Page rendered in 0.2324 seconds
59 querie(s) executed
— Where BTF's Members Investigate the Grand Old Game
Wednesday, April 11, 2001
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about the season just past and the season to come?one aspect of the book’s subject matter?they also responded to the historical content accompanying the more topical material.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)
a) 700-800 runs
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:
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.
You must be logged in to view your Bookmarks.
Loser Scores 2015
(12 - 2:28pm, Nov 17)
Loser Scores 2014
(8 - 2:36pm, Nov 15)
Winning Pitcher: Bumgarner....er, Affeldt
(43 - 8:29am, Nov 05)
Last: ERROR---Jolly Old St. Nick
What do you do with Deacon White?
(17 - 12:12pm, Dec 23)
Last: Alex King
(15 - 12:05am, Oct 18)
Nine (Year) Men Out: Free El Duque!
(67 - 10:46am, May 09)
Who is Shyam Das?
(4 - 7:52pm, Feb 23)
Last: RoyalsRetro (AG#1F)
Greg Spira, RIP
(45 - 9:22pm, Jan 09)
Last: Jonathan Spira
Northern California Symposium on Statistics and Operations Research in Sports, October 16, 2010
(5 - 12:50am, Sep 18)
Mike Morgan, the Nexus of the Baseball Universe?
(37 - 12:33pm, Jun 23)
Last: The Keith Law Blog Blah Blah (battlekow)
Sabermetrics, Scouting, and the Science of Baseball – May 21 and 22, 2011
(2 - 8:03pm, May 16)
Last: Diamond Research
Retrosheet Semi-Annual Site Update!
(4 - 3:07pm, Nov 18)
What Might Work in the World Series, 2010 Edition
(5 - 2:27pm, Nov 12)
Last: fra paolo
Predicting the 2010 Playoffs
(11 - 5:21pm, Oct 20)
SABR 40: Impressions of a First-Time Attendee
(5 - 11:12pm, Aug 19)
Last: Joe Bivens, Floundering Pumpkin