is this. Using the straightline approach, a calculation based on players with only a few plate appearances gets, umm. . .stupidlooking results. That’s the scientific term for it; it looks stupid. You get a lot of players (in history, not a lot in a season) whose only plate appearance is that they are used as a pinch runner, or who play only two games but go 4for6 with a homer and earn a Win Share or something; they’re liable to have a winning percentage, on a straightline approach, of 20.000. Looks stupid. Including the players with just one game includes in the data a lot of players with winning percentages of either zero or near 1.000. You pretty much have to exclude them when grouping players by the Linear Winning Percentage, but there is no real reason that we HAVE to exclude them from the study, when we are using the Pythagorean approach to winning percentage. So here’s the chart:
Range

Count

G

PA

Avg G

Avg PA

.950 to .99999

485

1492

2791

3

6

,900 to .94999

654

6393

20516

10

31

.850 to .89999

823

23235

90738

28

110

.800 to .84999

1250

74962

305258

60

244

.750 to .79999

1857

150843

618925

81

333

.700 to .74999

2475

227095

925796

92

374

.650 to .69999

3474

337267

1346622

97

388

.600 to .64999

3840

388483

1532457

101

399

.550 to .59999

4055

408297

1586850

101

391

.500 to .54999

3918

389011

1484788

99

379

.450 to .49999

3745

354032

1318047

95

352

.400 to .44999

3380

313522

1147277

93

339

.350 to .39999

2980

266168

958102

89

322

.300 to .34999

2628

222191

781737

85

297

.250 to .29999

2734

207862

730151

76

267

.200 to .24999

5407

374388

1260404

69

233

.150 to .19999

4000

270560

763971

68

191

.100 to .14999

1974

121535

349477

62

177

.050 to .09999

1472

63360

183841

43

125

.000 to .04999

3584

75291

165947

21

46

The ratio of players vs. the "opposite group" is very interesting here. Near the center of the chart, near .500, there are more players ABOVE .500 than UNDER .500. But when you get past .250 and .750, this changes dramatically. For every 100 plate appearances in the range of .500 to .550, there are only 89 in the range of .450 to .500—more above .500 than below.
For 100 PA in .500 to .550, there are 89 PA in .450 to .500.
For 100 PA in .550 to .600, there are 72 PA in .400 to .450.
For 100 PA in .600 to .650, there are 63 PA in .350 to .400.
For 100 PA in .650 to .700, there are only 58 PA in .300 to .350.
For 100 PA in .700 to .750, there are 79 PA in .250 to .300.
BUT
For 100 PA in .750 to .800, there are 204 PA in .200 to .250.
For 100 PA in .800 to .850, there are 250 PA in .150 to .200.
For 100 PA in .850 to .900, there are 385 PA in .100 to .150.
For 100 PA in .900 to .950, there are 896 PA in .050 to .100.
For 100 PA in .950 to 1.000, there are 5,946 PA in .000 to .050.
This sort of makes sense, without taking the time to dive into it with both feet and a clean swimsuit. There is more playing time used up by players who are somewhat above average than by players who are somewhat below average. But there is far, far more playing time used up by players who are truly terrible than by players who are truly outstanding. Overall, there are 7.91 million Plate Appearances by players who are above average, and 7.62 million by those who are below average, the slight imbalance caused by the fact that those who are below average are further below average than those who are above average are above average.
The standard deviation of winning percentage, figured with this group of players and figured in this manner, is .240. The standard deviation of winning percentage, figured in this manner but only including players with 100 or more PA, is .197, essentially the same as it was when figured with basically the same group of players but calculated with the linear approach.
4) Potential avenues of research opened up by this approach
Just this morning on twitter, someone posted a comment in my direction to the effect that the standard deviation of winning percentage is less now than it was years ago. While I would ASSUME this to be true, it is always better to know than to assume.
One would assume that, as players mature, the standard deviation of winning percentage would shrink, and that, after players turn 30, it would begin to increase again. There are many studies of this nature that could be done.
But there are vastly MORE studies which could be done just based on the winning percentage itself, rather than the standard deviation thereof. All of the things that we routinely study—Hall of Fame standards, MVP competitions, the composition of good teams and weaker teams, performance by age, etc.—all of those areas could at times be enlightened by the addition of Win Shares Winning Percentage—or WAR Winning Percentage—as an analytical tool.
The FIRST of those that needs to be studied, though, is winning percentage related to the retention of playing time. If one were to study, let us say, regular players (400 or more PA) who have winning percentages over .650, one would assume that virtually all of them would retain their jobs the next season, all except those who are injured or taken into military service or something.
If we were to study players with winning percentages of .350 and 400 or more PA, we would assume that most of them would lose playing time in the following season. But what exactly is the breakpoint, and where is the level at which playing time is lost?
It is a critical question in the development of WAR and other analytical tools—and the truth is, we don’t know where it is. I don’t believe that it has been systematically studied. This method makes it more available to study than it previously has been.
5) A few issues that we can get to
It is a generalization, but generally true, that only Hall of Famers and Most Valuable Players have winning percentages over 1.000 (linear method), or over .800 (Pythagorean approach.) (1.000 by the Linear Method is the same as .800 by the Pythagorean Approach.)
There are 18,608 player/seasons in my data in which a player had 400 or more plate appearances. Of those, 553 had Winning Percentages of 1.000 (.800) or higher. 553 of 18,608 is 3%, almost on the nose. Of those, 335 seasons were by players who are now in the Hall of Fame. Probably 100 to 150 more are by players who would appear to be certain Hall of Famers, but not yet elected—Mike Trout, Miguel Cabrera—or by stars who would be obvious Hall of Famers if not for the PED issue.
Of the 553 seasons, 79 were in the 19^{th} century, 116 were in the years 1900 to 1924, 109 were in the years 1925 to 1949, 100 were in the years 1950 to 1974, 81 were in the years 1975 to 1999, and 68 have come in the years 2000 to 2018. Obviously the frequency of "over the top" seasons is decreasing over time, consistent with the theory that the standard deviation of talent is decreasing over time.
Four players were over 1.000 in 2018—Mike Trout (1.329), Mookie Betts (1.199), Christian Yelich (1.026) and Alex Bregman (1.001). Three players were over 1.000 in 2017—Trout (1.164), Carlos Correa (1.052) and Jose Altuve (1.030). The other 2017 MVP, Giancarlo Stanton, was at .806—a very good number, obviously, but relatively low for an MVP. In 2016 there were two over 1.000Trout, and Daniel Murphy. In 2015 there were five—Trout, Bryce Harper, Miguel Cabrera, Paul Goldschmidt and Andrew McCutchen. Apparently this "trout" character is For Reals, I don’t know.
There are 173 MVP seasons in my data. 86 of them are overthetop seasons; 87 are not. The top top ten MVP seasons are as follows:
First

Last

Team

YEAR

WPct

Barry

Bonds

Giants

2002

1.835

Barry

Bonds

Giants

2004

1.817

Mickey

Mantle

Yankees

1957

1.766

Barry

Bonds

Giants

2001

1.762

Babe

Ruth

Yankees

1923

1.747

Tris

Speaker

Red Sox

1912

1.586

Mickey

Mantle

Yankees

1956

1.566

Ted

Williams

Red Sox

1946

1.562

Barry

Bonds

Giants

2003

1.542

Ty

Cobb

Tigers

1911

1.514

Oh, hell, let’s do the top 20:
First

Last

Team

YEAR

WPct

Barry

Bonds

Giants

2002

1.835

Barry

Bonds

Giants

2004

1.817

Mickey

Mantle

Yankees

1957

1.766

Barry

Bonds

Giants

2001

1.762

Babe

Ruth

Yankees

1923

1.747

Tris

Speaker

Red Sox

1912

1.586

Mickey

Mantle

Yankees

1956

1.566

Ted

Williams

Red Sox

1946

1.562

Barry

Bonds

Giants

2003

1.542

Ty

Cobb

Tigers

1911

1.514

Joe

DiMaggio

Yankees

1939

1.511

Joe

Morgan

Reds

1975

1.426

George

Brett

Royals

1980

1.422

Barry

Bonds

Giants

1993

1.407

Mike

Schmidt

Phillies

1981

1.376

Babe

Ruth

Yankees

1928

1.373

Mickey

Mantle

Yankees

1962

1.370

Barry

Bonds

Pirates

1992

1.367

Stan

Musial

Cardinals

1948

1.357

Joe

DiMaggio

Yankees

1941

1.354

Those are the top MVP seasons, not the top seasons. I’ll do the top 10 nonMVP seasons in a moment. The LOWEST winning percentage for an MVP was .558, by Andre Dawson in 1987. These are the ten lowest values for an MVP season:
First

Last

Team

YEAR

WPct

Andre

Dawson

Cubs

1987

.558

Roger

Peckinpaugh

Senators

1925

.564

Jake

Daubert

Dodgers

1913

.580

Marty

Marion

Cardinals

1944

.637

Thurman

Munson

Yankees

1976

.666

Robin

Yount

Brewers

1989

.673

Willie

Stargell

Pirates

1979

.683

Juan

Gonzalez

Rangers

1996

.684

Jimmy

Rollins

Phillies

2007

.693

Dustin

Pedroia

Red Sox

2008

.696

These are the 10 top NONMVP seasons ever:
First

Last

Team

YEAR

WPct

Honus

Wagner

Pirates

1908

1.831

Babe

Ruth

Yankees

1920

1.796

Fred

Dunlap

Maroons

1884

1.684

Babe

Ruth

Yankees

1921

1.663

Babe

Ruth

Red Sox

1919

1.631

Ty

Cobb

Tigers

1910

1.558

Honus

Wagner

Pirates

1906

1.549

Honus

Wagner

Pirates

1904

1.547

Ted

Williams

Red Sox

1941

1.525

Nap

Lajoie

Athletics

1901

1.521

These are the top 25 seasons of the expansion era:
First

Last

Team

YEAR

WPct

Barry

Bonds

Giants

2002

1.835

Barry

Bonds

Giants

2004

1.817

Barry

Bonds

Giants

2001

1.762

Barry

Bonds

Giants

2003

1.542

Mickey

Mantle

Yankees

1961

1.519

Joe

Morgan

Reds

1975

1.426

George

Brett

Royals

1980

1.422

Dick

Allen

Phillies

1967

1.422

Barry

Bonds

Giants

1993

1.407

Mike

Schmidt

Phillies

1981

1.376

Mickey

Mantle

Yankees

1962

1.370

Barry

Bonds

Pirates

1992

1.367

Mike

Trout

Angels

2018

1.329

Rickey

Henderson

Athletics

1990

1.309

Willie

Mays

Giants

1965

1.292

Norm

Cash

Tigers

1961

1.290

Will

Clark

Giants

1989

1.285

Dick

Allen

White Sox

1972

1.275

Willie

McCovey

Giants

1969

1.269

Jeff

Bagwell

Astros

1994

1.266

Mark

McGwire

Cardinals

1998

1.261

Joe

Morgan

Reds

1976

1.248

Frank

Thomas

White Sox

1997

1.235

Carl

Yastrzemski

Red Sox

1967

1.220

Jack

Clark

Cardinals

1987

1.216

I’m going to switch now to the Pythagorean approach Winning Percentages, and run charts from the 2018 season, by team. Players are listed with the team with which they FINISHED the season:
First

Last

Team

WPCT

HR

RBI

Avg

OPS

Mike

Trout

Angels

.876

39

79

.312

1.088

Andrelton

Simmons

Angels

.643

11

75

.292

.754

Justin

Upton

Angels

.517

30

85

.257

.808

Albert

Pujols

Angels

.249

19

64

.245

.700

Kole

Calhoun

Angels

.208

19

57

.208

.652

Alex

Bregman

Astros

.800

31

103

.286

.926

Jose

Altuve

Astros

.685

13

61

.316

.837

Yuli

Gurriel

Astros

.591

13

85

.291

.751

George

Springer

Astros

.573

22

71

.265

.780

Carlos

Correa

Astros

.466

15

65

.239

.728

Marwin

Gonzalez

Astros

.411

16

68

.247

.733

Josh

Reddick

Astros

.343

17

47

.242

.718

Evan

Gattis

Astros

.314

25

78

.226

.736

Martin

Maldonado

Astros

.282

9

44

.225

.627

Jed

Lowrie

Athletics

.725

23

99

.267

.801

Matt

Chapman

Athletics

.697

24

68

.278

.864

Khris

Davis

Athletics

.628

48

123

.247

.874

Stephen

Piscotty

Athletics

.611

27

88

.267

.821

Marcus

Semien

Athletics

.549

15

70

.255

.706

Matt

Olson

Athletics

.528

29

84

.247

.788

Mark

Canha

Athletics

.502

17

52

.249

.778

Jonathan

Lucroy

Athletics

.363

4

51

.241

.617

I’ll break for comments after every three teams. The A’s had a great season because they had 7 regulars (out of 8) who were playing above the average, above .500. No other American League team could match that, although two National League teams had 7 players at .500 or above.
Shohei Ohtani doesn’t Shohei Up on the chart above because he had only 384 Plate Appearances. It’s a good thing he doesn’t show up, for me, because as I mentioned earlier I can’t deal with those pitcher/position player combo guys. Not saying he wasn’t a really good player; not saying he’s not going to be a great player. My system as this point just doesn’t have anything to say about a player with that combination of skills.
First

Last

Team

WPCT

HR

RBI

Avg

OPS

Justin

Smoak

Blue Jays

.632

25

77

.242

.808

Randal

Grichuk

Blue Jays

.494

25

61

.245

.803

Aledmys

Diaz

Blue Jays

.452

18

55

.263

.756

Teoscar

Hernandez

Blue Jays

.353

22

57

.239

.771

Kevin

Pillar

Blue Jays

.333

15

59

.252

.708

Kendrys

Morales

Blue Jays

.309

21

57

.249

.769

Yangervis

Solarte

Blue Jays

.145

17

54

.226

.655

Ronald

Acuna Jr.

Braves

.691

26

64

.293

.917

Freddie

Freeman

Braves

.672

23

98

.309

.892

Nick

Markakis

Braves

.585

14

93

.297

.806

Johan

Camargo

Braves

.555

19

76

.272

.806

Dansby

Swanson

Braves

.500

14

59

.238

.699

Ozzie

Albies

Braves

.481

24

72

.261

.757

Ender

Inciarte

Braves

.469

10

61

.265

.705

Adam

Duvall

Braves

.126

15

61

.195

.639

Christian

Yelich

Brewers

.808

36

110

.326

1.000

Lorenzo

Cain

Brewers

.712

10

38

.308

.813

Travis

Shaw

Brewers

.631

32

86

.241

.825

Jesus

Aguilar

Brewers

.595

35

108

.274

.890

Ryan

Braun

Brewers

.577

20

64

.254

.782

Mike

Moustakas

Brewers

.543

28

95

.251

.774

Curtis

Granderson

Brewers

.479

13

38

.242

.782

Jonathan

Schoop

Brewers

.231

21

61

.233

.682

The Blue Jays had only one player, Justin Smoak, who was above average. It is interesting that Smoak comes in at .632 and Grichuk at .494 with very similar triple crown numbers and OPS, but that happens because Smoak drew 83 walks to Grichuk’s 27. Walks are undervalued in the OPS formula. They have more impact on Runs than they do on OPS. Bobby Grich has filed to seek an injunction ordering Grichuk to stop using his name.
First

Last

Team

WPCT

HR

RBI

Avg

OPS

Jose

Martinez

Cardinals

.777

17

83

.305

.821

Matt

Carpenter

Cardinals

.723

36

81

.257

.897

Yadier

Molina

Cardinals

.622

20

74

.261

.750

Paul

DeJong

Cardinals

.591

19

68

.241

.746

Marcell

Ozuna

Cardinals

.556

23

88

.280

.758

Jedd

Gyorko

Cardinals

.552

11

47

.262

.762

Harrison

Bader

Cardinals

.519

12

37

.264

.756

Kolten

Wong

Cardinals

.500

9

38

.249

.720

Ben

Zobrist

Cubs

.645

9

58

.305

.817

Javier

Baez

Cubs

.644

34

111

.290

.881

Kris

Bryant

Cubs

.618

13

52

.272

.834

Anthony

Rizzo

Cubs

.618

25

101

.283

.846

Jason

Heyward

Cubs

.585

8

57

.270

.731

Kyle

Schwarber

Cubs

.534

26

61

.238

.823

Willson

Contreras

Cubs

.466

10

54

.249

.730

Albert

Almora Jr.

Cubs

.453

5

41

.286

.701

Ian

Happ

Cubs

.450

15

44

.233

.761

Addison

Russell

Cubs

.363

5

38

.250

.657

Paul

Goldschmidt

D'Backs

.668

33

83

.290

.922

David

Peralta

D'Backs

.663

30

87

.293

.868

Eduardo

Escobar

D'Backs

.577

23

84

.272

.824

A.J.

Pollock

D'Backs

.513

21

65

.257

.800

Ketel

Marte

D'Backs

.495

14

59

.260

.768

Daniel

Descalso

D'Backs

.490

13

57

.238

.789

Nick

Ahmed

D'Backs

.454

16

70

.234

.700

Jon

Jay

D'Backs

.320

3

40

.268

.678

The Cardinals are one of the NL teams with 7 regulars over .500, and the eighth, Kolten Wong, is Mr. Average, one of two regulars in the majors who sits on the midpoint, the other one being Dansby Swanson.
The Cubs were one of three teams to have four .600 players—Zobrist, Baez, Bryant and Rizzo—although none of the four was near the MVP level, which generally starts about .750. I know some people thought Baez was an MVP candidate, but I don’t. Anyway, three teams with four .600 players—Cubs, A’s, and Red Sox. But one team, still to come, had FIVE .600 players in the lineup.
First

Last

Team

WPCT

HR

RBI

Avg

OPS

Justin

Turner

Dodgers

.749

14

52

.312

.924

Max

Muncy

Dodgers

.723

35

79

.263

.973

Manny

Machado

Dodgers

.690

37

107

.297

.905

Yasmani

Grandal

Dodgers

.583

24

68

.241

.815

Matt

Kemp

Dodgers

.580

21

85

.290

.818

Cody

Bellinger

Dodgers

.549

25

76

.260

.814

Chris

Taylor

Dodgers

.510

17

63

.254

.775

Kiké

Hernandez

Dodgers

.485

21

52

.256

.806

Brian

Dozier

Dodgers

.424

21

72

.215

.696

Yasiel

Puig

Dodgers

.423

23

63

.267

.820

Joc

Pederson

Dodgers

.403

25

56

.248

.843

Buster

Posey

Giants

.613

5

41

.284

.741

Brandon

Belt

Giants

.568

14

46

.253

.756

Brandon

Crawford

Giants

.542

14

54

.254

.719

Evan

Longoria

Giants

.189

16

54

.244

.694

Gorkys

Hernandez

Giants

.158

15

40

.234

.676

Jose

Ramirez

Indians

.728

39

105

.270

.939

Francisco

Lindor

Indians

.707

38

92

.277

.871

Jason

Kipnis

Indians

.516

18

75

.230

.704

Michael

Brantley

Indians

.483

17

76

.309

.832

Edwin

Encarnacion

Indians

.445

32

107

.246

.810

Yonder

Alonso

Indians

.363

23

83

.250

.738

Yan

Gomes

Indians

.357

16

48

.266

.762

The Dodgers are the other team with 7 "regulars" over .500. Of course, it is hard to say who is a regular on the Dodgers, with their everybodyplayseverywhere style. Picking up Dozier late in the year they had 11 regulars, more than any other team I think, and, while four of them were belowaverage, none of them was MUCH below average.
The general reaction to the Dodgers trading Puig and then signing Pollock was "He’s not Yasiel Puig, but he’ll fill the gap." But this system shows Pollock (.513) as being a better player last season than Puig (.423), and I’ll stand by that. I think Puig will have nearMVP numbers in Cincinnati this season, 2019, but I think he was below average last season. He drove in 63 runs and had a .327 on base percentage. Those are not star numbers. Pollock was a better player than Puig last year.
Longoria’s appallingly low number, .189, is surprising, but Longoria drew only 22 walks, giving him a .281 on base percentage—this from a player whose On Base Percentage from 2009 to 2012 was .355 to .372 every season. You might disagree that Longoria was THAT bad, but nobody thinks that he helped the team.
But I would not stand by the sub.500 number for Michael Brantley. Brantley hit .309, 38 doubles, 17 homers, 48 walks, .364 on base percentage. It’s a hitter’s park and his defense wasn’t great and 17 homers is a belowaverage number for a 21^{st} century outfielder, but he still seems like a .500+ player to me. I don’t know why he didn’t score well. There’s a similar case coming up later on, even more surprising than Brantley.
First

Last

Team

WPCT

HR

RBI

Avg

OPS

Mitch

Haniger

Mariners

.713

26

93

.285

.859

Jean

Segura

Mariners

.663

10

63

.304

.755

Nelson

Cruz

Mariners

.653

37

97

.256

.850

Denard

Span

Mariners

.564

11

58

.261

.760

Kyle

Seager

Mariners

.379

22

78

.221

.673

Dee

Gordon

Mariners

.303

4

36

.268

.637

Ryon

Healy

Mariners

.260

24

73

.235

.688

Mike

Zunino

Mariners

.254

20

44

.201

.669

J.T.

Realmuto

Marlins

.756

21

74

.277

.825

Brian

Anderson

Marlins

.695

11

65

.273

.757

Derek

Dietrich

Marlins

.521

16

45

.265

.751

Starlin

Castro

Marlins

.508

12

54

.278

.729

Miguel

Rojas

Marlins

.405

11

53

.252

.643

Lewis

Brinson

Marlins

.058

11

42

.199

.577

Brandon

Nimmo

Mets

.707

17

47

.263

.886

Michael

Conforto

Mets

.601

28

82

.243

.797

Amed

Rosario

Mets

.528

9

51

.256

.676

Todd

Frazier

Mets

.452

18

59

.213

.693

Wilmer

Flores

Mets

.440

11

51

.267

.736

Brian Anderson of the Marlins had to be the MVIP in baseball last year—the Most Valuable Invisible Player.
1) It was an extreme pitcher’s park, park factor of 75,
2) He had 62 walks and 16 HBP, giving him a .357 on base percentage,
3) He scored 87 runs,
4) He hit 34 doubles, and
5) He played aboveaverage defense in right field.
Nobody seems to be convinced that he is actually that good, which will make it all the more interesting if, in his second season, he steps it up a notch and plays even better, as many players do.
First

Last

Team

WPCT

HR

RBI

Avg

OPS

Anthony

Rendon

Nationals

.672

24

92

.308

.909

Bryce

Harper

Nationals

.628

34

100

.249

.889

Trea

Turner

Nationals

.623

19

73

.271

.760

Juan

Soto

Nationals

.585

22

70

.292

.923

Wilmer

Difo

Nationals

.158

7

42

.230

.649

Jonathan

Villar

Orioles

.510

14

46

.260

.709

Adam

Jones

Orioles

.296

15

63

.281

.732

Tim

Beckham

Orioles

.219

12

35

.230

.661

Trey

Mancini

Orioles

.167

24

58

.242

.715

Chris

Davis

Orioles

.017

16

49

.168

.539

Hunter

Renfroe

Padres

.477

26

68

.248

.805

Eric

Hosmer

Padres

.428

18

69

.253

.720

Freddy

Galvis

Padres

.368

13

67

.248

.680

Manuel

Margot

Padres

.304

8

51

.245

.675

Jose

Pirela

Padres

.242

5

32

.249

.645

The Padres were the only team in the majors that did not have a .500 player in the lineup, although overall they were, of course, much better than the Orioles. Chris Davis’ .017 Winning Percentage was the lowest in the majors (400 or more PA); another guy was lower with 288 PA, but he was released and then was murdered in Venezuela, so we won’t get into that. We’ve seen two guys so far who were under .100, Davis and Lewis Brinson, and there are two more of those coming on the last 12 teams.
First

Last

Team

WPCT

HR

RBI

Avg

OPS

Wilson

Ramos

Phillies

.728

15

70

.306

.845

Rhys

Hoskins

Phillies

.616

34

96

.246

.850

Asdrubal

Cabrera

Phillies

.596

23

75

.262

.774

Cesar

Hernandez

Phillies

.581

15

60

.253

.718

Carlos

Santana

Phillies

.525

24

86

.229

.766

Justin

Bour

Phillies

.496

20

59

.227

.746

Maikel

Franco

Phillies

.483

22

68

.270

.780

Odubel

Herrera

Phillies

.416

22

71

.255

.730

Nick

Williams

Phillies

.320

17

50

.256

.749

Scott

Kingery

Phillies

.232

8

35

.226

.605

Francisco

Cervelli

Pirates

.754

12

57

.259

.809

Starling

Marte

Pirates

.612

20

72

.277

.787

Gregory

Polanco

Pirates

.608

23

81

.254

.839

Corey

Dickerson

Pirates

.539

13

55

.300

.804

Colin

Moran

Pirates

.483

11

58

.277

.747

Josh

Bell

Pirates

.472

12

62

.261

.768

Jordy

Mercer

Pirates

.345

6

39

.251

.696

Robinson

Chirinos

Rangers

.549

18

65

.222

.757

Jurickson

Profar

Rangers

.497

20

77

.254

.793

Rougned

Odor

Rangers

.438

18

63

.253

.751

ShinSoo

Choo

Rangers

.437

21

62

.264

.810

Joey

Gallo

Rangers

.396

40

92

.206

.810

Ronald

Guzman

Rangers

.345

16

58

.235

.722

Nomar

Mazara

Rangers

.316

20

77

.258

.753

Adrian

Beltre

Rangers

.267

15

65

.273

.763

Elvis

Andrus

Rangers

.264

6

33

.256

.675

All three of these teams are led in the numbers by their catchers, although Wilson Ramos was actually with the Rays most of the season. Ramos, also a victim of Venezuelan violence, had a great season, but the .754 figure for Francisco Cervelli is one of the most surprising calculations in the data. A terrific walk rate and 15 HBP gave him an excellent on base percentage (.374). He had career highs in homers (12) and RBI (57); he drove in almost as many runs in 332 at bats as Yasiel Puig drove in in 406, and he threw out 37% of opposing base stealers.
I thought Nomar Mazara was going to have a great season, but he didn’t, to put it kindly. In that park, the kind of player he is, he would have to put up Juan Gonzaleztype numbers to be really valuable, and he didn’t come close. I have no use for Joey Gallo, despite the 40 homers, or for anybody who even LOOKS like Joey Gallo. Spare me. No love for that kind of player.
First

Last

Team

WPCT

HR

RBI

Avg

OPS

Joey

Wendle

Rays

.620

7

61

.300

.789

Matt

Duffy

Rays

.592

4

44

.294

.727

Mallex

Smith

Rays

.591

2

40

.296

.773

Tommy

Pham

Rays

.551

21

63

.275

.830

C.J.

Cron

Rays

.375

30

74

.253

.816

Carlos

Gomez

Rays

.201

9

32

.208

.634

Mookie

Betts

Red Sox

.852

32

80

.346

1.078

J.D.

Martinez

Red Sox

.797

43

130

.330

1.031

Xander

Bogaerts

Red Sox

.755

23

103

.288

.883

Andrew

Benintendi

Red Sox

.662

16

87

.290

.830

Jackie

Bradley Jr.

Red Sox

.530

13

59

.234

.717

Mitch

Moreland

Red Sox

.461

15

68

.245

.758

Ian

Kinsler

Red Sox

.429

14

48

.240

.681

Rafael

Devers

Red Sox

.300

21

66

.240

.731

Eduardo

Nunez

Red Sox

.191

10

44

.265

.677

Joey

Votto

Reds

.661

12

67

.284

.837

Eugenio

Suarez

Reds

.629

34

104

.283

.892

Scooter

Gennett

Reds

.578

23

92

.310

.847

Jose

Peraza

Reds

.363

14

58

.288

.742

Scott

Schebler

Reds

.319

17

49

.255

.777

Tucker

Barnhart

Reds

.272

10

46

.248

.699

Billy

Hamilton

Reds

.200

4

29

.236

.626

Joey Wendle was, I think, the Most Valuable Rookie in the majors last year; not saying he should have won the Rookie of the Year Award, he’s a 28yearold player with a modest minor league record, probably having a fluke season, but he played tremendous baseball, hit .300 with 33 doubles and plus defense at second base. 16 stolen bases in 20 attempts.
The Rays, in a sense, are the new wave of baseball. While much of baseball is stuck in the Joey Gallo/Chris Davis/Todd Frazier/Adam Duvall stage, still hoping for a comeback from Chris Carter, the Rays went to the type that has now become undervalued, brought in singles hitters (Wendle, Duffy and Mallex Smith), got a great halfseason or twothirds of a season out of Wilson Ramos, and won 90 games. Power to them; way to go, guys. Lik