Sociology of Sport Journal, 1999, 16,16-34
0 1999 Human Kinetics Publishers, Inc.
"Stacking" in Major League Baseball:
A Multivariate Analysis
Benjamin Margolis and Jane Allyn Piliavin
University of Wisconsin-Madison
This research studied stacking-position segregation by race or ethnicity in team sportsin the 1992 Major League Baseball season using a multivariate analysis, with control variables of height, weight, age, power, speed, and skill. The strong relationship between race
and centrality found in previous studies was confirmed; African-American players were
predominantly in the outfield positions, Latino players in the middle infield positions, and
white players in the most central position of catcher, as well as the other infield positions.
The multiple regression analyses revealed direct effects of some control variables on centrality; however, only the variable of speed was found significantly to reduce the bivariate
relationship between being African-Americans and centrality. A proportion of the variance
in allocation of African-Americans to the outfield may thus be due to this job-related ability; the residual race effects, which account for the majority of the explained variance, must
at present still be attributed to direct discrimination.
La prksente Btude porte sur I'entassement (la dgr6gation en fonction de la race ou de
l'ethnicit6 dans les positions en sports d'kquipe) dans la Ligue Majeure de Baseball en
1992. Une analyse multivarike est utiliske, ainsi que les variables de contrale de la taille, du
poids, de I'sge, de la puissance, de la vitesse et de l'habiletk. La forte relation entre la race
et la centralitk trouvke dans les ktudes prkctdentes a 6t6 conf1rm6e. Les joueurs africainsamkricains se retrouvaient surtout dans le champ extkrieur, les joueurs latino-amkricains,
au champ intkrieur, et les joueurs blancs, dans la plus centsale des positions, celle de receveur,
ainsi que dans les autres position du champ intkrieur. Les analyses de rkgression multiple
ont rev616 les effets directs de certaines variables de contrble sur la centralit6, mais il a Ctk
trouvk que seulement la variable vitesse rkduisait de fagon significative la relation bivari6e
entre le fait d'Ctre Africain-Am6ricainet la centralit6. Une proportion de la variance dans la
distribution des Africains-AmCricains au champ ext6rieur peut donc Ctre due B l'habiletk
associke & cette position. Les effets r6siduels de la race, qui sont associ6s B la majeure partie
de la variance expliquke, doivent donc, jusqu'h preuve du contraire, Ctre attribuks B la discrimination directe.
In their pathbreaking work on racial segregation in professional football,
Loy and McElvogue (1970) argued that there exists a disproportionate allocation
of players to central and non-central athletic positions on the basis of race or
ethnicity. Specifically,majority group players typically occupy those positions that
are considered central-those positions involving the most communication with
others as well as leadership potential and decision-making-while minorities occupy
J.A. Piliavin is with the Department of Sociology at the University of WisconsinMadison, Madison, WI 53706. She can be reached at <[email protected]>.
"Stacking"in Baseball
17
positions that are considered peripheral, involving mainly athleticism and "instinct."
The term now commonly used for this phenomenon is stacking. Although the initial work is three decades old, its findings have been consistently confirmed in
both football and baseball in the U.S. (Bivens & Leonard, 1994; Christian0 1988;
Jones, Leonard, & Schmitt, 1987; Kahn, 1991; Lewis, 1995; Schneider & Eitzen,
1986; Smith & Leonard, 1997).In baseball, African-Americansare overrepresented
among the three outfield positions and underrepresentedin the infield and at catcher;
in football, whites are heavily represented at quarterback, center, guard, linebacker,
and kicker, with African-Americans more frequently occupying the positions of
defensive back, running back, and receiver.
Staclung has been found in college football as well (Eitzen & Sanford 1975;
Jones et al., 1987; Schneider & Eitzen, 1979; Williams & Youssef, 1975).There is
only slight evidence currently for positional discrimination in college men's basketball (whites are more likely to play center) but slightly more in women's basketball, where whites are overrepresented at both center and guard (Berghorn,
Yetman, & Hanna, 1988; Leonard, 1987). Little else has been done in women's
sports, but Eitzen and Furst (1989) found that in volleyball, African-Americans
are overrepresented slightly as hitters and underrepresented as setters (the leadership position).
Researchers have also investigated the phenomenon in the professional sports
played in other countries, including Canadian football (Ball 1973; Stebbins, 1993),
Australian rugby league (Hallinan, 1991), English professional basketball (Chappell,
Jones, & Burden, 1996), English soccer and rugby league (Maguire, 1988, 1991;
Melnick, 1988; Norris & Jones, 1998), and cricket (Malcolm, 1997). Only in Canadian football and English basketball are the results not as expected. Kahn (1991),
in a survey article summarizing all of the existing research to that time on racial
discrimination in professional sports, concluded that although results in other areas such as wages, hiring, and spectator discrimination based on race tend to be
mixed, the stacking phenomenon has yet to be refuted.
While various explanations have been offered for stacking (Eitzen 1989;
Leonard, 1984), sport sociologists generally regard stacking in this country as a
consequence of U.S. racial and ethnic stratification patterns (Dougherty, 1976;
Edwards, 1973; Eitzen & Yetman, 1977). The centrality hypothesis as posited by
Loy and McElvogue (1970) was inspired by Grusky's (1963) theory of formal
structure.According to Grusky (1963), in a formal structure such as the organization of a team, a set of written rules and unwritten norms define certain positions
as central and others as non-central or peripheral. The centrality of a position is
governed by its location, the nature of its responsibilities, and the frequency of its
interactions with other positions. Once the centrality of a position is determined,
Loy and McElvogue (1970) argue that it is more likely to be inhabited by members
of the racial or ethnic group that is dominant in the larger society. The remaining
non-central positions are left over for the racial and ethnic minorities.
The specific causal pattern underlying this structural result is disputed. Some
have argued that African-Americans are kept out of positions viewed as "thinking" or "leadership" positions, based on negative stereotypes (e.g., Eitzen & Sanford,
1975;Williams & Youssef, 1975) or the prejudice of coaches and managers against
interacting with them. Medoff (1986) has put forth the prohibitive cost hypothesis,
which suggests that the expense of training athletes for the central positions, coupled
18
Margolis and Piliavin
with the low socioeconomic standing of minorities, is responsible for the absence
of black quarterbacks, pitchers, and catchers. That is, the minority athletes cannot
afford the training, and others (due to prejudice) are unwilling to invest in them.
This economic explanation is also consistent with the absence of minorities in the
"country club" sports. Along similar but more psychological lines, Lavoie and
Leonard (1994) suggest the uncertainty hypothesis-namely, that discrimination
is linked to the difficulty and lack of objectivity in assessing player performance at
a given position. They argue that the "central" positions carry more uncertainty;
thus, discrimination against minorities should be higher at those positions. The
data they present are only weakly supportive of this hypothesis. Whatever the
underlying mechanism, prejudice and discrimination are assumed to be the cause.
There has been little attempt to find explanations that do not involve some direct
form of prejudice and discrimination.
There clearly is great variation in physical size, body characteristics, and
specific skills needed to play the different positions in basketball, baseball, and
football. Offensive linemen in professional football these days need to weigh 300
pounds; basketball centers need to be seven feet tall; shortstops need to be able to
turn on a dime. No one would contest that first basemen are-and need to betaller than other infielders, or that it is desirable that they be left-handed. It is
possible that the distribution of these characteristics themselves is non-random
across different groups, for a combination of reasons, including genetics, early
nutritional status and later diet, training, cultural emphasis, or even demographics.
For example, Eitzen and Furst (1989) suggest that in basketball, because ''height is
an ovemding consideration in position assignment, especially center" (p. 47), whites
may predominate there simply because "whites are eight times more likely than
blacks to be at the upper end of the height continuum from which centers are
selected (because blacks comprise only 14% of the population)."
It is also likely that children from various racial groups are socialized
differently to desire to play different positions-not to mention different
sports-and therefore to develop (or already possess) different physical skills and
characteristics. For example, youngsters may disproportionately choose sport
"role models" of the same race and thus self-select into the same positions their
models play (McPherson, 1975). It may also be that there is differential recruitment of the members of different groups on the basis of different skills. It is widely
believed, for example, that recruiters go to certain towns in the Dominican Republic looking specifically for infielders and that boys in those towns practice hard at
the relevant skills.
Although many sport sociologists believe the evidence is sufficient to support a racist or discriminatory basis for stacking, there is a dearth of studies in
which control variables have been introduced to determine if the zero-order relationship between race and position allocation is sustained. In fact, the only study
of stacking that has attempted a multivariate analysis is Jones and colleagues (1987).
These researchers controlled only for football conference and time period in their
attempt to clarify the relationship between race and position allocation in college
football. While Christian0 (1988) employed a series of multiple regressions that
included variables such as batting average in addition to race, his study sought to
uncover economic discrimination-namely, differences in salaries for white and
African-American players-rather than stacking. Kahn and Scherer (1988) used
"Stacking" in Baseball
19
height, weight, and a host of performance variables in their regression analyses,
but, again, the dependent variable was economic compensation. No studies have
attempted to control for physical characteristics, abilities, or interests related to
position in order to see whether some of the segregation by position may reflect
job-related characteristics rather than discrimination, pure and simple.' If such an
analysis shows that they do not reflect job-related characteristics, the argument
that position segregation is due to discrimination is strengthened.
Baseball may be the best of the major sports for the investigation of these
questions. There exists a very clearly defined pattern of interaction among the nine
positions on the playing field. The initial two-tiered structure developed by Loy
and McElvogue (1970) consisted of the following: (a) the pitcher, the catcher, and
the infield (including first base, second base, shortstop, and third base) constituting the central positions; and (b) the three sutfield positions constituting the peripheral positions.
Most recently, Pattnayak and Leonard (1991) reanalyzed the positional segregation thesis in the case of professional baseball. They argued that the initial
two-tiered system in which African Americans were found to be overrepresented
at peripheral positions has become a three-tiered one, largely due to the injection
of many Latino players who are intermediate between the white center and the
African-American periphery. Pattanayak and Leonard (1991) thus argued that there
are degrees of centrality: (a) the pitcher and catcher positions are the most central;
(b) the infield, especially the second base and shortstop positions, are the semiperipheral positions; and (c) the three outfield positions are the most peripheral.
Gonzales (1996) has confirmed that by 1992,27% of second basemen and 36% of
shortstops were Latino, well beyond their representation in major league baseball
overall in that year (19.9%), while only 16% of either catchers (central) or outfielders (peripheral) were Latino.
In this paper, the positional segregation thesis is reexamined through a multivariate investigation of the players of the 1992 Major League Baseball season,
using a 6-point scale measure of centrality, modified from Pattanayak and Leonard's
three-tiered measure. This analysis introduces the control variables of height, weight,
age, power, fielding skill, and speed as potential intervening factors that could
reduce the bivariate relationship between race-ethnicity and a player's degree of
centrality. Two hypotheses are proposed:
Hypothesis 1: Consistent with previous research, players will be distributed
to positions non-randomly with respect to racial-ethnic group membership,
with white players overrepresented in central positions, Latino players overrepresented in semi-peripheralpositions, and African-Americanplayers overrepresented in peripheral positions.
Hypothesis 2: Physical characteristics and skills will be related to position
played.
We will first test Hypotheses 1 and 2. If both are confirmed, we will then
determine whether any of the physical characteristics related to position played
are also related to race and ethnicity. If they are, then it is possible that these characteristics may explain, in a statistical sense, some of the relationships between
race-ethnicity and centrality. No specific hypotheses are offered a priori regarding
Margolis and Piliavin
20
the central purpose of this research, which is to determine whether physical or skill
differences play a part in differential allocations of playing positions by race and
ethnicity.
Methods
Past studies of stacking in professional baseball have employed a variety of
methods of sample selection and measurement of the central variables of race and
centrality. Loy and McElvogue (1970), for example, obtained their data from the
1968 Baseball Register and included every player who played in at least 50 games
during the 1967 season. They hoped to eliminate the partial participant, such as the
pinch hitter or runner, the player brought up from the minor leagues on a part-time
basis, and the occasional utility man. However, they also excluded what they labeled "Latin Americans" from their analysis because it was impossible in terms of
the sources they used to "determine which Latin American athletes were Negroes"
(1970, p. 8).
Pattnayak and Leonard (1991) present an analysis based on information published in USA Today, which gave the race of baseball players team by team and
position by position. The listings did include Latinos along with African-Americans and whites. No mention was made of how the researchers selected their sample
or if they simply included everyone listed in the USA Today article. Jiobu (1988),
in his investigation of career length, extracted performance data from the widely
available Baseball Encyclopedia. By doing this, the researcher was able to identify
a player's date and place of birth but not his race. For race information, they consulted a "complete picture collection" from the Topps company, which has for
over 40 years produced baseball trading cards with the players' pictures on the
front of each card. From this collection, they judged whether a player was white,
African-American, or Latino.
Sampling
Data for the present analysis were obtained from two sources: the complete
set of Topps Baseball Cards for the 1992 baseball season and the 1993 edition of
The Great American Stat Book. Following Loy and McElvogue (1970), we wished
to leave out partial participants. However, we used what we consider to be a better
criterion than excluding from the sample every player who did not appear in 50
games. A "game played" can include being used as a pinch hitter, a pinch runner,
or even a defensive replacement. Therefore, one could appear in 50 games and still
really be a partial participant. On the other hand, one could start in fewer games
but accumulate a large number of at-bats. Thus, a player from the 1992 Major
League Baseball season was considered a part of this analysis if he had played in
40 games and done either one of the following: (a) accumulated 150 at-bats or (b)
accumulated 200 plate appearances (walks plus at-bats).This information was easily
obtained from the Topps baseball cards.
Measures
Race. Each player's baseball card contained two color pictures of that player
and also that player's name, place of birth, and current residence, which were used
to determine race and ethnicity.
"Stacking" in Baseball
21
1. An African-American player was operationally defined in this study as
one with a relatively dark complexion and a non-Spanish surname, who was neither born in nor currently resided in a Spanish-speaking country.
2. In this study, a player was identified as Latino, first, if he had a Spanish
surname and was either born in a Spanish-speaking country or currently resided in
one; or second, if the player was born in the U.S. but had a Spanish surname, and
his picture led the researchers to believe that he was Latino.
3. Players not classified as either African-American or Latino by these criteria were assigned the residual category of white. There were no obviously Asian or
Asian-American players in 1992.
If a player's category was still in question after reviewing these criteria, the two pictures were given more weight in the final decision. This occurred in only 4 out of the
325 players included in the sample. Outside help was sought in these instance^.^
A tally was kept for Latino players who had very dark skin and could possibly be categorized by observers as African-American players. Given the consistent results found in the stacking research and some of the interpretations of it, we
reasoned that they might possibly be allocated to less central positions than Latino
players with lighter complexions. Nineteen out of the 61 Latino players included
in the total sample were deemed to be "dark." A chi-square analysis provided no
evidence that Latinos with darker skin were allocated to less central positions than
other Latino players, and they were retained in the Latino category.
In total, 169 white players (52%), 61 Latino players (18.8%), and 95 African-American players (29.2%) were included in this study. The variable race was
originally constructed so that 1 indicated a white player, 2 indicated a Latino player,
and 3 indicated an African-American player. In the regression analysis, we have
employed two dummy variables, African-American and Latino, in order to be able
to examine the separate effects. When both the African-American effect and the
Latino effect are included in a regression equation, the "excluded group" becomes
white, non-Latino players.
A player's position was determined with the aid of the 1993 edition of The
GreatAmerican Baseball Stat Book (Gillette, pp. 243-253). Here, players are listed
alphabetically by team with the total number of games each player appeared in
during the 1992 season (including designated hitter). In addition, the number of
games started at each position is given. It should be noted that a player's total
games frequently were greater than his official games played, since that player
may have played in more than one position in some games (e.g., starting a game in
center field then moving to right field during the game). A player was assigned to
the position in which he appeared most often. Pitchers were excluded from this
study because of the uniqueness of their role, and the differences between the
measures used to assess their performance and those used for other players.
Centrality. Pattanayak and Leonard's (1991) three-tiered system was modified in this paper by the creation of a 6-point centrality scale. The catcher position
in this study is considered to be the most central, and any player determined to be
primarily a catcher was given a centrality rating of 1.3 Because of Pattanayak and
Leonard's (1991) suggestion regarding the developing role of Latino players as
"semi-peripheral," a centrality rating of 2 was given to the second base and shortstop positions. The third base and first base positions were given a centrality rating
of 3. The centerfield position was given a centrality rating of 4, while the left and
22
Mavgolis and Piliavin
right field positions were given centrality ratings of 5. The reasoning behind the
centerfield position being given a higher centrality rating than the other two outfield positions is the fact that the center fielder is often considered the "general" of
the outfield just as the catcher is considered the "general" of the infield. The
centerfield position carries more game responsibility and also involves more interaction with the infield. The centerfield position has a greater range, so the center
fielder touches more fly balls and line drives than the other two positions. The
centerfield position also requires more speed and a better throwing arm.The designated hitter position was included to see if minorities are overrepresented at a position that requires absolutely no interaction. It was given a centrality rating of 6.
For those few players who played equally often at two positions with different centrality ratings, the higher of the two centrality ratings was recorded. For
example, if a player played 40% of his games at catcher (centrality rating of 1) and
40% of his games in left field (centrality rating of 5), he would receive a centrality
rating of 1. This occurred in 9 out of 325 cases.
The following control variables were employed in this study in order to better understand the zero-order relationship between race and position allocation.
1. Player Height (in inches)-Different positions on the field demand different physical requirements. First base, for example, is a position requiring height
because of the need to stretch to receive throws. Thus, height may influence position placement.
2. Player Weight (in pounds)-Central positions such as the second base and
shortstop positions require players to be flexible and quick. Weight, along with
height, would also seem to be a determinant of where a player is positioned.
3. Power (slugging percentage)-This is a measure of a player's ability to
hit extra-base hits (doubles, triples, and home-runs). It is computed by dividing the
total bases a hitter obtains by that hitter's number of off~cialat-bats. Often, powerful hitters are such a priority that a manager will find a position for them on the
field even if they are poor defensive players. Also, powerful hitters are often bigger physically, which might limit where they can play on the field. Often, powerful
hitters are placed at first base, or left or right field or, in the American League, at
designated hitter. Slugging percentage for each player was found on the Topps cards.4
4. Adjusted Fielding Range (Am)-This was used as a measurement of a
player's defensive ability. A team's best defensive players will most often be found
at the most central positions on the field. This measurement was obtained from the
1993 edition of The Great American Baseball Stat Book (pp. 243-254). As can be
seen from the following brief discussion of how AFR is calculated, this measurement required an amazing amount of research and creative thinking.
First, the authors determined the opportunities that a player has to field
batted balls by counting the number of balls put into play by opposing
batters when he is on the field. A "ball in play" is defined as a hit (except
for home runs), an error, or an out.
In the second step, defensive equivalent games (DEG) are computed for
all players by counting the balls put into play while they are on the field
and dividing by the league average of balls put into play per game. (This
compensates for pitching staffs that are above or below league averages
in hits allowed or strikeouts, thus affecting their fielders' opportunities.)
"Stacking" in Baseball
23
Third, the number of "balls fielded" is calculated as the number of times
a player is the first to handle a batted ball that results in an out.
Aplayer's fielding range is computed by dividing his balls fielded by his
DEG. In addition, each player's range is adjusted to reflect the numbers
of left-handed and right-handed batters faced.
AFR is felt to be an accufate measurement of a fielder's performance, because it is
based on what really matters for fielders: their ability to position themselves to
field batted balls and turn them into outs. In addition, this measurement is also a
type of centrality measurement because it is specific to position. Infielders, for
example, get putouts in various ways that have nothing to do with their range and
everything to do with their position (e.g., receiving a throw at first base). Traditional ways of measuring fielding range are much less accurate, since they are
based simply on putouts, assists, or total chances, not on actual balls fielded. Also,
official fielding statistics add all plays in left, center, and right fields together,
giving one total for the three different positions. The raw AFR measure differentiates the centerfield position from the other outfield positions in these data.5This
measure cannot be used for catchers; thus, analyses using AFR omit catchers.
5. Skill (or StandardizedAdjusted Fielding Range)-AFR is so strongly related to position that we developed a measure of fielding ability independent of
position played by simply standardizing scores by position. Each player's score
was subtracted from the mean and divided by the standard deviation of the AFR of
his position.
6. Speed-Different positions on the field require more speed and quickness
than others (i.e., second base, shortstop, and especially center field). Therefore, it
was reasoned that speed might also be a determinant of where players are positioned on the field. Speed was operationalized in this study by stealing attempt
percentage, which indicates the number of attempts per 100 opportunities to steal
second base. This statistic was available for every player who attempted 10 or
more stolen bases in the 1993 edition of The Great American Stat Book (pp. 240242). Obviously this is not a direct measure of speed; rather, it indicates a player's
assessment of his likelihood of success in getting from first to second base safely
in the time available. It reflects the player's previous experience of success and
failure, however, which should be highly correlated with his speed and acceleration. There is a significant negative correlation of. 183 between weight and speed,
even though we have the speed measure for only 123 playem6
7. Age-As a player gets older, some of his skills may deteriorate, while
others may improve. It is also possible that selection may occur, such that only the
most skilled players are retained as they age. Many major league players start at a
more central position, and as their career goes on, are moved to a more peripheral
position. Age was determined by using the birth date of each player and calculating how old he was by October 4, the end of the 1992 season. This information
was found on the Topps cards.
Results
Table 1 shows the numbers and percentages of players by race at each of the
six points on the centrality scale. Like many professional sports, baseball has by
now attracted large numbers of Latino and African-Americanplayers. While whites
Margolis and Piliavin
24
still constitute the majority, the percentages of African-American and Latino players in major league baseball are larger than their percentages in the U.S. population. Of the 325 total players in this sample, 52% are white, 18.8% are Latino, and
29.2% are African-Ameri~an.~
From the centrality scale, it is seen that 13.8% of the players were primarily
catchers, 23.7% primarily played shortstop or second base, and 22.2% primarily
played first or third base. Twelve percent had center field as a primary position,
while 24.6% played right or left field. Designated hitters make up the remaining
3.7%.
As past studies have convincingly shown, race is clearly and strongly related to centrality. More than 80% of the catchers-the most central position-are
white, and nearly half of the outfielders are African-American. The differentiation
between center field and the other two outfield positions appears to make no difference, and the small sample of designated hitters seems similar to the outfielders
in racial-ethnic composition. Consistent with Pattanayak and Leonard's (1991)
and Gonzalez's (1996) findings, Latinos are concentrated at second base and shortstop (the 2 position on the scale). Thirty-one percent of players at these positions
are Latino, and 39% of Latino players (24 of 61) are in these positions.A test of the
overall relationship, as shown in Table 1, indicates that the likelihood that it is due
to chance is infinitesimal (x2= 59.5, p = .00001), confirming Hypothesis 1.
Individual comparisons of the racial composition of adjacent categories on
the centrality scale reveal that there are significantdifferences between Positions 1
and 2 ( x ~ =14.7, p < .001), 2 and 3 (x2= 7.53, p < .05), and between 3 and all the
positions above 3 (x2= 16.12, p < .001). Thus, these findings confirm and further
modify the three-tier system that Pattanayak and Leonard discovered in the 1989
Major League Baseball season. It would appear that the 6-point centrality scale we
employed makes one additional useful distinction, that between the central infield
Table 1 The Relationship Between Race and Positional Centrality
Race
White
f
1
2
3
4
5
6
Row
total
39
50.6
24
31.2
14
18.2
46
63.9
9
12.5
17
23.6
13
33.3
7
17.9
19
48.7
27
33.8
14
17.5
39
48.8
6
50.0
1
8.3
5
41.7
169
52.0
61
18.8
95
29.2
77
23.7
72
22.2
39
12.0
80
24.6
12
3.7
325
100.0
African
American
%
38
84.4
6
13.3
1
2.2
Column
total
f
%
45
13.8
%
Latino
f
%
f
Note. xZ= 59.50, d f = 10, and p < .0001.
"Stacking" in Baseball
25
(second base and shortstop) and the peripheral infield (first and third base), but distinctions among the most peripheral positions do not add to our understanding.
Control VariableAnalyses
In order for a control variable to be able to explain a relationship, it must
fxst be shown to relate to both the independent and dependent variable in that
relationship. Hypothesis 2 proposes that at least some of the control variables of
height, weight, age, power, AFR, and speed will be related to position and thus to
centrality. All of the ANOVAs testing the effect of position on the control measures are significant at a probability of .Ol or less (df = 713 17 except as noted), and
all of the relationships are consistent with position requirements. Designated hitters are on average 4 years older (32.67) than players in any other position, and
shortstops are the youngest (27.24). First basemen and DHs are the tallest and
heaviest and have the most power; shortstops and second basemen are the shortest
and lightest and have the least power. AFR is strongly related to position as expected-namely, it is highest in the middle infield positions, next at center field
and third base, and lowest at first base and right and left field (df = 61260). Because
it is standardized, skill is, by definition, unrelated to position. Outfielders have by far
the most speed (df = 71115). They attempted almost twice the number of steals (21) as
compared to first basemen or DHs (11). Hypothesis 2 is therefore confirmed.
The final requirement for a variable to serve as an explanatory variable is
that it be related to the independent variable in the relationship. We made no a
priori hypothesis here. However, race is found to be significantly related to age,
height, and weight, with Latinos being younger, shorter, and lighter than AfricanAmericans and whites, who do not differ from each other (p < .O1 by ANOVA for
all three comparisons).Power is also related to race; African-Americans have significantly higher slugging percentages than either Latinos or whites O, < .001),
c o n f i g Phillips's (1997) analysis. Raw A m scores are highest among Latinos,
then whites, and last African-Americans O, < .01). Skill is unrelated to race. That
is, controlling for position, there are no perceptible racial differences in fielding
a b i l i t ~Finally,
.~
the measure of speed indicates that African Americans are the
fastest, followed by Latinos, then whites ( p < .001). This is consistent with data
reported by Lavoie and Leonard (1994) for 1988, showing blacks stole more bases
than whites, even controlling for position played (p. 151).All of the characteristics
investigated differentiate among at least some of the racial-ethnic categories.
Given that race and position are both related to some of these control variables, it is possible that the introduction of these controls into the analysis relating
race to centrality could significantly reduce the association, indicating that some
job-related skills and characteristics in part mediate the race-centrality relationship. This would suggest that at least some of the association is not due to direct
discrimination. If these job-related skills do not reduce the relationship, the argument that position segregation is due to discrimination is strengthened.
Regression Analysis
A basic regression of centrality on race and all control variables except speed
and skill (because these variables are available for only a selected sample) was
performed with all 325 players. Because height and weight were highly correlated
Margolis and Piliavin
26
(.63) and multi-co-linearity problems were evident when both were in the equation, we developed a combined variable: size. This was computed by standardizing each player's height and weight measures, adding them together, and dividing
by
As seen in Table 2, when only the two race-ethnicity variables, African
American and Latino, are entered, the unstandardized regression coefficient, B,
for the African-American effect is 1.18, significant at p < .001, and indicating
more than a point difference between African Americans and whites on the centrality scale. The Latino effect is only .21 and is not significant. The adjusted R2
shows us that about 12% of the variability in centrality is explained at this point.
When the variables of age and size are brought into the regression analysis,
significantly (p < .001) more of the variability in centrality is explained, but these
variables do not explain the African-American effect on centrality. However, the
coefficient for Latino more than doubles, to .434, and becomes significant (p <
.05), indicating a suppressor effect. That is, only after age, height, and weight are
controlled are Latinos found to hold significantly more peripheral positions than
whites. This indicates more discrimination against Latinos than is evident from the
raw numbers. Taller and heavier players are on average significantly more peripheral. Latinos tend to be shorter and lighter. When these characteristics are controlled, then, Latinos are seen to be more peripherally placed than whites of the
same size.
When the variable of power is entered into the regression (Table 2), again
significantly (p < .01) more of the variability in centrality is explained, as the
adjusted R2 is increased to .171. Power is a very significant direct predictor of
Table 2 Regression of Centrality on Race and Control Variables
Variable
Step 1
Step 2
African
American
Latino
Size
Power
"Significanceindicators refer to change in R2.
***p < .001; **p < .01; *p < .05.
Step 3
"Stacking" in Baseball
27
being placed in a peripheral position. Looking at the betas, the standardized coefficients, power explains more variance than any other variable with the exception
of African-American. The coefficients for both the African-American and the Latino
dummy variables are reduced slightly (the Latino effect becomes just barely nonsignificant, at p = .058), but very little interpretation of the race effects is evident.
Two other control variables of interest are skill and speed. Unfortunately,
the raw AFR measure is correlated -70 with centrality, explaining half of the variance. The amount of variance remaining to be explained by the other variables is
therefore very small. Being standardized, the skill variable is no longer related to
centrality and is also not related to race. Thus it is impossible that it could serve as
an explanatory factor in the race-centrality relationship, and no test is possible for
statistical reasons.
The final control variable of interest is speed. Sample size is reduced very
significantly (to 123) when speed is included, since only those players who attempted 10 or more steals of second base were given speed scores. The percentage
of African-American players increases, and the players in the sample are somewhat smaller, but the mean values of the power, age, Latino, and centrality variables are unaffected. Although catchers are not automatically omitted from this
sample, only two remain; similarly, only 2 DHs attempted 10 or more steals, as is
Table 3 Regression of Centrality on Race and Control Variables (Plus Speed)
Step 1
Variable
AfricanAmerican
(B)
(SE, B )
Latino
(B)
(SE, B )
Size
(B)
(SE, B )
Age
(B)
(SE, B )
Power
(B)
(SE, B )
Speed
(B)
(SE, B )
step 2
Step 3
Step 4
P
P
P
P
P
P
R2 (adj~sted)~
df
"Significanceindicators refer to change in RZ.
***p < .001; **p < .01; *p < .05.
Margolis and Piliavin
28
consistent with their age. Thus the variance of the centrality scores is slightly decreased. As can be seen in Table 3, again only about 11% of the variance in centrality is explained in the equations that include only the two race variables. The African-American effect is again robust, while the Latino effect is positive but not
quite significant (p = .08). The addition of the block of age and size again leads to
the explanation of significantly (p < .001) more variance (adjusted R2 goes up to
19%),causes the Latino effect to become significant, and does not interpret the
African-American effect. These results are very similar to what was found in the
initial regression on the total sample, giving us more confidence that this sample is
not particularly unrepresentative.When power is brought in, results are again similar; as in the initial regression, the African-American effect and the Latino effect
decrease, but very little.
When the variable of speed is entered into the regression, the story is entirely different. With the large increase in the adjusted R2 to .280, more than a
quarter of the variance in centrality is now explained. In fact, the effect of speed on
centrality is relatively larger (looking now at the beta coefficients) than either the
African-American effect or the Latino effect, when also controlling for age, size,
and power. This statistical outcome seems to indicate that a significant part of why
players are allocated to peripheral positions-essentially to the outfield-is because of their ability to run fast. This is clearly a rational use of talent, given the
requirements of the outfield position, and should be surprising to no one.
Of the greatest interest, however, is the fact that the introduction of speed
into the equation significantly decreases the effect of being African American on
centrality. The African-American effect is reduced by roughly one third from essentially one point on the scale (.984) to .687. It remains highly significant, however-the second most important predictor in the equation. The Latino effect is
also reduced but only slightly. There are thus two findings in these analyses that
require discussion: the suppressor effect of size on the effect of the Latino variable
and the interpretive effect of speed on the strength of the African American effect.
Discussion
How does the partial interpretation of the African-American effect by differences in speed help us better understand how and why positional segregation occurs in professional baseball and possibly in other sports? First of all, it must be
remembered that the above findings are based on the measure we have called speed,
but which arguably is a direct indication of a player's ability to steal a base and at
best only an indirect measure of speed.I0 There are certainly better measures of
speed that could be employed (such as a player's time in the 40 yard dash), but
they were not available. It still makes sense that only the faster players will attempt many steals of second base because base stealing involves both high costs
and high rewards for the offense; failure leads to an out, but success can lead to a
much needed run.
The fact that when the variable of speed is included, the effect of being
African American is reduced by one third suggests that part of the reason AfricanAmerican players are positioned primarily in the outfield is because, for whatever
reason, they are faster. In this sample, African-American players and players who
are primarily outfielders are, overall, significantly faster by this measure. (White
"Stacking" in Baseball
29
outfielders are in fact somewhat faster than black infielders.) Consistent with past
studies, almost a majority of outfielders were African American.
We do not intend to raise here the issue of race-linked physical differences.
The well known sports sociologist, Stanley Eitzen, has concluded that at present
"we do not know whether African-American athletes actually possess physical
traits superior to those of whites or not. Even if it were finally proved that AfricanAmericans do have genetic advantages over whites, we believe that genetic differences will likely be less important than the social reasons for African-American
dominance" (1989, p. 270). John Hoberman (1997), in his much discussed book,
Darwin 3 Athletes, goes into great detail regarding the near impossibility of demonstrating such group differences under the best of circumstances and the foolishness of making such a claim, given the bad sampling, ambiguity of measures, and
suspect motivations of many of the authors who have pursued such research. It
seems safe to say that a majority of sociologists who study sports agree with this
line of thinking.
The fact still remains, however, that at least in this sample, African Arnericans were predominantly faster by our measure than white or Latino players, and
their speed accounted for part of the reason they were assigned to the outfield.
What might be some possible explanations for this finding?
Maybe it is not that African Americans in general are faster but that the
stereotype that they are faster and better suited for the outfield positions is ingrained in the minds of scouts and coaches at the high school, college, and professional levels. Maybe scouts go looking for speed when they go on recruiting trips
to African-American areas in the inner city or the rural South, just as they look for
"good hands" and mobility when they recruit in the Dominican Republic. Maybe
when coaches see an all-around good athlete who is African American, one quality
they notice, because they expect to see it, is speed. If coaches look for different
skills in blacks and whites, because they have positions with different requirements in mind for members of the two races, they will select blacks who are fast
while using other criteria for selecting whites. In an article on stacking in British
soccer, Maguire (1991) quotes an Afro-Caribbean winger explaining how he started
at that "speed" position: "Our coach at school, our master at school. Because like
you said, you're black, you're quick, you're on the wing" (p. 115). Williams and
Youssef (1975) found that coaches stereotype the mental, physical, and personality characteristics of players by both race and position. Leonard (1998) in discussing their study says, "The results of the study supported the matching hypothesis,
whereby coaches assign a disproportionatenumber of blacks, for example, to those
positions where successful execution depends on those characteristics judged to
be predominant in blacks" (p. 221).
Based on the same beliefs, coaches may also change the positions of blacks
who are fast-for example, to the outfield in baseball or to cornerback in football-to better utilize the slull that is most salient to them. African-American football players are more likely than are whites to be moved from central positions in
high school to peripheral positions as they move through college to the pros (Eitzen
& Sanford, 1975; Madison & Landers, 1976). Black quarterbacks have been told
that they are just "too athletic" to remain in that position, which makes no logical
sense. (Anecdotally, Babe Ruth was considered to be too good a hitter to remain a
30
Margolis and Piliavin
pitcher and was switched to the outfield, but this has to do with the number of
games in which he could play.) Regarding baseball in the 1960s Guppy (1983)
found that "many blacks are initially recruited as infielders and then later transferred to outfield positions. . . . Black movers (infield to outfield) are superior
infielders compared to whites" (p. 107).
It is not only whites who believe that blacks are inherently faster; it is part
of the culture shared by people of African ancestry. Thus, African Americans who
are fast may be more likely than whites who are fast to discover that they have this
ability, to select into a sport in which it can be valuable, and specifically, to select
into a position in which it can be best utilized. Maguire (1991) quotes another
Afro-Caribbean soccer winger: "You know a lot of us being graced with the gift of
pace and all the rest of it" (p. 114).
l African Americans may model famous black athletes; thus, when they go
out for sports, they choose the same positions. Castine and Roberts (1974) found
that among African-American college athletes, 52% had a same-race idol, and
48% played the same position as the idol when in college. Given that there will be
a lot of athletes vying for a limited number of those positions, only those who are
fast survive the competition. Whites may have a broader range of role models.
Brower (cited in Castine & Roberts, 1974) found that 90% of a small sample of
African-American athletes had only black role models. White players had role
models from both races. The selection for success in other positions is based less
on speed than on other criteria; thus, the incumbents of those positions, who are
largely white, will not be selected for speed.
l In a variant of this argument, it is also possible that, in emulation of famous African-American outfielders, African-American boys work on their speed
(as well as their batting skills) and a self-fulfilling prophecy results. Namely, they
become faster.
A more structural argument is offered by Leonard (1998). He suggests that
since there are closed occupational doors in other areas, a higher proportion of
African Americans have channeled themselves into sports, leading to a greater
possibility of finding the outstanding talent that exists in that group. He quotes
Edwards, "It is the inevitable result of all this talent channeled into a single area.
The white athlete who might be an O.J. Simpson is probably sitting somewhere
behind a desk" (1977, p. 59).
Whatever the reason may be that African-American baseball players in this
sample are faster and are found predominantly in the outfield, in this analysis
speed has been shown to be a significant determinant of where a player is positioned and partially to explain the African-American effect. What needs to be done
now is, first, to replicate this result, perhaps in football and if possible with a better
measure of speed in baseball. Second, the mechanism by which this occurs needs
to be clarified. It is only by understanding how apparent discrimination occurs that
we can hope to combat it.
A second interesting finding of this study is that the significant effect of
being Latino on centrality is partially masked by some of the control variables:
height, weight, and age. Only when these variables are included in the regression
equation do we find that Latinos are, on the average, in more peripheral positions
than whites of the same size and age. That is, there is discrimination against Latino
players that is not evident in most investigations of stacking.
"Stacking"in Baseball
31
Since the great majority of the literature on stacking has focused on blackwhite differentials, there has been little speculation regarding position segregation
of Latinos. In the only study that focuses on this group, Gonzalez (1996), who did
not use controls for age and size, finds as others have that Latinos are heavily
represented in nearly central positions, "defying the traditional stacking theory
that minorities are found mostly in noncentral playing positions" (p. 136). She has
no easy explanation for this, agreeing with Phillips, who writes, "It would seem
unlikely that prejudiced white coaches and managers would place foreign, colored
players who do not speak English well in positions requiring interaction and control. But they do, and the 'stereotyping7 version of the centrality theory does not
explain this" (1983, pp. 13-14, as quoted in Gonzalez, 1996, p. 154).
Gonzalez settles on a sociological explanation based on stereotype-driven
selection by managers, and especially scouts, as probably the most reasonable.
She writes,
Latino players are considered to be better infielders because they are relatively short, are more agile, and have better hands. Latinos are not seen as
having the power or the speed to be outfielders. . . . Managers, and especially scouts, seem to have preconceived notions that are based on their knowledge and beliefs regarding biology, culture, experience, and, above all, organizational needs. These beliefs then play a role in the decision-making process as to what type of players the scouts will seek. (1996, p. 155)
As with most sports sociologists, Gonzalez rejects an innate differences model.
The fact remains, however, that in our sample, Latinos are shorter and lighter
than white or African-American players. Our findings, controlling for these attributes, appear to solve the puzzle expressed in the Phillips quote. The managers
appear to be acting both on their expectations regarding the appropriate placement
of Latinos by assigning them largely to second base and shortstop and on their
prejudices. That is, controlling for size, they are also distancing them somewhat
from whites with comparable physiques. This finding should provide a cautionary
note for those who investigate position allocation in the future. The problem of
omitted variables can lead to erroneous interpretations of data in more ways than
one. It is not only that we can miss the effects of the variables themselves that are
omitted; we can also miss the effects of variables that are being suppressed by not
including those variables. Certainly this research also suffers from the omission of
relevant variables; however, we hope it has provided a step in the direction of a
more sophisticated analysis of these interesting and controversial phenomena.
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Notes
'Problems with communication have been offered as one of the explanations for
results regarding the underrepresentation of French speakers in defensivepositions in hockey,
although this has been contested (Lavoie, 1989).
2Whilethe researchers concede that the process for determining race may have been
susceptible to some bias, this does not seem to have been the case. The first author has
extensive knowledge of major league baseball, which was occasionally employed as validation. Memories of past interviews with a player, columns written about the player, and
even comments by game announcers about a player's background were used when necessary.
3The position of pitcher is also typically categorized with a centrality of 1. Regretfully, we have omitted pitchers from the sample because the main focus of this research was
34
Margolis and Piliavin
on the multivariate analysis, and the majority of them could not be assigned one or more of
the control variables. Details will be explained below. We thought that these problems would
make any sample of pitchers included in the analyses too unrepresentative.
4Half of all pitchers-those in the American League--could not be characterized
with a slugging percentage, because they never bat, which would have eliminated them
from analyses using the power variable. The variability of scores of pitchers who do bat
would have been very small.
appears to validate the separation of centerfield from the other two outfield
positions with regard to centrality, as was done in this study.
6Pitchers in the National League (who do bat) are seldom on base enough to have
much opportunity to steal. When they are, it would be unusual for them to be "sent" in any
case. Thus, most of them would have been eliminated in analyses using the speed variable.
'The informed reader will notice that, although the percentage of Latinos is very
close to the overall league percentage (about 17%), the sample contains a higher proportion
of African-American players than is true of all major league ballplayers (also 17%). There
are at least two possible reasons for this. First, pitchers are not included, and in 1992 this
position was heavily dominated by white players. Second, it has been demonstrated (Phillips,
1997) that on average, African-Americanplayers have higher batting averages than whites.
Thus, one might expect that our procedure for selecting the most active players may also
have increased the proportion of African Americans in the sample.
81tmay be of interest to note that the only other variable to which skill was related
was age. Older players had significantly more fielding range compared to younger players
at the same position (r = .147, p < .05).A separate measure of fielding ability among catchers revealed a similar relationship to age (r = .397, p < .01).
When both variables are in the equation, neither individually is significant, although
together they increase the variance explained.
' m e authors are grateful to an anonymous reviewer, who pointed out that in order to
be a successful base stealer, one must be able to "read" the pitcher's delivery, be aware of
the strategic circumstances of the moment, and be able to accurately synthesize quickly a
great deal of data on position of infielders, the hitter's skills, and so on. That is, it requires
analysis and judgment as well as raw speed. Many of these skills are also needed to be a
good outfielder or middle infielder.
Acknowledgments
This paper is based on a senior honors thesis prepared by the first author under the
direction of the second author. The authors would like to thank the two anonymous reviewers and the editor for extremely helpful comments and Irving Piliavin for statistical advice.