Celebrating the Olympics

A note from the editor: In recognition of the upcoming Olympics, The Sports Journal has “temporarily” allowed for the addition of unique perspectives on Olympic Sports. Please enjoy the commentary from Dr. John Cairney from the University of Queensland.

For the first time in over a decade, NHL players are set to return to the Olympic stage, sparking widespread excitement among ice hockey enthusiasts worldwide. Announced by NHL Commissioner Gary Bettman , this decision to participate in the 2026 and 2030 Winter Games ends a hiatus that has lasted since 2014. It reflects a strategic move to enhance international competition among the world’s elite hockey players, aiming to alternate between the Olympics and the World Cup of Hockey every two years.

The NHL’s withdrawal after the 2014 Olympics stemmed from logistical and financial concerns, including potential revenue losses and the risks of competitive imbalance and player injuries when resuming the season. The injury of John Tavares during the 2014 Sochi Olympics underscored the risk of injury, while also pointing to the demanding nature of Olympic play. Conversely, the break offered a rest period for those not participating, leading to concerns about unequal player fatigue and readiness. Players not competing in the Olympics could potentially benefit from the break, gaining an edge over those who did participate.

Despite these concerns, there was scant research at the time to evaluate their validity, even though professional sports, including ice hockey, are rich in data capable of informing such analyses. Our research team aimed to fill this gap by investigating the impact of NHL participation in the Winter Games on both team and individual player performance, with a
focus on injury and fatigue. Our findings offered some surprising insights.

Our first study looked at the team-level “fatigue effect,” suggesting that teams with more Olympic participants might experience a dip in performance post-Games due to player fatigue, potentially affecting their regular season play. We analysed goal differentials (goals for minus goals against) before and after the Olympics, taking into account the number of players each team sent and mid-season trades’ impacts. Although some Olympic years showed a trend towards a negative effect on goal differential, indicative of a potential fatigue effect, the overall impact on team performance was minor.

The second study focused on individual player performance, particularly during the 2014 Sochi Winter Olympics. We examined performance metrics before and after the Olympics to test the “fatigue theory” at an individual level. Our findings indicated that the number of Olympic minutes played had no significant effect on post-Olympic performance for players overall. However, a closer look at player positions revealed that forwards experienced a slight decrease in points per game post-Olympics if they played more minutes. Defensemen, on the other hand, were unaffected. Overall, our research suggests that concerns about performance declines due to Olympic participation may have been exaggerated.

Our studies provide reassurance that NHL players’ return to the Winter Olympics is beneficial for the sport. While issues related to scheduling, injury risks, and competitive balance remain, the evidence indicates that these factors minimally impact the league and its athletes. The advantages of Olympic participation, including sport promotion, player experience, and fan engagement, significantly outweigh the potential downsides. As the NHL sends its stars back to the Olympic ice, this move is celebrated not only by fans but also as a victory for the global prestige of ice hockey.

References

1https://www.nhl.com/blues/news/nhl-to-return-to-olympics-host-4-nations-face-off#:~:text=For%20the%20first%20time%20in,the%20first%20time%20since%202014.
https://www.cbsnews.com/boston/news/nhls-worst-nightmare-realized-star-player-john-tavares-hurt-during-winter-olympics/#:~:text=NHL%27s%20Worst%20Nightmare%20Realized%3A%20Star%20Player%20John%20Tavares%20Hurt%20During%20Winter%20Olympics,-February%2021%2C%202014&text=BOSTON%20(CBS)%20%2D%20New%20York,over%20Latvia%20in%20quarterfinal%20play.
2https://www.scitechnol.com/peer-review/the-impact-of-the-olympics-on-regular-season-team-performance-in-the-national-hockey-league-5ghI.php?article_id=4359
https://www.frontiersin.org/articles/10.3389/fspor.2020.00076/full

2024-05-08T11:05:32-05:00June 7th, 2024|Commentary, General|Comments Off on Celebrating the Olympics

For the Good of the Game: What Keeps Soccer Referees from Renewing Their Licenses

Authors: Dr. J Ross Pruitt1, Dr. Dexter Davis2


Corresponding Author:

J. Ross Pruitt* Professor Department of Agriculture, Geosciences, and Natural Resources

269 Brehm Hall University of Tennessee at Martin

Martin, Tennessee 38238

Phone: (731)881-7254 Fax: (731)881-7968 rpruit10@utm.edu

For the Good of the Game: What Prevents Soccer Referees from Renewing Their Licenses 

ABSTRACT

The United States faces a critical shortage of youth sports referees despite a growing interest in many sports. This issue is increasingly gaining attention from sanctioning bodies, referee associations, and researchers. There is a significant cost of referee turnover and implementing strategies implemented to increase retention of officials, especially in soccer. Correct identification of the issues resulting in non-renewal of referee licenses will increase the likelihood of retention strategies being successful. This study builds on existing research by using best-worst scaling to provide a preference share on the factors that result in non-renewal which Likert scales cannot provide. Current and former U.S. Soccer Federation referees in Tennessee were surveyed to determine which factors are most likely to motivate their decision to not renew their referee license. Findings from this research indicate that motivations are different from youth referees compared to adult referees. Youth referees find the cost of refereeing and assigning are resulting in non-renewal of licenses compared to lack of respect and changing work commitments among adult referees. Results of this research can be used to improve retention strategies across age groups.

Keywords: best-worst scaling, soccer referees, referee motivations, referee retention  

Organized sports are an important part of society within the United States as it allows recreational and entertainment opportunities for participants and spectators. Sports officials are often referred to as the “third team” and are a critical aspect to the success of organized sports. In recent years, the popular press has been bombarded with stories of referee shortages (e.g., Conlon, 2022; Medina, 2022; Yurkevich, 2023) and physical attacks (Mendola, 2014; Ortiz, 2015; Weir, 2022; Hamacher, 2023). A majority of states have enacted or are considering laws to protect referees according to the National Association of Sports Officials (NASO) who tracks the status of legislation impacting sports officials (NASO, n.d.).  

Even with increased awareness of the issues of referee shortages, verbal abuse and/or physical assaults, and growing legal protections, organized sports in the United States are still facing a shortage of officials. National and grassroots sport associations have enacted strategies to reduce the turnover to aid in recruitment (Titlebaum et al., 2009) and retention (Warner et al., 2013) of sports officials. These efforts will take time to minimize the impact of verbal abuse and physical assaults that are believed to result in the exodus of sports officials (Warner et al., 2013; Downward et al., 2023). Prior research has explored the factors that result in individuals deciding to become a sports official (Furst, 1991; Kellett and Warner, 2011; Johansen, 2015; Baldwin and Vallance, 2016) and continuing as a sports official (Rainey, 1999; Rainey and Hardy, 1999; Kellett and Shilbury, 2007; Kellett and Warner, 2011; Cuskelly and Hoye, 2013; Ridinger et al., 2017; Da Gama et al, 2018; Giel and Brewer, 2020; Orviz-Martinez et al, 2021; Downward et al., 2023), but the factors resulting in non-renewal of licenses needed to officiate is less clear in the literature.  

The internal and external factors that draw individuals to officiate sports are important motivators to keep renewing their license. When one or more of these factors dissipate or change, an official’s lagging desire to continue can result in non-renewal of the soccer refereeing license. Licenses to officiate soccer are typically renewed annually which requires a conscious decision to continue or not continue. This provides the official with the opportunity to reflect whether the benefits of officiating (e.g., financial, health, social) continue to exceed the costs (e.g. cost to renew the license, additional time away from family, job stress, verbal abuse). As very few soccer referees can rely financially on officiating income alone, the need to balance family, career, and officiating is present. The popularity of youth soccer results in a constant cadre of referees needing recruitment, introductory and advanced training, and retention at the youth and grassroots level. Past research (e.g., Gomes et al, 2021) has used Likert scales and qualitative interviews to determine factors that impact continued refereeing of soccer. This study adds to the existing literature by inviting current and former soccer officials to make a choice among the alternative factors included on the survey instrument. The method used in this study presents a direct ranking of factors not provided in Likert scales. This paper continues with a literature review of the existing literature of factors attracting individuals to officiate sports and what results in the decision to no longer referee followed by a description of our survey methodology. Our survey population included current and former U.S. Soccer Federation referees. Results are then discussed with suggestions for future research presented.  

Literature Review  

The reasons an individual becomes a sports official are complex, but often include altruistic motivations (Balch and Scott, 2007) and love of the sport (Burke, Joyner, Pim, and Czech, 2000). Furst (1991) and Balch and Scott (2007) state that officials continue to officiate for social and interpersonal reasons along with a commitment to the sport. Kellett and Shilbury (2007) discuss the importance of the social and interpersonal support provided between officials to cope with the stress of officiating sports. The stress is, in part, a reflection of the need to quickly and correctly apply the rules of the sport while being in the proper position to make a decision. Initial training of new sports officials often focuses primarily on knowing the rules of the sport with some field training to practically apply what is learned. Factors that are important to keep beginning officials engaged in officiating such forming interpersonal relationships (e.g., Furst, 1991; Balch and Scott, 2007; Kellett and Shilbury, 2007; Kellett and Warner, 2011; Baldwin and Vallance, 2016) and coping with stress (e.g., Voight, 2009) are not the primary focus of initial trainings.  

Officiating sports is a stressful experience due to the complexity of making quick decisions (Guillén and Jiménez, 2001; González-Oya, 2006; Gama et al., 2018) in an environment where positive feedback for correct decisions is limited. In younger and/or inexperienced officials, the lack of experience in these environments and ability to cope with the accompanying stress can contribute to referees no longer officiating (Cuskelly and Hoye, 2013). Prior research has focused on the connection between stressors and burnout (Rainey and Hardy, 1999; Voight, 2009; Da Gama et al., 2018; Gomes et al., 2021; Orviz-Martinez et al., 2021; Downward et al., 2023) with tools like the Burnout Inventory for Referees developed by Weinberg and Richardson (1990). Stressors experienced by sports officials are not always related to the sporting event but can be representative of other factors in their lives including work, family, and support of the organization for which they officiate (Voight, 2009; Cuskelly and Hoye, 2013).  

Reasons that individuals begin refereeing may not always be the reasons they intend to continue. Kellett and Shilbury (2007) document that the interpersonal relationships developed can overcome nervousness experienced by beginning officials. These interpersonal relationships can be a positive stressor, or an indication of commitment described in Cuskelly and Hoye (2013). These may be social in nature can result in officials who, “are likely to feel somewhat compelled to continue officiating through various social mechanisms” (Cuskelly and Hoye, 2013). The level of organizational support, or the official’s perception of support, can result in an intention to continue officiating (Rainey, 1999; Kellett and Warner, 2011).  

Giel and Breuer (2020) find the altruistic motives are not a significant factor in continuing to referee. This highlights the importance of the social relationships as the stress associated with officiating, balancing family, job, and officiating, the stress associated with maintaining the desired level of performance, or other factors can result in the official questioning their desire to continue. This contributes to the belief often expressed in the popular press that burnout and verbal abuse/physical assault are primary motivators to officials leaving the sport (Kellett and Shilbury, 2007). The ability to reframe the abuse as described in Kellett and Shilbury (2007) may limit the extent to which the perception is reality. Voight (2009) finds the conflict between family and officiating, making a controversial call, conflict between work and officiating, making the wrong call, and verbal abuse from coaches as the top stressors among college soccer officials. The least amount of stress can be attributed to the fear of physical harm (Voight, 2009).  

Methods 

The decision to not renew one’s soccer referee license reflects the costs of continuing to referee (whether financial, social, or emotional) relative to the benefits accrued by refereeing. We hypothesize that referees will consider not renewing their license prior to the actual decision where the license is not renewed (Rainey and Hardy, 1999; Cuskelly and Hoye, 2013). Factors that motivate the decision to not renew one’s license are presented in Table 1. Included factors represent those included in the literature (e.g., Furst, 1991; Rainey, 1999; Rainey and Hardy, 1999; Burke et al., 2000; Balch and Scott, 2007; Kellett and Shilbury, 2007; Cuskelly and Hoye, 2013; Johansen, 2014; Giel and Breuer, 2020) as well as those from our personal experiences refereeing and coaching soccer. After the factors shown in Table 1 were selected to include in the questionnaire, the staff and mentors of the U.S. Youth Soccer Region III Championships reviewed our factors and accompanying descriptions for thoroughness. Their suggestions are reflected in our final factors presented in Table 1.  

Use of best-worst scaling (Finn and Louviere, 1992) provides the relative importance that a factor can have on a referee’s continued interest in renewing their license. This method provides an improvement over qualitative interviews which can provide insight into motivations for referees, but not a hierarchical preference ranking that can be used by referee associations to assist in retention of referees. An additional benefit of best-worst scaling is the fact it provides a ratio scale for its results unlike a Likert rating scale that may result in the ordinal ranking not being consistent across respondents (Steenkamp and Baumgartner, 1998; Lusk and Briggeman, 2009). This provides greater insight into the obstacles for a referee to annually renew their license which can lead to increased retention efforts and educational efforts by clubs and sanctioning bodies to reduce the impact of factors resulting in non-renewal of licenses. 

Best-worst scaling provides the respondent the ability to select the factor that provides the most and least utility in a choice set which Likert scales do not provide. This approach has significant implications for marketing (Cohen, 2009; O’Reilly and Huybers, 2015; Massey, Wang, and Waller, 2015) to help identify specific factors that consumers find desirable. Use of this method has extended into the healthcare industry (Flynn et al., 2007) and the value of public information (Pruitt et al., 2014). Given J factors, there are J(J-1) combinations a respondent could select for each best-worst question. The choice of the most important factor j by individual i can by represented by λj on the utility scale with the latent level of utility determined by Iij = λj + εij which assumes that εij is the random error term. By selecting factor j as the most important factor and factor k as the least important is determined by the probability for all other J(J-1)-1 possible differences in the choice set.  

Results from best-worst scaling normally occurs through a multinomial or random parameters logit. Estimate coefficients have little interpretation aside from the magnitude of the coefficient. Preference shares for each factor’s impact on lack of interest in continuing to referee is calculated using the following equation preference share for factor.

Respondents were asked if they had actively considered not renewing their U.S. Soccer Federation (USSF) referee license in the past five years. Individuals that responded yes, were then asked best-worst questions using the factors that were identified and presented in Table 1. Using PROC OPTEX in SAS 9.4, a quasi-balanced incomplete block design (BIBD) was created. The design had a treatment D-efficiency of 90.78 and a block design D-efficiency of 99.86. This resulted in twelve best-worst questions with six factors present in each question. Each factor appeared six times to each respondent with an example of the best-worst questions is provided in Figure 1.

Figure 1. Example Best-Worst Question

Survey

A web-based Qualtrics survey was created that was distributed to current and former U.S. Soccer Federation referees implementing the best-worst questions discussed previously. Through contacts with the Tennessee Soccer Referee Program, we were able to distribute the questionnaire to 3,507 current and former referees. Our ability to contact referees who had not recertified in the previous four years is due to the Tennessee Soccer Referee Program adopting computer software that allows the program to track referees who do not re-certify from year to year. Inclusion of youth referees (between the ages of thirteen and eighteen) was approved by our university’s Institutional Review Board which allows for determination if factors vary by age. Per USSF policy, any email contact from a certified USSF assignor results in the parent/guardian also being contacted1. This resulted in parents/guardians of current and former youth referees also receiving the recruitment email. Initial questions identified if the respondent was at least eighteen years of age and then determined if the respondent was answering for themselves or as parent/guardian of a current or former youth soccer referee2. For youth referees, we included questions that determined if their parent/guardian had provided consent in addition to the minor providing assent. As the parent/guardian also received the recruitment email, email addresses for minors were collected in case the parent/guardian revoked consent necessitating removal of youth referee responses. No parent or guardian contacted us requesting removal of the youth referee’s responses.

A recruitment email was sent in early March 2023 to 3,507 current and former referees registered with USSF in the state of Tennessee with a follow-up email sent two weeks later. An incentive was offered to each respondent of a gift card worth $100 to a referee equipment supplier or a free registration for the 2023 year. Email addresses were collected at the end of the questionnaire and provided to the Tennessee Soccer Referee Program which was responsible in selecting and contacting the winners of the inducement. We received 107 usable responses for a response rate of 3.05%.

Results Demographic information is provided in Table 2. Total responses did vary by question as respondents were not required to answer every demographic question which were asked following the best-worst questions. Respondents were overwhelmingly male and Caucasian. Approximately forty percent of respondents were less than twenty-five years of age and an additional twenty-five percent between the ages of forty-three and fifty-four. Over sixty percent who responded were no longer refereeing soccer with approximately two-thirds believing they were assigned the appropriate number of matches given their skill and ability level. Those receiving the questionnaire were asked an open-ended question on how many years they refereed soccer. Of the 110 responses, many did not provide an exact number. For those who provided an exact number, the average number of years that survey participants had refereed was 8.63 years. Given responses not included in this calculation that stated they had refereed 10+, 20+, or 50+ years, this estimate of 8.63 understates the longevity of referees in this research. A histogram of responses for this question is presented in Figure 2. More than three-quarters of respondents refereed no more than sixty matches a year with the majority refereeing less than fifteen matches annually. Over ninety percent of respondents only refereed soccer. Nearly seventy percent of respondents had suffered verbal abuse in the past two years with approximately five percent having suffered a physical assault (e.g., touched, pushed, shoved, punched, kicked, or spat on) by a player, coach, fan, or parent. Parents and coaches were most likely to have been the source of verbal abuse with players being the source of physical assault.

As we were able to include youth referees (less than eighteen years old), we conducted t-tests for significant differences in means between those who had actively considered not renewing their USSF licenses for youth and adult referees. We did not test for differences in means in age and educational attainment categories since we compared those less than eighteen of ages to all other ages in this comparison. Differences in the mean at the 5% level of significance (p<0.05) were found in these groupings with less than fifteen matches officiated, whether the respondent felt they were under assigned, assigned the right number of matches for their skill/ability level, and whether they play organized soccer. Table 2 includes these results.

Table 2. Demographic Information

VariableMeanStandard Deviation
Gender (n=111)  
Male75.68%0.43
Female23.42%0.43
Prefer Not to Say0.90%0.09
Ethnicity (n=111)  
Caucasian76.58%0.43
African American0.00%0.00
Hispanic5.41%0.23
Native American0.00%0.00
Asian0.00%0.00
Multi-racial6.31%0.24
Other2.70%0.16
Prefer Not to Say5.41%0.23
Age (n=111)  
13-1722.52%0.42
18-2421.62%0.41
25-308.11%0.27
31-366.31%0.24
37-421.80%0.13
43-4811.71%0.32
49-5413.51%0.34
55-603.60%0.19
Over 6010.81%0.31
Education Level (n=111)  
Currently in Middle/High School27.03%0.45
High School Diploma or GED0.00%0.00
Trade, vocational, or technical school4.50%0.21
Associate Degree4.50%0.21
Bachelor’s Degree27.93%0.45
Master’s Degree15.32%0.36
Doctoral or Professional Degree7.21%0.26
Prefer Not to Say1.80%0.13
Household Income (n=110)  
Less than $40,00010.00%0.30
$40,000 to $60,0009.09%0.29
$60,001 to $80,0008.18%0.28
$80,001 to $100,0005.45%0.23
Greater than $100,00040.00%0.49
Prefer Not to Say27.27%0.45

Table 2. Continued

VariableMeanStandard Deviation
Residence (n=111)  
Urban Area14.41%0.35
Suburban Area66.67%0.39
Rural Area18.92%0.47
Levels Officiated1  
Youth recreational33.46% 
Club28.31% 
AYSO7.35% 
High School16.18% 
College3.68% 
Adult Amateur/Recreational10.29% 
Professional0.74% 
Approximate number of annual matches  
Less than 1530.91%20.46
16-3019.09%0.39
31-4513.64%0.34
46-6012.73%0.33
61-755.45%0.23
76-908.18%0.28
91-1052.73%0.16
Over 1057.27%0.26
Proper Assigning Level (n=109)  
Under assigned25.69%20.44
Over assigned7.34%0.26
Right number66.97%20.47
Sports Officiated besides Soccer  
None92.73%0.26
1-26.36%0.25
3-40.91%0.10
5 or more0.00%0.00
Play Organized Soccer (n=110)43.64%20.50
Verbally Abused in Last Two Years (n=109)68.81%0.47
Source of Verbal Abuse1  
Player18.96% 
Coach27.01% 
Fan22.27% 
Parent31.75% 

Table 2. Continued

VariableMeanStandard Deviation
Physically Assaulted in Last Two Years (n=109)4.59%0.21
Source of Physical Assault1  
Player  
Coach  
Fan  
Parent  
Injury of at Least Four Weeks (n=109)11.93%0.33
Attend Continuing Education (n=110)  
Once a year28.18%0.45
Twice a year7.27%0.26
Three to four times a year10.00%0.30
At least five times a year0.00%0.00
Does not attend47.27%0.50
Accepts unsanctioned matches (n=110)12.73%0.33
Anticipates refereeing soccer: (n=110)  
No longer refereeing60.91%0.49
Less than one year7.27%0.26
One to two years12.73%0.33
Three to four years8.18%0.28
At least five years10.91%0.31

1 Question allowed multiple responses and standard deviations are not presented as a result.
2 Denotes significant differences at the 5% level (p<0.05) between youth and adult referees who had actively considered not renewing their license.

Non-Renewal of Referee License

Respondents who answered they had actively considered not renewing their license in the past five years were shown a series of questions asking them to select the most and least important factors impacting why they would not renew their refereeing license. As our sample included youth referees (those less than 18 years of age), we estimated a combined model for all referees responding against the alternative models of youth and adult referees. Each of these models was estimated using a multinomial logit (MNL), an uncorrelated random parameters logit (RPL), and a correlated random parameters logit model. Significant differences were found to exist between youth and adult referees who were considering not renewing resulting in separate models being estimates for youth and adult referees. Likelihood ratio tests favored the use of MNL model for both youth and adult referees.

Youth Referees

Results for youth referees are presented in Table 3 uses Work as the base factor with results. Estimates for the MNL and RPL models are presented with the MNL preferred by use of a likelihood ratio test. Aside from their magnitude, the econometric estimates in Table 3 have no natural interpretation and equation 1 was used to calculate the shares of preference that are presented. The shares of preference for the uncorrelated RPL model were generated from 1,000 random draws using a normal distribution of the mean and standard deviation of a specific factor that might result in a referee not renewing their USSF license. Shares of preference were consistent between the two modeling techniques as there was not greater than ±0.01% difference for any factor. The cost to referee (i.e., Afford) was the number one reason that youth referees had considered not renewing their USSF license. This factor includes the inability to make it to matches for youth referees reflecting the need for an adult or friend to help them make it to assignments. Note that even with a small sample size of youth referees, fifteen of the eighteen youth referees were no longer refereeing. The youth referee’s opinion on how well they were assigned was the second most important factor with the lack of Respect from fans, players, and coaches third (depending on the model used). It should be noted that the fourth most important factor was Game Fees, indicating the cost to benefit ratio for youth referees is contributing to non-renewals. The use of best-worst scaling provides a clearer view of the magnitude of factors resulting in youth referees not renewing their licenses through the direct comparisons with the lack of Respect relatively not as important as other factors.

Table 3.  Relative Importance of Factor Impacting Non-Renewal of Youth Referee Licenses

FactorEconometric EstimatesShares of Preference
 MNLRPLMNLRPL
Respect0.995***1.000***0.1530.153
 (0.255)a(0.256)[0.000][0.002]
 [0.000]b[0.018]  
Youth Involvement-0.761***-0.763***0.0270.026
 (0.252)(0.254)[0.000][0.000]
 [0.000][0.000]  
Social Aspects0.271***0.2700.0740.074
 (0.258)(0.265)[0.000][0.002]
 [0.000][0.172]  
Family Commitments-0.865***-0.859***0.0240.024
 (0.243)(0.246)[0.000][0.000]
 [0.000][0.001]  
Injury-0.491*-0.496*0.0350.034
 (0.256)(0.258)[0.000][0.001]
 [0.000][0.000]  
Lack of Opportunities to Advance-0.189-0.1880.0470.047
 (0.255)(0.257)[0.000][0.001]
 [0.000][0.002]  
Cost to Referee1.123****1.130***0.1740.174
 (0.255)(0.256)[0.000][0.002]
 [0.000][0.005]  
Age-1.178***-1.138***0.0170.018
 (0.248)(0.279)[0.000][0.001]
 [0.000][0.355]  
Assigning0.999***1.009***0.1540.155
 (0.255)(0.258)[0.000][0.002]
 [0.000][0.031]  
Game Fees0.913***0.915***0.1410.141
 (0.265)(0.267)[0.000][0.003]
 [0.000][0.001]  
Lack of Organizational Support0.524**0.529**0.0960.096
 (0.258)(0.259)[0.000][0.001]
 [0.000][0.001]  
Work0.0000.0000.0570.058
(Base Factor)  [0.000][0.015]
     
Log Likelihood-625.138-624.910  
McFadden’s LRI0.0940.149  
Number of Respondents1818  

            ***, **, and * asterisks represent the factor is significantly different from the Work factor at the 1%, 5%, and 10% level, respectively.

a Numbers in parentheses are standard errors.
b numbers in brackets are standard deviations.

In addition to the shares of preference presented in Table 3, we generated Pearson correlations from the individual specific RPL estimates shown in Table 4. Several factors had correlations with at least ±0.3 with another factor. Given the limited number of responses, care should be taken when viewing Table 4, but it provides an indication of how youth referees view these factors influencing their decision to not continue refereeing. The more likely a youth referee viewed the lack of Social camaraderie, the higher an injury might factor into a non-renewal decision. Importantly, the lack of Social connections had a strong direct relationship with their views of Organizational Support provided to them. Concerns about how many games the referee was assigned had a positive relationship with Game Fees being an important factor in the decision to not renew the license. Game Fees tended to have large (positive or negative) correlations with many factors that were included in this research.

Table 4.  Pearson Correlations Between Factors from Individual Specific RPL Estimates of Youth Referees

Factor1234567891011
Respect (1)1.000          
Youth Involvement (2)0.0071.000         
Assign (3)-0.1400.1771.000        
Social (4)-0.1760.410-0.3011.000       
Injury (5)-0.007-0.439-0.4940.5051.000      
Advance (6)-0.211-0.8320.0180.2220.2121.000     
Age (7)0.1640.8740.100-0.248-0.448-0.9191.000    
Cost (8)-0.043-0.159-0.2640.2270.079-0.0730.0961.000   
Game Fees (9)-0.1230.7250.476-0.569-0.737-0.5380.694-0.0461.000  
Organizational Support (10)0.097-0.338-0.1720.5140.5440.189-0.3320.249-0.6691.000 
Family (11)-0.326-0.753-0.0300.4890.5640.723-0.860-0.025-0.6100.5311.000

Adult Referees

Results for adult referees who had considered not renewing their USSF license are presented in Table 5. As with youth referees, a MNL model was preferred to an uncorrelated RPL model with the estimates from both models presented. Unlike youth referees, the lack of Respect experienced by adult referees is the primary reasons resulting in the non-renewal decision. Work commitments or a change in them was the second most important factor. Nearly two-thirds of adult referees who had considered not renewing their license were no longer refereeing; fifteen were considering not renewing in more than the next two years with only four considering refereeing at least four more years.

Table 5.  Relative Importance of Factor Impacting Non-Renewal of Adult Referee Licenses

FactorEconometric EstimatesShares of Preference
 MNLRPLMNLRPL
Respect0.558***0.568***0.2370.238
 (0.113)(0.114)[0.000][0.003]
 [0.000][0.028]  
Youth Involvement-1.571***-1.582***0.0280.028
 (0.117)(0.119)[0.000][0.000]
 [0.000][0.004]  
Social Aspects-1.313***-1.312***0.0360.036
 (0.118)(0.131)[0.000][0.001]
 [0.000][0.3480]  
Family Commitments-0.688***-0.688***0.0680.068
 (0.112)(0.131)[0.000][0.002]
 [0.000][.450]  
Injury-0.794***-0.795***0.0610.061
 (0.117)(0.118)[0.000][0.001]
 [0.000][0.010]  
Lack of Opportunities to Advance-0.828***-.833***0.0590.059
 (0.116)(0.117)[0.000][0.001]
 [0.000][0.001]  
Cost to Referee-.481***-0.474***0.0840.084
 (0.115)(0.116)[0.000][0.001]
 [0.000][0.016]  
Age-1.455***-1.468***0.0320.031
 (0.117)(0.127)[0.000][0.001]
 [0.000][0.179]  
Assigning-0.491***-0.482***0.0830.083
 (0.116)(0.117)[0.000][0.001]
 [0.000][0.001]  
Game Fees-0.350***-0.348***0.0950.095
 (0.120)(0.121)[0.000][0.002]
 [0.000][0.009]  
Lack of Organizational Support-0.510***-0.514***0.0810.081
 (0.115)(0.116)[0.000][0.001]
 [0.000][0.001]  
Work0.0000.0000.1350.135
(Base Factor)  [0.000][0.014]
     
Log Likelihood-2843.485-2838.531  
McFadden’s LRI0.0660.097  
Number of Respondents7777  

***, **, and * asterisks represent the factor is significantly different from the Work factor at the 1%, 5%, and 10% level, respectively.

a Numbers in parentheses are standard errors.
b numbers in brackets are standard deviations.

As with the youth referees, Pearson correlations for the adult referees are presented in Table 6. A greater response rate among adults compared to youth referees provides more robustness in the correlations that are presented. It is interesting to note the strong negative correlation between Game Fees and Assign (-0.591) suggesting concerns about pay is not tied to assigning. Concerns about Game Fees and the ability to Advance had a strong positive relationship (0.607) indicating adult referees view the pay for higher level games isn’t a strong enough incentive to advance. Those referees who rated the inability to Advance highly was negatively correlated (-0.611) with concerns about being over or under assigned (Assign).

Table 6.  Pearson Correlations Between Factors from Individual Specific RPL Estimates of Adult Referees

Factor1234567891011
Respect (1)1.000          
Youth Involvement (2)0.0631.000         
Assign (3)-0.304-0.5781.000        
Social (4)-0.105-0.0520.0171.000       
Injury (5)0.1530.533-0.587-0.1241.000      
Advance (6)0.3000.387-0.6110.2170.4181.000     
Age (7)-0.172-0.2300.089-0.192-0.115-0.2871.000    
Cost (8)0.2750.179-0.4600.0280.1960.542-0.2821.000   
Game Fees (9)0.0460.444-0.591-0.1240.4010.607-0.0150.3361.000  
Organizational Support (10)0.129-0.036-0.055-0.0690.049-0.334-0.0930.007-0.1971.000 
Family (11)-0.255-0.2790.574-0.136-0.543-0.3380.147-0.483-0.3230.0361.000

Conclusions

Concerns about retaining sports officials are a pressing factor for many sports with referee abuse a concern among leagues and official associations. Factors influencing the decision to not renew referee licenses are not well understood in the literature. Prior research has focused on qualitative factors impacting the renewal decision which doesn’t quantitatively rank factors included in the research. This research surveyed current and former referees who had actively considered not renewing their referee license with a majority no longer refereeing soccer. There were significant differences between youth and adult referees in the factors that had led them to consider not renewing their referee license. For youth, the cost to referee and concerns about being over- or under-assigned were the top two reasons for considering not renewing their license compared to adults who were more concerned about the lack of respect and work commitments. For both age groups, concerns about organizational support were significant factors as it relates to continuing refereeing.

Our study is limited by the small sample size, but it is an important look into the factors that resulted in a majority of referees no longer renewing their U.S. Soccer Federation license. While we do not focus on the well-being of referees as in Downward and Webb (2023), our findings are consistent with theirs that a zero-tolerance approach will aid in adult referee retention. This reinforces the need for organizational support (Rainey, 1995; Voight, 2007; Ridinger et al., 2017; Downward and Webb, 2023), but also requires training by those organizations on what to include in post-match reports to have the backing. As over 75% of respondents in our survey did not attend more than one continuing education session annually, sanctioning bodies and referee associations need innovative ideas to aid in reaching this objective.

Future research should focus on expanding this to referees who have not recently considered non-renewal of their referee licenses. This portion of the referee community will likely have different factors motivating their continued renewals as was demonstrated by the differences observed in this paper based on the age of the referee. Identification of the factors that aid in retention of these referees may aid in development of strategies to limit the impact of factors discussed in this research. Given the nature of soccer in the U.S., future research should better control for the differences in length of refereeing and level officiated (e.g., recreational versus club). With the number of young referees who work matches in the U.S., the skills necessary to be successful may not have been developed to handle the stressors commonly associated with officiating (Rainey, 1995; Rainey and Hardy, 1999; Burke et al., 2000; Voight, 2009; Gomes et al., 2021). A more diverse respondent pool, in terms of locality, gender, and ethnicity, is also needed to better understand why referees continue to engage in a stressful avocation.

Acknowledgements

The authors express appreciation to Don Eubank, State Referee Administrator for Tennessee Soccer, for sending the questionnaire to soccer referees in the state and providing the incentive for respondents to complete the questionnaire. We also thank the staff and mentors of U.S. Youth Soccer Region III for helpful feedback on an early draft of the questionnaire. The authors are grateful for the helpful edits and suggestions from Marco Palma on an earlier draft of this paper.

Conflicts of Interest

J. Ross Pruitt is an active soccer referee with the U.S. Soccer Federation, Tennessee Secondary School Athletic Association, and National Intercollegiate Soccer Official Association.

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2024-05-08T12:27:56-05:00May 31st, 2024|General, Sports Coaching, Sports Management|Comments Off on For the Good of the Game: What Keeps Soccer Referees from Renewing Their Licenses

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.

  1. ShotResult: The dependent variable: whether the shooter made the shot. (Values: 0=Missed, 1=Made; Original variable: Fgm)
  2. 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)
  3. 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)
  4. 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)
  5. 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)
  6. 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)
  7. 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

VariableFrequencyLogitOddszp>zProbEV
DefDist       
0-3 ft.6%1.290.86
3-6 ft.54%0.251.294.740.00.341.02
6-9 ft.28%0.381.466.780.00.371.11
9+ ft.12%0.471.607.740.00.391.17
ShotClock       
0-2 secs.5%1.210.62
2-4 secs.7%0.631.888.020.00.330.99
4+ secs.88%0.772.1711.870.00.361.08
Catch       
Off catch75%1.361.07
Off dribble25%-0.09.0.91-3.210.00.341.01
Period       
124%1.371.11
224%-0.110.89-3.340.00.341.03
325%-0.050.96-1.340.18.361.08
4+27%-0.150.86-4.540.00.341.01
ShotDist       
22-24 ft31%1.381.13
24-25 ft.36%-0.090.91-3.250.01.361.06
25-26 ft.20%-0.170.84-5.120.00.341.01
26+ ft.13%-0.300.74-7.130.00.310.92
Venue       
Away50%1.351.04
Home50%0.051.052.140.03.361.07
…Constant-1.460.23-17.420.00.190.56

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 
0.25 0.77 -0.05 -1.46 -0.49 0.38 
0.47 0.77 -0.05 -1.46 -0.27 0.42 
0.47 0.77 -0.09 -0.05 -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 

VariableFrequencyLogitOddszp>zProbEV
DefDist       
0-3 ft.11%1.240.72
3-6 ft.55%0.892.442.440.02.431.29
6-9 ft.24%0.972.652.480.01.451.35
9+ ft.10%1.263.512.750.00.521.55
ShotClock       
0-2 secs.2%1.250.75
2-4 secs.3%2.108.172.190.03.722.15
4+ secs.95%0.792.211.100.27.421.25
Catch       
Off catch46%1 .401.21
Off dribble54%0.151.17.750.46..441.32
Period       
133%1.441.30
219%0.011.010.050.963.441.31
329%-0.030.97-0.120.902.431.28
4+19%-0.260.77-0.910.364.371.12
ShotDist       
22-24 ft16%1.551.65
24-25 ft.31%-0.750.47-2.460.01.371.11
25-26 ft.24%-0.510.60-1.580.11.431.28
26+ ft.28%-0.650.52-2.040.04.401.12
Venue       
Away54%1.411.23
Home46%0.111.12.560.58.441.31
…Constant-1.530.22-1.80.07.190.56

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

  1. Game_Id: The game’s unique identifier.
  2. Matchup: The teams competing.
  3. Location: Whether it was a home or away game for the shooter’s team.
  4. Outcome: Whether the shooter’s team won or lost.
  5. Final_Margin: By how many points the shooter’s team won or lost.
  6. Shot_Number: The shooter’s nth shot that game.
  7. Period: The period in which the shooter took the shot.
  8. Game_Clock: Minutes and seconds left in the period in which the shooter took the shot.
  9. Shot_Clock: Seconds remaining on the 24-second shot clock when the shooter took the shot.
  10. Dribbles: Number of dribbles the shooter took before shooting.
  11. Touch_Time: Number of seconds the shooter had the ball before shooting.
  12. Shot_Dist: Distance in feet from the center of the basket to the shooter.
  13. Pts_Type: Whether the shooter attempted a two- or three-point shot.
  14. Shot_Result: Whether the shooter made the shot.
  15. Closest Defender: Name of the defender closest to the shooter.
  16. Closest_Defender_Player_Id: The closest defender’s unique identifier.
  17. Close_Def_Dist: The closest defender’s distance to the shooter in feet.
  18. Fgm: Whether the shooter made the shot.
  19. Pts: The shot’s point value (0, 2 or 3).
  20. Player_Name: The shooter’s first and last name.
  21. 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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2024-05-21T13:46:56-05:00May 17th, 2024|General, Research, Sports Management|Comments Off on Advice on making the most of basketball three-point shot data

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: 

  1. States and/or provinces that host the most minor league teams; 
  1. Cities that host the most minor league teams; 
  1. 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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2024-05-01T12:50:45-05:00May 3rd, 2024|General, Research, Sports Management|Comments Off on An Analysis of the Geographic Distribution of Minor League Sports Teams

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.

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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