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 straight-line approach and the Pythagorean approach. The straight-line 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 straight-line approach is actually the more useful and more representative approach, but that winning-percentage-greater-than-1.000 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 straight-line 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 Pythagorean-derived 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 (15-15). One player is 14-13, one player is 13-11, and one player is 12-9. 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 one-third 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 19th 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 late-season 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 straight-line 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 straight-line 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 right-hand slice of the bell-shaped distribution curve—that is, with the greatest number of players being below-average—but with some elements of a normal or bell-shaped 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 straight-line Winning Percentage approach. For the Pythagorean approach, I’m going to include 54,735 players in my data. The difference is this. Using the straight-line approach, a calculation based on players with only a few plate appearances gets, umm. . .stupid-looking 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 4-for-6 with a homer and earn a Win Share or something; they’re liable to have a winning percentage, on a straight-line 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 19th 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 1.000-Trout, 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 over-the-top 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 non-MVP 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 NON-MVP 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 under-valued 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 everybody-plays-everywhere 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 below-average, 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 near-MVP 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 below-average number for a 21st 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 above-average 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
|
|
Shin-Soo
|
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 Gonzalez-type 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 28-year-old 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 under-valued, brought in singles hitters (Wendle, Duffy and Mallex Smith), got a great half-season or two-thirds of a season out of Wilson Ramos, and won 90 games. Power to them; way to go, guys.