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 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 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 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 1.000-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 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 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.   Like to see more of that. 

The Red Sox had two MVPs in the lineup and four regulars at .662 or above.  The only other teams with more than two players above .662 were the Yankees and the Dodgers, who had three each.   Eleven teams had NO ONE at that level, no one.  And all four of the Red Sox guys had 580 or more plate appearances. 

The Reds, for my money, had probably the most overrated and the most underrated players in baseball.  On the former, I still don’t think Billy Hamilton can play.  I never did, honestly; I mean, you don’t want to beat up on the young hotshot that everybody is raving about, but I honestly don’t believe he has a place on a major league roster, even as a backup center fielder/pinch runner.  

And on the other end of the scale, Jesse Winker.  Ordinarily, Win Shares and WAR are going to come out more or less in the same place in evaluating a player, not always and not exactly, of course, but as a rule.   But one of us really whiffed on Jesse Winker. 

Winker, who hit .299 and had a .405 on base percentage, is nonetheless evaluated by BB Reference WAR as a sub-replacement level player, based on their belief that he is a God Awful outfielder.   I saw him play a couple of games, and . . .yeah, he’s a God Awful outfielder, maybe not as bad as Duvall, but in the same ballpark.  In the same outfield, no less.  But 24 years old, left-handed hitter, .405 on base percentage. . . I’m in.   I’m not saying he is Wade Boggs, but I see a lot of Wade Boggs here. .. ..Wade Boggs, Edgar Martinez, Joey Votto.   I don’t see him getting to that level, but I see him as being in that pathway.   Maybe closer to Lou Piniella, who reached the major leagues in 1964 and was the Rookie of the Year in 1969.   People said that all he could do was hit.  That’s what they said about David Ortiz and Kevin Millar.   I like guys who can hit.   Give me all of those guys that you don’t need. 

I have Winker with a .649 Winking Percentage.   Fangraphs has him at +0.9 WAR, Baseball Reference has him at negative 0.1.   He doesn’t show up here because he had only 334 plate appearances.  But I like him. 

First	Last	Team	WPCT	HR	RBI	Avg	OPS
Nolan	Arenado	Rockies	.716	38	110	.297	.935
Trevor	Story	Rockies	.684	37	108	.291	.914
Charlie	Blackmon	Rockies	.654	29	70	.291	.860
DJ	LeMahieu	Rockies	.594	15	62	.276	.749
Carlos	Gonzalez	Rockies	.502	16	64	.276	.796
Gerardo	Parra	Rockies	.460	6	53	.284	.714
Ian	Desmond	Rockies	.317	22	88	.236	.729
Whit	Merrifield	Royals	.586	12	60	.304	.806
Salvador	Perez	Royals	.413	27	80	.235	.713
Alex	Gordon	Royals	.333	13	54	.245	.694
Alcides	Escobar	Royals	.063	4	34	.231	.593
Nicholas	Castellanos	Tigers	.680	23	89	.298	.854
Jose	Iglesias	Tigers	.558	5	48	.269	.699
Niko	Goodrum	Tigers	.465	16	53	.245	.747
Jeimer	Candelario	Tigers	.302	19	54	.224	.710
James	McCann	Tigers	.170	8	39	.220	.581
JaCoby	Jones	Tigers	.115	11	34	.207	.630
Victor	Martinez	Tigers	.070	9	54	.251	.651

 

Carlos Gonzalez is worth a line.   People talk about him as if he was ready to be put out to pasture, and I’m not really arguing, but my system still sees him as being an average player, not a good player anymore, but not a liability.  Four regulars in the majors were under a .100 winning percentage, two of them in this package—Chris Davis, Lewis Brinson, Alcides Escobar and Victor Martinez.   Victor’s a terrific person, but a .297 on base percentage and no power, from a DH. . . .ouch. 

Escobar’s replacement, Adalberto Mondesi, came in at .483 despite impressive power and speed numbers; problem is that 77-11 strikeout to walk ratio.   But .483 is really good for a 22-year-old.   Lot of guys have gone from .483 at age 22 to MVP Awards.   Mondesi looks like the 2023 MVP to me; not saying he is, but he looks like it.   He’s in the Yoan Moncada mold, but ahead of Moncada.   Last group of teams:

First	Last	Team	WPCT	HR	RBI	Avg	OPS
Joe	Mauer	Twins	.552	6	48	.282	.729
Robbie	Grossman	Twins	.512	5	48	.273	.751
Eddie	Rosario	Twins	.502	24	77	.288	.803
Max	Kepler	Twins	.369	20	58	.224	.727
Logan	Forsythe	Twins	.358	2	27	.232	.604
Jose	Abreu	White Sox	.563	22	78	.265	.798
Matt	Davidson	White Sox	.472	20	62	.228	.738
Yolmer	Sanchez	White Sox	.404	8	55	.242	.678
Daniel	Palka	White Sox	.373	27	67	.240	.778
Tim	Anderson	White Sox	.356	20	64	.240	.687
Yoan	Moncada	White Sox	.353	17	61	.235	.714
Adam	Engel	White Sox	.181	6	29	.235	.614
Aaron	Judge	Yankees	.689	27	67	.278	.919
Aaron	Hicks	Yankees	.679	27	79	.248	.833
Gleyber	Torres	Yankees	.672	24	77	.271	.820
Didi	Gregorius	Yankees	.651	27	86	.268	.829
Miguel	Andujar	Yankees	.617	27	92	.297	.855
Andrew	McCutchen	Yankees	.555	20	65	.255	.792
Giancarlo	Stanton	Yankees	.481	38	100	.266	.852
Brett	Gardner	Yankees	.386	12	45	.236	.690

 

I loved Max Kepler when he came to the majors, but he’s got a career batting average of .233 and is going backward.   This is a critical season for him; he’s got to break through in 2019, or his career is in the toilet. 

Jose Abreu, although he had his first off season, was still the White Sox’ only .500 position player. 

And the Yankees. . .well, here’s the biggest surprise in the numbers.   My system sees Giancarlo Stanton, in 2018, as a little bit less than an average player.  A .481 player. 

I don’t really believe in that.   It’s kind of a Jack Kralick thing; sometimes your formulas just don’t work, and you don’t really know why.   He didn’t have a great year, and I’ve always been the low man on Giancarlo; I remember one year I left him off my list of Top 10 Right Fielders when everybody else on Brian Kenny’s show had him in the Top 3.  

I certainly wouldn’t see him as a less-than-.500 player, but here’s how the system sees it:

1)      Little defensive value; spent most of the year as a DH although he played pretty well in the outfield while he was out there,

2)      Unimpressive .343 on base percentage,

3)      211 strikeouts,

4)      Grounded into 17 double plays,

5)      Hit 38 homers, yes, but 25 of them with the bases empty,

6)      Hit .241 with runners in scoring position,

7)      And it’s a hitter’s park.  

Most guys who strike out 200 times a season, at least they’re not grounding into double plays.  His 17 GIDP was one short of the record for a guy who struck out 200 times.  Chris Carter grounded into 18.

I’m not saying the system is right; I’m saying it has its reasons.  Giancarlo hit .229 in Yankee Stadium, .300 on the road, so it’s a great hitter’s park, but not for him, in his first season there. 

Let’s compare Giancarlo to Jason Heyward of the Cubs.  Heyward came in in this study at .585, and Stanton at .481.   The conclusion that Heyward played better than Stanton in 2018 would be, um, universally unpopular.

First of all, I am not asserting that Heyward had more value than Stanton; in fact, I have Stanton with 18 Win Shares and Heyward with 16, so Stanton was more valuable.   My system thinks that Heyward was more valuable, relative to his playing time.   Baseball Reference has Stanton at 4.0 WAR and 1.7 WAA, and Heyward at 1.6 WAR and 0.1 WAA.  Let’s get into it a little deeper.

Heyward created 67 runs; Stanton created 98.  Heyward, however, created 67 runs while making 332 outs.  Stanton created 98 runs while making 480 outs, which is a large number of outs.   Heyward is at 5.45 Runs Created per 27 outs; Stanton is at 5.51—not really much of a difference, per opportunity. 

The Cubs, however, scored 761 runs and allowed 645 in 163 games, or 4.31 runs per game.  The Yankees scored 851 and allowed 669 in 162 games, or 4.69 runs per game.  The cost of a win was higher for the Yankees, in terms of how many runs it takes them to win a ballgame, so who is actually better—Heyward, at 5.45 Runs Created in a 4.31 context, or Stanton, at 5.51 Runs Created in a 4.69 context? 

Defense?   Stanton played 648 innings in the field, almost evenly split between left field and right, and was a DH in a little over half of his games.   Heyward played 1005 innings of defense, 85% of them in right field and the rest in center.  I think everyone would agree that Heyward is a better fielder. 

Baserunning?  Stanton has a "surprise" number here.  Stanton moved up a base 27 times on a Wild Pitch, a Passed Ball, a Sacrifice Fly, a Balk or Defensive Indifference.  This was one of the highest totals in the major leagues.  Heyward moved up on 14 times on those events.  We call those "Bases Taken".   Also, Stanton was 5 for 5 as a base stealer, while Heyward was just 1 for 2. 

Otherwise, Heyward has a big advantage as a baserunner.  Heyward was 11 for 24 going first-to-third on a single, 46%; Stanton was 6 for 20, or 30%.  Heyward was 7 for 15 scoring from second on a single, or 47%; Stanton was 4 for 12, or 33%.  Heyward was 6 for 18 scoring from first on a double, 33%; Stanton was 3 for 10, or 30%.  Heyward batted 102 times in GIDP situations, and grounded into 7 double plays, or 7%; Stanton batted 151 times in GIDP situations, and grounded into 17 double plays, or 11%.  Overall, we grade Heyward at +16 bases, as a baserunner, and Stanton at +1, based mostly on his ability to move up a base on a Wild Pitch or something.  

Heyward hit .324 with Runners in Scoring Position, and hit 6 of his 7 home runs with men on base.  Stanton hit .241 with Runners in Scoring Position, and hit 25 of his 38 homers with the bases empty.  Stanton outhomered Heyward 25 to 1 with the bases empty, but 13 to 6 with men on base. 

The conclusion that Heyward outperformed Stanton, relative to playing time, seems reasonable to me.   I’m not saying it is right, but I looked carefully at it, and I can’t say that it is wrong, either. 

But while this line of analysis curiously dislikes Giancarlo, it likes the rest of the Yankee lineup, with Gregorius, Hicks, Judge and Gleyber all finishing between .650 and .700.  Miguel Andujar, who I love, I think he’s maybe a Hall of Famer, comes in at .617 despite his defensive struggles at third base.  

Miguel is kind of the new Jim Ray Hart.   The Giants in 1964, when they already had Mays and McCovey and Orlando Cepeda and the Alou brothers, came up with Jim Ray Hart, a third baseman who was brutal defensively, but a right-handed hitter who could RAKE.   Andujar so far is exactly the same story; he comes up with a team that is already loaded, and he’s a right-handed hitter who is trying to play third base, doesn’t seem to have any ability there, but Jeez, that man can hit.   Hart’s career didn’t go well; he was just another Big Bat for the Giants for four or five years and then faded away.   But I’d bet on Andujar to hit 400 or more homers with a good batting average. 

 And we’re not even going to talk about Luke Voit, who comes in at an MVP-like .838 Winning Percentage, albeit in only 148 plate appearances, and he’ll be 28 years old in a week or so, but still. . .

OK, two more little issues, and I’ll bring this 22-page article to a halt.  First, Roy Cullenbine.  I said early in the article that I would assume that everybody who has an effective Winning Percentage of .650 or better gets to keep his job.   But Roy Cullenbine, a 1940s outfielder who had the temerity to walk 137 times in 142 games in 1947, was sold that winter to the Philadelphia Phillies, the worst team in the National League, and then was released the next spring by the Phillies.   Cullenbine’s winning percentages, Pythagorean approach, in his last seven major league seasons:  .689, .690, .758, .595, .773, .850 and .645.   By the Linear approach, his winning percentage in his last season would be .674. 

              And finally, Bryce Harper and Manny Machado.   Maybe I’m wrong, but this is how I see it; not trying to put these guys down, they’re good players.   But PART of what is going here is that they’re good players; they’re not GREAT players.

              Years ago, when these guys came to the majors, the media decided that they were going to be Superstars!!.   19, 20 years old, already good players; my God, what will they be when they’re 26?  Bryce Harper was going to be (spoken reverently) The Face of Baseball. 

              But they’re just not.   Switching now to the Linear Approach version of Individual Winning Percentage. . . Rickey Henderson was a great player.  He had Linear Winning Percentages, in a five-year period about the same age, of 1.063, .807, .932, .896, 1.166.    Chipper Jones in his prime had figures of .803, .922, .771, .844, .927.   Manny Ramirez, in a five-year-period, went 1.105, 1.053, .798, 1.136 and .821.   Mark McGwire was .898 as a rookie, and regularly over 1.000 in his steroid phase.   And we’re not even going to talk about the REAL superstars, the guys like Willie Mays and Mickey Mantle and Mike Schmidt and Joe Morgan and Barry Bonds.   Those guys had numbers in the stratosphere. 

              Machado and Harper are good players, but they’re just not in that league.   I am amazed that people miss this point.    Machado over the last six years is .519, .619, .714, .758, .520 and .746. 

              Manny is like Albert Belle. . .not as good a player as Albert Belle, but like him in this way.   Albert was controversial.  People were always on him about something he had done, some kind of behavior with which he had given offense, but what people missed was:  he was always ready to play.  Game started, he was there.   Manny is the same way; when the game starts, he’s in the lineup.   His counting stats are terrific.  He should get more credit for that, and less talk about one-time-this-happened. 

              Harper, in the last six years, has winning percentages of .727, .417, 1.181, .604, .881 and .650.   That’s a good player, but people are expecting him to get a contract like A-Rod got from Texas.  A-Rod was over 1.000 three times in his career, over .900 six times—and he stayed healthy.   Machado and Harper are just simply not at that level.   People EXPECTED them to get to that level, but they didn’t. 

              And it isn’t a now-versus-then thing. I’m not saying this to put them down; this is my honest opinion.  Neither Harper nor Machado is one of the 25 best players in baseball.   You think I’m crazy?  Look at it.   Trout, Mookie, Aaron Judge, Altuve, Bregman, Lindor, Jose Ramirez.  .you can’t put Harper or Machado up against any of those guys.  I’d rather have J. D. Martinez or Gleyber Torres.  That’s nine players; we haven’t yet gotten to the National League, or to pitchers.   I’d rather have Xander Bogaerts than either one.  Paul Goldschmidt, Nolan Arrenado, Justin Turner.   Why, logically, would Manny Machado rank ahead of Freddie Freeman?  Would you rather have Bryce Harper than Ronald Acuna?  Would you rather have Bryce Harper than Juan Soto?   How about Kris Bryant?  Jose Baez?  Christian Yelich?   Carlos Correa had an off year, but you can put him in there if you want to. 

              PART of what isn’t happening here has to do with the market, yes, but part of it has nothing to do with the market.   It has to do with the produce.   They just never became the players that they were supposed to be.  The market has accepted that.  The media has not. 

             

 

 

 

 

 

 

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