Acknowledgements
Before I get into the article, I just wanted to give a quick shout out to Tom Tango and to Bill James Online member jgf704 for their help in steering me to the WAR master data file on baseball-reference.com. The raw data I was able to access is at the heart of this article, and I would not have been able to efficiently pull and organize the data the way I wanted without having that file available.
Introduction
What if you were trying to identify players who were good "across the board"? There are lots of different ways to approach that, starting with defining what "across the board" means. Does it mean a player exhibits excellence across a set of "tools" (speed, power, defense, etc.)? Does it mean doing well across different statistical categories? What exactly are you trying to capture?
I know Bill has alluded to this type of thing before. For example, in the New Bill James Historical Abstract (from 2001), Bill referred to Barry Larkin (his #6 ranked shortstop) as one of the most well-rounded stars in baseball history because he could hit .300, had good power, good speed, excellent defense, and was a good percentage player, saying he right up there with DiMaggio and Mays in that regard.
In his comment on George Grantham (#62 at second base) in the same book, he referenced a couple of studies he had done 20 years apart where he looked for players who were above average in several offensive categories and also played a key defensive position. In the first study, he came up with 2 players: Willie Mays and George Grantham. In the second one, it was again just 2 players, this time being Jackie Robinson and George Grantham.
Those types of things got me thinking about a similar type of search. What if we looked for players who were "positive" across all of the components of WAR? First, let’s do a quick primer on WAR and its 6 "components".
The Components of WAR – Basic Concepts
I would suspect most of you are familiar enough with the baseball-reference.com version of WAR, and probably have a pretty decent understanding of it and how it gets used in a variety of different contexts. And I’m also sure that many of you are also familiar as to what is known as the "components of WAR", but for the benefit of readers who may not be used to looking at that level, I wanted to take a few minutes to review the components, what they represent, and some of the results they generate.
The following is straight from Baseball-reference.com:
======================
WAR for position players has six components:
· Batting Runs
· Baserunning Runs
· Runs added or lost due to Grounding into Double Plays in DP situations
· Fielding Runs
· Positional Adjustment Runs
· Replacement level Runs (based on playing time)
The first five measurements are all compared against league average, so a value of zero will equate to a league average player. Less than zero means worse than average, and greater than zero means better than average.
These five correspond to the first half of our equation above (Player_runs - AvgPlayer_runs).
The sixth factor is the second half of the equation (AvgPlayer_runs - ReplPlayer_runs).
======================
Back to me now….
I’m not going to go into depth as to the calculations that are behind each of those components. You can read up on that if you like. I merely wanted to level set that I’m not just using WAR in the aggregate, but more interested in each of the building blocks.
Here’s a quick summary of the 6 components, including the abbreviations I’ll be using to reference them from here on (these are the same abbreviations you would find in a player’s "Player Value-Batting" section on his baseball-reference.com player page):
Component Name
|
Abbreviation
|
Measures
|
Compares to
|
Batting Runs
|
Rbat
|
Runs attributable to hitting
|
Average
|
Baserunning Runs
|
Rbaser
|
Runs attributable to baserunning events
|
Average
|
Double Play Runs
|
Rdp
|
Runs attributable to avoiding double plays
|
Average
|
Fielding Runs
|
Rfield
|
Runs attributable to fielding
|
Average
|
Position Adjustment Runs
|
Rpos
|
Bonus/penalty based on positions played
|
Average
|
Replacement Level Runs
|
Rrep
|
Runs above replacement level
|
Replacement Level
|
As the definition stated, the first 5 estimate how many runs above average a player was within each measure, and the last component estimates how much above replacement level he was. Adding it all up gives you total runs above replacement (which is referred to as RAR). Then, the final step to translate RAR into the more recognizable WAR figure is to apply a conversion factor of runs-to-wins. To quote Baseball-reference.com: If you had to pick one number over the history of baseball to convert runs into wins, it would be 10.
The exact conversion factor for the ratio of runs-to-wins varies (for example, the league run context has an impact), but generally if you simply take RAR divided by 10, it’ll get you pretty close to a player’s actual final WAR figure. As I look over the yearly data for players, that factor generally stays between 9 and 11 for the vast majority of them. If you take the top 500 position players in terms of WAR, the range goes from a low of 9.0 to a high of 11.6, and the average is 10.1. The main point is that the runs-to-wins conversion is applied to turn RAR into WAR, and over time that amounts to roughly 1 win per 10 runs.
Component Characteristics
Next, I wanted to investigate some basic statistical characteristics of these 6 components before applying it to the final study.
First, it’s worth noting that the Double Play Runs component (Rdp, which is a hitting component for being able to avoid a double play, not a fielding component of an ability to turn double plays), is zero for every player through the 1930 season. I didn’t see that directly addressed in the explanation of the WAR components, but I suspect it’s simply due to lack of necessary play-by-play data.
So, since I ultimately wanted to investigate players who were good across all component categories, I decided to limit the scope to 1931 or later. In addition, it looks like a similar effect holds true for the Negro Leagues (Rdp is zero for all Negro League players), so I will also eliminate Negro League stats from this study. At the end, I’ll do a separate quick summary for players in those last 2 groups.
So, the scope of my data will be based on:
· National League and American League only
· Using seasons from 1931 to 2021 only
· A minimum of 1,000 career games played during that time frame
Note that if a player had career stats both prior to and after 1931, I only included the years for 1931 and beyond, so someone like Babe Ruth, who only had 568 games played in 1931 and beyond, is not part of the final data set result.
This yielded a data set of 1,259 players after I removed pitchers Hoyt Wilhelm, Trevor Hoffman, Kent Tekulve, and John Franco, all of whom were in the original data set and played more than 1,000 games, since they’re not relevant.
Here are some summary stats for the players in the data. The "range" is the difference between the high (max) and the low (min):
Measure
|
G
|
PA
|
Rbat
|
Rbaser
|
Rdp
|
Rfield
|
Rpos
|
Rrep
|
Total Runs Above Replacement
|
Career WAR
|
Totals
|
1,955,968
|
7,549,552
|
76,334.1
|
3,727.3
|
(621.6)
|
8,985.0
|
(5,358.7)
|
255,113.4
|
338,233.2
|
33,180.9
|
Min
|
1,000
|
2,101
|
(304.9)
|
(39.0)
|
(46.6)
|
(253.3)
|
(203.8)
|
66.1
|
(48.7)
|
(6.9)
|
Max
|
3,562
|
15,890
|
1,128.4
|
143.8
|
56.2
|
293.6
|
161.7
|
506.6
|
1,646.3
|
162.8
|
Range
|
2,562
|
13,789
|
1,433.4
|
182.8
|
102.8
|
546.9
|
365.6
|
440.5
|
1,695.0
|
169.7
|
Average
|
1,554
|
5,996
|
60.6
|
3.0
|
(0.5)
|
7.1
|
(4.3)
|
202.6
|
268.7
|
26.4
|
A few observations about the basic data resulting from these 1,259 players:
· An important characteristic of WAR data is that negative values are present. Most traditional baseball stats are either counting stats (beginning at zero and then increasing) or a calculation/ratio/percentage of something (like batting average, on-base percentage, winning percentage, etc.). But because WAR components are values relative to something else (relative to an average, or relative to a replacement level), they can be positive or negative.
· It’s an over simplification, and it’s a little tricky given the negative figures, but about 75% of the total run above replacement (which is the sum of all 6 components) is represented by Rrep, the runs above replacement, and the other 25% is represented by the other 5 (the runs above average). Of that "above average" portion, Batting Runs (Rbat) is the biggest driver (it’s more than 90% of the above average figure).
· The largest range, by far, is in Batting Runs (Rbat). Rbat has the highest individual value (1,128.4 runs, Barry Bonds) and the lowest individual value (Negative 304.9 runs, Larry Bowa), a range of 1,433.4 runs, or roughly 143 "wins".
· The smallest range is in RDP, which ranges from a high of 56.2 runs (Ichiro Suzuki) to a low of negative 46.6 runs (Ernie Lombardi).
Here are some the individual highs and lows within my data set for each of the components:
Component
|
Best
|
Best Figure
|
Worst
|
Worst Figure
|
Rbat
|
Barry Bonds
|
1,128.4
|
Larry Bowa
|
(304.9)
|
Rbaser
|
Rickey Henderson
|
143.8
|
David Ortiz
|
(39.0)
|
Rdp
|
Ichiro Suzuki
|
56.2
|
Ernie Lombardi
|
(46.6)
|
Rfield
|
Brooks Robinson
|
293.6
|
Derek Jeter
|
(253.3)
|
Rpos
|
Ozzie Smith
|
161.7
|
David Ortiz
|
(203.8)
|
Rrep
|
Pete Rose
|
506.6
|
Dave Hansen
|
66.1
|
Notable, of course, is that David Ortiz has the lowest figures in 2 different categories – baserunning runs, and the position adjustment, probably not a surprise to any of you, considering how slow he ran and also how much of his career was spent at designated hitter. Ortiz also has negative figures in 2 other components (Rdp and Rfield), but he was such a valuable hitter than he was able to overcome all those negatives.
Here’s another summary table, showing how many players had positive (greater than zero) figures in each component. As you can see, every single player had a positive runs figure in the 6th component (Replacement Runs), which really just reinforces the notion that any player with 1,000 or more Major League games is undoubtedly someone who is above replacement level. Most of the other 5 components, except for Batting Runs, come out close to 50/50 splits, give or take a few percentage points:
Component
|
Total Players
|
Positive Runs
|
Negative or Zero Runs
|
% with Positive
|
Rbat
|
1,259
|
769
|
490
|
61%
|
Rbaser
|
1,259
|
610
|
649
|
48%
|
Rdp
|
1,259
|
595
|
664
|
47%
|
Rfield
|
1,259
|
677
|
582
|
54%
|
Rpos
|
1,259
|
609
|
650
|
48%
|
Rrep
|
1,259
|
1,259
|
0
|
100%
|
I think the following is an important point as well. The table below is from baseball-reference.com and displays the position adjustments that have been used in coming up with a player’s Rpos (the position adjustment) figures over time. I would say it is somewhat similar to (though not in perfect harmony) with the concept of the "Defensive Spectrum" that Bill introduced decades ago, but that it tries to capture the relative value of playing different positions in a more quantitative manner.
The original table on baseball-reference.com shows each season, but for brevity I’m only showing one decade at a time to show how the values have changed over time, starting with 1871 and then moving forward 10 years at a time, culminating with the last year shown in the table (2017).
In general, the sum of the position run adjustments tends to net out at zero or close to zero each year, with relative adjustments changing over time as individual positions become more or less "important" defensively. The list is color-coded with a green/yellow/red model, with greens being high, reds being low, and yellows falling in the middle. The more intense the color, the stronger the effect is in that direction.
Shortstops and catchers have been consistently high over history, starting as high as +10 runs in the early days. Catchers got down as low as +5 in the 30’s, 40’s, and 50’s, but have moved back up since. Shortstops started at +10, and are still relatively high at +7. Left field and right field have been consistently low, starting as low as -10, and improving slightly to the modern -7.
Positions like first base and center field are the most interesting, and have evolved in different directions. First base was more of a neutral position in the early days and then became increasingly less important over time, to the point where it now represents the biggest negative adjustment aside from DH. On the other hand, center fielders started off as one of the more negative adjustments, but evolved over time to more neutral territory and, now, represent a positive adjustment.
Note: "DH" is constant at -15 runs each year, and they are displayed even for years for which there was no DH.
Year
|
runs_c
|
runs_1b
|
runs_2b
|
runs_3b
|
runs_ss
|
runs_lf
|
runs_cf
|
runs_rf
|
runs_dh
|
1871
|
10
|
0
|
3
|
5
|
10
|
-10
|
-8
|
-10
|
-15
|
1881
|
10
|
0
|
3
|
5
|
10
|
-9.5
|
-8
|
-9.5
|
-15
|
1891
|
10
|
0
|
3
|
5
|
10
|
-9.5
|
-8
|
-9
|
-15
|
1901
|
10
|
-3.5
|
1
|
5
|
10
|
-8
|
-5
|
-8.5
|
-15
|
1911
|
10
|
-5
|
0
|
5
|
10
|
-8
|
-4
|
-8
|
-15
|
1921
|
6.5
|
-6.5
|
3.5
|
5
|
10
|
-7
|
-4
|
-7.5
|
-15
|
1931
|
5
|
-7
|
5
|
3.5
|
10
|
-7
|
-2.5
|
-7
|
-15
|
1941
|
5
|
-7
|
6.5
|
1
|
10
|
-7
|
-1.5
|
-7
|
-15
|
1951
|
5
|
-7
|
6.5
|
0
|
9.5
|
-7
|
-1
|
-7
|
-15
|
1961
|
8.5
|
-9
|
4
|
3
|
9
|
-8
|
-1
|
-7
|
-15
|
1971
|
9
|
-9
|
4
|
3
|
9
|
-8
|
-1
|
-7
|
-15
|
1981
|
9
|
-9.5
|
4
|
2
|
8.5
|
-7
|
-0.5
|
-7
|
-15
|
1991
|
8.5
|
-9.5
|
3
|
1
|
8.5
|
-7
|
1.5
|
-7
|
-15
|
2001
|
8.5
|
-9.5
|
3
|
2
|
7.5
|
-7
|
2.5
|
-7
|
-15
|
2011
|
9
|
-9.5
|
3
|
2
|
7
|
-7
|
2.5
|
-7
|
-15
|
2017
|
9
|
-9.5
|
3
|
2
|
7
|
-7
|
2.5
|
-7
|
-15
|
Anyway, I wanted to present that table to help explain why some players (example, Willie Mays or Tris Speaker) end up with a negative position adjustment despite spending most of their careers at what we would normally consider to be a "key" position. It has to do with the table above, and what position adjustments were in effect for the seasons they were active.
OK, so now you have a sense of each component. Next, I wanted to look at the results and who had positives across the board, and other related observations.
The Results
So, if you had to guess, how many of the 1,259 players in the data set have positives in all 6 components? As a clue, keep in mind the following:
1) Every player in the dataset has a positive value for Runs above Replacement.
2) About half the players get eliminated off the bat because they primarily played position(s) that result in a negative adjustment. In general, over this time frame, the players who end up with positive position adjustments have tended to be catchers, shortstops, second basemen, third basemen, and (sometimes) center fielders.
Those of you who are especially sharp might have wondered what would happen if you just simply multiplied each component’s "% of players with positive runs" (the percentages provided in the table above) by the total number of players. In other words:
1,259 players x .61 x .48 x .47 x .54 x .48 x 1.00 = 45 players.
Well, as it turns out….that would be a really good estimate, as there are actually 43 players (about 3.4%) that were able to pull off positives in all 6 components.
Let’s work our way down the WAR leaderboard and take a look at who doesn’t make the grade. Here are the top 13 by career WAR (and remember, this is only data for 1931 and later, so there are a few big names that won’t be present):
Name
|
G
|
Rbat
|
Rbaser
|
Rdp
|
Rfield
|
Rpos
|
Rrep
|
Total Runs Above Replacement
|
Career WAR
|
Barry Bonds
|
2,986
|
1,128.4
|
43.6
|
5.6
|
175.0
|
(101.2)
|
394.8
|
1646.3
|
162.8
|
Willie Mays
|
2,992
|
805.9
|
78.0
|
(8.4)
|
184.7
|
(18.9)
|
453.4
|
1494.6
|
156.1
|
Henry Aaron
|
3,298
|
877.2
|
44.1
|
(11.6)
|
97.6
|
(140.9)
|
496.1
|
1362.9
|
143.0
|
Stan Musial
|
3,026
|
868.7
|
9.9
|
17.4
|
49.8
|
(130.4)
|
458.3
|
1273.7
|
128.6
|
Ted Williams
|
2,292
|
1,050.5
|
5.3
|
7.5
|
(32.3)
|
(95.4)
|
294.1
|
1229.8
|
122.0
|
Alex Rodriguez
|
2,784
|
639.5
|
56.3
|
(5.1)
|
22.9
|
71.6
|
440.4
|
1225.5
|
117.6
|
Rickey Henderson
|
3,081
|
555.4
|
143.8
|
3.4
|
64.4
|
(97.3)
|
449.0
|
1118.9
|
111.1
|
Mickey Mantle
|
2,401
|
801.6
|
50.5
|
12.1
|
(36.9)
|
(34.4)
|
304.9
|
1097.8
|
110.2
|
Frank Robinson
|
2,808
|
728.7
|
34.5
|
(25.4)
|
21.9
|
(146.3)
|
418.0
|
1031.5
|
107.2
|
Mike Schmidt
|
2,404
|
526.8
|
(1.0)
|
(6.6)
|
127.2
|
42.0
|
332.5
|
1021.0
|
106.8
|
Joe Morgan
|
2,649
|
449.8
|
79.5
|
25.0
|
(48.1)
|
78.2
|
373.6
|
958.2
|
100.4
|
Albert Pujols
|
2,971
|
664.8
|
7.2
|
(43.4)
|
137.8
|
(170.8)
|
414.3
|
1010.1
|
99.6
|
Carl Yastrzemski
|
3,308
|
449.5
|
(2.1)
|
(2.5)
|
184.0
|
(171.3)
|
465.9
|
923.8
|
96.5
|
As you can see, none of these quite made the grade in terms of having 6 positives, although some are close. 10 of the 13 had negative position adjustments that took them out of the running, even Willie Mays, which might surprise some of you (it surprised me). Although Mays was primarily a center fielder, for most of his career the position adjustment for center fielders was a small annual negative (typically around minus-1 runs per year). Given a center fielder’s general perception as being an important "up the middle" defensive position, that may be hard for some to accept, but that’s the way the adjustment was applied. As we saw in the position adjustment table earlier, center fielders in more recent years do now receive a positive position adjustment. In any case, Mays also had a second negative runs result, that being in the Double Play Runs component, so that would have taken him out of the running as well.
So, as you can see, none of these top 13 were positive across the board. Barry Bonds, Stan Musial, and Rickey Henderson were positive in all components except for the position adjustment, as Bonds and Henderson were mostly left fielders with Musial splitting time among first base and all 3 outfield positions. Joe Morgan had solid positives in everything except Fielding Runs, which some might question because he had a good defensive reputation and took home a few Gold Glove awards. Alex Rodriguez was oh so close, but had a small negative figure in Double Play Runs. Hank Aaron, Ted Williams, Frank Robinson, Mike Schmidt, Mickey Mantle, and Albert Pujols all had 2 negatives, and Carl Yastrzemski had 3.
As you might have guessed, I stopped at 13 because the next player down is our first across-the-board positive:
Name
|
G
|
Rbat
|
Rbaser
|
Rdp
|
Rfield
|
Rpos
|
Rrep
|
Total Runs Above Replacement
|
Career WAR
|
Eddie Mathews
|
2,391
|
501.6
|
0.7
|
17.7
|
33.2
|
22.3
|
371.4
|
946.7
|
96.1
|
I was kind of surprised to see that Mathews ended up with positives in all components, because I don’t think he’s generally thought of as a great all-around player. He does just barely qualify in the Baserunning Runs component – that’s his closest call. But, anyway, he is our highest career WAR qualifier with 6 positive results.
The next 8 in career WAR after Mathews are Cal Ripken Jr., Roberto Clemente, Adrian Beltre, Mel Ott, Al Kaline, Wade Boggs, George Brett, Chipper Jones. Legends all, but each one has at least one negative WAR component. The next member of the 6-positive club is one that probably wouldn’t surprise you:
Name
|
G
|
Rbat
|
Rbaser
|
Rdp
|
Rfield
|
Rpos
|
Rrep
|
Total Runs Above Replacement
|
Career WAR
|
Ken Griffey Jr.
|
2,671
|
440.2
|
15.7
|
9.2
|
3.4
|
20.7
|
391.3
|
880.4
|
83.8
|
Now, Griffey Jr., like Mays, was primarily a center fielder, but by the time Griffey Jr. came along, the position adjustment for center fielders had turned from a slight negative to a slight positive, which worked in Griffey Jr’s favor. Griffey’s pure Fielding Runs figure is way behind Mays’, but he did at least come out on the positive side of the ledger.
For the sake of brevity, here is the full list of the 43 "across the board" positives. The list is sorted by Career WAR, with Hall of Famers highlighted in yellow.
Name
|
G
|
Rbat
|
Rbaser
|
Rdp
|
Rfield
|
Rpos
|
Rrep
|
Total Runs Above Replacement
|
Career WAR
|
Eddie Mathews
|
2,391
|
501.6
|
0.7
|
17.7
|
33.2
|
22.3
|
371.4
|
946.7
|
96.1
|
Ken Griffey Jr.
|
2,671
|
440.2
|
15.7
|
9.2
|
3.4
|
20.7
|
391.3
|
880.4
|
83.8
|
Arky Vaughan
|
1,817
|
356.9
|
12.1
|
34.0
|
21.0
|
97.1
|
244.4
|
765.6
|
78.0
|
Luke Appling
|
2,416
|
233.1
|
18.3
|
9.5
|
42.0
|
149.2
|
341.7
|
793.7
|
77.6
|
Lou Whitaker
|
2,390
|
209.4
|
32.1
|
16.0
|
76.7
|
71.3
|
344.2
|
749.7
|
75.1
|
Alan Trammell
|
2,293
|
132.3
|
25.0
|
14.4
|
76.9
|
133.3
|
323.2
|
705.1
|
70.7
|
Barry Larkin
|
2,180
|
200.2
|
80.7
|
3.7
|
17.6
|
124.4
|
282.9
|
709.5
|
70.5
|
Pee Wee Reese
|
2,166
|
30.3
|
55.7
|
8.2
|
117.0
|
132.2
|
339.7
|
683.2
|
68.4
|
Kenny Lofton
|
2,103
|
139.9
|
78.9
|
23.2
|
107.9
|
43.4
|
318.0
|
711.2
|
68.4
|
Ryne Sandberg
|
2,164
|
191.9
|
33.6
|
11.1
|
60.0
|
66.3
|
294.8
|
657.6
|
68.0
|
Chase Utley
|
1,937
|
172.5
|
44.8
|
23.9
|
131.0
|
43.6
|
243.5
|
659.4
|
64.5
|
Ken Boyer
|
2,034
|
183.7
|
13.5
|
6.1
|
73.0
|
29.3
|
305.8
|
611.4
|
62.8
|
Charlie Gehringer
|
1,591
|
289.9
|
33.9
|
15.5
|
42.0
|
53.7
|
246.7
|
681.7
|
62.8
|
Jackie Robinson
|
1,382
|
259.4
|
31.9
|
9.1
|
80.8
|
19.7
|
218.1
|
619.1
|
61.7
|
Sal Bando
|
2,019
|
205.9
|
11.4
|
3.5
|
36.3
|
37.7
|
289.4
|
584.2
|
61.5
|
Yogi Berra
|
2,120
|
225.7
|
11.6
|
11.0
|
33.7
|
58.6
|
254.3
|
595.2
|
59.4
|
Billy Herman
|
1,922
|
156.6
|
8.2
|
2.4
|
55.0
|
68.0
|
267.2
|
557.7
|
57.3
|
Jim Fregosi
|
1,902
|
138.5
|
9.4
|
9.5
|
2.8
|
74.7
|
240.3
|
475.3
|
48.7
|
Mike Cameron
|
1,955
|
70.5
|
40.9
|
5.0
|
71.2
|
33.5
|
268.4
|
489.7
|
46.7
|
Chuck Knoblauch
|
1,632
|
104.5
|
42.8
|
1.9
|
26.1
|
27.5
|
262.9
|
465.8
|
44.6
|
Lonny Frey
|
1,535
|
50.7
|
23.0
|
23.0
|
57.0
|
67.2
|
204.0
|
425.0
|
44.5
|
Ben Zobrist
|
1,651
|
122.9
|
10.0
|
11.4
|
51.0
|
5.3
|
239.6
|
440.0
|
44.5
|
Nomar Garciaparra
|
1,434
|
189.0
|
0.7
|
3.4
|
15.0
|
49.4
|
212.6
|
470.2
|
44.3
|
Lenny Dykstra
|
1,278
|
132.8
|
45.9
|
5.8
|
45.7
|
20.6
|
162.2
|
413.0
|
42.4
|
Harlond Clift
|
1,582
|
167.8
|
22.8
|
1.3
|
3.0
|
17.9
|
232.0
|
444.9
|
42.1
|
Eddie Stanky
|
1,259
|
127.2
|
10.7
|
2.0
|
24.8
|
57.4
|
188.0
|
409.9
|
41.4
|
Andy Van Slyke
|
1,658
|
122.1
|
30.7
|
7.3
|
26.1
|
1.5
|
211.1
|
398.6
|
41.3
|
Gil McDougald
|
1,336
|
93.7
|
6.7
|
1.9
|
89.7
|
43.3
|
164.7
|
400.1
|
40.6
|
Eric Chavez
|
1,615
|
94.3
|
9.1
|
2.6
|
40.7
|
17.9
|
229.8
|
394.6
|
38.3
|
Ray Lankford
|
1,701
|
172.5
|
4.7
|
3.3
|
2.7
|
9.9
|
205.6
|
398.5
|
38.2
|
Garry Maddox
|
1,749
|
7.3
|
15.0
|
13.1
|
99.9
|
3.2
|
222.8
|
361.5
|
36.8
|
Don Money
|
1,720
|
70.3
|
3.0
|
0.6
|
22.8
|
16.1
|
242.1
|
355.0
|
36.5
|
Johnny Pesky
|
1,270
|
86.8
|
15.5
|
16.6
|
17.6
|
45.3
|
154.7
|
336.4
|
34.3
|
Robby Thompson
|
1,304
|
67.3
|
13.7
|
5.7
|
36.1
|
37.8
|
168.4
|
329.0
|
33.8
|
Shane Victorino
|
1,299
|
28.0
|
37.9
|
15.3
|
72.0
|
1.5
|
161.6
|
316.5
|
31.5
|
Jacoby Ellsbury
|
1,235
|
20.4
|
36.1
|
7.9
|
30.0
|
20.0
|
188.8
|
303.2
|
31.2
|
Brian Roberts
|
1,418
|
17.5
|
13.1
|
4.2
|
8.0
|
34.5
|
220.9
|
298.3
|
29.5
|
Red Rolfe
|
1,175
|
21.9
|
24.8
|
27.1
|
40.0
|
18.9
|
180.1
|
312.8
|
29.1
|
Edgardo Alfonzo
|
1,506
|
65.7
|
8.6
|
1.0
|
16.6
|
32.9
|
188.8
|
313.6
|
28.7
|
Denard Span
|
1,359
|
39.9
|
16.7
|
6.1
|
8.0
|
12.3
|
197.5
|
280.5
|
27.9
|
Lloyd Moseby
|
1,588
|
17.5
|
22.8
|
7.3
|
8.4
|
3.0
|
224.2
|
283.1
|
27.6
|
Billy Goodman
|
1,623
|
28.6
|
11.8
|
10.4
|
20.2
|
5.5
|
193.2
|
269.7
|
27.2
|
Jean Segura
|
1,230
|
2.8
|
20.7
|
2.2
|
18.0
|
57.3
|
169.9
|
271.0
|
26.2
|
Because the Position Adjustment Runs is a key component, this is heavily dominated by players up the middle and third basemen, although Berra was the only catcher. If you classify by primary position, here’s the distribution:
Position
|
Count
|
2b
|
14
|
cf
|
11
|
ss
|
9
|
3b
|
8
|
c
|
1
|
At this point, I thought I’d get a little more selective, because even though the result was only about 3% of the data set, a lot of these players qualify as "positive" in certain categories by an oh-so-slim margin.
So, rather than looking for players who were simply "positive" across all 6, how about those who achieved a higher level in all 6? Below are the players from the previous result set who were in the 75th percentile or above (within the dataset) in all 6 categories:
Name
|
Rbat
|
Rbaser
|
Rdp
|
Rfield
|
Rpos
|
Rrep
|
Career WAR
|
Rbat Pctile
|
Rbaser Pctile
|
Rdp Pctile
|
Rfield Pctile
|
Pctile Rpos
|
Rrep Pctile
|
Gehringer
|
289.9
|
33.9
|
15.5
|
42.0
|
53.7
|
246.7
|
62.8
|
92.5
|
93.7
|
93.0
|
79.8
|
82.1
|
75.9
|
Appling
|
233.1
|
18.3
|
9.5
|
42.0
|
149.2
|
341.7
|
77.6
|
88.6
|
86.6
|
83.3
|
79.8
|
99.6
|
95.0
|
Whitaker
|
209.4
|
32.1
|
16.0
|
76.7
|
71.3
|
344.2
|
75.1
|
85.5
|
93.4
|
93.3
|
91.7
|
89.8
|
95.1
|
Sandberg
|
191.9
|
33.6
|
11.1
|
60.0
|
66.3
|
294.8
|
68.0
|
83.3
|
93.6
|
87.9
|
86.2
|
87.6
|
88.9
|
Utley
|
172.5
|
44.8
|
23.9
|
131.0
|
43.6
|
243.5
|
64.5
|
81.1
|
96.5
|
97.9
|
96.9
|
76.5
|
75.0
|
Lofton
|
139.9
|
78.9
|
23.2
|
107.9
|
43.4
|
318.0
|
68.4
|
76.4
|
99.3
|
97.6
|
95.5
|
76.3
|
92.1
|
So, now we’re down to just 6 players: 4 second basemen, a shortstop, and a center fielder. And, if we got even more restrictive (say, 80th percentile or above)? That would get us down to two:
Name
|
Rbat
|
Rbaser
|
Rdp
|
Rfield
|
Rpos
|
Rrep
|
Career WAR
|
Rbat Pctile
|
Rbaser Pctile
|
Rdp Pctile
|
Rfield Pctile
|
Pctile Rpos
|
Rrep Pctile
|
Whitaker
|
209.4
|
32.1
|
16.0
|
76.7
|
71.3
|
344.2
|
75.1
|
85.5
|
93.4
|
93.3
|
91.7
|
89.8
|
95.1
|
Sandberg
|
191.9
|
33.6
|
11.1
|
60.0
|
66.3
|
294.8
|
68.0
|
83.3
|
93.6
|
87.9
|
86.2
|
87.6
|
88.9
|
How about that? After all the whittling down, the only 2 players who were at the 80th percentile or above across all 6 components are the players who were generally considered the best AL and the best NL second basemen of the 1980’s (although some might opt for Willie Randolph over Whitaker in the AL). But, I think these 2 would generally be the consensus picks.
And, of course, those who know me would probably take this opportunity to remind me that I am generally not an advocate for Whitaker’s selection to the Hall of Fame (which is true). But, it does point out one thing that I do believe does hold true for Whitaker, and that he is a very good all-around player who didn’t really have any big weaknesses. He had a long career and had an extremely high number of good seasons, 3 and 4 WAR type seasons, but he didn’t have many high ones (5.0 or over) and he also didn’t have any real poor ones. He stayed in the middle lanes for virtually his entire career. This is kind of a similar dynamic – Whitaker was good across the board in all the components of WAR that help result in a positive figure, and didn’t have any bad categories. That’s basically the type of player he was.
Pre-1931 and the Negro Leagues
One last analysis before calling it a day. As mentioned earlier, data from pre-1931 and data from the Negro Leagues for Double Play Runs (Rdp) is just a bunch of zeros, so I excluded all pre-1931 seasons as well as excluding all Negro League seasons.
But what if we did a partial query? That is, what if we looked for players from those 2 segments who registered positives in the other 5 categories other than Rdp?
Using the same criteria (1,000+ games) for NL/AL players, there are 290 players in that dataset. 15 of those players had positives in the 5 non-Rdp categories (about 5%). The list is sorted by Career WAR, with Hall of Famers highlighted in yellow.
Name
|
G
|
PA
|
Rbat
|
Rbaser
|
Rfield
|
Rpos
|
Rrep
|
Total Runs Above Replacement
|
Career WAR
|
Honus Wagner
|
2,794
|
11,759
|
637.7
|
33.7
|
85.0
|
107.2
|
373.6
|
1237.4
|
130.8
|
Eddie Collins
|
2,826
|
12,082
|
627.8
|
39.2
|
35.0
|
36.5
|
437.9
|
1176.3
|
124.4
|
George Davis
|
2,372
|
10,178
|
278.3
|
19.6
|
146.0
|
91.0
|
349.5
|
884.7
|
84.9
|
Bill Dahlen
|
2,444
|
10,435
|
135.4
|
11.5
|
139.0
|
144.9
|
344.2
|
775.3
|
75.2
|
Frankie Frisch
|
1,565
|
6,981
|
168.3
|
28.7
|
141.0
|
53.7
|
207.2
|
598.7
|
59.9
|
Jack Glasscock
|
1,699
|
7,372
|
132.9
|
15.7
|
146.0
|
106.5
|
248.0
|
649.0
|
59.5
|
Wally Schang
|
1,812
|
6,347
|
159.3
|
1.8
|
4.0
|
55.7
|
238.9
|
459.9
|
48.1
|
Johnny Evers
|
1,784
|
7,226
|
80.8
|
2.3
|
127.0
|
3.4
|
226.4
|
439.9
|
47.7
|
John McGraw
|
1,067
|
4,815
|
304.2
|
12.1
|
9.0
|
33.9
|
157.5
|
516.8
|
45.7
|
Buck Ewing
|
1,232
|
5,380
|
176.3
|
21.8
|
70.0
|
29.4
|
180.2
|
477.8
|
44.2
|
Hughie Jennings
|
1,196
|
5,266
|
168.2
|
12.6
|
68.0
|
48.2
|
174.8
|
471.8
|
41.5
|
Art Devlin
|
1,313
|
5,245
|
68.3
|
7.4
|
46.0
|
40.2
|
166.1
|
328.0
|
36.1
|
Bid McPhee
|
1,227
|
5,448
|
74.3
|
1.8
|
87.0
|
20.9
|
182.2
|
366.3
|
33.0
|
Hank Gowdy
|
1,050
|
3,145
|
27.1
|
4.4
|
55.0
|
38.1
|
97.0
|
221.5
|
23.8
|
Bob O'Farrell
|
1,222
|
4,065
|
24.3
|
7.6
|
23.0
|
37.2
|
125.3
|
217.5
|
21.9
|
5 Shortstops, 4 second basemen, 4 catchers, and 2 third basemen.
The top players from that era who had one or more negative components include Ty Cobb, Tris Speaker, Babe Ruth, Rogers Hornsby, and Nap Lajoie. Cobb, Speaker, and Ruth had negative figures in the position adjustment component, and Ruth, Hornsby, and Lajoie had negative baserunning run figures.
How about Negro Leaguers? I reduced the games played threshold to 300 to account for the fact that we don’t have complete career data for Negro League players, and as a result the "games played" stats that have been captured tend to be significantly lower than what we tend to see for NL & AL players.
For this pull, there are 146 players in the dataset, with 7 (about 5%) showing 5 positives:
Name
|
G
|
PA
|
Rbat
|
Rbaser
|
Rfield
|
Rpos
|
Rrep
|
Total Runs Above Replacement
|
Career WAR
|
Willie Wells
|
1,039
|
4,538
|
309.8
|
5.9
|
36.7
|
64.7
|
147.3
|
564.6
|
51.0
|
Dobie Moore
|
453
|
2,008
|
118.5
|
0.1
|
52.3
|
29.8
|
66.6
|
267.3
|
24.5
|
Newt Allen
|
945
|
4,080
|
2.3
|
3.0
|
47.3
|
32.7
|
132.3
|
217.6
|
20.5
|
Bill Riggins
|
677
|
2,945
|
28.9
|
1.5
|
29.2
|
40.1
|
98.4
|
198.1
|
18.3
|
Sam Bankhead
|
629
|
2,710
|
1.1
|
4.3
|
19.1
|
21.4
|
86.0
|
133.8
|
12.1
|
Pythias Russ
|
311
|
1,260
|
35.2
|
1.8
|
9.7
|
14.5
|
42.9
|
104.0
|
10.0
|
Tom Young
|
345
|
1,190
|
18.1
|
1.5
|
2.6
|
9.6
|
40.7
|
72.5
|
6.9
|
These names probably aren’t as recognizable to most of you with the exception of Hall of Famer Willie Wells, although I’m sure some of you are familiar with at least some of the others like Moore and Allen. All of the other well-known Negro League stat hitters such as Turkey Stearnes, Oscar Charleston, Josh Gibson, Mule Suttles, Jud Wilson, Cool Papa Bell, Buck Leonard, Biz Mackey, Cristóbal Torriente, Bullet Rogan, and John Henry Lloyd had at least one negative category, typically either Baserunning Runs (Gibson, Suttles, Leonard, Mackey, Lloyd) or the Position Adjustment category (Stearnes, Charleston, Suttles, Wilson, Bell, Leonard, Torriente, Rogan).
Wrapping it Up
In conclusion, regardless of the league or the era, there are very few players who manage to post positive, across-the-board figures in all WAR-component categories. In the different pulls I did (which had minimum game thresholds), about 4% of the players were able to achieve that status. If we took all players without any regard to number of games, and if even if we included those with a 0.0 Rdp figure, it only happens about 0.5% of the time.
Thank you for reading,
Dan