As a kid, when the paper arrived I would immediately dive for the sports

section, a move closely followed by flipping past all the articles and heading

right for the “Scoreboard” page where I could scan the box scores over a bowl of

Chocolate Frosted Sugar Bombs. This frantic ritual had its pros (a quick summary

of yesterday’s action) and cons (I missed lots of headlines–for example, I didn’t

realize for two days that Buster Douglas had actually beaten Mike Tyson because

that somehow didn’t make the scoreboard page), but it did make me intimately

familiar with the old newspaper box score format.

Box scores are disappearing. While we still find them in their traditional

format in newspapers and across the Web, the ability to read several articles about

each game, daily updated stat reports, and play-by-play logs largely nullify the

need to manually keep track of how many home runs your favorite player has or to

discern the events of each game from a very limited set of numbers. The days of trying

to figure out how a player scored a run without an AB or why another player has

one fewer plate appearance than everyone else despite being in the middle of the

order are–for the most part–gone.

As the box score loses its place, certain stats become a little harder to find. In

that section just below the player-by-player lines, a quick summary of various game

events like errors, doubles, triples, home runs, and double plays was always

included. While those stats are meticulously and popularly maintained, there

hasn’t been as much discussion lately of our old friend LOB (left on base). Unless

you’re an A’s fan, a group that seems to be on the verge of having to add a counter

to the outfield bleachers to keep track of all the green and gold left stranded on

the bases.

Through Tuesday’s games, the A’s had scored 95 runs this season, good for second

to last in the majors, tied with Cleveland and ahead of Pittsburgh. During that

same time, they’ve left 209 men on base. That sounds like quite a lot, but let’s

see how it compares to the rest of the league. Pittsburgh, also down in the

basement with the A’s in run scoring, is at 178 LOB and 79 runs scored. Cleveland

has 173 and 95. So the A’s aren’t terribly outside the norm when looking at their

basement compatriots.

Since offense can be thought of in two component parts–getting men on base and

driving them in–LOB can be thought of as potential runs, fulfilling half of that

equation, but not the other. Thus, looking at runs scored as a percentage of LOB +

R (called “runner scoring percentage” for now) can give an idea of whether or not a

team is lacking in one department or the other. While this ignores events like

double plays and caught stealings, it should still give us a rough idea of offenses

that are good at one component, or the other.

In 2005, the Pirates have plated 30.74% of their potential runs. The A’s have

scored 31.25%, the Indians 35.45%. Just as before, the A’s look to be in the

middle of the pack when comparing them to the other two meager offenses this year.

But comparing the Pirates and A’s to every team since 1990, they come in dead last.

In the past 16 years, no two teams have been as bad at driving in runners they put

on base as the A’s and Pirates. For comparison’s sake, here are the top and bottom

ten teams since 1990 in runner scoring percentage:

YEAR TEAM LOB R AVG OBP SLG R% ---- ---- ---- --- --- --- --- ----- 1994 CLE 762 679 .290 .348 .484 47.12 2000 CHA 1127 978 .286 .352 .470 46.46 1996 COL 1108 961 .287 .350 .472 46.45 2004 CHA 1031 865 .268 .330 .457 45.62 1995 CLE 1018 840 .291 .358 .479 45.21 1996 BAL 1154 949 .274 .348 .472 45.13 1997 COL 1124 923 .288 .353 .478 45.09 2005 DET 161 132 .273 .331 .433 45.05 1999 CLE 1234 1009 .289 .370 .467 44.98 2000 COL 1198 968 .294 .358 .455 44.69 ---- ---- ---- --- --- --- --- ----- 1990 PHI 1242 646 .255 .324 .363 34.22 2003 LAN 1108 574 .243 .299 .368 34.13 1992 CHN 1148 593 .254 .303 .364 34.06 1990 SLN 1164 599 .256 .316 .358 33.98 1990 HOU 1132 573 .242 .309 .345 33.61 1992 BOS 1215 599 .246 .318 .347 33.02 1993 FLO 1189 582 .248 .311 .346 32.86 1992 LAN 1138 548 .248 .308 .339 32.50 2005 OAK 209 95 .237 .311 .338 31.25 2005 PIT 178 79 .230 .299 .359 30.74

When Will Carroll asked me about this on BP radio two weeks ago, I

quickly responded that the reason for the A’s struggles is their reliance on players

with high on-base percentages (OBP)

and not necessarily high slugging percentages (SLG).

Especially in the A’s situation, in which they’ve sought out players with

deceptively high OBPs built mostly on walks rather than batting average, a team

built on walks rather than slugging would seem to strand more runners than

one built on batting average (AVG).

The reason for this is quite simple: It’s hard to take the extra base on a walk.

But the Pirates don’t necessarily fall into that category. Their team line of

.230/.299/.359 is objectively terrible, but their SLG and isolated power (ISO)

are both higher than the A’s (.237/.311/.338) by margins of .021 and .028, and the

Pirates have a higher ISO than any team in the bottom 10 in runner scoring

percentage. If anything, Pittsburgh’s better power numbers should mean that they

would score a higher percentage of their runners on base than the A’s, but that’s

not the case.

Before jumping to any conclusions based on limited amounts of data, let’s expand

things to the full 15+ years worth of data we’ve got on hand. Of the three major

rate stats, SLG has the highest correlation to runner scoring percentage, meaning

that we can expect a team’s slugging percentage to account for most of the changes

in runner scoring percentage. In this case the correlation is positive, meaning

the higher the slugging percentage, the higher the runner scoring percentage.

Doing the same analysis for AVG and OBP reveals that all three stats have solid

positive correlations; so as offense increases overall, the percentage of runners

on base who score increases as well. Again, this is just a logical extension of the

fact that there are only three bases where runners can be stranded, so as teams put

more runners on those bases, more of them have to score.

Essentially, teams can only strand up to three runners per inning, but they can

hypothetically score an infinite number of runs.

So if all three major rate stats have positive correlations to runner scoring

percentage, we cannot say that teams with high OBP and low SLG will have a lower

runner scoring percentage–not exactly, anyway. Because both OBP and SLG encompass AVG to

some degree, the positive correlation of AVG may be overshadowing what we’re really

looking for. Instead, we can run a multivariable regression using all three major

rate stats against runner scoring percentage. Doing so yields the following

equation:

Runner Scoring Percentage = 0.40*AVG + -0.21*OBP + 0.69*SLG + 0.07 (+/- 0.01)

Note that when AVG and SLG are included in the regression, OBP actually has a

negative effect on runner scoring percentage. This is exactly what we suspected:

if AVG and SLG are held steady, increasing OBP (in this case only in the form of

walks because AVG is constant) results in more baserunners, but not nearly as many

runs as we’d expect if those baserunners reached on hits instead of walks. This

doesn’t mean that walks are a bad thing; it just means that teams with a

disproportionate percentage of their baserunners coming on walks will have a higher

percentage of their baserunners left on base than teams whose baserunners come from

hits.

Believe it or not, there’s actually some hope here for A’s and Pirates fans (and

even Cleveland fans). Instead of using AVG, OBP, and SLG in the regression, we can

try to remove the AVG component of OBP and SLG. For SLG, this is simply ISO. For

OBP, it can be a little trickier because the denominators for the two stats are

different, but to keep things simple, we’ll just use OBP-AVG and call it ISO_BB for

now. It’s not technically correct, but it still gives us a good idea of teams

whose OBP is built more on walks than hits. Running things again, we now get this

formula with a virtually identical correlation:

Runner scoring percentage = 0.88*AVG + -0.21*ISO_BB + 0.69*ISO + 0.07 (+/- 0.01)

Once again, the walks component of offense results in more baserunners but not

the corresponding number of runs based on runner scoring percentage. Note that the

coefficient for AVG has gone way up while the other two have remained very similar.

As mentioned above, the Pirates (.230), A’s (.237), and Indians (.226) have

struggled mightily in the batting average department. All three teams are likely

to see significant improvements in those numbers as the season moves along. As

their batting average increases, all three teams will start to score a higher

percentage of the runners they put on base.

(The other major point made frequently by the mainstream media is a team’s performance with

runners in scoring position. On the whole, teams tend to bat very similarly with

runners in scoring position than without and there doesn’t appear to be any

characteristics of teams that’s indicative of a group that bats better or worse

than expected with runners in scoring position. Part of the A’s and Pirates’

struggles is their ineptitude with runners on second or third, but those numbers

aren’t far out of line with their overall offensive performance and lend little to

no additional information about runner scoring percentages.)

As with any regression formula, forecasts for outliers are going to involve some

extreme regression to the mean. In this case, the Pirates, instead of scoring

30.74% of their runners, would be expected to score 35.07%. The A’s increase from

31.25% to 33.64% (Note that because of their higher-OBP, lower-SLG numbers

compared to the Pirates, the A’s don’t increase nearly as much. Scoring 33.64% of

their runners would still rank them fifth worst since 1990). Applying those

numbers to their actual run totals, the Pirates would be forecast to score 90 runs

instead of their actual 79; the A’s jump to 102 instead of 95. The Indians,

however, are already scoring 35.45% of their runners, very close to their predicted

average of 36.37%, a net of only two more runs. In Cleveland’s case, it isn’t that

the offense can’t get men home, it’s that it can’t get them on base in the first

place.

The A’s and Pirates are better offenses than they’ve shown so far this year, and

expecting them to maintain both their poor overall offensive pace and their poor

ability to score runners on base is like expecting **Brian Roberts**

to hit 48 home runs. Both teams should see a rebound, both because their team

batting averages will increase and because they’ve been underperforming their

runner scoring percentage so far this year. For now, they’ve both dug themselves quite

a hole, and it may be a while before they climb out of it.