Let me admit up front that this week’s article is a little short on the
analysis. I’m heading off to Atlanta for a family reunion this weekend, but
didn’t want to leave our readers without any kind of AFTH fix to get them
through the holiday weekend. So accept my apologies if this doesn’t meet
with the kind of thoroughness you’ve come to expect in this space.
This week’s question comes from Walter Davis, who writes:
How about an umpire-by-umpire analysis, which seems to be the third rail of
baseball. This would be similar to park effects–are more or fewer runs
scored with particular umpires behind the plate. The sample size won’t be
huge–each ump should work about 40 games behind the plate per year–but it
shouldn’t be a problem to look at data over a few years, including
season-dummies if necessary to control for that variation. Let’s really
find out who the hitters’ and pitchers’ umps are.
There were a couple of articles in the SABR Baseball Research Journal in the
mid-1990s investigating umpire effects. I don’t believe that they found any
kind of consistent effect of the home-plate umpire on scoring, although I
don’t have the specific citation handy.
Generally speaking, umpire-specific data is subject to the same kinds of
variability we see in other breakdowns. The usual caveat about small sample
sizes applies, as fewer games leads to correspondingly higher expected
standard deviations. Park effects come into play as well, since an umpire
who happens to call more games in Colorado or Houston will look
hitter-friendly, even if he’s not. Schedule or team effects can make an
umpire assigned to an Atlanta homestand look pitcher-friendly only because
he’s seeing more games thrown by Greg Maddux and John Burkett (side note:
who’d have thought that GM and JB would be mentioned in the same breath like
that at the season’s start?). All of these come into play, even if the
umpire has no actual influence on run scoring. Separating signal from
noise, if it exists, may be even harder to do than it is for park effects.
I’ve started tracking umpire data this season, and it’s interesting to at
least see the range of observed values regardless of whether it arises from
a specific umpire tendency or from external forces and randomness. I’ve
presented the data, through August 27 in the table below. This data will
soon be available on the Baseball Prospectus Web site, updated daily,
as will several more new statistical reports.
Thanks to Walter for the question, and everyone have a great Labor Day
weekend.
UMPIRE G INN RA H/9 BB/9 K/9 PA AVG OBP SLG Al Clark 13 229.0 4.56 9.7 3.4 5.5 998 .276 .346 .433 Alfonso Marquez 28 504.3 5.19 9.3 3.5 7.0 2189 .268 .339 .439 Andy Fletcher 26 472.3 5.85 10.3 2.9 6.3 2047 .290 .346 .481 Angel Hernandez 29 523.7 5.55 10.7 3.1 7.4 2331 .296 .356 .485 Bill Miller 29 514.7 4.16 8.4 2.8 7.4 2139 .248 .312 .399 Bill Welke 26 473.3 4.51 8.9 2.8 6.7 2002 .258 .324 .420 Brian Gorman 28 489.3 4.18 8.9 3.1 6.7 2088 .256 .326 .420 Brian O'Nora 26 462.0 4.81 9.4 3.0 7.5 1992 .269 .333 .429 Brian Runge 14 259.7 4.30 8.3 3.2 7.3 1083 .248 .318 .390 Bruce Froemming 28 508.7 5.33 9.7 3.9 6.2 2221 .280 .357 .460 C.B. Bucknor 26 454.7 4.55 8.9 3.3 7.3 1935 .258 .327 .411 Charlie Reliford 25 454.0 5.02 9.0 3.4 6.6 1958 .261 .331 .432 Charlie Williams 1 17.0 5.29 7.9 5.3 4.8 73 .238 .342 .365 Chris Guccione 23 401.0 4.94 9.1 3.4 6.2 1712 .263 .330 .410 Chuck Meriwether 26 469.3 4.26 8.3 3.5 7.1 1983 .245 .321 .392 Dale Scott 24 428.0 4.65 9.0 3.3 6.2 1827 .261 .332 .412 Dan Iassogna 29 516.3 4.17 8.9 2.7 7.1 2152 .261 .320 .421 Dan Morrison 12 215.3 4.64 8.9 3.8 6.8 934 .257 .338 .410 Dana DeMuth 27 483.7 4.65 8.8 3.8 6.0 2081 .257 .334 .405 Dave Phillips 14 248.7 5.03 10.0 2.7 6.2 1079 .278 .330 .433 Derryl Cousins 28 491.7 5.33 9.5 3.8 5.9 2155 .272 .347 .426 Doug Eddings 27 486.3 4.83 9.5 2.5 7.8 2066 .273 .328 .457 Ed Montague 26 463.0 5.21 9.4 3.5 6.3 1993 .272 .343 .435 Ed Rapuano 26 473.0 4.89 9.6 3.5 6.8 2044 .275 .346 .443 Eric Cooper 28 498.7 4.84 9.0 3.1 6.8 2121 .262 .323 .416 Fieldin Culbreth 26 466.0 4.77 8.9 3.5 6.8 2031 .256 .329 .390 Gary Cederstrom 26 461.0 4.35 8.5 2.7 6.4 1909 .250 .310 .414 Gerry Davis 28 496.3 5.11 9.3 4.0 5.8 2157 .269 .347 .421 Greg Bonin 13 228.3 5.20 9.3 2.6 7.3 969 .268 .326 .462 Greg Gibson 27 473.0 5.58 9.4 3.8 6.5 2064 .270 .345 .461 Hunter Wendelstedt 29 510.0 4.45 8.1 3.4 7.2 2150 .242 .317 .404 Ian Lamplugh 2 34.0 3.71 7.7 2.9 4.0 141 .228 .291 .346 Jack Samuels 1 17.0 7.41 14.3 1.6 3.2 76 .375 .395 .639 Jeff Kellogg 27 492.0 5.07 9.3 3.1 6.8 2095 .269 .334 .461 Jeff Nelson 26 489.7 5.07 9.7 3.4 6.8 2129 .278 .348 .438 Jerry Crawford 21 377.0 4.15 8.2 3.2 5.9 1577 .242 .315 .402 Jerry Layne 24 419.7 5.10 9.4 3.2 6.5 1787 .273 .335 .435 Jerry Meals 29 541.7 4.00 8.8 3.4 6.8 2294 .258 .330 .403 Jim Joyce 29 510.3 5.10 9.3 2.8 6.8 2170 .266 .325 .438 Jim McKean 17 307.7 4.74 9.4 2.8 6.9 1312 .270 .329 .425 Jim Reynolds 27 474.3 5.46 10.1 3.0 6.4 2049 .285 .343 .450 Jim Wolf 26 456.0 4.74 8.5 3.1 6.5 1913 .252 .319 .399 Joe Brinkman 28 494.3 4.90 9.4 3.9 6.0 2158 .271 .349 .438 John Hirschbeck 27 470.3 4.48 8.9 2.7 7.2 1969 .260 .316 .414 John Shulock 28 508.3 4.92 9.0 3.6 6.0 2172 .265 .338 .429 Justin Klemm 7 122.0 5.16 9.8 3.0 5.5 532 .276 .333 .429 Kerwin Danley 28 486.3 4.61 9.2 2.9 6.9 2072 .265 .325 .425 Kevin Kelley 2 35.0 3.60 8.2 1.8 8.5 137 .248 .285 .419 Lance Barksdale 31 544.3 5.09 9.5 3.1 6.7 2341 .271 .333 .438 Larry Young 23 404.7 5.03 9.0 3.4 7.8 1752 .258 .330 .428 Laz Diaz 28 504.7 5.03 9.1 3.1 6.7 2145 .262 .328 .448 Mark Barron 15 267.0 5.33 9.7 3.1 6.3 1151 .277 .340 .467 Mark Carlson 28 504.0 5.18 9.7 3.7 6.8 2202 .279 .352 .419 Mark Hirschbeck 29 529.0 4.07 8.3 3.5 7.1 2243 .244 .320 .378 Mark Wegner 27 474.0 4.48 8.5 3.6 6.9 2009 .252 .329 .411 Marty Foster 28 500.0 5.26 9.6 3.2 6.4 2153 .275 .338 .444 Marvin Hudson 25 433.0 4.24 8.8 3.0 6.8 1820 .255 .317 .413 Matt Hollowell 19 333.7 4.59 9.3 2.9 6.3 1418 .267 .327 .433 Mike DiMuro 8 140.0 4.56 9.5 3.5 6.8 599 .280 .357 .422 Mike Everitt 29 515.0 5.45 9.5 2.6 6.7 2199 .270 .325 .442 Mike Fichter 24 420.7 5.67 9.7 3.6 6.9 1846 .277 .350 .454 Mike Reilly 28 488.0 4.83 9.4 4.1 7.0 2129 .269 .349 .428 Mike Van Vleet 2 36.0 5.50 10.8 3.8 5.0 160 .303 .363 .493 Mike Winters 28 504.7 4.83 9.2 3.1 7.4 2140 .267 .328 .426 Morris Hodges 3 52.7 5.13 8.2 4.3 6.2 224 .251 .348 .435 Patrick Spieler 5 89.0 5.36 10.9 2.9 6.2 394 .301 .353 .474 Paul Emmel 25 461.3 5.25 9.6 3.2 6.2 2000 .275 .344 .450 Paul Schrieber 26 456.3 5.17 9.7 3.4 6.9 1979 .276 .342 .436 Phil Cuzzi 28 493.0 3.83 8.3 2.9 7.5 2040 .245 .308 .398 Randy Marsh 23 405.7 4.48 8.7 3.7 6.5 1729 .254 .329 .393 Rich Rieker 18 315.0 4.89 8.9 4.1 5.9 1367 .260 .345 .433 Rick Reed 28 505.7 4.95 9.1 3.4 6.8 2189 .261 .330 .420 Rob Drake 20 361.3 4.26 8.0 3.4 7.4 1495 .242 .320 .413 Rocky Roe 19 334.3 5.38 9.3 3.6 6.3 1453 .268 .341 .449 Ron Barnes 9 164.0 4.77 9.7 2.7 5.9 700 .277 .333 .436 Ron Kulpa 29 518.0 5.00 8.7 3.3 6.1 2208 .253 .325 .424 Scott Higgins 11 198.3 6.63 10.3 4.5 6.6 902 .286 .370 .453 Scott Packard 3 53.0 3.57 8.3 1.7 5.3 217 .243 .286 .411 Steve Rippley 21 378.7 4.63 9.4 2.9 7.7 1602 .272 .332 .424 Ted Barrett 28 494.0 4.74 9.3 3.4 7.7 2132 .267 .336 .439 Terry Craft 25 436.0 4.52 8.9 3.4 6.2 1879 .256 .326 .414 Tim McClelland 27 486.7 4.92 8.7 3.4 6.3 2077 .255 .325 .429 Tim Timmons 31 544.0 4.75 9.2 3.3 6.8 2330 .266 .333 .423 Tim Tschida 29 525.0 4.75 9.2 3.6 6.7 2293 .264 .339 .423 Tim Welke 27 489.3 4.75 9.1 3.2 6.9 2112 .262 .328 .421 Tony Randazzo 25 442.3 4.88 9.2 2.9 7.1 1883 .263 .326 .449 Travis Katzenmeier 4 70.0 6.69 9.4 2.8 5.0 294 .273 .330 .479 Wally Bell 30 532.3 4.97 9.5 2.9 6.6 2267 .273 .333 .442
Keith Woolner is an author of Baseball Prospectus. You can contact him by
clicking here.
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