The All-OBP Team

Don exams the practical limits of getting on base.

Seeking the Practical Limits of Offense

During the long run of the BBBA series, Brock Hanke and I were blessed   with readers who were both loyal and indulgent?more than that, they were pretty   darned smart. While they were interested in information about the season just   past and the season to come?one aspect of the book’s subject matter?they also   responded to the historical content accompanying the more topical material.   We’d often get interesting notes written on the back of an envelope or an order   form?communications that would send us off on some area of research that we   hadn’t thought of previously.

That tradition was continued in 2001, and one of the examples of this comes   from long-time BBBA reader Duane Thomas, who has bought enough copies   of BBBA and its predecessors over the past dozen years to remember our fixation   with "walkmen"?that special breed of batters who know how to keep   their bats on their shoulders.

Duane wrote in this year with the following question:

"If I had the following batting order for 162 games, how many runs would   my team score?

1.       Eddie Stanky 2b (1945)
  2.       Richie Ashburn cf (1955)
  3.       Eddie Yost 3b (1950)
  4.       Ferris Fain 1b (1951)
  5.       Roy Cullenbine rf (1946)
  6.       Eddie Joost ss (1949)
  7.       Elmer Valo lf (1949)
  8.       Eddie Lake dh (1945)
  9.       Wes Westrum c (1951)

a)       700-800 runs
  b)      800-900 runs
  c)       900-1000 runs
  d)      1000-1100 runs
  e)       1100+ runs
  f)        games go on too long"

Now before I go on to try and answer this question for Duane, I urge all of   you out there reading to stop right where you are, go to the primer "add   a comment" option (one of the things that makes this site unique, I might   add?), and choose between a) and e).

I think we can safely say that f) would also be the case, since we have the   largest collection of true "walkmen" in one lineup here. A "true"   walkman is a batter who draws walks on his batting eye, not on his fearsomeness   at the plate. Babe Ruth, Ted Williams and Frank Thomas are all "walkmen,"   but something close to half their walks occur because pitchers are afraid to   give them a good ball to hit.

That’s not the case with Duane’s team. There is one Hall of Famer in this lineup?Richie   Ashburn. The rest of the batting order features the cream of the post-WWII walk   boom?that temporary phenomenon between 1947 and 1955 when the base on balls   was king (for a little while, at least).

What’s intruiging about this question is that Duane has picked a team that   could probably be assembed on the cheap even today; it certainly could have   been back when theese players were in their heyday. This is a well-traveled   starting nine?only the catcher (Westrum) stayed with the team he came up with   for his entire career.

On-Base Times Slugging (OBP x SLG) and Run Scoring

Let’s try to answer Duane’s question. There is a simple tool, touted for years   by Brock Hanke, which can help us here. While many of us are now intimately   familiar with OPS (on-base plus slugging), it’s far less well-known that multiplying   these offensive components creates a useful runs per at-bat ratio that   can be used to project run scoring.

Let’s walk through it one time just to get the hang of it. Let’s say your team   has an aggregate OBP of .330 (that’s not good in today’s era of inflated offense,   but pretend you’re a long-suffering fan in a "small market" like Philadelphia).   Let’s say that the team is also underpowered (too many Doug Glanvilles), and   has a combined SLG of just .400 (the 2000 NL had an aggregate SLG of .432, just   for comparison).

Multiply .330 by .400 and you get a runs per at-bat ratio of .1320 (not too   far off MLB’s historical average, by the way). We’ve found that by multiplying   this figure by the team at-bats, and then multiplying the result by 1.035, you   get a pretty accurate projection of runs scored.

In this case, the calculation is (.330 x .400) = .1320 x 5500 = 726 x 1.035   = 751 runs.

Whether this is a good or a bad offense depends on the league context, of course.   It’s good in 1968, for example. But it’s not so good in 2000, and probably not   in 2001, either.

We’ve come to view the calculation above as expressing a range for run scoring   (a team with the above OBP x SLG will tend to score between 726 to 751 runs).   Teams fall into this range around 70% of the time, and when they are out of   the range, they’re not far out of it. There are more accurate methods, but none   are as simple and as easy to use as this one.

Armed with this tool, let’s tackle Duane’s question.

First, let’s take a look at some of the key offensive statistics for the players   on Duane’s team in the years he has selected to use. (Like the Legends of   Baseball league that you’ve read about in my weblog, Duane has chosen peak   seasons for his players.)

Player?????? Year??? BB??? BA?? OBP?? SLG
Stanky?????? 1945?? 148? .258? .417? .333
Ashburn????? 1955?? 105? .338? .449? .448
Yost???????? 1950?? 141? .295? .440? .405
Fain???????? 1951? ? 80  .344? .451? .471
Cullenbine?? 1946??  88? .335? .477? .537
Joost??????? 1949?? 149? .263? .429? .453
Valo???????? 1949?? 119? .283? .413? .404
Lake???????? 1945?? 106? .279? .412? .410
Westrum???   1951?? 104? .219? .400? .418
TOTAL????    1040?       .290? .426? .425

The salient feature of this team, of course, is its ability to get on base.   If this team could duplicate the chosen seasons, it would shatter the all-time   record for team OBP (.385, held by the 1950 Boston Red Sox and the 1921 Detroit   Tigers).

That’s reinforced by the walk total. Those 1040 walks were achieved in only   5300 plate apperances (Fain, Cullenbine, and Westrum were not full-time players   in the years chosen). Even so, this figure would also shatter the all-time record   for walks (835, held by the 1949 Boston Red Sox).

Extrapolating that figure out to a full season’s worth of plate appearances   (around 6300) gives us the amazing and probably impossible total of 1209. (Why   is that impossible? I’ll talk about that a bit later.)

Using OBP times SLG, we get a runs/at-bat ratio of .1811. Multiplying that   by 5100 at-bats (we subtract the number of walks from 6300 to obtain an estimate   of at-bats) and adding in the 3.5% boost, we come up with an estimate of 956   runs scored for Duane’s team.

That’s excellent, but it’s not especially close to the all-time record for   runs scored (we’re talking twentieth century): 1067, by the 1931 New York Yankees.

Why doesn’t a team with .040 more OBP than any team since 1901 score more runs?   Lack of power. That .425 SLG is better than average, but much of it comes from   a high batting average (.290). Duane’s walkman team only projects to hit in   the realm of 110-120 homers, which would place them at the bottom of the team   rankings now. Back in the 50s this would be a middle-of-the-pack total.

Another tip-off in terms of this team’s imbalance can be seen in its runs scored   and RBI totals. For the seasons Duane selected, the nine members of the walkman   team scored 813 runs, but drove in only 539. Only Westrum, who batted eighth   most of the time for the Giants due a low batting average, drove in more runs   than he scored. This is a team of table-setters, not table-clearers.

Something would have to change in the hitting dynamic with a team of this type.   One or more hitters would need to swing the bat more in order to drive in runs.   Pitchers would adjust, and throw more strikes, because these players are all   taking advantage of game and lineup contexts in terms of drawing excess walks.

In short, it would be impossible for this team to do as a unit what the members   of the lineup were able to do individually.

Oh, they’d still draw a lot of walks?they’d break the record with some to spare.   But they’d probably lose at least 20% of their margin, and finish with about   950 or so.

Holding all of the other factors even, the likely OBP for this team would be   more like .398 than .426.

Suprisingly, that reduces the estimated runs scored by only about 30-35 runs   (924 instead of 956). Why is that?

Because all that excess OBP, derived from walks, is less valuable in terms   of scoring runs than the other elements of offense. While it is fashionable   to call RBIs "a meaningless stat" and to deride them due to their   "situation dependent" nature, the truth of the the matter is that   offense has two components?getting on base (OBP) and driving runs in (SLG)?that   must work together in some kind of balance in order to be optimized.

The OBP x SLG model gives us a simple and reliable overview for that. More   sophisticated and specialized methods can eliminate distortions that have been   found at the extremes of such projections, but in the ranges that occur for   team performance, you don’t really need anything more complicated.

Career Year Reality Check

One final note about Duane’s team. It’s a fanciful idea, and one that’s fun   to contemplate. But it’s impossible?not just because of the theoretical elements   noted above.

Simply put, teams don’t reach a monolithic peak all at once. Duane’s walkman   team is a collection of players having their best years (or very close to them)   in the same year. This just doesn’t happen, except in simulations. And even   there, simulations such as Diamond   Mind Baseball won’t permit a team whose players combined for a   .440 OBP to produce anything close to that when the simulated season is run.

The reason for this is what Bill James called The Law of Competitive Balance.   This law operates in a different manner on teams than it does on individuals.   One is more complex than the other, and while we can do a reasonably good job   of projecting hitters’ future performance, the variables that exist there become   harder to manage when we’re asked to project an entire lineup or an entire roster.

So while we can get an accurate measure of run scoring after the fact from   the OBP x SLG model, we have no truly reliable way to project what those values   will be before the fact at the team level.

For a team today, we’d simply add up the individual projections based on age   and performance-to-date. For a team like Duane’s, where the players are all   long retired, we could try one of two possible approaches?career averages   or career-to-date including the "peak" year.

The hope is that such an exercise in modeling will "even out the errors"   in the individual performances and produce a reasonably accurate result.

Cutting Duane’s lineup down to the career averages of its hitters takes a lot   of starch of its run-producing ability. Interestingly, it takes more away from   the SLG component than the OBP component, which probably hurts its offense more   than if it were the other way around. Here are the values:

Type?????? OBP??? SLG? Runs

Peak???   .426?? .425?? 956

Career??? .384?? .375?? 820

Diff??   -.042? -.050? -130

What’s interesting here, I think, is that we get a range for how much run scoring   fluctuation is possible from a team composed of the same players from year to   year. It’s a crude figure, to be sure, but it’s probably a reasonable rule of   thumb.

Since teams don’t usually remain static year after year, of course, such a   range doesn’t express the total potential for variability. You might think of   this as representing the "inherent variability" in run scoring.

Will anyone ever assemble a team with a .400 OBP? It’s happened three times   in baseball history?all in the same year. This feat, which occurred in 1894?baseball’s   most offensive year ever?was not due to a proliferation of "walkman"   teams, however. A hard rain of singles, doubles and triples was the cause, as   three teams?the Baltimore Orioles, Boston Beaneaters, and Philaldelphia Phillies?all   hit .330 or higher. It took that level of batting average to push OBP to .400   and over, and it’s something that we’re likely never to see again.

The only place we’re likely to assemble a .400 OBP team, alas, is in our dreams.   But that’s no reason why Duane?and the rest of us?shouldn’t keep on dreaming.