Navigating Darkness: College Athlete Suicide, Support Systems, and Shadows of Depression

Authors: Matt Moore, Ph. D, MSW ¹, Anne M. W. Kelly, Ph. D ², Lana Loken, Ed. D. ATC ², Mastano N. Dzimbiri, MS ¹, Payton Bennett, student

Corresponding Author:

Matt Moore, Ph. D, MSW
Chair and Faculty, Family Science and Social Work Department
Miami University
501 E. High Street
Email: moorem28@miamioh.edu

Coaches’ Perspectives of the Influence of Safe Sport-Related Education

ABSTRACT

Purpose: An increase in mental health concerns and suicide among young adults led to a sharpened research focus on suicide and college athletes. In this study, we investigated the relationship between college athletes’ risk of depression, suicidality, and their support system and whether preventing suicide deaths requires identification of commonly cited risk factors. Methods: Voluntary college athletes aged 18-years-old or older and attending an NAIA member institution participated in the study (n = 361). They completed a web-based instrument that consisted of the following: (1) demographic questionnaire, (2) Patient Health Questionnaire (PHQ-9), (3) Berlin Social Support Scale, and (4) Columbia Suicide Severity Rating Scale. Results: Between 5-18% of college athletes responded affirmatively to one of the questions asking about suicidality. There was a significant moderate negative correlation between the suicide predictor and the PHQ-9 score and significant weak positive correlations between the suicide predictor and perceived emotional support and between the suicide predictor and perceived instrumental support. Conclusion: This study identified findings that might be useful to practitioners and opened new lines for future research. Applications in Sport: College athletic programs and university counseling centers are poised to enhance our understanding of student-athletes’ suicidal distress and how to respond by making use of qualitative research methods. We strongly recommend adopting this strategy to address depression and suicidal ideation.

Keywords: prevention, student-athletes, mental health, risk factors

Introduction
Despite growing openness about mental health struggles, a disparity still exists between physical and mental health (Gorczynski et al., 2023; Moore et al., 2022), fostering stigma and hindering help-seeking behavior (Moore, 2017), particularly among college students (Centers for Disease Control and Prevention [CDC], 2021). While mental health diagnoses in the college student population is a longstanding challenge, the COVID-19 pandemic increased stressors placed on the college student population leading to increased risks (Gupta & Agrawal, 2021; MacDonald & Neville, 2023).

According to the CDC (2021), mental health concerns and suicidal thoughts are increasing for youth and young adults. Forty percent of those surveyed showed signs and symptoms of depression and 20% said they had thoughts of suicide. These trends are similar to studies on college student mental health and suicidality (Barclay et al., 2023; Schmiedehaus et al., 2023). According to the Substance Abuse and Mental Health Services Administration (SAMHSA, 2017) individuals aged 18-25 reported a 3% increase in major depressive episodes from 2015-2017. Additionally,18.9% of individuals 18 and above reported experiencing a mental illness in the past year, with 7.5% reporting a serious mental health illness (SAMHSA, 2017). A second SAMHSA (2021) study found 33.7% of individuals aged 18-25 reported a mental illness and 11.4% reported a serious mental illness.
In addition to concerns about serious mental health illness, SAMHSA (2021) found an increase in rates of suicidal behavior. Specifically, 10.5% reported having serious thoughts of suicide, 3.7% created a suicide plan, and 1.9% attempted suicide. Research by Rosenthal et al. (2023) found higher rates with 13.7% of college students reporting suicide ideation, 7.6% making a suicide plan, and 3.2% reporting at least one suicide attempt. In 2021 suicide became the leading cause of death for those aged 20-24 (CDC, 2023).
One subset of the college student population is college athletes. Recently, discussion of their mental health increased. Researchers attempted to explore the intersectional identity of student athletes and the effect that this role strain may have on mental health (Gorczynski et al., 2023; Moore et al., 2022). Quantifying mental health and suicide risk in this group is challenging, with conflicting results on the link between depression, support systems, and suicide. Many researchers see sport participation as a protective factor for mental health risk due to the social support provided by the team (Hui et al., 2023; Sullivan et al., 2020). But additional pressures like failure to successfully compete or live up to expectations, loss of social structure due to injury or retirement from sport, or time demands of the sport in addition to being a college student can increase the risk (Moore, 2017; Moore et al., 2022). This study builds upon existing research by looking more closely at the relationship between a college athletes’ risk of depression, suicidality, and their support system.

College Athletes and Depression
According to the American Psychological Association (2020), depression is one of the most common mental health disorders in the United States. Depression might include emotional, cognitive, physical, and/or behavioral symptoms and is best understood on a continuum of severity, rather than either present or not present. Findings amongst college athletes demonstrate that depression rates align with rates of the general population of college students (hovering around 25%) (Prinz et al., 2016; Wolanin et al., 2016), and some revealed that athletes have higher rates of depression (over 30%) than the general population (Cox, 2015). While many studies find similar rates between college athletes and their non-athlete peers, others show participation in college athletics can decrease one’s risk for depression (Banu, 2019; Salehioan et al., 2012).
Although some research shows athletic participation may protect against mental illness, there is still reason for concern for college athletes. A current study by the National Collegiate Athletic Association (NCAA, 2022) surveyed almost 10,000 NCAA athletes from all three competitive division levels. Results showed athletes of all competition levels demonstrated elevated levels of mental exhaustion, anxiety, and depression. These levels were nearly two times higher than pre-pandemic levels. The top three factors negatively affecting mental health were academic worries (44%), planning for the future (37%), and financial worries (26%). Only 50% of college athletes believed mental health was a priority for their athletic department, 33% of college athletes did not know where to go to seek mental health services, and as many as 17% of college athletes reported feeling hopeless.

College Athletes and Suicide
Suicide risk in athletes is difficult to determine due to underreporting and misclassification of many sudden deaths. Over the past two decades the NCAA attempted to determine the risk of suicide specific to college athletes. Rao et al. (2015) reported that 7.3% of all athlete deaths were suicides, making suicide the fourth leading cause of death for college athletes. Previously, Miller and Hoffman (2009) found approximately 5% of student-athletes contemplated suicide. Much like research on college athlete depression, some research demonstrates sport protects against suicidality (Maron et al., 2014). This study’s findings highlight the importance of promoting participation in diverse sporting activities among college students given that engaging in such activities safeguards against depression and suicidal ideation by nurturing self-esteem and bolstering social support.

College Athletes and Social Support
The discrepancy in the literature may be accounted for by the supports that are available to college athletes and their willingness to seek such supports (Sullivan et al., 2020). One of the most discussed supports is the team environment. Sullivan et al. (2020) analyzed the effects of social supports on depressive symptoms in college athletes. They found emotional support from teammates, family, and friends was correlated with a decrease in depressive symptoms. Other more formal or instrumental supports that reduced depression included the availability of tutoring and health services, including mental health providers with specialization with athletes.
Social support has not been as extensively studied in the college athlete population. Studies show links between social support and burnout as well as social support and overall wellbeing in college athletes (Defreese & Smith, 2014). Research identified social support as an important component in allowing athletes to balance school and athletics (Carter-Francique, 2015). Many college athletes have strong social support networks naturally, such as relationships with teammates, coaches, medical staff, and other resources provided by the athletic department (Armstrong & Oomen-Early, 2009). They also have supportive relationships, such as family and friends, outside of athletics.
Despite knowledge of these available supports and benefits they offer college athletes, exploring the utilization of built-in athletic supports and personal supports unique to an individual athlete remains understudied. Much of the research tends to oversimplify social support. Due to its dynamic and complex nature, social support among college athletes merits further investigation. Research has not examined the differences in the type of perceived social support in collegiate athletics as it relates to levels of depressive symptoms and suicidality.

Present Study
Overall, the research on mental health issues, including depression and suicide in collegiate athletes is inconclusive. More research is needed to determine what factors put athletes at risk for severe mental health concerns and suicide. The purpose of this study was to investigate whether there is a relationship between levels of depression and suicide risk and levels of social support among National Association of Intercollegiate Athletics (NAIA) college athletes. The NAIA does not have data available on connectedness between depression, social support, and suicide.

Methods

Procedures

Research Design
The current exploratory study utilized a cross-sectional, web-based survey design to gather data from NAIA college athletes. Considering the size of the NAIA student-athlete population, confidence level, confidence intervals, statistical test, and statistical power, the minimum sample for this study was 47 college athletes (Faul et al., 2007). Researchers identified athletic trainers through the NAIA database to establish contact information. Athletic trainers provided survey information to their assigned college athletes. This approach was successful in other NAIA research efforts (Moore & Abbe, 2021).

Sampling
The exploratory study utilized a stratified random sampling procedure to identify college athlete participants. Researchers divided the NAIA college athlete population into subgroups, or strata, based on sports available throughout the NAIA. This included a stratum for each of the 17 sports with separate stratum for each gender that participates in a sport. Next, researchers identified NAIA member institutions that participated in each of the 17 sports. Each institution participating in a sport received a random number. Researchers selected random numbers to identify the member institutions that would participate in the survey from each sport. This approach ensured all member institutions participating in various sports had an equal opportunity for inclusion.

Participants
Voluntary college athletes aged 18-years-old or older and attending an NAIA member institution participated in the study (n = 361). Most participants were 18-21 years old (53.5%, 46.5% indicated being over the age of 21). Survey participants were primarily juniors (30.7%, 23.8% sophomores, 23.1% first years, 22.1% seniors of graduate students). More women completed the survey (59.8%, 40.2% men). Most participants who reported race/ethnicity were White/Caucasian (55.4%, 21.9% Hispanic or Latino, 14.9% Black or African American, 6.6% multiracial, 1.2% from other groups).

Table 1.

NAIA Institutional Demographic Information

University Demographic	%
Private	20.2%
Public	79.8%
Suburban	33.3%
Urban	33.9%
Rural	32.8%
Faith Based	62.9%
Non-Faith Based	37.1%

Participants recorded which NAIA athletic team they were primarily affiliated with (20.2% baseball, 19.9% soccer, 12.5% track volleyball, 8.0% softball, 6.4% cross country, 6.1% basketball, with all other sports being under 5% each [e.g., football, bowling, cheer, dance, track and field, swimming and diving, golf, tennis, and lacrosse]). Participants were further examined regarding NAIA college/university demographics (See Table 1). Participants also responded to whether or not they receiving mental health training from their college of university before participating in sport. The largest majority (n = 229, 63.7%) indicated they did not receive such training. The other 36.3% (n= 132) indicated they did receive some form of training.
[Insert Table One]

Measures and Instruments

College athletes completed a web-based instrument that consisted of the following: (1) demographic questionnaire (see above demographics), (2) Patient Health Questionnaire (PHQ-9; Kroenke et al., 1999), (3) Berlin Social Support Scale (BSSS; Shulz & Schwarzer, 2003), and (4) the Columbia Suicide Severity Rating Scale (C-SSRS; Posner et al., 2011).

Patient Health Questionnaire (PHQ-9)
The PHQ-9 is a self-administered version of the PRIME-MD diagnostic instrument for common mental disorders (Kroenke et al., 2001). It is used to make criteria-based diagnoses of depressive and other mental disorders commonly encountered in primary care. This is a 9-item depression module upon which the diagnosis of Diagnostic and Statistical Manual (DSM) depressive disorders is based. Reliability and validity of the tool have indicated it has sound psychometric properties. Internal consistency of the PHQ-9 has been shown to be high (American Psychological Association, 2020). There is precedent for using the PHQ-9 in research with college athletes (DaCosta et al., 2020; LoGalbo et al., 2022).

Berlin Social Support Scale (BSSS)
The researchers measured the degree of emotional and tangible support using the BSSS (Schulz & Schwarzer, 2003). This scale measured perceived emotional and instrumental supports, need for support, and support seeking. There are 17 items on the BSSS that are answered using a five-point Likert scale with endpoints “1 = Strongly Disagree” and “4 = Strongly Agree.” The researchers used a mean score for each of the subscales (perceived emotional support, perceived instrumental support, need for support, and support seeking). The scale has a Cronbach’s alpha of 0.83 for perceived social support, 0.63 for need for support, and 0.83 for support seeking (DiMillo et al., 2017). The scale has a prior history of use within college athletics (Sullivan et al., 2020)

Columbia Suicide Severity Rating Scale (C-SSRS)
The C-SSRS was developed by researchers from Columbia, Pennsylvania, and Pittsburgh Universities to evaluate suicidal ideation and behavior (Posner et al., 2011). The scale provides a brief assessment of severity and intensity of suicidal ideation, suicidal behavior, and lethality (Syndergaard et al., 2023). The screener version used in this study consisted of six “yes” or “no” questions. Based on participant responses to the six questions, participants were considered low, moderate, or high risk. The C-SSRS has excellent internal consistency (α = 0.95). Principal components analysis revealed a two-factor solution, accounting for 65.3% of the variance across items (Madan et al., 2016). There is limited research on the use of the C-SSRS with the athlete population (Costanza et al., 2021).

Data Collection
Researchers contacted the athletic training staff at all sampled NAIA member institutions. Athletic training staff received the list of teams from their institution for inclusion in data collection. Researchers provided athletic training staff detailed instructions for data collection and a copy of the informed consent. Athletic training staff distributed the electronic survey to their college athletes. College athletes were able to opt-out of the survey at any time. The survey took approximately 15-20 minutes to complete. Researchers recorded survey results into a statistical software program (SPSS 28) on a secure, private platform.

Data Analysis
Researchers utilized descriptive statistics to provide details about the sample and overall survey results. Researchers used inferential statistics to infer information from the sample data to the overall NAIA student-athlete population.

To investigate the first research objective, an initial correlation analysis was conducted to examine whether having any safe sport training was related to increases in coaching outcomes. The safe sport training variable was transformed so that coaches who answered “yes” to completing any of the safe sport training courses were coded as 1 and coaches who had answered “no” to completing all the safe sport training courses were coded as 0 (i.e., no SS training=0, any SS training=1). This variable was included in a correlation analysis with all coaching outcomes: knowledge & confidence, safe sport stress, stress over athlete well-being, and efficacy to support others. To investigate the second research objective, four separate linear regression models were constructed with the sum of completed safe sport training courses (range =1-12) as the independent variable, and the following coaching outcomes as respective dependent variables: knowledge & confidence, safe sport stress, stress about athlete well-being, and efficacy to support others. In all four models, the coaching context, whether training was required (0=no, 1=yes), and whether training was free (0=no, 1=yes) were included as covariates. To address the third research objective, ANOVAs were conducted with individual safe sport courses as independent variables, and the following coaching outcomes as dependent variables: knowledge & confidence, efficacy to support others, safe sport stress, stress about athlete well-being and efficacy to support others. All analyses were conducted using IBM SPSS Statistics (Version 28) (20).

Results

Results
Descriptive Statistics
College athletes answered each item from the C-SSRS. Descriptive findings from this scale indicated that 18.3% of participants wished to be dead, 18,3% had non-specific active suicidal thoughts, 13.6% had active suicidal ideation without intent to act, 6.1% had active suicidal ideation with some intent to act, and 5.0% had active suicidal ideation with a specific plan and intent to act. Of the 361 college athlete respondents, 25.8% answers “yes” to at least one of the questions on the scale.

College athletes completed the PHQ-9 as a brief screening tool for potential depressive symptoms. Results of the PHQ-9 and the percent of athletes at risk of depression for each item can be found in Table 2.

Table 2. PHQ-9 Scores for NAIA College Athletes

Question	Mean (SD) (% At Risk)
Little interest or pleasure in doing things?	1.81 (0.91) (22.1%)
Feeling down, depressed, or hopeless?	1.68 (0.81) (14.1%)
Trouble falling asleep or sleeping too much?	2.06 (1.05) (30.2%)
Feeling tired or having little energy?	2.17 (0.92) (29.1%)
Poor appetite or overeating?	1.81 (0.96) (21.3%)
Feeling bad about yourself?	1.75 (0.93) (18.6%)
Trouble concentrating on things?	1.69 (0.96) (17.2%)
Moving or speaking so slowly that people could have notice? Or more fidgety and restless than usual?	1.34 (0.69) (7.8%)
Thoughts that you would be better off dead?	1.21 (0.53) (4.1%)

Evaluation of Assumptions

College athletes also completed the BSSS. Results of the BSSS and the percent of athletes at risk of limited social support in various areas can be found in Table 3. These are only the scale items where there were significant concerns about perceived emotional support, perceived instrumental support, need for support, and support seeking.

BSSS Scores for NAIA College Athletes

Question	Mean (SD) (% At Risk)
Whenever I am not feeling well, other people show me that they are fond of me?	3.14 (0.82) (17.2%)
When everything becomes too much for me to handle, others are there to help me?	3.21 (0.83) (18.3%)
I get along best without any outside help?	2.48 (0.81) (48.7%)
In critical situations, I prefer to ask others for their advice?	3.00 (0.79) (23.0%)
Whenever I am down, I look for someone to cheer me up again?	2.51 (0.89) (49.6%)
When I am worried, I reach out to someone to talk to?	2.69 (0.93) (38.2%)
Whenever I need help, I ask for it.	2.70 (0.96) (39%)

Researchers used correlation analysis to assess the relationship between a college student-athletes predictor of suicide with their score on the PHQ-9, perceived emotional support, perceived instrumental support, level of needed support, level of support sought, and mental health training.

Prior to conducting the analysis, researchers generated several statistics and graphs to examine the tests of assumption, including level of measurement, related pairs, absence of outliers, and linearity.

Results of the Correlational Analysis
Researchers computed a Pearson product-moment correlation coefficient to assess the relationship between a college student-athletes suicide predictor and their PHQ-9 score, perceived emotional support, perceived instrumental support, level of needed support, and level of support sought. There was a significant (p < 0.001) moderate negative correlation, r = -.462, N = 361 between the suicide predictor and score on the PHQ-9. There was a significant (p < 0.001) weak positive correlation, r = .236, N = 361 between the suicide predictor and perceived emotional support. A similar significant (p < 0.001) weak positive correlation, r = .255, N = 361 between suicide predictor and perceived instrumental support. A college student-athlete’s exposure to mental health training, perceived level of needed support, and level of support sought did not appear to be suicide predictors.

Discussion

In this study, we investigated whether preventing suicide deaths requires the identification of factors that are associated with people’s risk of suicidal behavior. Commonly cited risk factors for suicidal thoughts and behaviors are depression and inadequate support. Association between major depressive disorder (MDD) and suicide attempts or ideation has been well-documented. Accordingly, depression has been considered a necessary or sufficient cause of suicidal thoughts. But much is unknown about the characteristics that increase suicide risk among people living with depression (Bradvik, 2018). Many mechanisms could play a role in suicidal behavior among people with MDD, and, although suicidal behavior occurs among people with major depressive disorder, depression is not necessarily a useful tool for understanding the complexity of suicide (Orsolini et al., 2020).

Most people with depression do not attempt suicide. Diagnosis of MDD requires a simultaneous presentation of several specific symptoms. Approximately, 17 million American adults will have symptoms of MDD each year, but only around 45,000-50,000 Americans will die by suicide during that same time. Considered independently of other risk factors, MDD may put one at greater risk, meaning that those with this disorder are more likely than those without it to die by suicide. But still very few of those with MDD will go on to die by suicide; reliance on depression to predict suicidality is inadvisable. This is supported by Ribeiro et al. (2018), who reviewed existing literature on the subject and showed that although depressive symptoms were reported to confer risk of suicidality, the effects were weaker than expected.

Melhem et al. (2019) demonstrated that the most severe depressive symptoms and variability over time were the only predictors of suicide attempt in young adults, especially when combined with other factors (e.g., childhood abuse, history of attempt, substance use disorder, and parental attempt). But prediction was marginally better than chance, perhaps because suicidal risk varies during a psychiatric illness and may be linked to other factors that appear during depressive episodes. Orsolini et al. (2020) showed that anxiety disorders co-occurring with MDD are among the main predictors of attempts. Several factors interact and contribute to suicidal behavior and death by suicide. These may include major depressive disorder, but interactions with other factors, such as genetic vulnerability, stress, psychiatric comorbidities, and social aspects need to be evaluated to improve prevention (Orsolini et al., 2020).
Results from our research showed a moderate negative correlation between the suicide predictor and score on the PHQ-9, challenging the assumption that depression is a necessary or sufficient cause of suicidal thoughts. This lends support to the idea that traditional risk factors can be problematic and that their predictive value has not improved over the past 50 years (Franklin et al., 2017; Fortune & Hetrick, 2022).

Bradvik (2018) also acknowledged that depression is related to suicidal ideation and attempt but is not a good predictor. Bradvik (2018) pointed to results from the Australian Rural Mental Health Study in which only 364 out of 1051 respondents reported life-time depression. Of those 364 respondents, 48% reported life-time suicidal ideation and 16% reported a suicide attempt. Gender, age of depression onset, and possibly psychiatric comorbidities were somewhat predictive of suicide behavior, but no other predictive factors were revealed. These results were echoed by Melhem et al. (2019).

The limits of risk factors to accurately predict suicide is further strengthened by our finding that an increase in emotional social support was weakly associated with an increase in suicide risk, contradicting earlier research that showed suicidal distress was worse when emotional social support was low (Ayub, 2015; Otsuki et al., 2019). Similarly, instrumental social support (i.e., support that helps people with practical tasks) was weakly associated with suicide risk, contradicting findings from Otsuki et al. (2019).
After a concussion, athletes experience a range of psychological symptoms, with depression and anxiety being among the most reported (Kontos et al., 2012). Symptoms can include loss of interest in activities that were once enjoyable, persistent sadness, physical and mental fatigue, and changes in sleep patterns. These negative outcomes may be more pronounced in athletes who attach a great degree of importance to the athlete’s role in relation to other activities (Brewer et al., 1993; Raedeke & Smith, 2001) and can be made worse by changes in lifestyle, the loss of social support that team members provided, and even personality traits. One such trait is maladaptive perfectionism.
Maladaptive perfectionists are overly critical of mistakes. They strive for excessively high and ultimately unobtainable goals. This usually results in failure, which can be painful, especially for athletes with maladaptive perfectionism, who may lack resilience to bounce back from stressful experiences. This unhealthy perfectionism is associated with higher levels of depressive symptoms (Egan et al., 2011; Olmedilla et al., 2022). Additionally, perfectionists can struggle with time management, not setting realistic timelines for getting things done or because they are paralyzed by the prospect of failure. Time management is one of the most difficult aspects of participating in college sports (Rothschild-Checroune et al., 2013).

Taken together, injury and concussion, personality traits (e.g., maladaptive perfectionism), and external factors (e.g., time constraints) can contribute to negative mental health outcomes among student-athletes and may increase suicidal distress. College athletic programs and university counseling centers are poised to improve our understanding of the nature of suicidal distress among student-athletes face and how to respond by making use of qualitative research methods, which we recommend. We urge university administrators to dedicate more resources to building and integrating academic and co-curricular resilience programs into their campuses and rely less on risk assessment that focuses on commonly cited factors (e.g., depression) to predict suicide.

Study Limitations
While efforts were made to decrease discomfort with the survey, it is possible college athletes felt pressure to respond in particular ways out of personal and/or athletic concerns. This study also relied upon self-reported data. Without having the ability to verify participant responses, there was no way of knowing the legitimacy or honesty of participants’ responses. The study was unable to control the multiple covariates or confounding variables that influence a college suicidality and mental health. Finally, our study lacked a detailed exploration of how specific socio-demographic characteristics, such as race, gender, and class status, might influence suicidal ideation and other risk behaviors among college athletes.

Future Research
The complex interplay between core risk factors in individuals and heightened suicide risk among athletes necessitates further exploration. Future research should focus on understanding the repercussions of escalated demands on athletes’ mental well-being, particularly the impact of significant situational factors such as career-ending injuries on their mental health and suicide vulnerability. Additionally, there is a need to delve into the connection between suicide rates, race, and gender among collegiate students for a more comprehensive understanding of these dynamics.

Conclusion
This study examined the relationship between college athletes’ risk of depression, suicidality, and their support system and whether preventing suicide deaths requires identification of commonly cited risk factor. The results are quite different from previous research findings, revealing a moderate negative correlation between the suicide predictor and scores on the PHQ-9, adding nuance to the presumption that depression is either a necessary or sufficient factor for the emergence of suicidal thoughts. College athletic programs and university counseling centers are poised to enhance our understanding of student-athletes’ suicidal distress and how to respond by making use of qualitative research methods. We strongly recommend adopting this strategy to address depression and suicidal ideation.

Applications in Sport
Studying suicide in college sports has practical applications that can help improve the well-being and safety of college athletes. By examining the factors that contribute to suicidal ideation and behavior in college sports, researchers and practitioners can develop targeted interventions and support systems to address mental health challenges. For instance, such studies may lead to the creation of tailored mental health resources for student-athletes, including counseling services and peer support networks. Furthermore, understanding the unique stressors faced by student-athletes, such as performance pressure and balancing academics with athletics, can inform the design of preventative measures such as stress management and resilience training programs. Additionally, awareness campaigns can be created to destigmatize mental health struggles in sports, encouraging athletes to seek help when needed. Overall, studying suicide in college sports can lead to a safer and more supportive environment for student-athletes, promoting their overall health and success.

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Qshequilla Parham Mitchell2024-07-03T13:38:41-05:00July 5th, 2024|General, Research, Sport Education, Sports Studies and Sports Psychology|Comments Off

Coaches’ Perspectives of the Influence of Safe Sport-Related Education

Authors: Anthony Battaglia¹, Ph.D., Gretchen Kerr², Ph.D., and Stephanie Buono², Ph.D.

Corresponding Author:

Anthony Battaglia, Ph.D., CMPC

Faculty of Kinesiology and Physical Education

University of Toronto

55 Harbord Street, ON, Canada, M5S 2W6

Email: anthony.battaglia@mail.utoronto.ca

Anthony Battaglia, Ph.D., CMPC is a Postdoctoral Fellow and lecturer in the Faculty of Kinesiology & Physical Education at the University of Toronto. His research interests focus on youth athletes’ sport experiences, relational dynamics in sport, athlete maltreatment, and strategies for advancing developmentally appropriate and safe sport.

Gretchen Kerr, Ph.D. is a Full Professor and Dean of the Faculty of Kinesiology and Physical Education at the University of Toronto. She is also a co-Director of E-Alliance, the Canadian Gender Equity in Sport Research Hub.

Stephanie Buono, Ph.D. is a research associate in the Faculty of Kinesiology & Physical Education at the University of Toronto and an instructor in the Department of Applied Psychology & Human Development at the University of Toronto.

Coaches’ Perspectives of the Influence of Safe Sport-Related Education

ABSTRACT

To combat growing concerns of sport being unsafe for athletes, compulsory safe sport education has been developed worldwide. Much of this education has focused on the role of the coach, largely due to their position of power, prevalence rates that highlight coaches as common perpetrators of harm, and their direct contact with athletes. However, there is a lack of research examining the impact of such education for coaching-related outcomes. The purpose of this study was to explore the influences of safe sport training on coaches’ knowledge and confidence, efficacy to support others, stress about athlete well-being, and stress related to safe sport issues. In an online survey, 1365 coaches reported completion of any of 12 possible safe sport training courses and their knowledge and confidence, efficacy to support others, stress about athlete well-being, and stress related to safe sport issues. Regression analyses indicated that completing any of the 12 safe sport-related training courses was related to perceived increased efficacy to support others. Completing a higher number of safe sport training courses was related to perceived increases in efficacy to support others and knowledge and confidence, but not stress related to safe sport or athlete well-being. All 12 courses were related to increased knowledge and confidence, and several courses were related to increased efficacy to support others and reduced safe sport stress, while one course was related to reduced stress about athlete-well-being. Future research is needed to examine whether improvements in coaching outcomes associated with safe sport training translate into practice.

Key Words: Safe Sport; Coaches; Education; Coaching Outcomes;

Over the last several years, numerous reports of concerning behaviors in sport, such as maltreatment have emerged worldwide (15, 25). Maltreatment, which refers to “volitional acts that result in or have the potential to result in physical injuries and/or psychological harm” (12, p. 3), which include psychological, sexual, physical abuse, and neglect, harassment, bullying, and discrimination. To combat such concerns, policies and educational initiatives have been developed and implemented under the term ‘safe sport’ (26). The term safe sport initially emerged in response to scandals involving sexual abuse but has since expanded to refer to participation in sport free from all forms of violence, abuse, discrimination, and harassment (21, 39). More recently, broader conceptualizations of safe sport have also considered issues of environmental and physical safety (e.g., dysfunctional equipment, performance enhancing drugs), and the optimization of the sport experience (i.e., inclusive, accessible, growth-enhancing, and rights-based participation for all) (18). To advance safe sport, compulsory education has been developed; examples of existing safe sport education programmes include Australia’s Play by the Rules, U.S. Center for SafeSport Training, and the UK’s Child Protection in Sport Unit (24, 26).

Although safe sport education is needed for all sport stakeholders, including athletes, coaches, parents, administrators, officials and support staff, to-date, education has focused largely on coach-athlete dynamics, addressing issues such as harmful coaching practices, power relations, and duty to report harm (24, 26). There is a strong rationale for safe sport training focused on coaches. Consistent across many bodies of research in sport is acknowledgement of the presence and effects of the position of power and authority held by coaches over stakeholders in the sport ecosystem, including subordinate coaches, parents, athletes, and administrators (23, 38). When used inappropriately, these positions of power leave others vulnerable to experiences of harm. For example, psychological abuse (or what some refer to as psychological violence), the most prevalent form of athlete maltreatment, is most often perpetrated by coaches (42, 45, 48). Given their direct contact with other coaches, support staff, athletes and/or teams daily, coaches also significantly impact the type of culture promoted (e.g., win-at-all-costs versus caring or athlete-centred) and the nature and quality of athletes’ experiences (32). Coaches who are provided professional development and educational opportunities regarding positive sport practices are more likely to create environments where athletes experience enjoyment, competence, meaningful relationships, learning, satisfaction, reduced anxiety, and sport maintenance (6, 16, 36).

Although growing awareness of athlete maltreatment and the role of the coach in preventing these experiences has resulted in the proliferation of safe sport education initiatives for coaches globally, little research exists on the impact of such education for coaching-related outcomes (24, 26). In 2013, McMahon (28) investigated how a narrative pedagogical approach (i.e., athletes’ stories) might help swim coaches from amateur and elite levels understand the welfare implications for athletes subjected to emotionally or physically abusive coaching practices. Findings revealed that coaches gained increased empathy and undertook a more athlete-centered approach to coaching post-education, however, dominant cultural ideologies (e.g., winning) persisted in the coaches’ thinking and practice. Likewise, in 2018, Nurse (30) examined child sexual abuse prevention training for adults who work with children in schools, churches, and athletic leagues; with regards to coaches specifically, the training improved coaches’ knowledge on the topic and increased their confidence in their ability to identify abuse. These preliminary findings highlight the potential benefits of training for coaches; however, it is important to note that the education programmes were restricted to specific populations, sports, forms of harm, small sample sizes, and the effects of long-term behavioral change remained unclear. Further research examining the impact of safe sport training for coaches is required.

In Canada, the country of interest in this study, safe sport educational modules (e.g., NCCP Make Ethical Decisions, Safe Sport Training) (7, 9) have been developed by the Coaching Association of Canada (CAC), which is responsible for certifying and educating coaches across Canada. The CAC has also promoted safe sport standards and expectations for organizations and its coaches, including the Responsible Coaching Movement- a pledge to learn and apply consistent safety principles. The pillars of the Responsible Coaching Movement include the Rule of Two, which attempts to ensure all interactions and communications are in open, observable, and justifiable settings; background screening; and ethics training (8). In the province of Ontario, the Coaches Association of Ontario- an independent, non-profit organization that supports coaches from community level to high performance across all sports in Ontario- has adopted similar safe sport efforts and developed resources, such as Safe Sport 101 and the Ontario Coaches Conference (10). The goals of such initiatives include but are not limited to improving the knowledge of coaches with respect to safe sport, increasing their confidence in enacting desirable coaching behaviors, creating positive sport climates, and facilitating the holistic development of athletes. To-date, the extent to which these educational initiatives meet these goals for Canadian coaches has not been examined.

While safe sport education for coaches has commonly focused on enhancing knowledge of harmful or prohibited conduct, enhancing confidence in using desired behaviors, and supporting stakeholders’ (e.g., athletes, coaches, support staff) development and well-being, there remains a lack of research examining the influence of safe sport training on coaching-related outcomes (24, 26). In this study, the constructs of knowledge, confidence, efficacy, and stress were of interest. Despite recognizing their influential role, many coaches admit inadequate knowledge to cultivate safe sport environments (25); as cultivating safe sport environments is also a collective effort, it remains important that coaches feel efficacious in their ability to support all participants (31). Given the prevalence of mental health challenges in sport, coaches have expressed stress related to supporting athletes’ mental well-being (1, 3). Further, in response to the public attention paid to cases of athlete maltreatment and the focus on coaches as perpetrators of harm, coaches have reportedly felt stress about potential false accusations; specifically, concerns of negative touch have been identified in research and practice, resulting in coaches and sport personnel being fearful and unsure of how to be around athletes with whom they interact (40).

The purpose of this study therefore to explore the influences of safe sport training on Ontario coaches’ knowledge and confidence, efficacy to support others, stress about athlete well-being, and stress related to safe sport issues. Specifically, the first objective was to examine whether safe sport training improved coaches’ knowledge and confidence, efficacy to support others, stress about athlete well-being, and stress related to safe sport issues. The second objective was to examine whether the effect of safe sport training on coaches increased with the number of safe sport training courses. The third objective was to examine whether certain courses were related to coaches’ knowledge and confidence, efficacy to support others, stress about athlete well-being, and stress related to safe sport issues.

Methods

Procedures

This study was conducted in partnership with the Coaches Association of Ontario (CAO). CAO is an independent, non-profit organization that supports coaches across all levels and sports in Ontario. Ontario has the largest population of all provinces in Ontario with over 15 million people and one in four Ontarians have coached in their lifetime (10). The CAO selected the safe sport-related courses of interest for evaluation (see Table 1). As such, within the context of the current study, a broad perspective of safe sport (i.e., from injuries to drug-free sport, planning appropriate practices, and maltreatment) was adopted. Upon receiving approval from the University of Toronto Health Sciences Research Ethics Board, coaches were contacted through the Coaches Association of Ontario (CAO) email listserv and social media posts (Facebook, Instagram, Twitter). Recruitment communication provided details about study eligibility/requirements, the purpose of the study, the voluntary nature of the study, confidentiality and anonymity, and the link to the online survey. The survey was administered with RED Cap electronic data capture. Participants were required to meet the following eligibility criteria to complete the online survey: 1) Ontario resident; 2) over the age of 16; and 3) had coached in the last two years. Following the confirmation of eligibility, participants were able to complete the survey, which took approximately 15-25 minutes (M=19.25) to complete.

Table 1. An overview of the Safe Sport Education modules evaluated in the current study.

Course	Overview
NCCP Emergency Action Planning https://coach.ca/nccp-emergency-action-plan	Upon completion of this module, coaches will be able to: describe the importance of having an EAP; identify when to activate the EAP; explain the responsibilities of the charge person and call person when the EAP is activated; and create a detailed EAP that includes all required information for responding to an emergency.
NCCP Planning a Practice https://coach.ca/nccp-planning-practice	Upon completion of this module, coaches will be able to: explain the importance of logistics in the development of a practice plan; establish an appropriate structure for a practice; and identify appropriate activities for each part of the practice. To receive full credit for this module, coaches must also complete NCCP Emergency Action Planning.
NCCP Making Head Way https://coach.ca/nccp-making-head-way-sport	Upon completion of this module, coaches will understand how to: prevent concussions; recognize the signs and symptoms of a concussion; what to do when they suspect an athlete has a concussion; and ensure athletes return to play safely.
NCCP Leading Drug-Free Sport https://coach.ca/nccp-leading-drug-free-sport	Upon completion of this module, coaches will be able to: understand and demonstrate their role in promoting drug free sport; assist athletes to recognize banned substances and the consequences as identified by the Canadian Centre for Ethics in Sport; reinforce the importance of fair play and the NCCP Code of Ethics; educate and provide support to athletes in drug testing protocols; and inform athletes on nutritional supplements.
NCCP Prevention and Recovery https://coach.ca/nccp-prevention-and-recovery	Upon completion of this module, coaches will be able to: identify common injuries in sport, prevention and recovery strategies; design and implement programs/activities to optimize athlete training, performance and recovery; and support athletes’ return to sport through awareness and proactive leadership.
Commit to Kids https://protectchildren.ca/en/get-involved/online-training/commit-to-kids-for-coaches/	Upon completion of this module, coaches will be able to: understand and recognize child sexual abuse and the grooming process; ways in which to handle disclosures of sexual abuse; the implications of sexual abuse; how to create a child protection code of conduct; and ways in which to enhance child and youth safety in sport.
Standard First Aid and CPR https://www.redcross.ca/training-and-certification/course-descriptions/first-aid-at-home-courses/standard-first-aid-cpr	Upon completion of this module, coaches will be able to: understand and apply vital life-saving knowledge/skills essential for meeting a variety of workplace/professional requirements.
HeadStartPro https://headstartpro.com/coach-course/	Upon completion of this module coaches will be able to: understand and develop a set of coaching tools to improve team performance and injury-prevention; and assist athletes and/or teams in achieving their full potential with performance-driven injury prevention training.
NCCP Making Ethical Decisions https://coach.ca/nccp-make-ethical-decisions	Upon completion of this module coaches will be to: analyze challenging situations and determine the moral, legal, or ethical implications; and apply the NCCP Ethical Decision-Making Model to respond in ways that are consistent with NCCP Code of Ethics.
NCCP Empower+ (Creating Positive Sport Environments) https://coach.ca/nccp-creating-positive-sport-environment	Upon completion of this module, coaches will be able to: describe the characteristics and benefits of participant-centered coaching; explain the types of harm that may occur when a coach misuses their power; respond to suspicions or knowledge of maltreatment; and implement positive coaching strategies to foster learning, performance, and create a safe sport environment.
CAC Safe Sport https://coach.ca/safe-sport-training	Upon completion of this module, coaches will be able to: understand the critical role of all stakeholders in promoting safe sport, how the misuse of power leads to maltreatment, and principles of the Universal Code of Conduct; understand types of maltreatment and how to recognize signs and symptoms; and respond when maltreatment is suspected and create a safe sport culture for all participants.
Respect in Sport https://www.respectgroupinc.com/respect-in-sport/	Upon completion of this module, coaches will be able to: recognize, understand, and respond to issues of bullying, abuse, harassment, and discrimination.

Note. For further detail on course descriptions and/or objectives see the corresponding webpages indicated in the table.

Participants

Participants were 1365 coaches from the Coaches Association of Ontario (CAO). Of the respondents, 61% identified as men (n=823), 38% identified as women (n=514; n=28 did not disclose), 86% identified as White (n=1087), while 4% (n=53) identified as Black, 4% (n=51) identified as East/Southeast Asian, 2% (n=31) identified as Indigenous, and less than 2% identified as Latinx (n=19), South Asian (n=18), Middle Eastern (n=16), or another race category (n=27). Coaches reported working in a variety of contexts including grassroots (e.g., recreational, community sport, house league, intramural; n=273, 22%), school sports (e.g., primary and secondary school; n=141, 11%), development (e.g. competitive, club, travel, city, all-star; n=600, 49%), post-secondary (e.g., Support, CCAA, OUA, Inter-university; n=74, 6%), provincial (e.g., Canada Games, National Championships, OHL; n=90, 7%), international (e.g., International Competitions, Worlds, Pan Am, Commonwealth, Olympics; n=36, 3%), and masters or professional (e.g., Senior, NHL, NBA, CEBL; n=20, 2%). Coaches’ tenure in their current position ranged from 1-10 years (n=804, 65%), 11-20 years (n=238, 19%), and more than 20 years (n=194, 16%). Training in safe sport was required for 78% of coaches (n=782) and provided free of cost for 51% of coaches (n=535).

Measures

Safe sport training was measured with a “yes” or “no” response from coaches to indicate whether they had taken each of the following courses: NCCP[1] Emergency Action Planning, NCCP Planning a Practice, NCCP Making Head Way, NCCP Leading Drug Free Sport, NCCP Prevention and Recovery of Injury, Commit to Kids, Standard First Aid and CPR, HeadStart, NCCP Make Ethical Decisions, NCCP Empower+ (Creating Positive Sport Environments), CAC Safe Sport Training, Respect in Sport.

Knowledge & confidence to create a safe sport environment was measured using a 3-item scale (a=.7), which asked coaches about their knowledge of safe sport concepts and their confidence in creating a safe sport environment. Example items included, “I am confident in my abilities to create a safe sport environment” and “I am familiar with the responsible coaching movement.” Coaches responded to each item on a scale from 1 (strongly disagree) to 5 (strongly agree).

Safe sport stress was measured using a 3-item scale (a=.68), which asked coaches about the stress they experience over creating a safe sport environment. An example item includes, “I often stress about being the subject of a harassment or abuse claims”. Coaches responded to each item on a scale from 1 (strongly disagree) to 5 (strongly agree).

Stress about athlete well-being was measured with 2 items (a=.59): “I often stress about my ability to manage athletes’ mental well-being”, and “I often stress about my ability to manage athletes’ physical well-being.” Coaches responded to each item on a scale from 1 (strongly disagree) to 5 (strongly agree).

Efficacy to support others was measured using a 5-item scale (a=.87), which asked coaches about how confident they feel in their ability to support athletes and other coaches. An example item includes “I am confident in my abilities to support my athletes with performance issues”. Coaches responded to each item on a scale from 1 (strongly disagree) to 5 (strongly agree).

[1] NCCP refers to the National Coaching Certification Program offered by the Coaching Association of Canada.

Data Analysis

Results

Safe Sport Training & Coaching Outcomes

Range, mean, and standard deviation scores for all variables included in subsequent analyses are included in Table 2. Of the coaches in this sample, 65% (n=890) reported completing at least one of the education courses, while 35% (n=475) reported not having taken any of the education courses. Results of the correlation analysis (Table 3) demonstrate that having any safe sport training was significantly related to increases in efficacy to support others, but not knowledge and confidence, safe sport stress, or stress about athlete well-being.

Table 2. Descriptive statistics for all variables

	Range	Mean	SD
Coaching Context (0=Grassroots)	0-7	1.81	1.37
Training Required (0=No)	0-1	.59	.49
Training Free (0=No)	0-1	.49	.50
Any Safe Sport Training	0-1	.6	.13
Number of Safe Sport Training	0-12	3.64	3.42
Knowledge & Confidence	-4-2	0	1
Safe sport stress	-4-2	0	1
Stress over athlete well-being	-4-2	0	1
Efficacy to Support	-4-2	0	1
n=1365

Table 3. Correlations between any safe sport training and coaching outcomes
	Any Safe Sport Training	Knowledge Confidence	Safe Sport Stress	Athlete WB Stress	Efficacy to Support
Any Safe Sport Training	1.00	.06*	.04	.002	-.03
Knowledge Confidence	.06*	1.00	-.02	.00	.29^**
Safe Sport Stress	.04	-.02	1.00	.34^**	-.09^**
Athlete WB Stress	.002	.00	.34^**	1.00	-.20^**
Efficacy to Support	-.03	.29^**	-.09^**	-.20^**	1.00

**. Correlation is significant at the 0.01 level

*. Correlation is significant at the 0.05 level

Number of Safe Sport Training & Coaching Outcomes

Figure 1 demonstrates the number of safe sport courses taken by coaches in this sample based on influential covariates such as coaching context, training requirement, and training accessibility (i.e., whether the training was provided free of cost). Significantly more safe sport courses were completed by coaches in Post-Secondary, Provincial, International, Masters and Professional contexts, and by coaches for whom training and education is required and free.

Initial correlation analysis (Table 4) demonstrated being a coach at a high level of competition (e.g., provincial, international) was related to taking more safe sport courses, higher knowledge and confidence, and higher efficacy to support others. Having access to free training was related to taking more safe sport courses and higher knowledge and confidence. Finally, taking more safe sport training courses was related to higher knowledge and confidence and efficacy to support others. Safe sport stress and stress about athlete well-being were not related to any of the independent variables.

Table 4. Correlations between number of safe sport training courses, covariates and outcome variables
	Coaching Context	Training Required	Training Free	Safe Sport Training	Knowledge Confidence	Safe Sport Stress	Athlete WB Stress	Efficacy to Support
Coaching Context	1.00	-.04	-.03	.11^**	.07^**	.01	.00	.08^**
Training Required	-.04	1.00	.11^**	-.02	.08^**	.06	.03	-.05
Training Free	-.03	.11^**	1.00	.09^**	.08^*	.00	-.06	.01
Safe Sport Training	.11^**	-.02	.09^**	1.00	.26^**	.05	.01	.10^**
Knowledge Confidence	.07^**	.08^**	.08^*	.26^**	1.00	-.02	.00	.29^**
Safe Sport Stress	.01	.06	.00	.05	-.02	1.00	.34^**	-.09^**
Athlete WB Stress	.00	.03	-.06	.01	.00	.34^**	1.00	-.20^**
Efficacy to Support	.08^**	-.05	.01	.10^**	.29^**	-.09^**	-.20^**	1.00
**. Correlation is significant at the 0.01 level
*. Correlation is significant at the 0.05 level

The results of the first regression analysis (Table 5) demonstrated that the number of safe sport training courses coaches completed was related to increases in knowledge and confidence and efficacy to support others, when training requirements, access to training, and context of the sport environment were held constant. The number of safe sport training courses coaches took was not related to safe sport stress or athlete well-being stress.

Table 5. Linear Regression Analyses for General Coach Training
	Knowledge & Confidence		Safe Sport Stress		Athlete WB Stress		Efficacy to Support
	B	SE	B	SE	B	SE	B	SE
Coaching Context	.03	.02	.01	.02	.00	.02	.08^*	.02
Training Required	.09^*	.07	.06	.07	.03	.07	.04	.08
Training Free	.08^*	.06	.01	.06	.06	.06	.001	.06
Safe Sport Training	.31^**	.01	.05	.01	.003	.01	.12^**	.01

Adj. R-Square	.12		.01		.00		.03
n=1365
^**Coefficient is significant at the 0.01 level
^*Coefficient is significant at the 0.05 level

Individual Safe Sport Courses and Coaching Outcomes

The results of the final analysis demonstrated that all courses were significantly related to improved knowledge and confidence. NCCP Emergency Action Planning, NCCP Leading Drug Free Sport, Commit to Kids, HeadStartPRO, and NCCP Empower+ (Creating Positive Sport Environments) were significantly related to reduced safe sport stress. Commit to Kids was significantly related to reduced athlete well-being stress. Finally, NCCP Planning a Practice, NCCP Leading Drug-free Sport, NCCP Prevention and Recovery, Commit to Kids, HeadStartPRO, NCCP Empower+ (Creating Positive Sport Environments), and CAC Safe Sport were significantly related to efficacy to support others (Table 6).

Table 6. Effects of Individual Safe Sport Courses
	Knowledge Confidence		Safe Sport Stress		Athlete WB Stress		Efficacy to Support Others
	F	Sig.	F	Sig.	F	Sig.	F	Sig.
NCCP Emergency Action Planning	60.97	<.001	5.67	.017	1.45	.229	3.75	.053
NCCP Planning a Practice	53.82	<.001	.13	.722	.44	.509	7.23	.007
NCCP Making Head Way	64.15	<.001	.10	.754	.08	.772	.35	.557
NCCP Leading Drug-free Sport	72.82	<.001	5.65	.018	.25	.618	22.49	<.001
NCCP Prevention and Recovery	47.18	<.001	3.29	.070	.08	.777	14.21	<.001
Commit to Kids	35.88	<.001	5.16	.023	8.91	.003	11.29	<.001
Standard First Aid and CPR	17.96	<.001	.31	.580	.69	.406	9.73	.002
HeadStartPRO	7.08	.008	10.31	.002	.06	.814	9.15	.003
NCCP Making Ethical Decisions	22.26	<.001	.17	.680	.01	.931	.01	.91
NCCP Empower+ (Creating Positive Sport Environments)	15.21	<.001	7.92	.04	.315	.575	16.42	<.001
CAC Safe Sport	89.17	<.001	.16	.690	3.91	.532	8.41	.004
Respect in Sport	32.62	<.001	.07	.797	.07	.797	3.64	.056
n=1365

Discussion

The purpose of this study was to explore the influences of safe sport training on sport coaches’ knowledge and confidence, safe sport-related stress, efficacy to support others, and stress about athlete well-being. Specific focus was directed towards examining the relationship between the number of safe sport courses completed and the effects of specific safe sport courses for these coaching outcomes. The results of this study demonstrated that having any training or education was related to increased efficacy to support others. Having completed a higher number of safe sport training courses was related to increased efficacy to support others and knowledge and confidence, and all safe sport courses were related to increased knowledge and confidence.

Although a plethora of safe sport education exists to-date, a prominent criticism has been the lack of empirical evaluations examining the impact or effectiveness of such training (24, 26). The findings of the current study help to address this knowledge gap by providing preliminary, empirical evidence regarding the influence of safe sport education. According to the results, coaches in more professional contexts took more safe sport training courses, which supports the notion that at elite levels of sport, coaches may have more access to professional development opportunities and/or devote more time improving their coaching skills (11, 27). Coaches who were provided access to free training in the current study also took more safe sport courses. These findings suggest that when provided the opportunity, coaches engage in professional development, however, as issues of cost and accessibility remain prevalent barriers, the advancement and development for many coaches remains limited (19, 43. Online modalities have been advocated as a cost-effective, time efficient, and readily accessible way to educate coaches (13, 14) yet, for many coaches, online professional development opportunities still present financial demands. For example, of the twelve courses examined in the current study, only three (e.g., NCCP Emergency Action Planning, CAC Safe Sport, NCCP Making Headway) are listed as online and free for coaches; in the current study, it was not known if affiliated organizations where coaches instruct reimbursed education/training and, if so, for which courses. Access or lack thereof to safe sport-related education may impact the extent to which safe, inclusive, and welcoming spaces are promoted by all coaches (22, 47). This is particularly important for coaching at the youth sport level where the delivery of sport programmes is highly dependent on volunteers who, despite recognizing their critical role for nurturing developmentally appropriate and safe environments, often lack the requisite knowledge to do so (2, 44, 46).

The completion of more safe sport training courses and all courses examined in the current study was related to enhanced coaches’ knowledge and confidence. Exposing coaches to diverse topics which include but are not limited to safety, positive development, harmful practices, and mental health, are critical to improving coaches’ awareness and ability to create safe sport environments (6, 28, 30). The coaches also reported increased knowledge of the Rule of Two and the Responsible Coaching Movement; these safe sport efforts provide additional safety principles for Ontario and Canadian coaches more broadly on background screening, appropriate interactions, and ethics training (8). Findings may be interpreted to suggest that not only does safe sport education positively influence coaches’ knowledge and confidence to create safe environments but also facilitates greater awareness of safe sport efforts in the Canadian sport context, thus providing coaches with a more comprehensive perspective on ways to stimulate safer sport.

Nurturing athletes’ holistic development is a key responsibility of coaches; however, coaches may not have the necessary education and training to adequately support their athletes (41). The current findings indicate that the completion of more safe sport education as well as specific courses (e.g., NCCP Empower+, CAC Safe Sport) may nurture coaches’ expertise and confidence to actively support their athletes with personal and performance challenges. The extent to which athletes report positive coach-athlete dynamics and feel supported in their relationships with coaches has been known to influence whether they experience learning, growth, and safe sport environments (32). Safe sport training also influenced coaches’ confidence to support coaching peers/support staff with personal and performance issues; these findings are particularly important as learning by doing, having a coach mentor, and observing others are important sources of knowledge and development for coaches (43). Collectively, the improvements in coaches’ efficacy to support others (athletes and coaches) suggests that safe sport training may serve as an effective mechanism through which knowledge dissemination and learning amongst stakeholders is achieved.

Many coaches (uninformed on the benefits of positive touch) have adopted a risk-averse perspective when interacting with athletes (i.e., “no touching”) to avoid being accused of misconduct or having their behaviors misconstrued as harmful (33, 34). In the current study, no significant relationship resulted between the number of safe sport training courses completed and coaches’ perceived safe sport stress (e.g., fear of maltreatment allegations). Specific courses were identified as decreasing safe sport stress, however, some of the courses (e.g., NCCP Emergency Action Planning, HeadStartPro, NCCP Leading Drug-free Sport) focus on physical injury prevention and/or drug-free sport and do not necessarily provide broader content on maltreatment that might warrant the reported lower coach stress regarding potential accusations of harm or safe sport issues. Although coaches have commonly reported concerns about touching in sport (33), there has also been growing awareness of psychological harm and toxic cultures in sport (38, 48). The lack of reported stress regarding safe sport concerns may be reflective of coaches being less fearful of false accusations related to psychological forms of harm as opposed to sexual harms. As the survey questions referred to coach stress in relation to abuse and harassment claims broadly, further research attention is needed to assess whether education may impact coaches’ safe sport stress differently depending on the form of harm (e.g., sexual versus psychological).

It is also interesting that while safe sport education was related to coaches’ improved efficacy to support athletes with personal and performance issues, the number of completed courses was not significantly related to stress about managing athlete physical and mental well-being. Only one course (Commit to Kids) reduced coaches’ perceived stress for managing athlete well-being. Commit to Kids focuses exclusively on providing education on sexual harms; while education on sexual harms is needed to advance safe sport, psychological harm and neglect are reported far more frequently by athletes (25, 48) and thus coaches’ perceptions of their ability to manage athletes’ well-being may be limited in scope.

NCCP Empower+ (Creating Positive Sport Environments) was associated with enhanced knowledge and confidence, improved efficacy to support others, and lower safe sport stress, whereas CAC Safe Sport Training was linked to improved knowledge and confidence and efficacy to support others. Interestingly, Commit to Kids was the only course to positively impact all coaching outcomes, despite focusing exclusively on sexual harms. As sexual harm continues to receive the most media and research attention (4, 25), education on sexual harms may be interpreted by coaches and those in the sport community to be most relevant and important for creating safe sport. Further, in Ontario and Canada more broadly, sport organizations frequently identify course equivalents where coaches may complete different courses, including CAC Safe Sport Training, Respect in Sport, NCCP Empower+, and Commit to Kids but still satisfy the safe sport-related requirements needed to instruct. The lack of an integrated approach and the various safe sport education options available may expose coaches to different experiences and levels of learning, thus providing a plausible explanation for the reported influences on coaching outcomes in the current study. To advance safe sport,evidence-informed education for coaches and stakeholders more broadly is needed (5, 47).

Limitations and Future Directions

Although this study contributes to research and practice in safe sport by providing insights into the reported benefits of safe sport education for coaches, the findings must be interpreted within the context of the current study. Considering the CAO selected the safe sport-related courses of interest for evaluation, a broad perspective of safe sport (i.e., injuries, drug-free sport, planning appropriate practices, maltreatment) was required. The data were also collected from coaches in a specific geographic region (Ontario, Canada) and thus many of the safe sport courses evaluated were exclusive to this coaching sample. The courses evaluated in the current study should not be considered an exhaustive list of all safe-sport courses; for example, since the completion of the study, several courses (e.g., Support Through Sport, Safe Sport 101 Playbook) have been revised and/or developed. Additionally, as the sport domain has been referred to one that reinforces toxic cultures, there are several education courses in Ontario and Canada more broadly on creating positive culture and inclusive environments (e.g., NCCP Coaching Athletes with a Disability), that were not included and require future consideration regarding their impact on coaches and advancing safe sport.

The study findings highlighted a relationship between safe sport education and improvements in coach knowledge and confidence and efficacy to support others, suggesting that practitioners should explore ways to make safe sport education free of cost and accessible. However, as this study did not assess knowledge translation, future research is needed to examine if coaches’ improved knowledge, confidence and efficacy from education contributes to behavior change and the use of more developmentally appropriate and safe coaching practices. Organizational influence also remains an area of interest; for example, it would be beneficial to explore how an organization’s cultural values, priorities (e.g., win-at-all-costs vs holistic development), and support (e.g., free training), may impact coach education uptake and subsequently the effectiveness of safe sport education on coaching outcomes. Future researchers may consider a case study approach to examine the impact of safe sport education for coaches within a specific organization; for example, Likert-scales may be used to assess attitudes, beliefs, and perceptions, semi-structured interviews may help to gain deeper insights on coaches’ interpretations regarding safe sport courses, and participant observation may shed light on issues of coach behavior change resulting from safe sport education.

Conclusion

Safe sport education for coaches has been consistently advocated as a recommendation for advancing safe, inclusive, and welcoming environments, however, the influence of safe sport education remains largely unknown (24, 26). The current study contributes to the sport literature by providing an examination of the influences of safe sport training for coaches. Findings revealed a relationship between the number of safe sport training courses coaches completed and increases in their knowledge and confidence and efficacy to support others. However, the number of safe sport training courses completed was not associated with stress related to safe sport matters or athlete well-being. All safe sport courses were reportedly associated with improved coach knowledge and confidence. Several training courses were also linked to improvements in coaches’ efficacy to support others and reductions in their safe sport stress, with only one course contributing to coaches’ reduced stress related to athlete-well-being. Although the findings suggest favorable influences of safe sport training for coaches, the current study did not assess behavioral change. Future research is needed to explore whether reported improvements (e.g., knowledge and confidence) associated with safe sport education translates to coaching practice.

Applications in Sport

Safe sport education in the current study was reportedly associated with enhanced coach knowledge and confidence to create safe environments and efficacy to support athletes and other coaches/support staff. Unfortunately, as a large portion of the sport sector is run by a volunteer workforce (e.g., volunteer coaches), sport organizations remain reluctant to enforce training requirements for fear of further burdening these coaches who frequently report stress and burnout (2, 35). However, the extent to which sport organizations and their leaders prioritize and support safe sport, has been shown to impact the effectiveness of safe sport efforts (17, 37, 49). In some cases, merely having safe sport education initiatives may have little impact on creating and sustaining safer environments and appear as superficial gestures towards change, further reproducing harms (29, 31). Sport and coaching organizations are confronted with the challenge of maintaining low time and cost demands for many volunteer coaches while also providing adequate education for volunteer (and paid) coaches (19, 46).

Acknowledgements

The authors would like to thank the coaches who participated in this study along with Coaches Association of Ontario who contributed to the design and recruitment of this study.

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

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George Minoso2024-06-20T12:01:59-05:00June 21st, 2024|General, Research, Sport Education, Sports Coaching, Sports Exercise Science|Comments Off

Advice on making the most of basketball three-point shot data

Authors: George Terhanian1

Corresponding Author:

George Terhanian, PhD
200 Hoover Avenue, #2101
Las Vegas NV, 89101
george.terhanian@gmail.com
646-430-3420

¹George Terhanian founded Electric Insights after holding executive positions at The NPD Group, Toluna, and Harris Interactive. He has also served on boards or advisory groups for several organizations, including the US National Academy of Sciences, the Advertising Research Foundation, and the British Polling Society. He is known for conceiving how to make survey data, including pre-election forecasts, more accurate through statistical matching methods.

Making the most of basketball three-point shot data

ABSTRACT

This study’s primary goal is to help National Basketball Association (NBA) and other basketball teams worldwide increase their three-point shooting accuracy and decrease their opponents’, a key to winning more games. A related goal is to explain how a combination of good data, logistic regression analysis, likely effects reporting in probabilities or percentage points, and self-serve simulation can improve communication among data analysts, basketball coaches, and players, and enhance each group’s effectiveness. Logistic regression analysis of 32,511 NBA three-point shots shows six factors affect the three-point shooting percentage: closest defender’s distance to the shooter, time left on the 24-second shot clock, whether the player shot after dribbling or catching the ball, game period, shot distance, and venue. In the past, data analysts conveyed the results of such analyses to coaches and players using terms such as regression, logits, and odds. Some NBA executives say doing so again would be disastrous. An alternative is to emphasize probabilities and percentages in communication and create self-serve simulators coaches and players can use to predict how changes in critical factors affect three-point shooting percentages. NBA and other teams worldwide can apply this approach to new and existing datasets they maintain, enhance, and build.

Key Words: self-serve simulation, predicted probabilities, logistic regression, likely effects reporting, psychotherapy

INTRODUCTION

The National Basketball Association (NBA) releases specific three-point shot characteristics, such as shooter name and shot distance. Aside from the 2014-15 season’s first 903 of 1,230 games (and 2015-16’s first 631, though the latter data are no longer publicly available), the released data exclude a variety of individual shot characteristics such as the closest defender’s distance to the shooter, a crucial defensive effectiveness measure (14). Teams are said to consider the excluded characteristics proprietary. As Mike Zarren, assistant general manager and chief legal counsel for the NBA’s Boston Celtics, explained, “You can’t share stuff with other teams…We are not at an equilibrium point where all the teams know what everyone else is doing. There are some advantages that some teams have over others” (15) (51:47).

The analyses here use the 2014-15 shot dataset, the last and largest single-season one containing full shot data that is publicly available. The main goal is to help NBA and other basketball teams worldwide increase their three-point shooting accuracy and decrease their opponents’. Teams that do so should win more games. A related goal is to explain how a combination of good data, logistic regression analysis, likely effects reporting in probabilities or percentage points (e.g., “Shooting off the catch rather than the dribble is associated with a two-percentage-point increase in our three-point shot make percentage.”), and self-serve simulation can improve communication among data analysts, basketball coaches, and players, and enhance each group’s effectiveness. NBA and other teams worldwide can apply this approach to new and existing datasets they maintain, enhance, and build. Aspects of the approach are also portable to many other issues and areas where the key outcome variable is binary (26).

This paper has seven additional sections (excluding references and other ancillary information). The first summarizes basic rules and strategies for NBA basketball, highlighting the importance of the three-point shot. It also explains why data analysts seeking to communicate effectively with coaches and players should consider using non-technical language. The second section describes the three-point shot data used in this paper’s analyses. It then provides the rationale for relying on logistic regression analysis for model building and prediction. The third section reports the results of the analyses and suggests how data analysts might share them with coaches and players. It also explores why academic researchers tend not to report likely effects in probabilities or percentage points. The fourth details how data analysts can build self-serve simulators that report likely effects in probabilities or percentage points. The limitations of this paper’s analyses are discussed in the fifth section. The next-to-last section describes how teams might apply the approach described here, while the final section provides concluding remarks.

NBA Basketball: Basic Rules and Strategies

NBA games have two teams with five players competing for four 12-minute periods (excluding possible five-minute overtime periods). To score, a team needs to shoot the ball through the basket. With the clock running, a successful shot is worth three or two points, depending on the shooter’s distance from the basket. The clock stops for free throws, which are uncontested 15-foot shots worth a single point awarded for specific infringements. One can calculate each shot’s expected value (EV) by multiplying its potential value by its average make percentage. For the 2022-23 regular season, the expected value of a three- and two-point shot was almost identical: 1.08 points (3*.36) for a three-pointer and 1.10 (2*.55) for a two-pointer. Each free throw’s expected value was .78 points (1*.78) or 1.56 for a more typical pair (3). A recent example shows why the expected value measure can be strategically important.

In the second round of the 2020-21 playoffs, the Atlanta Hawks shocked the heavily favored Philadelphia 76ers, coming from behind to win the seven-game series four to three. The Hawks’ decision to foul Ben Simmons repeatedly to force him to shoot free throws contributed to the victory. As Hall-of-Fame player Earvin “Magic” Johnson observed, “…it fueled the Hawks’ comeback” (13).

Simmons shot just 33% (15 for 45) from the free-throw line for the series, far below his 61% (and the league’s 77%) regular season average. Simmons’s 33% figure suggests the Hawks expected him to score only .66 points for two free throws in a series in which his team made 40% of its three-pointers (for an expected value of 1.22 points) and 52% of its two-pointers (for a 1.05 expected value). That means the Hawks expected to gain .56 points (1.22 – .66) for a replaced three-point shot and .39 points (1.05 – .66) for a replaced two-pointer with the foul Simmons strategy. Perhaps more notably, it may have affected Simmons’s decision-making. To his team’s detriment, Simmons chose not to attempt an open lay-up or dunk with 3:30 remaining in game seven (4), arguably for fear of getting fouled and having to shoot free throws (21, 27).

Overstating three-point shooting’s significance is difficult. In 2022-23, the Toronto Raptors, Charlotte Hornets, and Houston Rockets won 41, 27, and 21 (of 82) regular season games, too few to qualify for the post-season playoffs; their three-point shooting percentages of 34%, 33%, and 33% were the league’s worst. The Philadelphia 76ers, Golden State Warriors, and Los Angeles Clippers won 54, 44, and 44 games, enough to compete in the playoffs; they were top performers in three-point shooting at 39%, 39%, and 38%. These data and separate multi-season analyses (18, 20) suggest that winning in the NBA hinges heavily on making (and defending) three-point shots.

Clear Communication

An excellent statistical model is “a simplified version of reality, like a street map that shows you how to travel from one part of a city to another” (28) (p. ix). But that map will not help you find your way if it includes esoteric terms or unfamiliar signs or symbols. Likewise, if data analysts use uncommon language when giving advice, coaches and players may feel lost. Mike Zarren would agree. If Celtics’ data analysts were to apply logistic regression to three-point shot data, he would tell them to communicate what they learn “without using the word regression because that’s a disaster” (15) (11:18). Terms like logits, standard deviations, odds, odds ratios, and z scores also would be off-limits. Zarren does not believe coaches and players are unintelligent. Even good data analysts can find aspects of logistic regression challenging. That is why DeMaris (7) (p. 1,057) observed, “…there is still considerable confusion about the interpretation of logistic regression results.” And why Gelman and Hill (11) (p. 83) commented, “…the concept of odds can be difficult to understand, and odds ratios are even more obscure.”

Washington Wizards’ assistant coach Dean Oliver’s views on clear communication resemble Zarren’s. “When I directed quantitative analysis for the Denver Nuggets and would prepare stuff for coaches,” he said, “there were actually very few numbers in there. It was usually words because it was easier for them to absorb…” (15) (48:54).

An alternative to avoiding numbers is to report key predictor variables’ likely effects with familiar ones like probabilities and percentages—the NBA reports various descriptive statistics and cross-tabulations on its website, emphasizing percentages, hence coaches’ and players’ familiarity.

Methods

Data

The NBA has used technology to gather detailed player performance data since the 2013-14 season via SportVU, then Second Spectrum. The analyses here use SportVU data, described as “real-time and innovative statistics based on speed, distance, player separation, and ball possession for comprehensive analysis of players and teams” (25). How did the SportVU system work? In each arena’s rafters, six cameras recorded information throughout each game in .04-second intervals, producing 25 images per second. A computer algorithm then plotted the locations of the ball, basket, and 10 players. SportVU delivered data and reports to each team and the league as a last step.

As noted earlier, the NBA made available SportVU raw, shot-level data—including the defender distance variable—for three-quarters of the 2014-15 regular season. (The NBA also made available raw, shot-level data early in the 2015-16 season before discontinuing the practice entirely in January 2016. The latter dataset is no longer publicly available.) The 2014-15 dataset (17)—the last and largest single-season one publicly available—contains 21 variables and 128,069 three- and two-point shots, as described in the Appendix. After making minor changes (e.g., removing two-point shots), the remaining three-point shots totaled 32,511—11,426 makes and 21,085 misses—taken from October 28, 2014, through March 4, 2015.

Analysis Method

Logistic regression models the relationship between a binary outcome (e.g., made or missed three-point shots, or nearly anything with a yes or no interpretation) and, typically, several predictor or explanatory variables. It is ideal for identifying and estimating the effects of actions to increase or decrease the size or proportion of the group of interest, specifically, made three-point shots. It can also predict each three-point shot’s probability of belonging to the “made” rather than the “missed” group. Many academic researchers consider it “the standard way to model binary outcomes” (11) (p. 79), “dominating all other methods in both the social and biomedical sciences” (2) (para. 1).

RESULTS
The final logistic regression model comprises one dependent and six predictor variables. The predictor variables were selected based on their relationship with the dependent variable, one another, theory, availability, and their effect on the model’s predictive accuracy. Below are descriptions of the seven variables and brief explanations for how they may differ from the original ones described in the Appendix.

ShotResult: The dependent variable: whether the shooter made the shot. (Values: 0=Missed, 1=Made; Original variable: Fgm)
DefDist: The closest defender’s distance to the shooter in feet (ft.). Basketball players and coaches recommended a four-category variable after discussions and preliminary analyses. (Values: 1=0-3 ft., 2=3-6 ft., 3=6-9 ft., 4=9+ ft.; Original variable: Close_Def_Dist)
ShotClock: The number of seconds (secs.) on the 24-second shot clock. Analyses showed steep drops in the make probability at the 4- and 2-second marks, thus the decision to create a variable with three categories. (Values: 1=0-2 secs., 2=2-4 secs., 3=4+ secs; Original variable: Shot_Clock)
Catch: Whether the shooter took the shot off the catch or dribble. The original variable reported the number of dribbles the shooter took before shooting. Basketball players and coaches recommended a two-category variable after discussions and preliminary analyses. (Values: 1=Off Catch, 2=Off Dribble; Original variable: Dribbles)
Period: The game period when the shot was taken, with fourth period and overtime shots pooled because of their similar make percentages. (Values: 1=1, 2=2, 3=3, 4=4+; Original variable: Period)
ShotDist: The distance in feet from the center of the basket to the shooter. Basketball players and coaches recommended a four-category variable after discussions and preliminary analyses. (Values: 1=22-24 ft., 2=24-25 ft., 3=25-26 ft., 4=26+ ft.; Original variable: Shot_Dist)
Venue: Whether it was a home or away game for the shooter’s team. (Values: 0=Away, 1=Home; Original variable: Location)

Table 1 reports the logistic regression analysis results, notably, standard information such as logit coefficients, odds, z scores, and a measure of statistical significance (i.e., p>z). It also reports useful non-standard information such as frequencies, (predicted) probabilities, and expected values. The rationale for reporting standard and non-standard information, to borrow from the statistician Frederick Mosteller, is to “let weaknesses from one…be buttressed by strength from another” (16) (Ch. 4, p. 116), a concept he referred to as “balancing biases.” As envisioned, data analysts can rely on standard information when building and evaluating logistic regression models, and non-standard when communicating the results and their implications to coaches and players.

Table 1.

Results of final logistic regression analysis

Variable	Frequency	Logit	Odds	z	p>z	Prob	EV
*DefDist*
0-3 ft.	6%	–	1	–	–	.29	0.86
3-6 ft.	54%	0.25	1.29	4.74	0.00	.34	1.02
6-9 ft.	28%	0.38	1.46	6.78	0.00	.37	1.11
9+ ft.	12%	0.47	1.60	7.74	0.00	.39	1.17
*ShotClock*
0-2 secs.	5%	–	1	–	–	.21	0.62
2-4 secs.	7%	0.63	1.88	8.02	0.00	.33	0.99
4+ secs.	88%	0.77	2.17	11.87	0.00	.36	1.08
*Catch*
Off catch	75%	–	1	–	–	.36	1.07
Off dribble	25%	-0.09	.0.91	-3.21	0.00	.34	1.01
*Period*
1	24%	–	1	–	–	.37	1.11
2	24%	-0.11	0.89	-3.34	0.00	.34	1.03
3	25%	-0.05	0.96	-1.34	0.18	.36	1.08
4+	27%	-0.15	0.86	-4.54	0.00	.34	1.01
*ShotDist*
22-24 ft	31%	–	1	–	–	.38	1.13
24-25 ft.	36%	-0.09	0.91	-3.25	0.01	.36	1.06
25-26 ft.	20%	-0.17	0.84	-5.12	0.00	.34	1.01
26+ ft.	13%	-0.30	0.74	-7.13	0.00	.31	0.92
*Venue*
Away	50%	–	1	–	–	.35	1.04
Home	50%	0.05	1.05	2.14	0.03	.36	1.07
*…Constant*	–	-1.46	0.23	-17.42	0.00	.19	0.56

Note. n=32,511. Log pseudolikelihood, starting value: -21,078.18; final value: -20,827.69. Likelihood ratio (degrees of freedom=13): 498.44, p > chi² = 0.00. Tjur R²: 0.014; McFadden R²: 0.012. Stukel chi²(1) = 4.10, p > chi² = 0.043

Standard versus Non-Standard Interpretations

Table 1 shows that the defender distance variable (DefDist) affects the outcome variable. A standard interpretation would emphasize odds ratios and statistical significance:

Controlling for other variables’ effects, three-point shots taken with the closest defender 9+ feet away have a:

60% higher odds (i.e., 1.6/1) of going in than those taken with the closest defender 0-3 feet away,
24% higher odds (i.e., 1.6/1.29) than those with the defender 3-6 feet away, and
10% higher odds (i.e., 1.6/1.46) than those with the defender 6-9 feet away.

Each effect is statistically significant, as their z scores show.

Although the standard interpretation is correct from a technical standpoint, coaches and players may not understand or act on it, given Zarren’s and Oliver’s comments (as well as those of DeMaris, Gelman, and Hill). Now consider a non-standard interpretation (that relies on Table 1’s non-standard information). Note that each percentage’s associated expected value is in parentheses.

All else unchanged, the percentage of three-point makes would decrease from 35% (1.05 pts.) to:

29% (0.86 pts.) with the defender always 0-3 feet away from the shooter, and
34% (1.02 pts.) with the defender always 3-6 feet away.

It would increase from 35% to:

37% (1.11 pts.) with the defender always 6-9 feet away, and
39% (1.17 pts.) with the defender always 9+ feet away.

NBA coaches and players would probably prefer the non-standard interpretation. Arguably, reporting the likely effect in percentage points instead of odds is more intuitive and actionable (26, 30).

Calculating Each Shot’s Make Probability

Another number to note in Table 1 is the constant of -1.46 logits which translates to a predicted make probability of 19% (0.56 pts.). The -1.46 number represents a three-point shot with the lowest value on each predictor variable:

Defender 0-3 feet away
0-2 seconds on the shot clock
Off the catch
First period
Shot distance of 22-24 feet
Away game

An implication is that it is possible to calculate the predicted make probability of each of the 32,511 shots. Such information can spark curiosity and foster improved performance for a player scrutinizing his own (or opponents’) shot data. For example, Row 1 of Table 2 reports the logit coefficients associated with the first three-point shot Klay Thompson of the Golden State Warriors attempted in 2014-15. In the third period of an away game versus the Sacramento Kings with 4.6 seconds on the shot clock, Thompson missed from 22 feet off the catch with the defender 3.9 feet away. As the column titled Prob shows, that shot’s predicted make probability was 38% (.38*100), calculated by applying the following formula to select Table 2 numbers: exp (sum of logit coefficients + constant)/ (exp (sum of logit coefficients + constant) +1).

Upon closer examination, Thompson could have asked the team’s data analysts how that shot’s make probability would have changed had the defender been 9+ rather than 3.9 feet away. To respond, an analyst could have replaced the DefDist logit coefficient of 0.25 with 0.47, the one corresponding to a 9+ feet value. As shown in Row 2, the make probability would have risen to 42%, a four-percentage-point increase or likely effect.

Thompson next might have asked how shooting off the dribble rather than the catch would have affected the 42% probability. After replacing the Catch logit coefficient of 0 with-0.09, an analyst could have reported that the probability would have dropped to 39%, as Row 3 of the Prob column shows.

Thompson, an excellent shooter, would probably work to improve specific aspects of his shooting if he had such data for all his three-point shots (31).

Table 2.

Simulating the effect of changes on a single shot’s make probability

Row	DefDist	ShotClock	Catch	Period	ShotDist	Venue	Cost	Total	Prob
1	0.25	0.77	0	-0.05	0	0	-1.46	-0.49	0.38
2	0.47	0.77	0	-0.05	0	0	-1.46	-0.27	0.42
3	0.47	0.77	-0.09	-0.05	0	0	-1.46	-0.36	0.39

Predicting the Likely Effect of Multiple Changes to Multiple Predictor Variables

Coaches thinking more broadly might focus on all 32,511 shots and ask analysts to predict the likely effect of multiple changes to the values of multiple predictor variables. Building on the Thompson example, analysts could approach the task by conceptualizing changes as scenarios. Below, and graphically in Figure 1, are three illustrative ones.

Scenario 1. Players take all 32,511 three-point shots with the defender 9+ ft. away.

Prediction: 39% of all three-pointers will go in, an increase of four percentage points compared to the 35% baseline, translating to 1,297 more makes and 12,723 total ones.

Scenario 2. Players take all 32,511 three-point shots:

with the defender 9+ feet away
from 22-24 ft. away from the basket

Prediction: 42% of all shots will go in, a three-percentage-point gain vs. Scenario 1. This translates to 808 more makes and 13,531 total makes.

Scenario 3. Players take all 32,511 three-point shots:

with the defender 9+ ft. away
from 22-24 ft. away from the basket
with 4+ seconds on the 24-second shot clock

Prediction: 43% of all shots will go in, an increase of another percentage point compared to Scenario 2, translating to 370 more makes and 13,901 total ones.

Figure 1.

Percentage of predicted makes by scenario

Each scenario’s likely effect results from all-or-nothing simulation. How does it work? For any predictor variable, such as Catch, data analysts select one target value—either “Off Catch” (occurring 75% of the time) or “Off Dribble” (25%). Assume they choose “Off Catch,” with a logit coefficient of 0, as Table 1 shows. For the 8,127 “Off Dribble” shots, they would replace the coefficient of -0.09, also shown in Table 1, with 0 and calculate the new likely effect: 158 more made three-pointers for the season, translating to 11,584 total makes.

Adopting a fine-tuning approach is another possibility. After examining the frequency distribution of the Catch values, analysts could specify a new distribution, such as 92% “Off Catch” and 8% “Off Dribble,” ensuring the total sums to 100%. They would keep the original 24,384 “Off Catch” values (i.e., 75%) and change the -0.09 coefficient to 0 for another 2,600 selected randomly from the original 8,127 “Off Dribble” values to achieve the 92:8 ratio. The change would result in 11,530 made three-pointers, 54 less (i.e., 11,584-11,530) than if players had taken all shots off the catch.

If coaches and players embrace simulation, there could be too many scenarios for data analysts to handle. To stay ahead of demand, they could build self-serve simulators tailored explicitly for coaches’ and players’ use. Finding prototypes in academic research will be a struggle, however, arguably because of the non-linear relationship between logits and probabilities (26, 30) and its dampening effect on reporting likely effects in probabilities or percentage points. Figure 2 plots illustrative logit and probability values to cast light on that relationship.

Figure 2.

The non-linear relationship between logits (x-axis) and probabilities (y-axis)

Note how a one-logit increase from zero to one on the x-axis corresponds to a .23 probability increase (from .5 to .73) on the y-axis. Yet a one-logit increase from four to five (or minus 5 to minus 4) translates only to a tiny probability increase. As shown in Table 1 (and later in Table 3), it is still possible to report the effect of a predictor variable, x, on a binary outcome, y, in probabilities or percentage points (e.g., a one-unit change in x is associated with a three-percentage-point increase in y, all else being equal). Arguably, it is also sensible to do so, not least because NBA players make roughly 35% of their three-point shots and the relationship between logits and probabilities is reasonably linear between .2 and .8 on the probability scale, as Figure 2 shows. But in more extreme cases, as Figure 2 suggests, the effect size will depend heavily on the value of y and the values of the model’s other predictor variables. More precisely, the size of the effect will decrease near 0 and 1. As a result, x’s effect on y in probabilities percentage points “…cannot be fully represented by a single number” (19) (p. 23). That may be why some logistic regression experts (6-8) have advised against using probabilities or percentage points to report and interpret logistic regression coefficients’ overall effects. It also may be why most major statistical software packages do not produce effects in probabilities or percentage points through pre-packaged procedures or built-in modules. As an unintended consequence, some data analysts seeking guidance likely have had to fend for themselves.

A GUIDE TO BUILDING SELF-SERVE SIMULATORS
Data analysts can use this guide to build simulators that report likely effects in probabilities or percentage points. (For convenience, references are made to the three-point shot data used in this paper’s analyses, although the guide is general and should work across areas of interest.) Several steps are involved in the process:

Step 1. Ensure sufficient three-point shot data are available to conduct logistic regression analysis, which should be a straightforward task for NBA teams given the league’s business relationship with Second Spectrum (which replaced SportVU). How does one define sufficient? As a rule of thumb, at least 10 shot attempts are needed for each predictor variable in logistic regression model, adjusting for the expected shot make rate (or miss rate if it is lower than the make rate). For context, this paper’s main analysis with six predictor variables and a 35% expected make rate required a minimum of 171 three-point shot attempts: 10 * (6 /.35). For non-NBA teams requiring raw data, assistant coaches can record key shot characteristics with paper and pencil or specialized hand-held apps.

Step 2. Develop a model to predict successful 3-point shots, the binary outcome of interest. Logistic regression produces a weight—a logit coefficient—for each category of each predictor variable. In an optimal model, those weights maximize the predicted probability gap between the mutually exclusive outcomes (1).

Step 3. To calculate a single 2014-15 three-point shot’s make probability, sum the weights corresponding to its characteristics and add the constant. After that, apply the formula shown earlier to the result: exp (sum of logit coefficients + constant)/ (exp (sum of logit coefficients + constant) +1). Alternatively, request the predicted probability from the statistical software.

Step 4. Do the same for the 32,510 remaining shots, sum all 32,511 probabilities, then take the average to compute the overall make probability. If the model predicts players will make 35% of all three-point shots, it translates to 11,426 makes (.35*32,511).

Step 5. To enable the simulator to work online or in a mobile app, develop an algorithm using JavaScript. The simulator’s purpose is to let users see how changes they make to the values of the predictor variables affect the .35 probability.

Step 6. Design a user interface, possibly by enlisting the support of someone familiar with website and app development.

Step 7. Keep things simple initially—permit users to change only one value of one predictor variable. If it has two response choices like Away and Home, let the user change every Away response to Home or vice versa. Think of this as the all-or-nothing option.

Step 8. For all 32,511 three-point shots, change the corresponding Away or Home logit coefficient (but no others) to align with the user’s selection, then recalculate the predicted make probability. The likely effect is the difference between the new and starting probability (and the new and starting makes).

Step 9. Follow the same process to let users change the values of several predictor variables simultaneously.

Step 10. Go further and allow users to change any predictor variable’s frequency distribution as they please, ensuring the distribution sums to 100%. Think of this as the fine-tuning option. The algorithm will need rules to accommodate the changes.

What would all-or-nothing and fine-tuning self-serve simulators look like, and how would they function? Figure 3 shows a screenshot of a working all-or-nothing simulator (accessible at https://www.electricinsights.com/hoops1). The first column contains the predictor variables and their values. Column 2 shows the changes (in blue) the user made to the 2014-15 frequencies; the third column displays the original frequencies.

Figure 3

All-or-nothing simulation

As Figure 3 shows, the user selected values of “0-3 ft.” for “Defender Distance,” “0-2 secs.” for “Time Left on Shot Clock,” “Dribble” for “Off Catch or Dribble?” and “26+ ft.” for “Shot Distance.” The likely effect is a 22-point decrease in the make probability, translating to 7,229 fewer makes and 4,197 total ones.

Personalized simulators for players like Klay Thompson and Stephen Curry could be more beneficial (and accurate) than a generic, all-player one. To support this point, Table 3 reports the results of a new analysis of Curry’s 2014-15 three-point shots. Note how the values of many key measures, such as frequencies and expected values, differ substantially from their Table 1 counterparts. Table 3 shows, for instance, that Curry took 54% of his three-pointers off the dribble with an expected value of 1.32 points per shot. But Table 1 showed NBA players (including Curry) took only 25% of their three-pointers off the dribble with a 1.01 points-per-shot expected value. Curry is not your average three-point shooter, hence the need for personalization.

Table 3.

Results of Steph Curry logistic regression analysis

Variable	Frequency	Logit	Odds	z	p>z	Prob	EV
*DefDist*
0-3 ft.	11%	–	1	–	–	.24	0.72
3-6 ft.	55%	0.89	2.44	2.44	0.02	.43	1.29
6-9 ft.	24%	0.97	2.65	2.48	0.01	.45	1.35
9+ ft.	10%	1.26	3.51	2.75	0.00	.52	1.55
*ShotClock*
0-2 secs.	2%	–	1	–	–	.25	0.75
2-4 secs.	3%	2.10	8.17	2.19	0.03	.72	2.15
4+ secs.	95%	0.79	2.21	1.10	0.27	.42	1.25
*Catch*
Off catch	46%	–	1	–		.40	1.21
Off dribble	54%	0.15	1.17	.75	0.46	..44	1.32
*Period*
1	33%	–	1	–	–	.44	1.30
2	19%	0.01	1.01	0.05	0.963	.44	1.31
3	29%	-0.03	0.97	-0.12	0.902	.43	1.28
4+	19%	-0.26	0.77	-0.91	0.364	.37	1.12
*ShotDist*
22-24 ft	16%	–	1	–	–	.55	1.65
24-25 ft.	31%	-0.75	0.47	-2.46	0.01	.37	1.11
25-26 ft.	24%	-0.51	0.60	-1.58	0.11	.43	1.28
26+ ft.	28%	-0.65	0.52	-2.04	0.04	.40	1.12
*Venue*
Away	54%	–	1	–	–	.41	1.23
Home	46%	0.11	1.12	.56	0.58	.44	1.31
*…Constant*	–	-1.53	0.22	-1.8	0.07	.19	0.56

Note. n=j. Log pseudolikelihood, starting value: -305.04; final value: -294.46. Likelihood ratio (degrees of freedom=13): 21.16, p > chi² = 0.07. Tjur R²: 0.047; McFadden R²: 0.035. Stukel chi²(1) = 4.38, p > chi² = 0.11.

A working fine-tuning simulator—a complement to the Curry analysis—is available at https://www.electricinsights.com/curry1. It lets users change any value of any predictor variable by any amount and see the likely effect. In the screenshot shown in Figure 4, the user changed Curry’s 2014-15 season frequencies (in parentheses) for “Defender Distance,” “Off Catch or Dribble?” and “Shot Distance.” The likely effect is a seven-percentage-point increase to his 42% average make probability, translating to 31 more makes (i.e., 220-189).

Figure 4

Steph Curry’s fine-tuning simulator

Discussion

If the sample size of three-point shots allows, data analysts can build all-or-nothing and fine-tuning simulators that include all teams and players, each team, and each player. Given sufficient demand, they can also do so with data for other major shot types (i.e., two-pointers and free throws).

Several caveats are in order before describing how basketball teams might act on the results the approach described here, using the results (and simulators) shown earlier for illustration. First, inferences drawn from the 2014-15 dataset may no longer apply because of the time gap. Nor did this dataset include several three-point shot characteristics (e.g., closest defender’s height and reach, the game score at each shot) that could be important, which is a second caveat.

A third caveat concerns the “all else the same” assumption, a logistic regression analysis theoretical staple. In practice, it may not hold up. Giving excellent three-point shooters more playing time, for example, could worsen teams defensively. Deciding who plays and why, a type of optimization, lies outside this paper’s scope.

Another caveat involves ease of implementation. Building and updating simulators like Curry’s for NBA players who shoot, say, 175 or more three-point shots per season may require automation. To characterize the task as trivial would be misleading.

Humility and ignorance are two key factors to consider as the fifth caveat. Some NBA data analysts may have already adopted an approach combining good data, logistic regression, likely effects reporting in probabilities or percentage points, and self-serve simulation. As noted earlier, they work mainly in secrecy. And when they make comments at analytics conferences or similar forums, some are instructed “to go up on stage and talk about something without saying anything” (15) (51:37), according to Zarren.

Application In Sports

Good basketball coaches position their players to make the highest percentage of three-pointers possible, all else equal. They also implement a defense to minimize opponents’ three-point make percentage. The analyses presented here suggest six factors affect the make percentage:

Closest defender’s distance to the shooter
Time left on the 24-second shot clock
Whether the player shot off the dribble or catch
Game period
Shot distance
Venue

How might coaches act on these findings? There are numerous possibilities, starting with game pace. Fast ball movement from defense to offense (e.g., before the defense sets) gives the offensive team more time to find an open three-point shot, preferably before the four-second mark on the shot clock where shooting percentages dip, and unquestionably before the two-second mark where they plummet. As the NBA’s all-time leading three-point shooter, Steph Curry understands this well. Table 3 showed he attempted only two percent (compared to a five percent NBA average) of his three-point shots with less than two seconds on the shot clock.

Coaches should design offensive plays and patterns to create at least three feet of space between the shooter and defender. A 22-24-foot shot’s make probability with the defender 0-3 feet away is only 29%, all else equal. It increases to 34% with the defender 3-6 feet away. Space is critical for Curry, too. He shot 11% of his three-pointers with the defender 0-3 feet away versus the NBA average of 6%, reducing his overall make percentage. It could have been worse. Had he taken all 448 of his shots with the defender 0-3 feet away, all other factors being equal, his make probability would have dropped from 42% to 24%.

Making sure players understand the characteristics of a desirable three-point shot is another opportunity. Personalized simulators like Curry’s can make each player’s shooting strengths and weaknesses obvious. For instance, some players may make a higher percentage of three-pointers off the dribble than catch. Others may suffer only a slight percentage point decline when guarded tightly or shooting from 26+ rather than 22-24 feet. And if those simulators contain opponents’ shot data, coaches could use them to determine how to exploit specific opponents’ weaknesses.

Analyses show the three-point make percentage drops in the fourth period. Player fitness could be a contributing factor. Without applicable data (e.g., feet, meters, or miles logged since tip-off), it is difficult or impossible to test the hypothesis. Maybe the players on the court lack the skills needed to shoot higher percentages. Or game stress could affect shooting performance—data on the game score at each shot would clarify the matter. For context, the all-or-nothing simulator would show that the highest probability three-point shot (46%) has these characteristics:

Defender 9+ feet away
4+ seconds on the shot clock
Off the catch
First period
22-24 feet from the basket
At home

The simulator would also show that the 46% make probability drops to 42% in the fourth period, changing nothing else. That means players have grown tired, different players are on the court, game pressure has taken its toll, or unknown variables caused the drop. So how should head coaches make sense of this? Working with assistant coaches and data analysts, they can explore ways to increase players’ fitness levels, optimize substitution patterns, and help players cope better with pressure. If teams can access variables that were unavailable for analysis here, their analysts can include them in new models to estimate their likely effect.

Players make a higher percentage of three-point shots at home than on the road, all else equal. Crowd noise, characteristics (e.g., lighting) of the less familiar setting, travel effects (e.g., uncomfortable hotel beds), or some combination of these may explain why. Coaches can look outside the league for ideas to help players overcome such obstacles. For instance, former US Navy SEAL commander Mark Divine prepares SEAL candidates for training by replicating the challenges they are likely to encounter, including Hell Week during which “each candidate sleeps only about four total hours but runs more than 200 miles and does physical training for more than 20 hours per day” (5).

Contrary to conventional wisdom, Divine’s SEALFIT program places particular emphasis on skills like positive visualization, breath control, and meditation because, as he said, “People who haven’t learned to control their mind and emotions quit or they get hurt” (10). Does SEALFIT work? Divine reports that nine of 10 SEAL candidates who complete SEALFIT training become SEALs (versus a 20% norm). He is confident that NBA players would benefit from the program (M. Divine, personal communication, March 11, 2022).

A complementary tool for improving performance is psychotherapy. As described earlier, Ben Simmons’s decision to avoid attempting an open lay-up or dunk (arguably) for fear of being fouled and having to shoot free throws may have cost his team the 76ers a 2021 playoff series to the Hawks. As his teammate Joel Embiid declared, “That was the turning point” (12) (1:08). Psychotherapist Richard Schwartz, who developed the Internal Family Systems (IFS) therapeutic model (23), would probably concur then speculate that Simmons’s widely criticized decision (21, 27) originated from past trauma linked to his poor free-throw shooting. After citing evidence (24) of IFS’s effectiveness, Schwartz might posit that a protective part of Simmons’s mind—a “guardian of [his] inner world” (23) (p. 184)—compelled him to pass rather than shoot to prevent a traumatized part—think of it as a deeply wounded child—from re-experiencing pain or shame at the free throw line. Were Schwartz to work with Simmons, he would likely try to communicate with his mind’s traumatized part as if it were an actual person, restore its faith in Simmons’s free-throw shooting abilities, and encourage the protective part to undertake different tasks. The more traditional coaching approach of advising, or even requiring, Simmons to practice harder with expert guidance did not—and may never—work. As Early (9) observed, “Simmons has been reluctant to seek help from top shooting coaches…He reportedly clashed with his former team (the 76ers) years ago over who he would work with, preferring to practice with his brother rather than team shooting coach John Townsend.”

Coaches can use the same strategies to reduce their opponent’s three-point shooting percentage they use to improve their own. Table 1 data (and the all-or-nothing simulator) suggest the key lies in forcing opponents to shoot with less than four seconds on the clock, off the dribble, from long distances while being closely guarded. Stepping up the defensive intensity in the first and third periods where the likelihood of making a three-point shot is relatively high, and motivating the home crowd to unsettle opponents makes sense, too.

Coaches can also think about implementing a full- or three-quarter court press more often, maybe for entire games. The goals of a 2-2-1 three-quarter court press, for example, are control and containment, not turnover generation. As envisioned, its use would slow down the game and force opponents to shoot a higher percentage of difficult three-pointers with less time on the clock, reducing their make percentage. As Hall-of-Fame coach Jack Ramsay explained in Pressure Basketball, “The tempo of the game is controlled by the defensive team and the best manner of control is through the exertion of pressure at some point on the court” (22) (p. 80).

Good data, logistic regression analysis, and self-serve simulation can also promote truth and trust, positive attributes for any coach or leader. Maybe tongue in cheek, former NBA coach Jeff Van Gundy (15) (17:40) confessed to lying to his players. “If I saw what I wanted to change,” he said, “I would either use numbers to support it or make them up because the players are not going to know the difference.” Giving players tools that predict the likely effects of their potential actions would be more truthful and potentially more effective, too.

Conclusions

Keeping things simple is critical in basketball. According to Zarren (15) (7:00), “There are 20 things in (the coach’s) head that will get us X number of wins per season, but you can only focus on six of them in practice, and the players might only remember four and actually execute one in a game. So you’ve got to pick your battles if you’re a stats guy who…needs to talk to a coach. But if you’re a coach, you need to pick your battles, too.”

Van Gundy (15) (16:51) offered data analysts and coaches strong advice related to this point from his coaching experience. “I wouldn’t tell a guy you’re 38% on three to four dribbles so dribble a fifth time because you go up to 40%,” he said. “You better be pretty sure about what you’re saying…You want players to feel confident. You don’t want them out there saying, ‘Was that [four] dribbles or [five] when I pull up?’”

To mitigate the risk of generating harmful insights, data analysts should actively engage coaches and players in making key analytical decisions (e.g., ensuring predictor variables and their levels are meaningful), not least because Van Gundy and others who share his philosophy consider basketball sense—the capacity to make wise choices that benefit the team—to be of paramount importance.

Arguably, self-serve simulation with likely effects reporting in probabilities or percentage points is steeped in such basketball sense. As a benefit, data analysts will not need to rely on technical terms (e.g., “he shoots two standard deviations below the league average when you force him to the left” (15) (48:20)), as former Memphis Grizzlies’ executive John Hollinger once did. Instead, they can speak with more authority using plain language (e.g., “his make probability drops to 28% when you force him to the left”). Or they can make self-serve simulators available to players (and coaches) and let them figure it out on their own. They may appreciate it, even cynics sharing Hall-of-Fame player Charles Barkley’s views: “Analytics don’t work at all. It’s just the crap that some people who are really smart made up to try to get in the game because they had no talent” (29) (2:05).

NBA and other basketball teams worldwide should consider adopting an approach that combines good data, logistic regression analysis, likely effects reporting in probabilities or percentage points, and self-serve simulation. The possible benefits are myriad. It can help teams increase their three-point shooting percentages while lowering their opponents’; improve communication among data analysts, coaches, and players; enhance each group’s effectiveness; and lead to more wins.

Appendix

Variables in the 2014-15 NBA shot dataset

Game_Id: The game’s unique identifier.
Matchup: The teams competing.
Location: Whether it was a home or away game for the shooter’s team.
Outcome: Whether the shooter’s team won or lost.
Final_Margin: By how many points the shooter’s team won or lost.
Shot_Number: The shooter’s nth shot that game.
Period: The period in which the shooter took the shot.
Game_Clock: Minutes and seconds left in the period in which the shooter took the shot.
Shot_Clock: Seconds remaining on the 24-second shot clock when the shooter took the shot.
Dribbles: Number of dribbles the shooter took before shooting.
Touch_Time: Number of seconds the shooter had the ball before shooting.
Shot_Dist: Distance in feet from the center of the basket to the shooter.
Pts_Type: Whether the shooter attempted a two- or three-point shot.
Shot_Result: Whether the shooter made the shot.
Closest Defender: Name of the defender closest to the shooter.
Closest_Defender_Player_Id: The closest defender’s unique identifier.
Close_Def_Dist: The closest defender’s distance to the shooter in feet.
Fgm: Whether the shooter made the shot.
Pts: The shot’s point value (0, 2 or 3).
Player_Name: The shooter’s first and last name.
Player_Id: The shooter’s unique identifier.

Note: The original dataset contained 128,069 two- and three-point shots. After removing all two-point shots, and all three-point shots with a missing (or unimputable) value on the Shot_Clock variable, the size decreased to 32,511. For a value to be imputable, there had to be 24 seconds or less on the game clock when the player took the shot. In that case, the decision was made to replace the missing Shot_Clock value with the Game_Clock value.

ACKNOWLEDGEMENTS

The author would like to thank David Clemm, Robert Eisinger, Ward Fonrose, John Geraci, Ryan Heaton, Adam Hoeflich, Priam Lacassagne, Roxane Lacassagne, and Mark Naples for reviewing earlier versions of this paper, and for providing helpful comments and suggestions. The author is particularly thankful to Dan Dougherty (who passed away in 2022) and Tom Northrup for their indirect contribution. Their longstanding beliefs and ideas about how basketball should be played permeate this paper’s “implications for coaches” section.

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George Minoso2024-05-21T13:46:56-05:00May 17th, 2024|General, Research, Sports Management|Comments Off

An Analysis of the Geographic Distribution of Minor League Sports Teams

Authors: Dr. Mark Mitchell¹, Richard Flight², and Sara Nimmo³

Corresponding Author:

Mark Mitchell, DBA

Professor of Marketing

Associate Dean, Wall College of Business

NCAA Faculty Athletics Representative (FAR)

Coastal Carolina University

P. O. Box 261954

Conway, SC 29528

mmitchel@coastal.edu

(843) 349-2392

¹Mark Mitchell, DBA is Professor of Marketing at Coastal Carolina University in Conway, SC.

²Richard Flight, PhD is Associate Professor of Marketing at Coastal Carolina University in Conway, SC. He previously worked in minor league baseball with the Memphis Redbirds and Birmingham Barons as well as in DI collegiate athletics at Samford University.

³Sara Nimmo currently serves as Assistant Director of Marketing for San Diego State University Athletics. She previously served as a Fan Engagement Assistant with MiLB’s Myrtle Beach Pelicans.

An Analysis of the Geographic Distribution of Minor League Sports Teams

ABSTRACT

Purpose: The purpose of this study is to evaluate the geographic distribution of minor league sports teams in the United States and Canada.

Methods: A census of minor league sports teams was assembled by collecting data from league websites and other sources. Then, the data was sorted by city and state (or Canadian province). This process allowed the identification of the cities and states/provinces that host the largest number of minor league teams and leagues.

Results: Minor league sports teams can be found in 43 of 50 U.S. states (86%) and the District of Columbia (i.e., Washington, DC) and 8 of 10 (80%) Canadian provinces. There are 12 North American cities or metropolitan areas that host four or more minor league teams: Atlanta, GA; Austin, TX; Birmingham, AL; Dallas-Fort Worth, TX; Des Moines, IA; Las Vegas, NV; New York, NY; Oklahoma City, OK; Salt Lake City, UT; San Antonio, TX; San Jose, CA; and Toronto, Ontario. Additionally, there are 24 cities that host three minor league teams that are distributed across 20 different states and provinces.

Conclusions: While select cities have attracted multiple minor league teams to their communities, these teams tend to be dispersed all over the United States and Canada. As expected, states with larger populations tend to host more teams. States with weather that allows year-round outdoor play tend to host more teams. Cities with successful franchises can use that demonstrated fan support to attract new teams and leagues to their communities.

Applications in Sport: In addition to offering family entertainment, the minor leagues offer both players and professional staff the opportunity to enter the business of professional sports and work toward careers at the major league level. The results of this study illustrate where minor league teams can be found in the United States and Canada. From this list of cities, sports fans can watch up-and-coming players develop. Furthermore, sport educators can direct their students (i.e., aspiring sport administrators) to the cities and teams that may provide them with an entry-point into the field of sports administration.

Key Words: Minor league sports, sports expansion possibilities, minor league team affiliations

INTRODUCTION

Organized sports may be thought of as the games people play. However, there is a very large business and financial infrastructure behind the scenes to allow those games to be played and the related fan experiences to be realized. Plunket Research estimated the total U.S. sports and recreation industry to be valued at over $550 billion in 2020 with the global market estimated to be worth $1.5 trillion (28).

Players making it to the major league of their sport have had to successfully navigate a developmental path by playing in the minor league system and earning successive promotions to earn a spot on a major league roster. In some cases, such as baseball, basketball, and hockey, these minor league teams represent hierarchical levels in a player development path that is clearly laid out. This focus on player development prompted Major League Baseball to restructure its minor league system beginning with the 2021 season. The new model provided for increased player salaries, modernized facilities, and reduced travel time and costs. The restructuring reduced the number of affiliated teams from 160 to 120 (12, 20).

Many colleges and universities offer sport management programs to serve interested students. Currently, there are 421 sport management programs in the United States at the Associates, Bachelors, Masters, and Doctoral levels (33). At the undergraduate level, Sport Management is the 38th most popular major among students. Each year, over 11,000 bachelor’s degrees in sport management are awarded (10). Furthermore, students from other disciplines (e.g., business, physical therapy, nutrition, hospitality, and others) often seek to apply their skills in the business and operation of sports teams. Much like athletes who seek to secure a position in the minor leagues to begin their hopeful path to the major leagues, many people interested in careers in sports administration and sports management begin their careers in the minor leagues as well.

The purpose of this study is to conduct an analysis of the geographic distribution of minor league sports teams and leagues in the United States and Canada. The results of this study will illustrate the cities, states, and provinces that currently host the most minor league teams. From this data, sports fans can incorporate a minor league game into their travel plans while prospective employees can see where their opportunities may be found and focus their job search activities accordingly. First, a broad overview of major and minor league sports is provided, including a look at the possible affiliations between major and minor league teams. Second, the geographic distribution of minor league teams will be provided to illustrate those states and cities that host multiple teams. Finally, the matrices of major and minor league cities are examined to identify the communities most likely to be discussed as expansion cities for major league sports.

THE ORGANIZATION OF MAJOR LEAGUE AND MINOR LEAGUE SPORTS

In the sections that follow, the teams and leagues involved in the major spectator team sports are profiled. Sports that have a longer professional history (such as football, baseball, or basketball) have a clear path of player development and a delineation between their ‘major’ and ‘minor’ leagues. For these sports, the minor league teams are included in this study.

Other newer professional leagues (such as women’s soccer, women’s ice hockey, or men’s lacrosse), have not yet established a hierarchical path for player development. Rather, it is evolving and, in some cases, changing annually. As such, the athletes who do progress to compete at the highest available professional level (i.e., NWSL, PWHL, or NLL) do realize a pinnacle or ‘major’ achievement. However, these teams and leagues are more similar operationally (attendance, budgets, etc.) to minor league sports rather than the traditional major league sports of football, baseball, or basketball. For these sports, these teams and leagues are included in this study. In the future, with the stability and expansion of these leagues, these sports may attain the classification of ‘major’ league sports.

Men’s Baseball

There are currently 30 Major League Baseball (MLB) teams operating in the United States and Canada (18). Each of these teams has an affiliated Triple-A, Double-A, High-A, and Low-A team. Additionally, MLB operates two leagues for first-year players: Arizona Complex League (ACL) and the Florida Complex League (FCL) where games are played at the Spring Training sites of MLB teams. Additional teams bring the total to 179 teams across 17 leagues in 43 states and 4 provinces (20). A list of minor league baseball teams is provided in Appendix A.

Appendix A: Major League Baseball and Minor League Affiliates

Major League	Triple-A	Double-A	High-A	Low-A
Arizona Diamondbacks	Reno Aces	Amarillo Sod Poodles	Hillsboro Hops	Visalia Rawhide
Atlanta Braves	Gwinnett Stripers	Mississippi Braves	Rome Braves	Augusta GreenJackets
Baltimore Orioles	Norfolk Tides	Bowie Baysocks	Aberdeen IronBirds	Delmarva Shorebirds
Boston Red Sox	Worchester Red Sox	Portland Sea Dogs	Greenville Drive	Salem Red Sox
Chicago Cubs	Iowa Cubs	Tennessee Smokies	South Bend Cubs	Myrtle Beach Pelicans
Chicago White Sox	Charlotte Knights	Birmingham Barons	Winston-Salem Dash	Kannapolis Cannon Ballers
Cincinnati Reds	Louisville Bats	Chattanooga Lookouts	Dayton Dragons	Daytona Tortugas
Cleveland Guardians	Columbus Clippers	Akron RubberDucks	Lake County Captains	Lynchburg Hillcats
Colorado Rockies	Albuquerque Isotopes	Hartford Yard Goats	Spokane Indians	Fresno Grizzlies
Detroit Tigers	Toledo Mud Hens	Erie SeaWolves	West Michigan Whitecaps	Lakeland Flying Tigers
Houston Astros	Sugar Land Skeeters	Corpus Christi Hooks	Asheville Tourists	Fayetteville Woodpeckers
Kansas City Royals	Omaha Storm Chasers	Northwest Arkansas Naturals	Quad Cities River Bandits	Columbia Fireflies
Los Angeles Angels	Salt Lake Bees	Rocket City Trash Pandas	Tri-City Dust Devils	Inland Empire 66ers
Los Angeles Dodgers	Oklahoma City Dodgers	Tulsa Drillers	Great Lakes Loons	Rancho Cucamonga Quakes
Miami Marlins	Jacksonville Jumbo Shrimp	Pensacola Blue Wahoos	Beloit Snappers	Jupiter Hammerheads
Milwaukee Brewers	Nashville Sounds	Biloxi Shuckers	Wisconsin Timber Rattlers	Carolina Mudcats
Minnesota Twins	St. Paul Saints	Wichita Wind Surge	Cedar Rapids Kernels	Fort Myers Mighty Mussels
New York Mets	Syracuse Mets	Binghamton Rumble Ponies	Brooklyn Cyclones	St. Lucie Mets
New York Yankees	Scranton/Wilkes-Barre RailRiders	Somerset Patriots	Hudson Valley Renegades	Tampa Tarpons
Oakland Athletics	Las Vegas Aviators	Midland RockHounds	Lansing Lugnuts	Stockton Ports

Major League	Triple-A	Double-A	High-A	Low-A
Philadelphia Phillies	Lehigh Valley IronPigs	Reading Fightin Phils	Jersey Shore BlueClaws	Clearwater Threshers
Pittsburgh Pirates	Indianapolis Indians	Altoona Curve	Greensboro Grasshoppers	Bradenton Marauders
San Diego Padres	El Paso Chihuahuas	San Antonio Missions	Fort Wayne TinCaps	Lake Elsinore Storm
San Francisco Giants	Sacramento River	Richmond Flying Squirrels	Eugene Emeralds	San Jose Giants
Seattle Mariners	Tacoma Rainiers	Arkansas Travelers	Everett AquaSox	Modesto Nuts
St. Louis Cardinals	Memphis Redbirds	Springfield Cardinals	Peoria Chiefs	Palm Beach Cardinals
Tampa Bay Rays	Durham Bulls	Montgomery Biscuits	Bowling Green Hot Rods	Charleston RiverDogs
Texas Rangers	Round Rock Express	Frisco RoughRiders	Hickory Crawdads	Down East Wood Ducks
Toronto Blue Jays	Buffalo Bisons	New Hampshire Fisher Cats	Vancouver Canadians	Dunedin Blue Jays
Washington Nationals	Rochester Red Wings	Harrisburg Senators	Fredericksburg Nationals	Fredericksburg Nationals

Source: (20).

Men’s Basketball

There are currently 30 National Basketball Association (NBA) teams playing in the United States and Canada; 28 of these teams have an affiliated G-League (or, minor league) team (27). Two teams (G League Ignite of Las Vegas, NV; Capitanes Ciudad De Mexico of Mexico City) operate independently and without NBA team affiliation (1). A profile of NBA G-League teams is provided in Appendix B.

Appendix B: G-League Teams and NBA Affiliations

G-League Teams	Location	NBA Affiliation
Capital City Go-Go	Washington, DC	Washington Wizards
College Park Skyhawks	College Park, GA	Atlanta Hawks
Maine Celtics	Portland, ME	Boston Celtics
Long Island Nets	Uniondale, NY	Brooklyn Nets
Greensboro Swarm	Greensboro, NC	Charlotte Hornets
Windy City Bulls	Hoffman Estates, IL	Chicago Bulls
Cleveland Charge	Cleveland, OH	Cleveland Cavaliers
Texas Legends	Frisco, TX	Dallas Mavericks
Grand Rapids Gold	Grand Rapids, MI	Denver Nuggets
Motor City Cruise	Detroit, MI	Detroit Pistons
Santa Cruz Warriors	Santa Cruz, CA	Golden State Warriors
Rio Grande Vipers	Hildago, TX	Houston Rockets
Fort Wayne Mad Ants	Fort Wayne, IN	Indiana Pacers
Agua Caliente Clippers of Ontario	Ontario, CA	Los Angeles Clippers
South Bay Lakers	El Segunda, CA	Los Angeles Lakers
Memphis Hustle	Southaven, MS	Memphis Grizzlies
Sioux Falls Skyforce	Sioux Falls, SD	Miami Heat
Wisconsin Herd	Oshkosh, WI	Milwaukee Bucks
Iowa Wolves	Des Moines, IA	Minnesota Timberwolves
Birmingham Squadron	Birmingham, AL	New Orleans Pelicans
Westchester Knicks	White Plains, NY	New York Knicks
Oklahoma City Blue	Oklahoma City, OK	Oklahoma City Thunder
Lakeland Magic	Lakeland, FL	Orlando Magic
Delaware Blue Coats	Newark, DE	Philadelphia 76ers
Stockton Kings	Stockton, CA	Sacramento Kings
Austin Spurs	Austin, TX	San Antonio Spurs
Raptors 905	Mississauga, ONT	Toronto Raptors
Salt Lake City Stars	Salt Lake City, UT	Utah Jazz

Source: (27).

Women’s Basketball

There are currently 12 Women’s National Basketball Association (WNBA) teams playing in the United States (40). There is no existing minor league development system for the WNBA. With just 12 teams and a maximum of 12 roster spots per team (compared to 15 roster spots for the NBA), the competition for one of these coveted roster spots is intense. Players selected in the three-round draft are not guaranteed a roster spot. There has not been any recent expansion of the WNBA despite calls to expand opportunities for women athletes (39).

Men’s Hockey

There are currently 32 National Hockey League (NHL) teams playing in the United States and Canada (24). The American Hockey League (AHL) serves as the top development league for the NHL. There are currently 32 AHL teams playing in the United States and Canada (6). The vast majority of AHL players were selected in the NHL draft and have been signed to player development contracts (17). A level below the AHL is the ECHL (formerly known as the East Coast Hockey League) with 28 teams, with each team affiliated with an AHL and NHL team (11). A list of AHL and ECHL teams is provided in Appendix C.

Appendix C: American Hockey League Teams and Affiliated NHL Teams

NHL Team	ACL Affiliated Team	ECHL Affiliated Team
Anaheim Ducks	San Diego Gulls	Tulsa Oilers
Arizona Coyotes	Tucson Roadrunners	Atlanta Gladiators
Boston Bruins	Providence Bruins	Maine Mariners
Buffalo Sabres	Rochester Americans	Cincinnati Cyclones
Calgary Flames	Calgary Wranglers	Rapid City Rush
Carolina Hurricanes	Chicago Wolves	Norfolk Admirals
Chicago Blackhawks	Rockford Icehogs	Indy Fuel
Colorado Avalanche	Colorado Eagles	Utah Grizzlies
Columbus Blue Jackets	Cleveland Monsters	Kalamazoo Wings
Dallas Stars	Texas Stars	Idaho Steelheads
Detroit Red Wings	Grand Rapids Griffins	Toledo Walleye
Edmonton Oilers	Bakersfield Condors	Fort Wayne Komets
Florida Panthers	Charlotte Checkers	Florida Everglades
Los Angeles Kings	Ontario Reign	Greenville Swamp Rabbits
Minnesota Wild	Iowa Wild	Iowa Heartlanders
Montreal Canadians	Laval Rocket	Trois-Rivieres Lions
Nashville Predators	Milwaukee Admirals	No ECHL team affiliation
New Jersey Devils	Utica Comets	Adirondack Thunder
New York Islanders	Bridgeport Islanders	Worchester Railers
New York Rangers	Hartford Wolf Pack	Jacksonville Icemen
Ottawa Senators	Belleville Senators	Allen Americans
Philadelphia Flyers	Lehigh Valley Phantoms	Reading Royals
Pittsburgh Penguins	Wilkes-Barre/Scranton Penguins	Wheeling Nailers
San Jose Sharks	San Jose Barracuda	Wichita Thunder
Seattle Kraken	Coachella Valley Firebirds	Kansas City Mavericks
St. Louis Blues	Springfield Thunderbirds	No ECHL team affiliation
Tampa Bay Lightning	Syracuse Crunch	Orlando Solar Bears
Toronto Maple Leafs	Toronto Marlies	Newfoundland Growlers
Vancouver Canucks	Abbotsford Canucks	No ECHL team affiliation
Vegas Golden Knights	Henderson Silver Knights	Savannah Ghost Pirates
Washington Capitals	Hershey Bears	South Carolina Stingrays
Winnipeg Jets	Manitoba Moose	No ECHL team affiliation

Source: (13).

Men’s Soccer

There are currently 29 Major League Soccer (MLS) teams playing in the United States and Canada (19). The USL Championship League is sanctioned by the U.S. Soccer Federation as a Division II professional league. The USL Championship League includes 24 teams located in the United States with expansion teams planned. A level below, the USL League One has 12 teams with 2 expansion teams planned. (36). A list of USL Championship and USL League One teams is provided in Appendix D.

Source: (36).

Women’s Soccer

There are currently 14 National Women’s Soccer League (NWSL) teams competing in the United States (26). A list of NWSL teams is provided in Appendix E. The United Soccer League (USL) is introducing the USL W League in Summer 2024. There are plans for 44 teams located in 20 different states. The USL W League hopes to “bring elite women’s soccer to communities across the U.S., creating more opportunities to play, watch and work in the women’s game.” The USL W league will be introduced as a para-professional league, meaning the players will retain their amateur status (37). For this reason, these teams are not included in this analysis.

Men’s Football

There are currently 32 National Football League (NFL) teams competing in the United States (23) and 9 Canadian Football League (CFL) teams competing in Canada (9). Over time, there have been competing and/or feeder leagues to the NFL, including the World Football League (WFL), the United States Football League (USFL), the Extreme Football League (XFL), and the Spring League. In December 2023, it was announced that the USFL and XFL would merge to create the United Football League (UFL) and begin play in the spring of 2024 (32). Through the merger process, eight teams were retained and eight teams ceased operations. One city (Houston, TX) previously hosted both USFL and XFL teams prior to the merger. The XFL Houston Roughnecks ‘survived’ the merger while the USFL Houston Gamblers did not. The following cities lost their USFL and XFL teams beginning in the 2024 season (16):

New York/New Jersey Metro

New Orleans, LA

Philadelphia, PA

Pittsburgh, PA

Orlando, FL

Seattle, WA

Las Vegas, NA

Indoor or Arena Football has been played in various locations since the mid-1980s with the Indoor Football League (IFL) being the longest-running league. There are 16 IFL teams playing in 2024. IFL personnel, including players, coaches, scouts and front office professionals have transitioned to the National Football League (15). In addition, the National Arena League (NAL) operates a 6-team league (22). A review of the various non-NFL football teams is provided in Appendix F.

Men’s Lacrosse

There are currently 15 National Lacrosse League (NLL) teams competing in the United States and Canada (25). The league plays its games in indoor arenas, often the same arenas that host minor league hockey and NBA G-League basketball teams. A list of NLL teams is provided in Appendix G. Beginning in Summer 2023, the Premier Lacrosse League started play with 8 teams in the United States. In its inaugural season, all 8 teams travelled to a select city for competition each weekend. City names are not attached to teams (29). As such, these teams are not included in this analysis.

Women’s Professional Hockey

The Professional Women’s Hockey League (PWHL) began its inaugural season in January 2024. The newly-created league consists of 6 teams across the United States and Canada with teams located in Boston, Minneapolis, Montreal, New York City, Ottawa, and Toronto (30).

Miscellaneous: Athletes United

Since 2020, Athletes Unlimited has introduced professional leagues in women’s basketball, volleyball, lacrosse, and softball. The leagues state they are ‘player-centric’ while avoiding the traditional model of city-identified teams. With this model, many American athletes can play professionally in their home country rather than competing abroad (7). However, teams are not based in home cities. As such, these teams are not included in this analysis.

METHODOLOGY

The minor league teams and leagues profiled above that operated in the 2023-24 seasons were identified and assembled into a database to allow the analysis of the location of the teams. The sorting function in Microsoft Excel allowed the researchers to identify the frequency of occurrence for city, state, and province, resulting in the identification of the following groups:

States and/or provinces that host the most minor league teams;

Cities that host the most minor league teams;

Cities that are most likely to be considered for league expansion in the future.

RESULTS

While select cities have attracted multiple minor league sports teams to their communities, these teams tend to be dispersed all over the United States and Canada. In the United States, 43 of 50 states (86%) host at least one minor league team. The states that do not current host a team are Alaska, Hawaii, Louisiana, Montana, North Dakota, Vermont, and Wyoming. In the Lower 48 states (excluding Alaska and Hawaii), minor league sports can be found in 43 of 48 (90%) of the states with the missing states being sparsely populated (with the notable exception of Louisiana).

In Canada, minor league teams can be found in 8 of 13 Canadian Provinces or Territories. The provinces that do not current host a team are New Brunswick, Northwest Territories, Nunavut, Prince Edward Island, and Yukon. Similar to the pattern found in the United States, teams can be found in 8 of 10 Canadian provinces (80%) with no teams located in the three more sparsely-populated Canadian Territories of Northwest, Nunavut, and the Yukon.

A city-by-city mapping of each minor league team located in the United States and Canada is presented in Figure 1. The heat mapping function in Microsoft Excel was used to generate Figure 2, a visual presentation of the frequency of location of minor league teams per state and province.

Interestingly, minor league teams have been located previously in Hawaii (baseball), Louisiana (baseball), Montana (baseball), North Dakota (indoor football), Vermont (baseball), and Wyoming (baseball). However, no teams existed in these states during the 2023-24 season. In fact, some of these baseball teams were among the 40 teams affected by the realignment of minor league baseball to begin the 2021 season (see 20, 31).

State-by-State Analysis

The following states host the largest number of minor league teams:

California (26 teams in 17 different communities)

Texas (25 teams in 15 different communities)

Florida (23 teams in 16 different communities)

New York (19 teams in 12 different communities)

North Carolina (17 teams in 12 different communities)

Pennsylvania (12 teams in 9 different communities)

Ohio (10 teams in 7 different communities)

Georgia (9 teams in 8 different communities)

Iowa (8 teams in 5 different communities)

Michigan (8 teams in 5 different communities)

South Carolina (8 teams in 4 different communities)

Oklahoma (7 teams I 2 different communities)

Washington (7 teams in 4 different communities)

Arizona (7 teams in 3 different communities)

Indiana (7 teams in 3 different communities)

Virginia (7 teams in 5 different communities)

Province-by Province Analysis

The following Canadian provinces host the largest number of minor league teams:

Ontario (6 teams in 3 communities)

British Columbia (3 teams in 2 communities)

Quebec (3 teams in 2 communities)

Alberta, Manitoba, Newfoundland and Labrador, Nova Scotia, and Saskatoon (1 team each)

It must be noted that junior hockey is a very popular spectator sport in Canada. However, most junior hockey players are classified as ‘amateurs’ (2). For this reason, Canadian junior hockey teams are not included in this analysis.

City-by-City Analysis

As illustrated above, many communities host more than one minor league team. Furthermore, some cities with minor league teams also host major league sports teams. For example, Charlotte, North Carolina hosts an NFL team (Carolina Panthers), an NBA team (Charlotte Hornets), and an MLS team (Charlotte FC) in addition to hosting minor league teams in baseball, hockey, and soccer. Nearby Greensboro, North Carolina also hosts three minor league teams in basketball, indoor football, and baseball but hosts no major league teams.

Table 1 provides an overview of the 12 cities that host four or more minor league teams. The reader will note that some the cities are larger metropolitan areas with teams located both in the city and the suburbs. Atlanta, for example, has one team in the city but four teams in its suburbs in close proximity to central Atlanta. These communities with a concentration of minor league teams often host additional sporting events, such as golf tournaments, auto races, or college football bowl games.

San Diego is an interesting case. In addition to hosting the San Diego Padres (MLB), the city previously hosted an NFL team (San Diego Chargers) and two NBA teams (San Diego Rockets and San Diego Clippers). All three of these professional teams continue to exist but relocated to other cities. San Diego has effectively attracted minor league teams to fill the voids left by the departure of these teams. Recently, the San Diego Loyal soccer team (USL Championship League) ceased operations after the 2023 season after failing to find a long-term home stadium option (14). However, an MLS expansion team (to be known as San Diego FC) will begin play in the 2025 season (34).

Table 2 provides an overview of cities that host three minor league teams. Included in Table 2 is each city’s ranking in size as a media market (21). Also, any professional teams in these same cities are shown with their table cell shaded. Sports not currently playing in those communities represent opportunities to expand a city’s minor league sports portfolio. It is interesting to note that some of these 3-team cities (such as Worchester, MA or Tacoma, WA) are very close to neighboring cities of top 15 media markets.

DISCUSSION

As expected, larger states with larger populations tend to host more minor league teams. Concurrently, cities with larger populations (and larger media markets) tend to host more minor league teams. The three states with largest number of minor league teams (California, Texas, and Florida) also offer a climate conducive to year-round outdoor activities. Cities with successful franchises can use that demonstration of fan support to attract new teams and leagues to their communities. Furthermore, shared facilities (such as an arena that can host basketball, hockey, and arena football) can help bring new teams to a community.

As previously noted, many cities host both major and minor league teams. Intuitively, these locations should attract the most attention should leagues consider expansion as the fan bases have demonstrated sufficient levels of support to sustain a major league team. These cities are listed in Table 3. Additionally, these cities tend to be the larger media markets with larger numbers of consumers. As an illustration, at the time of this writing the Oakland Athletics are strongly considering moving to Las Vegas, NV and have already received the approval to move by Major League Baseball owners (3-5).

INSERT TBL3

A Cautionary Note – Minor League Baseball Relocations

In 2020, Major League Baseball issued new facility standards for minor league teams, including: minimum clubhouse sizes for both home and visiting teams; food preparation and dining areas attached to clubhouses; better field lighting; more and better training space for players; separate space for female staffer, and others (31). Given that many minor league stadiums are municipally-owned, some communities may be unwilling or unable to make the needed investments in upgrades and may see their teams migrate to other communities, particularly at the A- and AA-levels.

In fact, some team movement has already been announced as the Kinston, North Carolina team (now known as the Down East Wood Ducks) have been purchased by Diamond Baseball Holdings (the largest owner of minor league baseball franchises) and will relocate to a new yet-to-be-built stadium in Spartanburg, South Carolina and assume a new team name as early as the 2025 season (8). This move marks the return of minor league baseball to Spartanburg, which previously hosted the Spartanburg Phillies from 1963-1980 and again from 1986-1994 (38).

CONCLUSIONS

Minor league sports teams are widely distributed across the United States and Canada with 86% of U.S. states and 80% of Canadian provinces hosting at least one minor league team. These 43 U.S. states host 97% of the U.S. population while the 8 provinces host 96% of the Canadian population. The highest concentration of teams can be found in four geographic areas in the United States: (1) the southeast Atlantic corridor from Virginia south through Florida; (2) the eastern Midwest and Northeast including Pennsylvania, New York, and Massachusetts; (3) the Southwest including Texas and its border states; and (4) the West coast primarily concentrated in California. In Canada, Ontario (i.e., the Toronto area), British Columbia (i.e., the Vancouver area), and Quebec host more minor league teams than the other provinces.

In addition to offering family entertainment, the minor leagues offer both players and professional staff the opportunity to enter the business of professional sports and work toward careers at the major league level. The results of this study illustrate where minor league sports teams can be found in the United States and Canada. From this list of cities, sports fans can watch up-and-coming players develop. Furthermore, sport educators can direct their students (i.e., aspiring sport administrators) to teams for internships and entry-level employment opportunities.

APPLICATION IN SPORT

In team sports, most professional athletes go through a player development process that includes some stint in the minor leagues in the hopes of earning a spot on a major league team. Similarly, many sport administrators begin their careers working for minor leagues and affiliated teams as they learn their craft and assemble the needed experiences for (hopeful) promotion to the major league level. The results of this study allow interested parties to easily identify the communities with greater access to minor league sports (for both fans and prospective employees). Sports fans should find this information helpful as minor league sports provide a good financial value in family entertainment. College students may find internship and employment opportunities with these minor league teams to aid their entry into a career of sport administration and management. Sport administration educators may find this information helpful as they advise and counsel their students for internships, co-operative employment opportunities, and job placement after graduation.

The communities identified here with multiple sports properties may allow a student to work in multiple sports in the same city (say, basketball in winter and baseball in spring, summer, and fall). In many instances, there may be an overlap in the ownership groups of minor league teams. This overlap in ownership may expand professional opportunities for employees as well-performing employees are offered additional positions and responsibilities elsewhere in the organization.

These communities also tend to host other events, such as college football bowl games or golf tournaments. These special events will need qualified staff to deliver these events, which will include people already living and working in those communities in the sports industry. Much like athletes in the minor leagues work to advance toward the major league ranks, so, too, can staff personnel ‘climb the ladder’ toward careers in the major leagues.

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George Minoso2024-05-01T12:50:45-05:00May 3rd, 2024|General, Research, Sports Management|Comments Off

Male Competitive Powerlifters relationship with Body Image: Utilising the Multidimensional Body Image Self Relations Questionnaire (MBSRQ)

Authors: Dr. Mark Chen¹, Dr. Andrew Richardson²

1School of Health and Life Sciences, Teesside University, UK (corresponding author)
²Population and Health Sciences Unit, Newcastle University UK

Corresponding Author:

Corresponding Author: Mark Chen
Campus Heart, Southfield Road, Middlesbrough
TS1 3BX, Tees Valley
m.chen@tees.ac.uk

Dr Mark Chen is a Senior Lecturer in Sport and Exercise Science at Teesside University and is a Chartered Psychologist with the British Psychological Society (BPS). Dr Chen’s research interests include psychological consequences of sports injury and attentional aspects of sports performance.

Dr. Andrew Richardson is a Chartered Heath and Activity Practitioner with the Chartered Institute for the Management of Sport and Physical Activity (CIMSPA) and is currently a Research Associate within the Population and Health Sciences Institute at Newcastle University. Andrew’s other research interests include body image, performance enhancing drugs, transgender sport, esports and public health..

Male Competitive Powerlifters relationship with Body Image: Utilising the Multidimensional Body Image Self Relations Questionnaire (MBSRQ).

ABSTRACT

Purpose: There is growing evidence to suggest that competitive male athletes in aesthetic sports that scrutinize their body image may experience undesirable mental health outcomes. However, there is limited research to address these issues in strength sports, particularly the sport of Powerlifting. Methods: This study employed the Multidimensional Body Image Self Relations Questionnaire (MBSRQ), which recruited 365 male participants across the following subgroups. Powerlifters (P) (n = 133), Active Subjects (AS) (n = 79), Appearance Based Sports (ABS) (n = 68), Strength Sports (SS) (n = 47) and Other Sports (OS) (n = 38). Results: One–way ANOVA showed significant (p < 0.05) results between all groups across six of the nine MBSRQ subscales. Post hoc comparisons found nine significant results with the powerlifting group achieving two of them against OS (p < 0.01) and AS (p < 0.01) groups respectively. Conclusions: Overall, the results showed that male powerlifters expressed their bodies-as-function rather than their bodies-as-object with regard to health evaluation and fitness orientation. This is supported by their stable and balanced scores across the MBSRQ subscales which indicates they have healthier and lower perceptions of negative body image concerns. The powerlifters results implied that a focus on objective performance improvement and maintaining a healthy body to prevent injury had body image benefits. Applications in Sport: The study concludes that male powerlifters present healthy body image perceptions compared to the other males in their respective sports and focus on their body functionality objectively rather than the subjective perception and presentation of their body image.

Keywords: Powerlifting, Body Image, Weight Classed Sports

INTRODUCTION

For this paper, the definition of Body image is referred to as “relating to a person’s perceptions, feelings and thoughts about his or her body, and is usually conceptualized as incorporating body size estimation, evaluation of body attractiveness and emotions associated with body shape and size” [1-2]. There has been extensive work conducted on the influence of body image in the media [3], in Western culture [4] and job roles such as the fitness industry [5]. Other comparisons include comparing body image within a range of demographic factors such as between athletes and non-athletes [6], age [7], nationality and ethnicity [8]. Cash and Pruzinsky [9] have defined five dimensions of body image, which work together to create an overall body image. However, these dimensions fails to mention the broader cultural and social contexts that influence body image [10]. They suggested that athletes dealing with sporting and societal pressures may experience adverse outcomes such as eating disorders or a negative perception of their body image. Such factors may lead to these pressures as a result of media and advertisements [11], supplements [12] and the use of image and performance-enhancing drugs [13].

Background of Powerlifting

Powerlifting athletes are scored on objective performance measures rather than appearance evaluations. Powerlifting tests athletes on their objective strength and has traditionally been male-dominated [14]. However, in the last twenty years, female participation has significantly increased [15]. Richardson and Chen [16] state that powerlifting is a competitive strength sport comprising three techniques: the Squat, the Bench Press and the Deadlift [17-18]. The aim is to lift the most weight across the three movements for nine attempts [18]. Sports similar to powerlifting that heavily rely upon strength include Olympic weightlifting [19], strongman [20], highland games [21] and the shot–put [22], to name but a few examples. Not all of these sports mentioned have a weight class or a weight requirement, but for those that do, depending on the rules of the competition, this weight requirement may be evaluated within twenty-four or even forty-eight hours prior to the event [23]. Weight classes help ensure fairness in competition and increase the pre-competition demands of participants to enter the weight category that maximizes their advantages. Experts argue that making weight places psychological demands on athletes who may be inclined to make drastic weight cuts to gain a competitive advantage [24]. However, as powerlifters are evaluated on the amount of weight lifted, the training is based on objective scoring criteria. As scoring is objectively determined, and not a third party as in aesthetic sports, this has important implications for positive psychological adaptations [25].

Theoretical models and frameworks

Theoretical models of body image, such as Objectification theory, focus on the impact on men of a culture that increasingly objectifies men’s bodies. It suggests that men, like women, may experience self-objectification [26]. For men, the dual focus on both leanness and muscularity characterizing the male body ideal may motivate a particularly maladaptive set of behaviors designed to achieve these goals, such as rigid exercise routines, hidden use of image and performance-enhancing drugs (IPEDs) [27]. Subsequently, the literature has claimed that men may suffer from body image concerns and dysfunctional behavior [28]. Some research argues that young men experience societal pressure to achieve the muscular mesomorphic body shape, and this behaviour leads to a drive for muscularity [29].

Further, studies have demonstrated that sociocultural pressures mediated by social comparisons and internalization of muscular and low-fat ideals are associated with men’s body dissatisfaction and drive for muscularity, which might lead to disordered eating [30]. Most research has focused on aesthetic sports such as bodybuilding [31-32]. These explanations fail to consider how individuals think, feel and behave concerning their body functionality [33]. How powerlifters think, feel, and behave about their body functionality in a sport concerned with achieving objective demands is essential to achieving a more complete and holistic understanding of body image in this context [34].

Theoretically, the subjective perception of muscularity depends on the individuals’ perception of body image, which for powerlifting tends toward a functional muscularity rather than aesthetic muscularity due to the sport’s rules. Critically, the self-objectification model does not consider the functionally orientated nature of sporting competition and its impact on male psychology [35]. Therefore, the athletes have a strong sense of control and need to prepare, train and diet concerning maximizing objective performance criteria, not gaining approval from judges based on aesthetics. The environmental demand to achieve an objective standard has essential implications for broadening body image, as Ginis et al., [36] reported. They found that the idea of muscularity and physical competence in men [37] are central to their evaluations of their bodies.
According to Conceptualisation theory, men are socialized to focus more attention on their body functionality than body-as-object (image) [38]. Therefore, powerlifting males are likely to value the functionality of their body over appearance, not only due to socialization processes that favour the achievement of tangible performance-based outcomes [39-40] but also due to the specific environmental demands of powerlifting which reward objective performance results. In contrast, perceptions of leanness and body fat percentage are less relevant to powerlifters performance. Franzoi [38] defined body-as-process as comprising physical capabilities and internal processes, which is similar to body functionality. The demand for functionality adds sources of experience, such as training to execute specific external and internal demands, that requires knowledge of body functionality (movement) and is, therefore, adaptive for how male powerlifters individuals think and feel about their body image [38].

For example, Richardson and Chen [16] found that female powerlifters, despite presumably having been socialized to experience higher levels of self-objectification and greater body-as-object identification than men, as predicted by self-objectification theory, nevertheless enjoyed their appearance in their sporting environment, indicating that it was not a source of anxiety, presumably due to the enjoyable experience of functional powerlifting training and competition reward. This was evident in other studies using smaller sample sizes and qualitative interviews in the same sport and sex [14 & 41]. Bordo [42] found that individuals who presented with large muscular physiques symbolized strength and masculinity.

Competition achievement and social reward within a sport based on tangible athletic goals [43-44] and psychological characteristics such as aggression when preparing to lift [45] will strongly mitigate against excessive rumination around body appearance and identity. Further reasoning supports the powerlifting community’s emphasis on body functionality [46-47]. From this perspective, male powerlifters likely develop a functional appreciation of their body that is valued separately from its appearance. This construct of functionality appreciation has only recently been investigated in the context of positive body image. It is positively associated with positive body image facets, such as body appreciation [48].

Franzoi [38] proposed that individuals hold more positive attitudes toward their body functionality than their body image. Therefore, it can be predicted that males with this orientation will hold performance adaptive attitudes toward their bodies. Body conceptualization theory offers a rationale for the body functionality being adaptive and reflective of positive male body image and improved mental health, compared to a body image orientation. This theorizing gives scope that negative body image attitudes can be adaptive and motivational within a performance-based environment based on objective rather than subjective and image-based criteria. For the male powerlifters, this would be the performance their bodies execute to meet the environmental needs (e.g., the sporting demands of their event). For example, Gattario and Frisen [49] found that males stated that finding a social context in which they found belonging and acceptance that allowed them to develop agency and empowerment allowed them to move from a negative to positive body image. With this logic, it could be predicted that competitive powerlifters will differ in their positive body image compared to individuals who are active but don’t compete.

Nevertheless, functionality measures have focused predominantly on physical capacities and internal processes and have typically concerned physical strength and muscularity. These aspects of body functionality can be conflated with physical appearance and are accentuated by male appearance ideals and the male gender role emphasizing dominance, power, and strength [50-51]. There has been some research into the body image perceptions of athletes in strength sports. Goltz et al [52] divided 156 male athletes into weight-class sports, endurance sports and aesthetic criteria sports and found no differences in body shape concerning self-depreciation due to physical appearance. Richardson and Chen [16] found no association between negative perceptions of appearance for female powerlifters compared to aesthetic sports individuals. These results suggested that the powerlifting group had contentment with their appearance, perhaps due to the decreased emphasis on body image compared to the increased emphasis on body functionality and focus on improving their skills and strength for their sport.

Apart from these few studies, research has yet to be done on body image and functionality in male powerlifting. The association of the physical body with functional sporting competition achievement based on objective standards may reduce the potential for internalizing negative body image and lead to healthy adaptations based on physical demands. This research will explore what functionality means for male powerlifters and how this impacts body image and functionality. This study aims to compare the body image of male powerlifting athletes against other subgroups of male athletic participation. The aim is to examine if male powerlifting athletes express different body image satisfaction or dissatisfaction with their body image and weight compared to subgroups of active and or sporting males.

Aim and Objectives of the Study

Aim

To compare the body image differences of male powerlifters against a range of male athletic subgroups.

Objectives

● The first objective was to determine if the powerlifters have significantly lower scores regarding their bodyweight perception when compared to other male groups in the study.

● To determine if powerlifters present an emphasis on body-as-process rather than body-as-object.

METHODS

Participant Information

An opportunity sample of 365 males was recruited through Facebook and Instagram. The recruitment period lasted three weeks in length and generated the following subgroups. There were 133 Powerlifters (P), 79 Active Subjects (AS), 68 Appearance Based Sports (ABS) participants, 47 Strength Sports (SS) participants and finally, 38 Other Sports (OS) participants within their respective subgroups. The group sample means and standard deviations for their age were 28.65 (± 7.44), height was 178.58cm (± 13.3cm), and their weight was recorded at 89.99kg (± 18.20kg).

Within Table 1.0, each subgroup’s means and standard deviations were recorded for their age, height, weight and the length of time they have spent training. The powerlifting (P) group mean age was 27.71 ± 6.86 years, the mean weight was 92.73kg ± 21.24kg, and the mean height was 176.67 ± 15.27cm. Appearance Based Sports (ABS) group mean age was 28.04 ± 7.59 years, mean weight was 86.89 ± 14.55kg, and height was 177.11 ± 12.32cm. The active Subjects (AS) group’s mean age was 30.30 ± 8.19 years, the mean weight was 84.99 ± 12.81kg, and the mean height was 179.85 ± 14.91cm. The strength Sports (SS) group’s mean age was 29.19 ± 7.26 years, the mean weight was 97.41 ± 20.11kg, and the mean height was 181.69 ± 7.02cm. In the final subgroup Other Sports (OS) group, the mean age was 28.95± 7.49 years, the mean weight was 87.19 ± 15.53kg, and the mean height was 181.47 ± 7.87cm. No ethnic identity data was recorded. The study was conducted after obtaining ethical approval from the Teesside University School of Social Science Business and Law Ethical Approvals Committee.

Measures

Multidimensional Body Self Relations Questionnaire (MBSRQ): The MBSQR measures Body Image divided into cognitive and behavioral components [53]. Items are ranked on a 1 to 5 Likert scale, where 1 = Definitely disagree, and 5 = Definitely agree. The MSBRQ subscales include Appearance Evaluation (AE), Appearance Orientation (AO), Fitness Evaluation (FE), Fitness Orientation (FO), Health Evaluation (HE), Health Orientation (HO), Illness Orientation (IO), Body Areas Satisfaction (BASS), Overweight Preoccupation (OWP) and Self-Classified Weight (SCW). Illness Orientation is not included as a separate subscale, as it is already reliably accounted for under Health Orientation. The MBSRQ is significantly evidenced in Body Image research [9 & 53] as a well-validated measure [54] through comparison with other tools of Body Image. The MBSRQ has a proven reliability and validity record for body image research [53]. The composite reliability was calculated using an SPSS Omega Macro [55] and is within the acceptable range (Cronbach’s omega = 0.79). The primary author constructed demographic questions to collect information about the participant’ background. These questions included (but were not limited to) sex, age, height, weight, and years spent training.

Procedure

Both the MSBRQ and Demographic Questionnaire were developed using Google Documents. Data gathered was stored under the General Data Protection Act [56]. Participants were assigned to groups 1.00 (Powerlifters – P), 2.00 (Appearance Based Sports – ABS), 3.00 (Active Subjects – AS), 4.00 (Strength Sports – SS) and 5.00 (Other Sports – OS), based on their answers from the demographic questionnaire. Participants were given no monetary or external incentive to take part. Participants read the pre-questionnaire information, participant information form and questionnaire instructions. Once read, participants were prompted to check a box that confirmed their consent to the study. All participants completed the questionnaire individually and received no communication from the researcher during data entry. A glossary was provided for technical terminology. Demographic questions were formatted as short answers, rating scales, and multiple-choice. Participants were informed they could opt out anytime during the study for any reason. In total, the questionnaires took about 10-15 minutes to complete.

Data Analysis

An independent group design was used to investigate the differences between the MBSRQ scores of the four. The dependent variables measured the differences in body image between the groups across nine subscales. All data were analyzed using Microsoft Excel version 2016 and Statistical Package for Social Science (SPSS) Version 27. Means and Standard Deviations were calculated for all the subscales. Data were checked for equality of variance between groups and assumptions for the one–way ANOVA where the alpha value was set at 0.05. Post hoc tests were calculated to compare the powerlifting group with the other three groups across the MBSRQ subscales. The post hoc alpha values were corrected for type one error rates using p < 0.01. To estimate the effect size of post hoc mean differences between groups, the Cohens d statistic size was interpreted using the following guidelines: .00-.2 (small), .40-.79, (medium) and .80 + (Large) [57] and 95% Confidence Intervals (CI) were reported. The Hedges g statistic was used if one or both groups being compared had n < 20, otherwise, Cohens d was reported.

RESULTS

The descriptive statistics associated with the MBSRQ scores across the five groups are reported in Table 2.0. It can be observed that the powerlifting group was associated with higher, consistently stable and healthy body image scores in comparison to the other four male sub-groups. Six of the nine MBSRQ subscales reported p-values below 0.05.

Below are the graphs of the nine subscales from the MBSRQ presented to showcase the differences in mean scores for each domain of body image.

DISCUSSION
This study aimed to compare the body image of male powerlifters with sporting and physically active males. There were multiple significant results across six of the nine MBSRQ subscales between the groups. Overall, the results of this study suggest that male powerlifters have a healthy relationship with their physical body when compared to all other groups. The powerlifters on average, evaluated both their health and fitness orientation were higher compared to both physically active males and males in other sports. Comparing the groups anthropometrics, all groups expressed similar heights, weights and mean age. Most participants from the powerlifting group were in the late twenties, average weight at 92.73kg and standing around 178cm in height. Nolan, Lynch and Egan [58] used a male sample that was comparable to the current study in size and age. Other studies recruiting male powerlifters all had smaller sample sizes and younger age ranges [59-60] compared to the current study.

The first objective was to determine if the powerlifters had significantly lower scores regarding their bodyweight perception when compared to other male groups in the study. There was no evidence to support this prediction, as the powerlifting group levels of overweight preoccupation and self-classified weight area satisfaction were not significantly different from the other groups. The Powerlifting group had scored 2.49 for the OWP subscale which was higher than both SS and OS groups but lower than AS was the powerlifting and ABS groups. This would appear to indicate that the male powerlifters either do not ruminate on their body-as-object to the detriment of their mental health or that the nature of engagement with the powerlifting competitive demands lends itself toward a functional conceptualization of the body over an image-based focus [61]. These results taken together do not imply that powerlifters demonstrated a negative perception of their body image. Rather, the results suggest that powerlifters link their body image toward objective performance related goals. Although, this is speculative, the intense regime of powerlifting training for competition would result to improved perceptions of body image due to perceived changes in strength over time.

Theoretically, powerlifters interpreting their body-as-process rather than the body-as-object is consistent with larger differences in Fitness Orientation, Health Evaluation and Overweight – Preoccupation compared to the sport male and physically active male groups. These subscales relate more to objective performance concerns, such as physical capacity, rather than the subjective interpretation of body image, thus appear to be accentuated by perceptions of power and strength [50-51]. Fitness orientation refers to, “Extent of investment in being physically fit or athletically competent. High scorers value fitness and are actively involved in activities to enhance or maintain their fitness. Low scorers do not value physical fitness and do not regularly incorporate exercise activities into their lifestyle” [53]. Richardson and Chen [16] found their sample of female powerlifters scored the highest out of this subscale when compared to other groups.

Health Evaluation is defined as, “Feelings of physical health and/or the freedom from physical illness. High scorers feel their bodies are in good health. Low scorers feel unhealthy and experience bodily symptoms of illness or vulnerability to illness” [53]. Richardson and Chen [16] found that their sample of female powerlifters scored the highest on this subscale compared to other sporting females.

Overweight preoccupation reflects “fat anxiety, weight vigilance, dieting, and eating restraint.” [53]. Richardson and Chen [16] found, for their powerlifting group, very stable scores around the normative values with little deviation from the mean, therefore indicating that the group were happy and content with their weight for the function of powerlifting. The Powerlifting group had higher OWP compared to the other two groups but not low enough to indicate extreme weight cutting, dieting or weight anxiety, Although, the nature of powerlifting does require some weight monitoring due to the weight classes requirement, the score was not concerning. An individual-by-individual analysis would need to be considered to accurately assess if an athlete is expressing extreme body weight anxiety or concerns.

Certainly, this does contrast with the findings of the Active subjects (AS) group who had a moderate effect size of greater overweight preoccupation (OWP) and self-classified weight (SCW) compared to Other Sports (OS) and Strength Sports (SS). These difference of the control group (AS) adds further weight for the difference between the powerlifters and the other groups body image. The active subjects were composed of individuals who don’t compete in any sport, but their recreational exercising still did not prevent them from having pre-occupation with their physique. Male exercisers can be as pre-occupied with outward appearance as women due to their motivation for muscularity [62] and also as non-athletes they may lack the functional body appreciation that male athletes possess [63].

The second objective was to determine if powerlifters present an emphasis on body-as-process rather than body-as-object. Theoretically, body functionality can be understood in contrast to appearance ideals and gender roles for men, which emphasise the importance of physical strength, prowess, and bodily control [64]. The absence of negative body image perceptions in the males only lends indirect evidence for a higher emphasis on functional cognitions related to objective performance. There were two significant differences between powerlifters with OS and AS in health evaluation and fitness orientation. There was a moderate effect size difference for health evaluation, with the powerlifting group showing more robust health behaviours than the other sports group.

The other sport group was the smallest group (n=31) and consisted of people who recreationally participated in a variety of sports of which Soccer, Cross fit and Athletics were the most numerous. The health cognitions of the powerlifters place an emphasis on being prepared to execute maximum effort in their training and respecting the possibilities and limit of what they can achieve [65]. Compared to sports such as Athletics and Soccer, which place more emphasis on diverse interceptive open skills in a changing environment and / or endurance, Powerlifting requires maximum and intense concentration to prepare for one explosive movement. Therefore, the powerlifters need to have a healthy attitude toward diet, for example, as performance is related to performing at their physical limits but is not essential for skilled footballers. These results contrast with Goltz et al., [52] who found no differences in self-depreciation due to physical appearance in comparing weight-class sports, endurance sports and aesthetic criteria sports.

The powerlifting group also showed stronger fitness orientation compared to the active subjects groups. This may mean that the powerlifters monitoring of their pre-performance health results in stronger fitness evaluations compared to individuals who only exercise and also individuals in sports with less physically explosive demands [65]. This seems to reinforce the first finding, that male powerlifters have a positive rather than negative view of their body image, in terms of the value they place on health and fitness related cognitions to help prepare for competition. The fitness-orientation aspect can be interpreted for body functionality qualities, as this subscale would support behaviours and cognitions conducive to maintaining good physical condition and a positive view of the body [66]. An explanation in terms of body conceptualization theory is that the functionality of powerlifting competition allows the participants to engage in a wider range of strategies to maintain fitness rather than being concerned with aesthetics, compared to individuals who only exercise [49].

Comparing this to the appearance-based sport (ABS) group, they too also undergo intense and regimented training, as competitors will need to ensure they are in the best condition for competition, although still based on aesthetics. However, where the ABS group differ from the powerlifters is a moderate effect size for overweight preoccupation compared to the OS group. There was also a moderate effect size for self-classified weight compared to the strength sports group. These two subscales are more in line with previous findings [67], in that aesthetic sport participants need to put more effort in body monitoring and judgements related to weight loss or gain. In powerlifting, research has shown that to overcome confounding issues that may affect athletic performance, athletes reported that the following factors help relieve or reduce competition day stressors include, the coach, mental attitudes, technical instruction, training partners and social isolation [67]. When comparing between sexes, the results revealed no fundamental difference in these confounding factors by male and female powerlifters [66]. Within both studies, it was noted that there was no mention of body image when competing to be a compounding factor, which supports the current findings. Nevertheless, the powerlifters body image or perception of their own image was not given as an option in their studies so results may have been different if participants had been given an option to select.

The AS group reported two medium effect sizes against the other sports group and strength sports group, which were in the overweight preoccupation and self-classified weight subscales, but the powerlifting group scored a moderate effect size against the AS group in fitness orientation. The reason for this can be linked to multiple variables. Firstly, the AS group participants as stated earlier in this manuscript are not training to improve their performance within a specific sport or event. They are active males who are training but with no sport specific goal in mind. Hence, these individuals may be more critical of themselves when it comes to focusing on their bodyweight. This can be easily demonstrated in the subscale of SCW where the AS group scored the lowest when compared to the OS and SS groups. As individual in these sports may compete at a weight they are comfortable at, this yields them the best performance advantages when in competition.

Notwithstanding, the AS group did score closer to a mean normative value for their OWP subscale and scored higher than both OS and SS groups. The reason may be that higher scores focus more on weight vigilance and weight anxiety. However, the OS and SS groups scoring lower than AS and having low OWP scores indicates that their sports don’t require, or these athletes didn’t express any worry about their weight when competing.

Nevertheless, there is research to suggest that those who train for body image and pursue masculine muscular ideals may be motivated for these appearances through unhealthy means. These include self – blame and or internalised shame as reported by Larison and Pritchard [68] found that men who scored higher on these variables also reported higher levels of eating disorder symptomology. Yet, in the same study, those same men who scored higher for internalised shame also scored higher on the drive to be more muscular. Finally, Swami and Bedford [69] found that men’s drive for muscularity was significantly predicted by neuroticism and their drive for body appreciated was significantly predicted by neuroticism and extroversion when considering BMI and subjective social status as drivers. However, in other studies the opposite findings have been reported. Reina et al., [67] also reported that males in non-aesthetic sports were more dissatisfied with their body image and were 1.5 times more likely to use exercise to lose weight than non-sport participants.

Limitations
The MBSRQ is a valid and reliable and well stablished body image assessment tool and is appropriate for out study [53]. Nevertheless, the MBSRQ does not measure disordered eating or specific ideals of muscularity as compared to other aforementioned assessment tools. The powerlifting group in this study as in the female study by Richardson and Chen [16] is centred around one sport and unlike the other groups they are made up of multiple sports. Ultimately, this will have impacted their scores within their groups and comparing between groups. The powerlifting group as a whole had more training experience than the other groups which is reflected in their larger sample size and more stable scores which has to be factored into the analysis.

CONCLUSIONS
In summary, the findings report the powerlifters presented with stable and positive outlooks and evaluations of their body image. This highlights a productive relationship with their own body image and their sport of powerlifting as a body-as-function role instead of body-as-object [47]. Comparing the powerlifters with other sport groups showed similar results. The powerlifters presented with significantly (p < 0.05) better scores for HE and FO subscales in the MBSRQ when compared to the AS and OS groups. The majority of the groups displayed stable MBSRQ subscale scores and healthy outlooks on their body image. The study found that powerlifters did not express or display any extreme perceptions of their body image despite them competing within a defined weight category. These results also find that the athletes recruited for the powerlifting group train for performance and are less concerned about their body image. By positioning their focus on objective performance (lifting as much weight as possible) this appears to have psychological benefits which helps negate negative body image as recorded in the female samples of Richardson and Chen [16] and Vargas and Winter [14]. Future research should focus on qualitative interviews with male powerlifters and additional sports to understanding the relationships between their body image and their sport.

APPLICATIONS IN SPORT
The majority of previous research concerning male body image is associated with negative behaviour outcomes such as aggression, violence and or the use of PEDs [70]. This study has taken a different approach to show strength training for males has a positive outlook on their body image helping to create healthy and stable relationships with their mental health using an objective measurement. In this instance, it is the sport of powerlifting that focuses the athletes on the performance to lift as much weight as possible across three events.

Competing in a weight class sport does not necessarily produce extreme group scores and or undesirable behaviours concerning their bodyweight or body image. This implies that strength training methods such as powerlifting for males (and females as shown in Richardson and Chen [16] when seeking to improve their health and fitness are beneficial. The focus on objective strength gains via tracking their lifting through increments using progressive overload allows positive body appreciation. As a positive by-product, they will also develop improved physique through increased levels of physical activity and adherence to a training program. Furthermore, by seeing continued progressions through improving their technical proficiency doing the movements and increased muscle hypertrophy will lead to a better outlook on their mental health and body image. As they are viewing their body for its function not as an object they place less emphasis on subjective body image changes but rather on performance. In populations that include body image disorders and eating disorders, using this form of training will help support clinicians in helping return their patients to exercise routines to support a holistic recovery pathway [71].

Author roles
Dr. Mark Chen: Conceptualization, Methodology, Formal analysis, Writing – original draft, Writing – review & editing, Supervision, Project administration.

Dr. Andrew Richardson: Conceptualization, Methodology, Formal analysis, Data curation, Writing – review & editing, Project administration.

Conflict of Interest Statement:
The authors declare that have no conflict of interest when writing and or submitting this manuscript for peer review publication to The Sport Journal.

Funding
No funding was sought or requested for the formation of this manuscript

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George Minoso2024-04-22T08:06:50-05:00April 20th, 2024|General, Research, Sport Training, Sports Exercise Science|Comments Off