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In the performance analysis community, it has long been shouted that
OBP is the single most important statistic of the cadre of official baseball
metrics. It’s so important in fact that one of Baseball Prospectus’
original stated goals was to have ESPN post OBP in their broadcasts.

Having achieved that modest target, it’s time to suggest some changes. Certainly, suggesting any changes to OBP is likely to draw shouts of protest from across the baseball community, but I’ll disregard the growing din and take it on anyway.

OBP is designed to measure the percentage of times that a player reaches
base. Of course, it’s not really that simple because no statistic is truly
great unless you can base it on someone’s opinions. OBP excludes fielder’s
choices–despite some colorful
arguments to the contrary
–because the official scorer has ruled that
the batter would have been out if the defense hadn’t chosen to retire a
different runner. There are all sorts of holes in this line of thinking,
but it’s not the issue I’m looking to tackle here.

The other big piece missing from OBP is the fact that reached on error
(ROE) has also been excluded. (For BP’s prior work on ROE, see Keith Woolner’s articles here and here.) If you watch enough baseball, thoughts start
to creep into your head, wondering whether certain players can “generate”
errors to get on base. The poster boy for this line of thinking is
Ichiro Suzuki (or Ichiro! if you live within 100 miles of
Derek Zumsteg). Ichiro!’s speed and batting style certainly appear to make
defenses rush, maybe bobbling a few more balls and leaving him standing on
first after a routine ground ball for anyone else. Others may argue that
there’s a case for players who hit the ball harder than others. Perhaps
they too generate errors, but instead of speed making fielders rush, it’s
the velocity of the ball forcing the error. Thus, since those ROE are the
result of some talent of the batters and not necessarily the fault of the
defense, those plate appearances, rather than being counted against OBP,
should be counted for OBP.

There are several problems with this line of thinking. First and
foremost, there’s still the inherent problem of the official scorer and his
tendencies to rule various identical events as hits or errors, depending on
other factors not relevant to the play at hand. Players who play in front
of “hometown” official scorers will have more of their borderline calls
ruled as hits than players whose scorers who hold the defense to a higher
standard.

Second, there may be a difference between infield and outfield
ROE. While there’s certainly an argument that players can generate ROE in
the outfield by hitting a plethora of nearly fieldable line drives, most of
the influence we’re searching for empirically comes from infielders and
their rush to throw out a speedy runner.

Regardless, it’s still educational to peruse the stats and see who would
benefit the most from an adjustment of OBP thinking. If the “pressure on
the defense” idea is true, we should see plenty of fast players at the top
of the list, while the glacier racers should be down at the bottom. Since
2000, here are the 25 players with at least 500 PA whose OBP would be helped
the most by including ROE:


Batter            PA    OldOBP  NewOBP   Diff
Pat Meares        798    .281    .306    .025
Devon White       600    .333    .355    .022
Calvin Murray     692    .311    .332    .021
Olmedo Saenz      813    .341    .362    .021
Ty Wigginton      944    .322    .341    .019
Darren Bragg      657    .317    .336    .019
Mike Lansing      930    .290    .309    .019
Damon Buford      645    .307    .326    .019
Bobby Estalella   798    .321    .340    .019
Rey Ordonez      1317    .288    .307    .019
Aaron Boone      2100    .328    .347    .019
Ken Harvey        759    .335    .354    .019
Jose Macias      1487    .295    .313    .018
Wendell Magee     795    .294    .312    .018
Tsuyoshi Shinjo   960    .296    .314    .018
Dee Brown         632    .283    .301    .018
Bill Haselman     546    .315    .333    .018
John McDonald     589    .267    .285    .018
Rondell White    2036    .343    .361    .018
Keith Ginter      725    .342    .360    .018
Jack Wilson      1892    .294    .311    .017
Raul Casanova     573    .309    .326    .017
Jeff Cirillo     2140    .342    .359    .017
Shawon Dunston    577    .272    .289    .017
Greg Vaughn      1450    .335    .352    .017

Well, that didn’t exactly go as planned. Olmedo Saenz?
Ken Harvey? Pat Meares? Greg
Vaughn
? These are not exactly the kind of guys who make fielders
rush throws. Our case study, Ichiro!, comes in at number 67 out of 464.
Here’s a look at the bottom 25:


Batter              PA    OldOBP  NewOBP   Diff
Doug Mientkiewicz  2011    .375    .380    .005
Luis Lopez          621    .291    .296    .005
Dave Hansen         677    .383    .388    .005
Shawn Wooten        704    .317    .322    .005
David Ortiz        2076    .350    .355    .005
Jay Gibbons        1646    .318    .323    .005
Chad Kreuter        634    .372    .377    .005
Daryle Ward        1255    .308    .313    .005
Mo Vaughn          1366    .356    .361    .005
Brady Anderson     1220    .343    .348    .005
Steve Cox          1380    .341    .346    .005
Barry Bonds        2660    .526    .530    .004
Dave Martinez       772    .346    .350    .004
Austin Kearns       877    .381    .385    .004
Troy O'Leary       1447    .318    .322    .004
Greg Norton         966    .318    .322    .004
Jason Giambi       2905    .445    .449    .004
Todd Pratt          749    .375    .379    .004
Carlos Pena        1257    .322    .325    .003
Joe Crede          1079    .300    .303    .003
Brian Schneider     965    .310    .313    .003
Russ Branyan       1221    .320    .322    .002
Nick Johnson        990    .376    .378    .002
Armando Rios        966    .322    .323    .001
Travis Hafner       633    .362    .363    .001

This group actually fits very well with our preconceptions. Mo
Vaughn
, Shawn Wooten, Jason
Giambi
, Todd Pratt…it’s like GIDP heaven.

In order to check to see if there is any correlation between speed and
the likelihood of ROE, I’ll attach a metric not terribly unlike the old Bill
James Speed Score. This one, which I’ll call Speed Factor for now, will be
simpler. Speed Factor will take the total stolen base attempts multiplied
by the stolen base success rate and again multiplied by the percentage of
doubles and triples that are triples. I’ve excluded events like runs scored
per time on base because they’re entirely too team-dependent. And while
triples rate is highly park dependent, it’s a small adjustment that doesn’t
dramatically alter the scores, so I’ll defer to Occam’s Razor for now. For
the math inclined:

Speed Factor = SB * (3B/(2B+3B))

A quick sort on Speed Factor shows a top 10 of:


Batter         Speed Factor
Juan Pierre       45.097
Dave Roberts      43.951
Luis Castillo     43.922
Alex Sanchez      34.517
Tom Goodwin       32.000
Roger Cedeno      31.927
Carl Crawford     30.823
Carlos Beltran    29.840
Tony Womack       29.647
Cristian Guzman   29.537

And the bottom:


Batter         Speed Factor
Eddie Perez         .000
Todd Hundley        .000
Tony Clark          .000
Matt LeCroy         .000
Jason Phillips      .000
Albert Belle        .000
Cal Ripken Jr.      .000
Greg Colbrunn       .000
Greg Myers          .000
Tom Wilson          .000

In general, the metric fits well with what we see on the field. But how
does it fit with the increase in OBP by adding ROE? Not at all. The
relationship between OBP with ROE and Speed Factor borders on complete
randomness. (Again, for the numerophiles: R-squared = 0.0106). This lack
of relationship may be due in some part to the very limited range of
increase from ROE (a maximum of .025 in this study), but even increasing the
precision of OBP to further decimals reveals minimal increase. In this
case, it appears that speed in general is not related to the propensity to
reaching base on an error.

This relational failure holds true for the other proposed theories as
well. Returning to the question of players who hit the ball harder than
others, I tried mapping any variety of power statistics and came up with
nothing. Additionally, combining Speed Factor with any kind of power
statistics yielded virtually no correlation. Even looking at batters who
put a larger percentage of balls in play yielded virtually no correlation.

(One final possible justification for including ROE in OBP is that OBP
with ROE may map better to overall team run scoring. This test also fails.
From 2000 to 2003, OBP maps to team run scoring marginally better than OBP
with ROE.)

As such, using arguments about various players “generating errors” by
putting pressure on the defense to justify including ROE in OBP appear
unfounded. Players who reach on an error the most often have no
characteristics to distinguish themselves from those who do not. This all
boils down to the fact that some players are simply lucky and some are not.
Of course, it could also mean there should be better regulation of official
scorers and their decisions, but that’s another article altogether.