Sex Roles

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Are Men Better Leaders? An Investigation of Head Coaches’ Gender and Individual Players’ Performance in Amateur and Professional Women’s Basketball

Original Article

Abstract

Male-dominated industries such as sport contain stereotypical and subjective notions of leadership ability (Burton et al. 2009; Fink 2008). These gender stereotypes often manifest themselves within varying levels of leadership, but specific to the sport industry, they are the most visible within the head coach role. Men hold the majority of head coach positions within the professional and amateur levels of sport, and these hiring practices can be based on gender-role stereotypes (Acosta and Carpenter 2014). In an attempt to challenge stereotypical gender based leadership preferences, leadership ability and performance should be objectively examined. Therefore, in the present investigation we aimed to examine the presence of gender stereotypes in the sport industry by determining whether the gender of a head coach for two women’s basketball leagues, the Women’s National Basketball Association (WNBA) and the National Intercollegiate Athletic Association (NCAA), impacted individual player performance. Data were collected for 1522 players for 19 WNBA seasons (1997–2015) and 4000 players for three seasons of NCAA Women’s Basketball (2013–2016). Results indicated that head coach gender does not appear to impact individual player performance in the WNBA or in the NCAA thereby providing objective evidence to challenge the traditional gender stereotypes found within the sports industry.

Keywords

Organizational and occupational outcomes Gender equality Stereotypes Leadership Sport 

Women in leadership positions are an anomaly and within male-dominated industries such as sport, women hold top leadership positions (e.g., head coach) at a much lower rate than their male counterparts do (Acosta and Carpenter 2014). Within the U.S. National Collegiate Athletic Association (NCAA), men currently hold the majority of head coaching roles of women’s teams (57.1%), whereas women represent only 43.4% of head coaches for similar women’s programs (Acosta and Carpenter 2014). Moreover, this percentage of women head coaches has drastically decreased from 90% since the passage of Title IX (Acosta and Carpenter 2014). (Title IX is a United States policy that addressed. Discrimination on the basis of sex within educational activities hosted by federally funded institutions and which subsequently aided in creating additional opportunities for girls and women in interscholastic and intercollegiate sport.) Based on this decline, previous research within the sport industry has understandably been devoted to examine the lack of women in leadership roles, specifically focusing on gender stereotypes (Heilman 2012), access and treatment discrimination (Burton 2015; Fink 2015), gendered hiring practices (Darvin and Sagas 2017, self-limiting behaviors and future career intentions (Sagas et al. 2006), and gender restrictive leadership roles (Walker and Sartore-Baldwin 2013).

Research outside the sport context has investigated varying personality traits that are considered essential to successful leadership and have determined that both men and women maintain traits that are necessary for success in leadership roles. These results also indicated that women report higher levels of empathy and emotional intelligence, which may enhance their overall leadership abilities (Eagly and Carli 2007). Hiring practices within the sport industry, however, do not reflect an acknowledgement or understanding of personality traits that may favor women in their leadership endeavors. Given that women may possess traits more conducive to success in these roles, additional research should aim to investigate the current hiring landscape and determine whether the disproportionate number of men leaders is due to performance or stereotypical notions of leadership ability.

Results of previous inquires have offered several possible explanations for the lower representation of women in leadership roles of the sport industry (i.e., head coaching positions). First, women’s lower representation could be based on supply due to a lack of interest in coaching by women for various reasons, including work-home life balance (Dixon and Bruening 2005; Inglis et al. 2000; Rhode and Walker 2008), Second, supply within the market can be affected by discrimination against women. This discrimination can come from employers (Brooks and Foster 2010; Humphreys 2000), from co-workers (Shaw 2006), and from role discrimination that discourages women from entering the coaching profession (Aicher and Sagas 2010; Hoffman 2010) or from coaching men’s sports (Walker and Sartore-Baldwin 2013). A third explanation for the lack of women head coaches could be that men are more productive head coaches than women are, even when both sets of coaches (men and women) receive the same set of resources (Von Allmen 2013).

For the purposes of the current study, we focused on the third of the aforementioned explanations: the notion that men coaches are more productive. In order to do so, we analyzed whether the gender of a head coach impacted productivity in coaching through objective measures of individual player’s performance. The purpose of our study was to analyze the contributions made by both men and women head coaches at the professional level (i.e., in the Women’s National Basketball Association; WNBA) as well as within the amateur level (i.e., in NCAA Division I Women’s Basketball). These U.S. organizations were selected for two distinct reasons: (a) the WNBA and NCAA employ both men and women head coaches and (b) players’ performances in the WNBA and NCAA can be accurately measured. Therefore, the WNBA and NCAA both provide an ideal dataset to examine how men and women impact the performance of the workers they lead (i.e., the players).

Our investigation will build upon similar research that has concluded that a coach’s gender does not impact outcomes within NCAA softball (Von Allmen 2013). However, Von Allmen (2013) analyzed team outcomes; such an approach fails to differentiate between a coach’s ability to recruit athletes and their ability to alter the performance of athletes on their roster. Therefore, we conducted our analysis of coaching based on the ability of a coach to alter the productivity of a player (i.e., individual player performance). Our study also built upon the work of Berri et al. (2009), which found that most NBA coaches do not have a statistically significant impact on the performance of their players or the performance of their team. However, given that women head coaches within the NBA are non-existent, their study did not specifically test for any gender differences in coaches’ impact.

In contrast, Dawson et al. (2000) argued that coaches have both a direct and indirect impact on team winning performance. Specifically, coaches seek not only to maximize wins given a certain amount of playing inputs, but also to improve the quality of inputs employed. Said differently, head coaches are responsible not only for developing and implementing strategies for success, but also for developing the players who will be responsible for the execution of their strategies during an event or competition. Given the contradicting arguments and outcomes surrounding the nature of a head coach in developing individual players, additional examinations of head coaches’ performance should be conducted. Therefore, the current investigation served as an extension of Berri et al. (2009) and Dawson et al. (2000) because we incorporated head coaches’ gender as an additional variable under consideration. Consequently, our analysis serves as the first known to objectively examine whether the gender of a head coach impacts the performance of individual players in any sports league.

Gender stereotypes

Gender stereotypes often dictate the roles to which men and women are assigned within the sport industry with little regard for productivity measurements (Burton 2015). Recent research has confirmed the perception of gender bias in the hiring of coaches in the NCAA and noted that male coaches face fewer obstacles in their career progression and compensation (Sabo et al. 2016). Therefore, our examination utilized the presence and subsequent outcomes of gender stereotypes as a basis for conducting an objective measure of head coach productivity. Specific to the current study, it has been found that gender stereotypes assigned to men often suggest that they are more agentic, in that they are assumed to be more assertive, confident, and powerful, whereas women are often considered to be communal, commonly described as pleasant, likeable, and trustworthy (Reid et al. 2009).

Although these gender-specific traits may not seem entirely harmful, agentic traits are most often assigned with and define the leadership role and, as such, men are often assumed to possess more appropriate leadership abilities (Heilman et al. 1995). These assignments of stereotypical agentic-male and communal-female traits have been found to lead directly to two forms of prejudice wherein (a) women are less likely to be endorsed as a leader because male trait stereotypes are more closely aligned with leaders and (b) the actual leadership carried out by a woman is evaluated less favorably because demonstrations of assertive behavior by a woman is perceived as threatening and undesirable, particularly by men (Heilman 2012). Therefore, these stereotypes often give way to damaging outcomes for women in their ability to obtain leadership roles, with little regard for distinct measurements of performance.

The aforementioned gender stereotypes consist of two distinct forms: descriptive and prescriptive (Heilman 2012). Prescriptive gender stereotypes suggest what men and women should be like, whereas descriptive gender stereotypes suggest that men and woman are alike (Heilman 2012). Specific to the sport industry, descriptive stereotypes are typically linked with the lack of women in leadership roles because they often promote negative expectations about a woman’s performance due to a perceived lack of fit (Heilman 2012). This lack of fit often stems from assumptions about the attributes women are thought to possess and the attributes thought necessary for success in traditionally male positions (e.g., head coach) (Heilman 2012). Therefore, challenging these stereotypical notions of ability is crucial within male-dominated industries such as sport.

Overall, and due in large part to these traditional gender stereotypes, not only are women granted restricted access to roles as leaders in sport organizations, but also when they reach these top levels of leadership, they are not treated with the same amount of respect as their male counterparts (Burton 2015; Cunningham and Sagas 2008). As such, although it is clear that gender stereotypes have created barriers for women in gaining leadership positions, one expansion of the literature should explore whether gender does in fact impact leadership performance. To this point, women have been outgained by their men counterparts due, at least in part, to these more subjective notions of leadership ability rather than any definitive or objective measures of performance. Thus, our study aimed to challenge these current gender stereotypes in the sport industry by conducting an objective analysis of head coach performance.

The gendered nature of sport

The gender stereotypes that often plague the sport industry have reinforced the general notion that sport is a gendered space (Burton 2015; Fink 2016). Specifically, gendered sport leadership processes have shaped the sport industry and often discouraged women from aspiring toward leadership roles. These gendered processes are often compounded when people typically assign stereotypical characteristics to women, men, and leadership positions. The assignment of stereotypical traits contributes to the challenges women face in obtaining leadership roles and performing well in them (Koenig et al. 2011). For example, specific to the head coaching role, Cunningham and Sagas (2008) found not only that women have less access to positions as head coaches, but also that when women are able to obtain these positions, they are not treated with the same level of respect that their male colleagues are afforded. Processes such as these reinforce the subconscious formation of leadership stereotypes and serve to perpetuate sport as a gendered space. These stereotypes often exist despite a lack of evidence and objective measurements of coaching performance.

Previous research has further suggested that gender stereotypes often contribute to access and treatment discrimination throughout the sport industry. Both access and treatment discrimination have been found to greatly influence the lack of women in leadership positions of sport organizations (Cunningham and Sagas 2008; Knoppers and Anthonissen 2008; Walker and Sartore-Baldwin 2013.). These processes continue to hinder women in the pursuit of leadership roles in sport with little regard for specific measurements of leadership outcomes. Specifically, these forms of discrimination suggest that the “old boys’ network,” or exclusive networks in general, prevent certain individuals (i.e., women) from entering the field (Burton 2015; Walker and Sartore-Baldwin 2013). These networks have also been found to impact the specific jobs that are accessible to women. For example, whereas men are given access to head coaching positions for both men’s and women’s teams, women are for the most part only given the opportunity to lead women’s teams (Walker and Sartore-Baldwin 2013). This gendered role assignment is especially evident when considering professional sports because the both the WNBA and the National Basketball Association (NBA) have comprised of a majority, if not solely, men head coaches.

Moreover, and specific to the current study, the treatment of women in the gendered sport industry has been found to impact leadership attainment. Specifically, scholars have examined the work experiences of women holding positions in intercollegiate athletics (Sabo et al. 2016; Sagas et al. 2006). According to Sagas et al. (2006), not only are women obtaining leadership roles at a lower rate than their men counterparts are, but women coaches also scored lower than men did regarding their intentions and attitudes toward obtaining a head-coaching role in the future. Further, and in conjunction with gender stereotypes, it has also been determined that due to gender stereotypes of assumed natural ability, women have been denied crucial opportunities to grow and learn in their positions (Peachey and Burton 2011).

Proper evaluations of leadership outcomes aimed at challenging traditional gender stereotypes and the subsequent gendered nature of sport are lacking. Gender stereotypes not only have led to a lack of opportunities in coaching for women, but have restricted women in gaining the necessary skill sets to obtain positions as head coaches for men’s or women’s programs in the future (Burton 2015; Hoffman 2010; Tiell et al. 2012; Walker and Sartore-Baldwin 2013). Therefore, objective measures of leadership outcomes are necessary in order to combat the subjective and stereotypical notions of successful leadership traits. Once men and women are evaluated in terms of production and performance, perhaps the emphasis placed on stereotypical abilities will begin to diminish.

Preference toward male leadership

Men and masculinity continue to receive enormous privilege within sport organizations because jobs are frequently associated with gender-role stereotypes (Cunningham and Sagas 2008). Not only have women been restricted in their leadership endeavors, but also gender stereotypes have naturalized men’s power and privilege over women through hegemonic or normative notions of masculinity (Burton 2015; Fink 2008; Walker and Bopp 2010; Whisenant et al. 2002). Within the sport industry, women are assigned to more traditionally feminine roles within sport organizations (e.g., lower level coaching, lower level administration), whereas men maintain the majority of leadership positions both in coaching (e.g., head coach) and administration (e.g., athletic director) (Acosta and Carpenter 2014). For example, despite a larger percentage of women assistant coaches within NCAA women’s basketball, softball, soccer, and volleyball, the proportion of women head coaches for these sports has continued to decline since the passage of Title IX (Darvin and Sagas 2017. Therefore, regardless of the increase of women assistant coaches, arguably a prerequisite in obtaining a head coaching position, women are not obtaining these roles at the same rate as their male counterparts (Darvin and Sagas 2017. This phenomenon calls into question the more subjective evaluations of head coaches, as well as the hiring practices for head coaching positions in both professional sport and the NCAA.

This male privilege also extends past the specific positions men and women obtain in sport and influences the levels at which men and women are seen in these roles. For example, there is a greater number of women head coaches in the NCAA Division III level (47.3%), whereas men maintain the majority of head coaching roles in Division I athletics, the highest level of NCAA athletics (56.6%) (Acosta and Carpenter 2014). Similarly, men have held the majority of head coaching roles in the WNBA over the last 19 seasons, with 29 men head coaches and 27 women head coaches leading WNBA teams (WNBA 2016). For the NBA, there has yet to be a woman head coach hire. Thus, it would appear that the reinforcement of men’s power and control in sport creates limitations for women in gaining access to head coaching roles at the highest levels of these industries (e.g., NCAA Division I; professional leagues).

The emphasis placed on gender stereotypes and notions of success that have been associated with masculinity in sport have excluded more inherent feminine attributes from being considered valuable, and they affirm men’s power not only over sport, but over women as well (Whisenant et al. 2002). More specifically, it has been suggested that sport operates as a space to define and reproduce masculinity and works to encourage boys and men to exhibit, value, and reproduce traditional notions of masculinity (Anderson 2009; Burton 2015). Given this stereotypical notion of sport outcomes, women are not often considered equipped with the inherent qualities necessary to lead men’s programs. For example, in accordance with traditional gender stereotypes, it has been found that, in coaching, masculine attributes such as dominance, aggressiveness, and independence are considered to be much more desirable in head coaches when compared with feminine attributes such as affectionate, sympathetic, and tender (Aicher and Sagas 2010). This emphasis placed on masculinity subsequently suggests that traditional gendered attitudes can have an impact on an overall lack of encouragement in hiring women to be head coaches (Walker and Bopp 2010). As a result, women have been somewhat powerless to improve their access to leadership positions in sport. Therefore, in order to increase access for women to head coaching roles, these traditional gender stereotypes should be challenged.

Research aimed at challenging the presence of gender stereotypes within the sport industry has not objectively confronted these coaching preferences and stereotypes. In order to challenge these more subjective and stereotypical notions of coaching ability, the next phase of research should aim to determine whether the gender of the head coach has any impact on the success of the individual players. In other words, it needs to be determined if men, as the dominant gender in head coaching positions, are in fact better at developing the players that they lead. To begin the process of objectively measuring head coach outcomes and ability it is critical to determine if the preference toward men head coaches is indeed warranted. Therefore, the aims of the current investigation are to determine if a head coach’s gender impacts individual players by posing two research questions: (a) Does the gender of the head coach in the WNBA impact the productivity of the individual player? and (b) Does the gender of the head coach in NCAA Division I Women’s Basketball impact the productivity of the individual player?

Model 1: The WNBA

The representation of head coaches within the sports industry is mostly male, and women struggle to obtain roles of leadership even within female programs (Acosta and Carpenter 2014; Lapchick et al. 2012). The fact that men currently hold the majority of head coaching roles suggests not only that gender stereotypes have dominated the landscape of sport, but also that men are better coaches than women. To test this assumption and potentially challenge the current state of gender stereotyping in sport, we explicitly examined how the gender of a head coach impacted the performance of their individual players. The approach taken for this examination followed Berri et al.’s (2009) study of NBA head coaches.

Traditionally, coaches are evaluated in terms of team wins and losses, but such an approach may overlook the quality of players on a specific team. In order to control for the quality of players for whom a coach is responsible, Berri et al. (2009) examined how coaches impacted the productivity of the individual players they were leading. In the current study we utilized the same approach as Berri et al. (2009). More specifically, we did not examine all aspects of the head coach role, such as whether or not a coach is better at recruiting players (i.e. selling a prospect on a program). What we did examine is closer to what many perceive as the fundamental role of a coach (Berri et al. 2009). Specifically our analysis was concerned with testing whether there is a difference in the ability of female and male head coaches in making their individual players better. This will be accomplished through an analysis of individual player statistics for 19 WNBA seasons (1997–2015). To be included in the analysis, a WNBA player had to have had participated in a minimum of 250 min played during the season examined as well as the season prior.

Method

Sample

The data utilized to estimate this model were collected for 19 WNBA seasons (1997–2015). Data for each player were obtained through online databases such as archived team rosters and archived player profiles. This dataset includes every player in the WNBA who played between the years of 1997 and 2015, but to be included in the model, a player had to have consecutive seasons with at least 250 min of total playing time. If a player does not play enough minutes, then player statistics will not fully capture a player’s abilities. Therefore, cutoffs had to be made to restrict the sample. Berri et al. (2009) included a player in their study of the NBA if a player competed in at least 25% of NBA games (approximately 20 games) and averaged at least 12 min per game (i.e., 25% of a game). Under these guidelines, players were included in the Berri et al. (2009) sample if they played at least 246 min per season. To remain consistent with this previous examination, our sample considered minutes-played cutoffs ranging from a low of 250 min to a high of 500 min.

Under this guideline, the total player-season observations for the WNBA amounted to 1522 participants. For each of these 1522 WNBA players, data collected included, in addition to the gender of the coach, each player’s age, position, minutes played, and a collection of box score statistics (i.e., points per game, rebounds per game, assists per game, steals per games, turnovers per game). Further, two additional variables that are estimated within the model, NewTeam and NewCoach were concerned with whether coming to a new team or coming to a new coach influenced an individual’s productivity. Our study included 58 coaches, of whom 29 were men, and the player observations were tilted toward the male coaches, with about 59% of the player observations being linked to a male head coach.

Measures

When examining player productivity in the sport of basketball, previous research has utilized a measure, which is detailed in Berri and Schmidt (2010), Berri (2008), and Berri and Schmidt (2010), that is known as wins produced. This metric begins with a regression of team wins on team offensive and defensive efficiency, wherein efficiency is defined by how many points a team scored and surrendered per possession. As detailed in Berri (2008), offensive efficiency is points per possession employed, where possession employed is calculated as: Field Goals Attempted +0.44*Free Throws Attempted + Turnovers – Offensive Rebounds. Defensive efficiency is points per possession acquired, where possession acquired is calculated as: Opponent’s Field Goals Made +0.44*Opponent’s Free Throws Made + Defensive Rebounds + Opponent’s Turnovers + Team Rebounds. Given the estimated results, productivity of a player (PROD) can be measured with Eq. 1:
$$ \mathrm{PROD}={\mathrm{Points}}^{\ast }(0.033)+\mathrm{Field}\ \mathrm{Goal}\ {\mathrm{Attempts}}^{\ast}\left(-0.031\right)+\mathrm{Free}\ \mathrm{Throw}\ {\mathrm{Attempts}}^{\ast}\left(-0.014\right)+\mathrm{Offensive}\ {\mathrm{Rebounds}}^{\ast }(0.031)+{\mathrm{Turnovers}}^{\ast}\left(-0.031\right)+\mathrm{Defensive}\ {\mathrm{Rebounds}}^{\ast }(0.032)+{\mathrm{Steals}}^{\ast }(0.031)+\mathrm{Personal}\ {\mathrm{Fouls}}^{\ast}\left(-0.020\right)+\mathrm{Blocked}\ {\mathrm{Shots}}^{\ast }(0.20) $$

Due to the fact that players often participate in contests for differing amounts of time, we then calculated each player’s PROD per 40 min played (PROD40). Following Berri and Schmidt (2010) and Berri and Krautmann (2013), PROD was adjusted for the player’s assists as well as the teammates’ production of assists, defensive rebounds, and statistics as well as opponent’s field goals made, opponent’s free throws made, opponent’s turnovers (that are not steals), and team rebounds (all of which are only tracked for the team). In the end, a measure is produced that, when summed across all players on a team, tracks very closely to team wins.

Therefore, the current investigation requires the estimation of the following multivariate regression model. The dependent variable is the measure of player performance (PROD40). In addition to the independent variables listed, the estimation of this multivariate regression included player fixed effects. Player fixed effects are essentially dummy variables for each WNBA player in the sample. These are included to control for player-specific characteristics such as the quality of the player. In essence, we are asking if there is something beyond the characteristics of the player that cause productivity to systematically change. Of the factors we consider, the most important to the present study is the gender of the coach. Specifically, we wish to know if the gender of the coach systematically causes player productivity to change. The complete WNBA model is reported in Eq. 2:
$$ \mathrm{PROD}40={\upgamma}_0+{\upgamma}_1\ \mathrm{GENDER}+{\upgamma}_2\mathrm{AGE}+{\upgamma}_{\mathrm{s}}\mathrm{SQAGE}+{\upgamma}_4\ GP+{\upgamma}_5\ \mathrm{TMPROD}+{\upgamma}_6\mathrm{NewTeam}+{\upgamma}_7\mathrm{NewCoach}+{\upgamma}_8\ \mathrm{DBIG}+{\upgamma}_9\ \mathrm{DGUARD}+\upvarepsilon $$
where GENDER is a dummy coded variable for coach’s gender (1 = female; 0 = male); AGE is age of the player; SQAGE is age squared of each player (the squared term is included because this is not expected to be a linear relationship:; GP is number of games played in the last two seasons (to control for injury); TMPROD is productivity of teammates; NewTeam is a dummy variable for coming to a new team; NewCoach is a dummy variable for coming to a new head coach; DBIG is a dummy variable equal to 1 if the player played power forward or center; DGUARD is a dummy variable equal to 1 if the player played point guard or shooting guard.

To ascertain the impact of the gender of the head coach on player performance, the analysis controlled for other factors that might impact player performance. The factors after GENDER listed in Eq. 2 were each employed in the aforementioned study by Berri et al. (2009). This list of additional factors begins with the age of the player, and because we expect that players will first improve when they are younger and then decline as they age, we also include age squared (SQAGE). To control for injury we include the number of games played (GP) the past two seasons. In addition, following Berri et al. (2009), we controlled for the productivity of teammates (TMPROD), which is defined as the production of wins by a player’s teammates. TMPROD was included in order to test for the possibility of diminishing returns. More specifically, if a player’s teammates are more productive, it is possible that that player will produce less. Finally, we controlled for the impact of coming to a new team (NewTeam), new coach (NewCoach), and whether a player plays in the frontcourt (DBIG) or backcourt (DGUARD) to see if any differences existed by position played.

Model 2: NCAA women’s division I basketball

For the second research question, we applied the same method to evaluate coaching and the impact of head coach gender in the WNBA to the NCAA. Once again, we were concerned with the role of the head coach in developing their individual players, not all aspects of the head coach role (e.g., recruiting). For the NCAA model we again followed the approach taken by Berri et al. (2009). Similar to our approach taken with the WNBA, we estimated the NCAA model with seven different minutes-played cutoffs from 250 to 500 min although we will only report results based on the 250 min-played cutoff. (Additional minutes-played results are available as an online supplement.)

Method

Sample

The data required for the present investigation were collected from NCAA Statistics (2017). Data found on this source only reported player minutes back to the 2012–13 NCAA Women’s Basketball season. Thus, this restricted our ability to measure PROD40 and TMPROD for the 2012–13, 2013–14, and 2014–15 seasons, and the estimation of PROD40 for the NCAA followed the same approach used for the WNBA. Despite this limitation within the available data, each of these seasons consisted of more than 300 teams; therefore both the team and player samples for the NCAA were larger than the samples for the WNBA. A similar minimum amount of minutes-played guideline was used for our second research question, and therefore NCAA players with a minimum of 250 min of playing time per season were included. Under this guideline, data were collected for 3313 NCAA women’s basketball player observations.

Measures

Similar to the model for the WNBA, Eq. 3 is a multivariate regression, but because the sample was limited to 3 years of player data, using player-specific fixed effects would not be the best approach. Instead, a measure of lagged player performance was included in order to control for the quality of the player. In addition, this model included conference-specific fixed effects in order to control for the quality of competition each player faced. The complete NCAA model is reported in Eq. 3.
$$ \mathrm{PROD}40={\upgamma}_0+{\upgamma}_1\ \mathrm{GENDER}+{\upgamma}_2\mathrm{Class}+{\upgamma}_3\ GP+\kern0.5em {\upgamma}_4\ \mathrm{TMPROD}+{\upgamma}_5\ \mathrm{DBIG}+{\upgamma}_6\mathrm{DGUARD}+{\upgamma}_7\ \mathrm{TOTEXPPER}+{\upgamma}_8\ \mathrm{LagPROD}40\kern0.5em +\upvarepsilon $$
where GENDER is a dummy coded variable for coach’s gender (1 = female; 0 = male); Class is the year of study for each player (1 if in first year, 2 if second year, etc.); GP is number of games played in the last two seasons (to control for injury); TMPROD is productivity of teammates; DBIG is a dummy variable equal to 1 if the player played power forward or center; DGUARD is a dummy variable equal to 1 if the player played point guard or shooting guard; and TOTEXPPER is the total expenditure on program per person.

In order to isolate the impact of the coach’s gender, the model also controlled for additional factors that would cause performance to deviate from what was seen in the past. These variables are similar to the model for the WNBA and additional considerations included games played, the productivity of teammates, and whether a player played in the frontcourt or backcourt. Beyond these factors, the analysis also considered two additional variables that were specific to college-level sport. First, instead of age of the player, the NCAA model considered how many years an athlete was in school (i.e., CLASS) to track changes in productivity over time from first year to graduation. Second, following a study conducted by Von Allmen (2013), our analysis also considered the amount of money each school spent on their respective program (i.e., TotExp). This is included to see if the level of school spending is related to changes in players’ performance.

Results

WNBA

Results for the WNBA were estimated with a linear multivariate regression model with player-specific fixed effects. The following results are reported using the 250-min cutoff in playing time. (Results for the additional cutoffs (300, 350, 400, 450, 500) are available in an online supplement; see Table 1s). Descriptive statistics are reported in Table 1. Descriptive statistics indicated that 41% of the WNBA player observations are linked to a female coach. In addition, the average player age in the sample is 27.3 years (M = 27.35, SD = 3.95), approximately 23% of the players came to a new team in the season under consideration and 48% had a new coach, and the average number of games played for the previous two seasons was 62 games (M = 61.54, SD = 6.43).
Table 1

Descriptive statistics for variables in Eq. 1 involving the WNBA

Independent variables

M

SD

Minimum

Maximum

Player’s productivity (PROD40)

.155

.104

−.182

.555

GENDER of head coach (0 = male)

.409

.492

.000

1.000

Player’s age

27.349

3.951

20.000

41.000

Games Played (GP)

61.538

6.427

27.000

70.000

Productivity of teammates (TMPROD)

.098

.030

−.011

.191

Coming to a new team (NewTeam = 1)

.230

.421

.000

1.000

Coming to a new coach (NewCoach = 1)

.475

.500

.000

1.000

Played power center of forward (DBig = 1)

.412

.492

.000

1.000

Played point guard or shooting guard (DGuard =1)

.389

.488

.000

1.000

The model estimation results are reported in Table 2. Results of the regression analysis for the 250 min-played cutoff indicated that gender of the head coach (GENDER) did not significantly predict individual player’s productivity. Results also indicated that age of the player, the productivity of teammates, and a player coming to a new team (NewTeam), were not significant. The only predictors significantly related to individual player’s performance were the number of games played (GP) and coming to a new coach (NewCoach).
Table 2

Estimation results for Eq. 1 involving the WNBA

Independent variables

Coefficient

t

p

GENDER of head coach (0 = male)

−.0017

−.36

.72

Age

.0104

1.09

.28

Age2

−.0002

−1.28

.20

Games Played (GP)

.0010

2.18

.03*

Productivity of teammates (TMPROD)

−.0448

−.52

.60

Coming to a new team (NewTeam = 1)

.0047

.77

.44

Coming to a new coach (NewCoach = 1)

−.0126

−2.53

.01*

Plays power forward or center (DBig = 1)

.0139

.98

.33

Plays point guard or shooting guard (DGuard =1)

−.0187

−1.48

.14

Constant

−.0115

−.08

.94

R2

.74

  

Adjusted R2

.66

  

Observations

1522

  

Minimum Minutes

250

  

Note. The model reported here is for a minimum of 250 player minutes. (Models for 150, 200, 250, 300, 350, 400, 450, and 500 player minutes are available in an online supplement; see Table 1s)

*p < .05

NCAA division I women’s basketball

Results for the NCAA were obtained through a linear multivariate regression model with fixed effects for the player’s conference. Results are reported for the 250 min-played cutoff. (Results for additional cutoffs—300, 350, 400, 450, and 500—(in both the current and lagged season) are available in an online supplement; see Table 2s). Descriptive statistics are reported in Table 3. Descriptive statistics indicated that players in the sample averaged 59.6 (SD = 6.02) games played (GP) and were roughly 2.60 (SD = 1.12) years into their collegiate experience (Class) and that total expenditures (TotExp) were on average $109,865.00 per team (SD = $70,100.00).
Table 3

Descriptive statistics for variables in Eq. 2 involving the NCAA

Independent Variables

M

SD

Minimum

Maximum

Player’s productivity (PROD40)

.183

.101

−.125

.661

GENDER of head coach (0 = male)

.606

.489

.000

1.000

Player’s Class Year (Class)

2.593

1.119

1.000

4.000

Games Played (GP)

59.588

6.018

22.000

79.000

Productivity of teammates (TMPROD)

.100

.040

−.047

.296

Played power center of forward (DBig = 1)

.410

.492

.000

1.000

Played point guard or shooting guard (DGuard =1)

.392

.488

.000

1.000

TotExp

109,865

70,100

$0

512,194

The estimation results are reported in Table 4. Results of the analysis for the 250-min played cutoff indicated that gender of the head coach (GENDER) did not significantly predict individual player’s productivity. Results also indicated that the productivity of teammates (TMPROD) was not significant. Additional results indicated that games played and players’ class year were significant predictors of individual player’s productivity.
Table 4

Estimation results for Eq. 2 involving the NCAA

Independent Variables

Coefficient

t

p

GENDER of Head Coach (0 = male)

−.0031

−1.03

.30

Players Class Year (Class)

.0042

2.14

.03*

Games Played (GP)

.0025

7.79

<.001*

Productivity of Teammates (TMPROD)

−.0526

−1.17

.24

Plays power forward or center (DBig = 1)

.0194

4.65

<.001*

Plays point guard or shooting guard (DGuard =1)

−.0018

−.40

.69

TotExp

4.14E-08

1.21

.23

LagProd

.5083

28.17

<.001*

Constant

−.0980

−5.14

<.001*

R2

.24

  

Adjusted R2

.23

  

Observations

3313

  

Minimum Minutes

250

  

Note. The model reported here is for a minimum of 250 player minutes. (Models for 150, 200, 250, 300, 350, 400, 450, and 500 player minutes are available in an online supplement; see Table 1s)

*p < .05

Discussion

The current investigation aimed to challenge the subjective, and considered by some (e.g. Burton 2015) stereotypical, hiring practices for leadership roles within the sport industry. In order to do so, we conducted an objective analysis of leadership outcomes. Specially, we examined whether head coaches’ gender significantly impacted individual players’ performance within two women’s U.S. basketball leagues, the WNBA and the NCAA. Our examination built upon previous studies that have analyzed the role of the head coach in the development of individual players (e.g., Berri et al. 2009; Dawson et al. 2000), while serving as the first known to consider the gender of a coach as a variable under consideration.

Our findings suggest that individual players’ performance was not influenced by the gender of the head coach and that both men and women were finding similar levels of success as head coaches in developing their individual players within both the WNBA and NCAA women’s basketball. Interestingly, our findings may support the recent WNBA and NCAA team success landscape. Based on the 2016 rankings for WNBA teams as well as the 2016 NCAA Division I Women’s Basketball championship tournament outcome, we see similar levels of team success for both men and women head coaches. For example, when considering the top six WNBA teams during the 2016 season, men and women were head coaches for three of these top six programs (WNBA.com). Similarly, within the NCAA Women’s Division I basketball tournament, half the teams competing in the opening round through the regional round were coached by women (NCAA.org).

Once again, our results indicate that the gender of the head coach did not significantly impact individual players’ performance and may subsequently challenge the gender stereotypes that are common within the institution of sport. These results may also contest the gendered nature of the industry because they suggest that men are not outperforming women in one of sports more visible leadership positions–the head coach (Acosta and Carpenter 2014; Burton 2015). Further, while ours is the first known examination that objectively examined gender-based leadership outcomes using a predictive model approach, our results do not suggest that one gender is somehow better in the role of a head coach. More specifically, although there are certainly additional factors that contribute to coaching success, leadership stereotypes that have been fueled by more implicit gender associations may not be warranted. Additionally, and beyond individual players’ performance, our results may also be extended to suggest that both team and organizational success may not be impacted by a leaders’ gender. More specifically, because coaches are considered responsible for the workers whom they lead (i.e., players) within a specific organization (i.e. team, university, professional organization), and individual player performance contributes significantly to team success, it appears that one gender is not outperforming the other in overall team and organization achievement.

Our results also challenge presumed preferences toward male leadership within the sport industry. Gender stereotypes often manifest themselves as barriers to female leadership advancement in that women are often underrepresented in leadership roles (e.g., head coach). As such, current preferences within the sport industry result in a greater number of male leaders (i.e. head coaches) with little regard for objective measures of productivity (Acosta and Carpenter 2014). One rationale for this occurrence that is directly related to gender stereotyping is the assumption that men are better equipped for success as head coaches in a more masculine industry (Heilman 2012). Instead, based on our results, it would appear that both men and women are experiencing similar levels of success within the WNBA and the NCAA levels of women’s basketball.

Similarly, these results run counter to the gender stereotypes that often discourage women from obtaining or accessing leadership roles in sport (Burton et al. 2009). Our results suggest that these reinforced gender stereotypes may not be entirely predictive of organizational outcomes and that a preference toward male leadership may not be based in how to best improve the productivity of the players. Rather, our results suggest that these preferences appear to be subjective and based in stereotypical biases toward the traits more commonly assigned to men, rather than the best overall candidate (Burton et al. 2009).

Overall, it is important to emphasize that our results run counter to the hiring patterns observed within the sport industry. While men are currently preferred in the market for the role of head coach for both men’s and women’s teams, our results suggest that hiring patterns may be based on more subjective and stereotypical notions of leadership abilities. Further, hiring patterns that suggest women are somehow less fit to lead men’s programs may be inconsistent. Based on our results and other findings (e.g., Berri et al. 2009) that have examined the impact of head coaches on teams’ and players’ performances, it appears that both men and women are readily equipped for success in the coaching profession (and in some cases, women coaches have been more successful than the male coaches they replace; Aicher and Sagas 2010). As such, our results imply that the absence of women coaches for men’s teams is not grounded in any objective or reliable evidence.

Instead, our results point to the possibility of subjective and stereotypical hiring preferences based on potentially subjective performance and productivity arguments. Individual players’ performances in the WNBA and NCAA Women’s Division I Basketball is not impacted by the gender of the team’s leader, indicating that the privilege men may have experienced in obtaining leadership roles in sport (i.e., as head coaches) could in fact be a result of reinforced gendered processes rather than ability and productivity. Therefore, although it is evident that head coaching jobs in professional basketball predominantly go to men (for both the WNBA and the NBA), our results suggest that women candidates should be given full consideration for future head coaching roles.

Limitations and future research

Our investigation was not free from limitations and leaves an opportunity for future studies to occur. First, although the sport industry provides a unique opportunity to objectively examine leadership performance, this does not mean that critiques of leadership ability should rely solely on objective measurements. These objective measures do not incorporate all of the varying aspects of the head coach’s role, and they may not be the preferred method of evaluation by hiring managers. That being said, in order to combat traditional gender stereotypes within sport, future examinations of sport industry leadership should aim to investigate additional leagues, teams, and perhaps individual sports. For instance, any sport that can provide statistical data per player for multiple seasons should be examined. Overall, future analyses should continue to objectively investigate gender stereotypes in traditionally male-dominated fields.

Second, individual players’ performance is certainly not the only objective measurement available to researchers in their quest to investigate leadership ability. Future research should also investigate other variables that may, at this point, impact a woman’s ability to obtain a leadership role in a particular sport. For example, variables such as a coach’s previous playing experience could be examined in order to determine if that type of experience affects players’ performance. Additionally, pay differences based on gender have long been noted (Humphreys 2000; Von Allmen 2013), and future research could incorporate salary information along with gender of the head coach to assess whether teams are overpaying for productivity. These research avenues in coordination with one another may provide additional insights and perhaps combat traditional gender stereotypes just as our study has done.

Finally, the purpose of our investigation was to focus on the top leadership roles within the sport industry, such as the head coach role. Therefore, our study was not concerned with the role that assistant coaches’ gender may play in the development of individual players. Findings have indicated, however, that over the past 15 years, there have been larger percentages of women than men assistant coaches for women’s teams. Although this pipeline certainly lends one to question the lack of women head coaches, it also may suggest that women are greatly influencing the development of individual players. Thus, given that there is a larger percentage of women than men assistant coaches for women’s teams at the NCAA level (Darvin and Sagas 2017, future investigations should aim to consider the roles not only of the head coach, but also of assistant coaches in the development of individual players.

Practice implications

In the coaching world, women face more biases than their male counterparts do when applying for a position of leadership. A recent study of 2219 respondents found that a majority of coaches felt men had an easier time gaining top-level coaching jobs (65%), negotiating salary increases (75%), achieving promoted (54%), securing a multi-year contract (52%), and being rewarded with salary increases for successful performance (53%) (Sabo et al. 2016, p.2). The findings of our study however, illustrate that the gender of a coach does not have an effect on the productivity of the players. Thus, it is important to also discuss what may be done to help shift these perceptions and improve the work environment for women.

From a practical standpoint we offer the following suggestions based on our research findings. First, hiring managers may want to employ more objective forms of critique when considering head coach candidates. For instance, although sport often places a great deal of emphasis on exhibiting masculine attributes (Burton 2015; Fink 2008), results from our investigation suggest that these attributes are not necessarily a prerequisite in becoming a successful head coach.

Recent research has uncovered a gender bias in coaching at the NCAA level and revealed that women coaches are more likely to perceive gender bias than their male counterparts are (even in women’s sports) (Sabo et al. 2016). Therefore, organizations wishing to hire more women in leadership roles will have to first evaluate the climate for gender bias in their organization, with a first step being an audit of the compensation practices for women and men. Because these organizations cannot assume that hiring managers are free of bias, they will need to consider educational programs to remedy the situation. And lastly, organizations should consider a more data-analytic approach to hiring that would include various forms of performance measurement that are not open to human bias in their interpretation.

Conclusion

Previous research suggests that there is an emphasis placed on the demonstration of masculine traits in order to obtain a successful leadership role in a sport organization (Burton et al. 2009). It can be argued then that the masculine identity of sport causes the subconscious formation of stereotypes that are then associated with the desirable gender of a sport industry leader. So long as masculine traits are emphasized, women are facing biased opinions regarding their coaching abilities. Not only are women facing a more difficult task in obtaining leadership roles in sport, but the specific roles available to women also are limited. In order to challenge these more subjective notions of ability, objective measures should continue to be examined. The results of the present investigation assist in further challenging these stereotypes associated with leadership ability through an objective measure of followers’ performance and suggest that both men and women are achieving similar levels of success as head coaches.

Notes

Compliance with Ethical Standards

Conflict of Interest

The authors declare that they have no conflict of interest.

Supplementary material

11199_2017_815_MOESM1_ESM.docx (46 kb)
ESM 1(DOCX 45 kb)

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Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  1. 1.Department of Tourism, Recreation, Sport ManagementUniversity of FloridaGainesvilleUSA
  2. 2.Department of Sports AdministrationLaurentian UniversitySudburyCanada
  3. 3.Department of EconomicsSouthern Utah UniversityCedar CityUSA

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