George Minoso2024-05-08T12:27:56-05:00

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

George Minoso2024-05-08T12:27:56-05:00May 31st, 2024|General, Sports Coaching, Sports Management|

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

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 λ_j on the utility scale with the latent level of utility determined by I_ij = λ_j + ε_ij which assumes that ε_ij 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

Variable	Mean	Standard Deviation
Gender (n=111)
Male	75.68%	0.43
Female	23.42%	0.43
Prefer Not to Say	0.90%	0.09
Ethnicity (n=111)
Caucasian	76.58%	0.43
African American	0.00%	0.00
Hispanic	5.41%	0.23
Native American	0.00%	0.00
Asian	0.00%	0.00
Multi-racial	6.31%	0.24
Other	2.70%	0.16
Prefer Not to Say	5.41%	0.23
Age (n=111)
13-17	22.52%	0.42
18-24	21.62%	0.41
25-30	8.11%	0.27
31-36	6.31%	0.24
37-42	1.80%	0.13
43-48	11.71%	0.32
49-54	13.51%	0.34
55-60	3.60%	0.19
Over 60	10.81%	0.31
Education Level (n=111)
Currently in Middle/High School	27.03%	0.45
High School Diploma or GED	0.00%	0.00
Trade, vocational, or technical school	4.50%	0.21
Associate Degree	4.50%	0.21
Bachelor’s Degree	27.93%	0.45
Master’s Degree	15.32%	0.36
Doctoral or Professional Degree	7.21%	0.26
Prefer Not to Say	1.80%	0.13
Household Income (n=110)
Less than $40,000	10.00%	0.30
$40,000 to $60,000	9.09%	0.29
$60,001 to $80,000	8.18%	0.28
$80,001 to $100,000	5.45%	0.23
Greater than $100,000	40.00%	0.49
Prefer Not to Say	27.27%	0.45

Table 2. Continued

Variable	Mean	Standard Deviation
Residence (n=111)
Urban Area	14.41%	0.35
Suburban Area	66.67%	0.39
Rural Area	18.92%	0.47
Levels Officiated1
Youth recreational	33.46%
Club	28.31%
AYSO	7.35%
High School	16.18%
College	3.68%
Adult Amateur/Recreational	10.29%
Professional	0.74%
Approximate number of annual matches
Less than 15	30.91%2	0.46
16-30	19.09%	0.39
31-45	13.64%	0.34
46-60	12.73%	0.33
61-75	5.45%	0.23
76-90	8.18%	0.28
91-105	2.73%	0.16
Over 105	7.27%	0.26
Proper Assigning Level (n=109)
Under assigned	25.69%2	0.44
Over assigned	7.34%	0.26
Right number	66.97%2	0.47
Sports Officiated besides Soccer
None	92.73%	0.26
1-2	6.36%	0.25
3-4	0.91%	0.10
5 or more	0.00%	0.00
Play Organized Soccer (n=110)	43.64%2	0.50
Verbally Abused in Last Two Years (n=109)	68.81%	0.47
Source of Verbal Abuse1
Player	18.96%
Coach	27.01%
Fan	22.27%
Parent	31.75%

Table 2. Continued

Variable	Mean	Standard 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 year	28.18%	0.45
Twice a year	7.27%	0.26
Three to four times a year	10.00%	0.30
At least five times a year	0.00%	0.00
Does not attend	47.27%	0.50
Accepts unsanctioned matches (n=110)	12.73%	0.33
Anticipates refereeing soccer: (n=110)
No longer refereeing	60.91%	0.49
Less than one year	7.27%	0.26
One to two years	12.73%	0.33
Three to four years	8.18%	0.28
At least five years	10.91%	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

Factor	Econometric Estimates		Shares of Preference
	MNL	RPL	MNL	RPL
Respect	0.995***	1.000***	0.153	0.153
	(0.255)a	(0.256)	[0.000]	[0.002]
	[0.000]b	[0.018]
Youth Involvement	-0.761***	-0.763***	0.027	0.026
	(0.252)	(0.254)	[0.000]	[0.000]
	[0.000]	[0.000]
Social Aspects	0.271***	0.270	0.074	0.074
	(0.258)	(0.265)	[0.000]	[0.002]
	[0.000]	[0.172]
Family Commitments	-0.865***	-0.859***	0.024	0.024
	(0.243)	(0.246)	[0.000]	[0.000]
	[0.000]	[0.001]
Injury	-0.491*	-0.496*	0.035	0.034
	(0.256)	(0.258)	[0.000]	[0.001]
	[0.000]	[0.000]
Lack of Opportunities to Advance	-0.189	-0.188	0.047	0.047
	(0.255)	(0.257)	[0.000]	[0.001]
	[0.000]	[0.002]
Cost to Referee	1.123****	1.130***	0.174	0.174
	(0.255)	(0.256)	[0.000]	[0.002]
	[0.000]	[0.005]
Age	-1.178***	-1.138***	0.017	0.018
	(0.248)	(0.279)	[0.000]	[0.001]
	[0.000]	[0.355]
Assigning	0.999***	1.009***	0.154	0.155
	(0.255)	(0.258)	[0.000]	[0.002]
	[0.000]	[0.031]
Game Fees	0.913***	0.915***	0.141	0.141
	(0.265)	(0.267)	[0.000]	[0.003]
	[0.000]	[0.001]
Lack of Organizational Support	0.524**	0.529**	0.096	0.096
	(0.258)	(0.259)	[0.000]	[0.001]
	[0.000]	[0.001]
Work	0.000	0.000	0.057	0.058
(Base Factor)			[0.000]	[0.015]
Log Likelihood	-625.138	-624.910
McFadden’s LRI	0.094	0.149
Number of Respondents	18	18

            ***, **, 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

Factor	1	2	3	4	5	6	7	8	9	10	11
Respect (1)	1.000
Youth Involvement (2)	0.007	1.000
Assign (3)	-0.140	0.177	1.000
Social (4)	-0.176	0.410	-0.301	1.000
Injury (5)	-0.007	-0.439	-0.494	0.505	1.000
Advance (6)	-0.211	-0.832	0.018	0.222	0.212	1.000
Age (7)	0.164	0.874	0.100	-0.248	-0.448	-0.919	1.000
Cost (8)	-0.043	-0.159	-0.264	0.227	0.079	-0.073	0.096	1.000
Game Fees (9)	-0.123	0.725	0.476	-0.569	-0.737	-0.538	0.694	-0.046	1.000
Organizational Support (10)	0.097	-0.338	-0.172	0.514	0.544	0.189	-0.332	0.249	-0.669	1.000
Family (11)	-0.326	-0.753	-0.030	0.489	0.564	0.723	-0.860	-0.025	-0.610	0.531	1.000

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

Factor	Econometric Estimates		Shares of Preference
	MNL	RPL	MNL	RPL
Respect	0.558***	0.568***	0.237	0.238
	(0.113)	(0.114)	[0.000]	[0.003]
	[0.000]	[0.028]
Youth Involvement	-1.571***	-1.582***	0.028	0.028
	(0.117)	(0.119)	[0.000]	[0.000]
	[0.000]	[0.004]
Social Aspects	-1.313***	-1.312***	0.036	0.036
	(0.118)	(0.131)	[0.000]	[0.001]
	[0.000]	[0.3480]
Family Commitments	-0.688***	-0.688***	0.068	0.068
	(0.112)	(0.131)	[0.000]	[0.002]
	[0.000]	[.450]
Injury	-0.794***	-0.795***	0.061	0.061
	(0.117)	(0.118)	[0.000]	[0.001]
	[0.000]	[0.010]
Lack of Opportunities to Advance	-0.828***	-.833***	0.059	0.059
	(0.116)	(0.117)	[0.000]	[0.001]
	[0.000]	[0.001]
Cost to Referee	-.481***	-0.474***	0.084	0.084
	(0.115)	(0.116)	[0.000]	[0.001]
	[0.000]	[0.016]
Age	-1.455***	-1.468***	0.032	0.031
	(0.117)	(0.127)	[0.000]	[0.001]
	[0.000]	[0.179]
Assigning	-0.491***	-0.482***	0.083	0.083
	(0.116)	(0.117)	[0.000]	[0.001]
	[0.000]	[0.001]
Game Fees	-0.350***	-0.348***	0.095	0.095
	(0.120)	(0.121)	[0.000]	[0.002]
	[0.000]	[0.009]
Lack of Organizational Support	-0.510***	-0.514***	0.081	0.081
	(0.115)	(0.116)	[0.000]	[0.001]
	[0.000]	[0.001]
Work	0.000	0.000	0.135	0.135
(Base Factor)			[0.000]	[0.014]
Log Likelihood	-2843.485	-2838.531
McFadden’s LRI	0.066	0.097
Number of Respondents	77	77

***, **, 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

Factor	1	2	3	4	5	6	7	8	9	10	11
Respect (1)	1.000
Youth Involvement (2)	0.063	1.000
Assign (3)	-0.304	-0.578	1.000
Social (4)	-0.105	-0.052	0.017	1.000
Injury (5)	0.153	0.533	-0.587	-0.124	1.000
Advance (6)	0.300	0.387	-0.611	0.217	0.418	1.000
Age (7)	-0.172	-0.230	0.089	-0.192	-0.115	-0.287	1.000
Cost (8)	0.275	0.179	-0.460	0.028	0.196	0.542	-0.282	1.000
Game Fees (9)	0.046	0.444	-0.591	-0.124	0.401	0.607	-0.015	0.336	1.000
Organizational Support (10)	0.129	-0.036	-0.055	-0.069	0.049	-0.334	-0.093	0.007	-0.197	1.000
Family (11)	-0.255	-0.279	0.574	-0.136	-0.543	-0.338	0.147	-0.483	-0.323	0.036	1.000

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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George Minoso2024-05-21T13:46:56-05:00

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

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

Authors: George Terhanian1

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.

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).

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

Predicting the Likely Effect of Multiple Changes to Multiple Predictor Variables

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

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

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

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

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

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

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

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

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

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.

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:

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George Minoso2024-05-01T12:50:45-05:00

An Analysis of the Geographic Distribution of Minor League Sports Teams

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

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

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):

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: 

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

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.

References

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George Minoso2024-04-22T08:06:50-05:00

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

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

Authors: Dr. Mark Chen¹, Dr. Andrew Richardson²

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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George Minoso2024-04-08T09:47:11-05:00

Decision-making on injury prevention and rehabilitation in professional football – A coach, medical staff, and player perspective

George Minoso2024-04-08T09:47:11-05:00April 8th, 2024|General, Research, Sports Management|

Authors: Mads Røgen Noesgaard ¹& Stig Arve Sæther²

Corresponding Author:

Stig Arve Sæther
Department of Sociology and Political Science
Norwegian University of Science and Technology, NTNU, Dragvoll, 7491 Trondheim, Norway
E-mail: stigarve@ntnu.no, https://orcid.org/0000-0002-1429-4746

Mads Røgen Noesgaard is educated as a physiotherapist and holds a master’s degree in sport science from the Norwegian University of Science and Technology. He has an extent experience as a physiotherapist from professional sports especially related to football and handball.

Stig Arve Sæther is an associate professor in sport science at the Norwegian University of Science and Technology, with an extensive research portfolio in talent development within sports and especially football. Sæther is head of the sport science staff, head of education at the department of Sociology and Political science and head of the research group Skill and Performance Development in Sports and School (SPDSS).

Decision-making on injury prevention and rehabilitation in professional football – A coach, medical staff, and player perspective

ABSTRACT

Purpose
The aim of this study is to research how the decision-making on RTP from the medical staff impact on the perceived short- and long-term performance of the player and the team, from a coach, medical staff, and player perspective. Methods: Two professional football players, one physical coach, one physiotherapist and one assistant coach were interviewed in-depth and recruited because of their insight, experience, and expertise from one Norwegian premiere league club. Results: The decision-making process on RTP in the club were partly based on the hierarchy in the club, where the coach was on the top among these actors. Despite that the actor´s describes the process as a natural dynamic, and felt a shared responsibility in the process, their different roles impact on the decisions. The RTP decision was affected by aspects such as the period in the season, earlier injury experience of the player and the medical staff and coach collaboration. Conclusions: Even though the medical staff and the injury prevention could mean that the player could have a longer career, the choices made in the process of RTP is often based on short term player and team performance. Applications in sport: Professional football players have competition as a living and are expected to enjoy and embrace competing against both other teams related to winning trophies and teammates related to a place on the team in matches. This degree of competition was also seen as a part of the RTP process since the competition with teammates gave the players motivation to overcome their injury situation and get back to compete for their “spot” on the team. Even though this study only includes experiences from one professional football club, it gives insight into how the RTP process is done in a professional football context. Future studies should consider recruiting representatives from the club management, which also could give insight on how the macro aspects of a club impact on the RTP decisions in the coaching team of a professional football club.

Keywords: return-to-play, professional sports, communication

INTRODUCTION

The development of professional football player is complex and consist of a myriad of factors, including injury prevention and rehabilitation through the return to play (RTP) (38). Even though the development of injuries in European professional football has decreased over the last two decades (10), the impact of injuries still plays a major role in both team and individual player development and success (7). Time loss in on field training and matches may have a negative impact on the players development, which makes it vital to minimize the duration of rehabilitation and RTP process. The responsibility of injury prevention, treatment and following RTP has in the literature been described as the responsibility of the medical staff, even though a strong coach and player involvement has been recommended (10). Even so, lack of needed authority in this process, have been highlighted as a challenge since both the coaching team and especially the head coach, and the players are expected to be a part of the decision process, hereby creating a dilemma (26). The need for a high performing medical team is thereby indicated crucial for the present success, but also future accomplishments (7).

Knowing that the major predictor in future injury being previous injury (13, 27-28, 35, 45), it has become standard procedure in European professional football clubs to screen and evaluate both in-squad players and potential investments even though research points to a lack of predictive capabilities (29, 46). Hereby the screening process is arguably/potentially increasing the consequences of previous injuries and treatment of such and the importance of injury preventive measures. In the pursuit of securing the best possible squad at all times injury preventive programmes such as FIFA11+, seems common but often adjusted based on either screening results or coaches’ preferences and hereby losing its evidence-based merits (29-30, 34, 46). Another promising preventive strategy is tracking and managing of load and restitution of the individual player and indicated to both increase the “here and now” short-term performance and the long-term performance. The main aim is to reduce the risk of injuries and illness (19, 24, 36), but it also presents a risk of withdrawing players from training and matches unnecessary.

The rehabilitation process of a player must address and manage the psychological and sociological health of the player (12). Though the general plan and goals of the rehabilitation is clear there is a lack of gold-standard and consensus for RTP which complicates the last steps before returning to training and competition (22). The literature advocates a shared-decision-making process to optimize this process. Coaches, medical staff, physical coaches, and the individual player all possess insight about the state of the player seen in a bio-psycho-social framework (5-6, 8, 47). A process as such is nonetheless challenged by the different profession’s confidence in their own decision, but also potentially with a lack of trust in others, hereby creating a dilemma where authority and power becomes more important than teamwork (9-10, 20). To increase the overall medical effort, the literature advocates an SDM-approach to minimize injuries and rehabilitation periods and improve RTP (1). Still, Paul et al. newly published editorial are highlighting that there has been identified concerns surrounding the social complexities of elite sports and the difficulties of truly applying this concept in practice (37).

Most of the research on this subject and in professional football have used a quantitative approach (7) and there seems to be a need of qualitative insight on how this process unfolds in practice, and how and by whom the decisions are made. An exception is Law and Bloyce (25) who interviewed professional football managers behavior towards injured players. The results indicated that managers at the lower levels felt more constrained to take certain risks related to injured players. The aim of this study is to research how the decision-making on RTP from the medical staff impact on the perceived short- and long-term performance of the player and the team, from a coach, medical staff, and player perspective.

METHODS

Participants 

Two professional football players, one physical coach, one physiotherapist and one assistant coach were interviewed in-depth and chosen based on strategic selection because of their insight, experience, and expertise in the field and their long-term involvement within one Norwegian premiere league club. The two players have in total more than 15 years in the club, while the physiotherapist and the physical coach has been in the club’s medical team for more than five years and altogether more than 20 years of experience in the field. The assistant coach has more than seven years of coaching experience. The participants are described in table 1.

 

All interviews were conducted in person and the location chosen by the interviewee. The length of each interview varied from 50 to 90 minutes with a mean at 70 minutes. Each interview was initiated with general questions to start the conversation and to get more background information on each participant. Prior to the interviews the questions were largely prepared to facilitate the conversation into different themes and topics of interest, with prepared follow up questions when depth and more context was needed. The questions varied specificity from general questions about the interviewee’s thoughts on the injury-period (e.g. “How do you think a player can develop while injured”) to more defined questions about the different actors’ actual role in the decision-making process about RTP (e.g. What role does the player has in the RTP-decisions). With these types of specific questions, the former mentioned extensive experience and expertise in the field was highly prioritized in the selection of participants. This made the insight in the specific club more extensive and gave the answers more depth. In addition, all participants were giving the opportunity to read through the transcript and afterwards able to withdraw parts or the interview in full, which none of the participants did. None of the participants neither wanted to alter the transcription. All interviews were audio-recorded and transcribed verbatim. By using pseudonyms for each participant, the transcriptions ensured the interviewee’ confidentiality and furthermore, ethical approval was in accordance with and approved by the Norwegian Social Sciences Data Services (number: 678375).          

Analysis
The analysis of data was done with the six steps of theme-centred approach as described by Braun and Clarke (2-3). The process was initiated by the transcription by the first author who afterwards read and reread the data twice. This was followed by initial coding, phase two of the chosen method. In this process the transcription was revisited multiple times until the final codes were discovered and presented to the second writer for discussion. The total of 47 codes were structed using a mind-map, which visualised the third phase of the process and used to structure the data into nine higher-order themes. Phase four was a back-and-forth process rereading the transcript, revising the raw material for clarifying questions, reviewing the codes all in all to elaborate the emerged themes. Through dialog and discussion within the research group the final three/four themes were identified, and subgroups reviewed and hereby phase five concluded. Finally, phase 6 was a detailed process and highly interwoven with the analysis of data. To present the findings in an argumentation related to research question and to illustrate the story of the data it was important to revise the extracts and go back to the both the higher order themes and the final themes in the writing of the report to ensure that the essence of the data was captured and presented. The final report presents the experienced everyday life of the participants in this specific Norwegian Premier League football club, how they perceive the decision-making process in the context of both development and performance and how the structure and reality of modern football plays and important role in both injury prevention and RTP after injury.

RESULTS

The actors in the RTP process – the club hierarchy
According to the actors (medical staff, coach and players), the prevention of injury and RTP practice has changed throughout the last decades, from a collective focus to a more specific and individual practice, described as a positive change by all the actors. RTP was described as a process, with benchmarks which was considered a motivational factor in the overall rehabilitation process. The decision-making process in the professional football club related to decisions on injured players and their capacity to play were affected to some degree by a hierarchy in the club. Even though the actor´s in the present study describes the process as a natural dynamic, and that they agree on their shared responsibility of the process, the different roles impact on the decisions.

Highest in the hierarchy are the coaches, and even though they highlight that the medical staff has an impact on their decision, the coaches seem to be the final decision maker in the process. This is indicated as a natural order because the coach is the one to take the ”fall” when the decisions shows to be wrong or more precisely have a negative output and also the final responsibility for the team performance. The coach described therefor a need to keep the medical staff on their toes, which the medical staff described as a challenge of their decisions, often based on what they considered external pressure on performance and results. This again meant that the medical staff had to make the “right” decision to keep their authority in the collaboration with the coaches.

The players felt in this regard that the medical staff had a two-sided role or responsibility both towards the coaches and the players, but that they still according to the players weigh the perspective of the player the heaviest. This double role was considered challenging and could mean lack of support in cases of doubt, while the medical staff considered that the final decision was taken by the coaches and the player. From the player perspective the trust was described as essential in this process. So even though trust, communication and collaboration are fundamental elements to keep a squad of players performing, there is also a need for a trust in the actors’ competencies and loyalty, both highlighted by the coach Lars: “Despite thinking about the result, first and foremost, we of course think: “The best for the player”. Because the player performs best when he is 100% healthy, both physically and mentally.” The physical coach Thomas stated this on the matter:

Thomas: “Because the vast majority of players understand deep down what the point is. They know when they shouldn’t go out there. They want to have hope, that: “yeah, it’s allright” and so sometimes our job is actually just to say: “Yes, it’s actually allright”, even if it’s 50/50, if it’s the last match on the season and they wanna take the chance anyways. Okay, then we have to see that and then just say: “This is allright”.

Thomas argued that their role in the process was to inform the coaches and even though the decision was not always in line with their suggestions, they felt that their opinions was considered vital for the final decision-making.

The factors that impact the decision process

Because of the complexity and uncertainty of who decides which players could play, the medical staff experience situations where at times they felt pressured to clear a player for playing, which in their experience often leads to a longer injury period. And despite the open communication, the pressure got more intense especially before important matches and at the end of the season, as this conversation and the following quotes indicates: Physiotherapist Hans: “You get a player who runs at 60%?”, Coach Lars: “Yes, but he is so important for us in set-pieces, so we have to have him”. This becomes even more prominent at the end of the season as physical coach Thomas highlights: “The fewer matches left, the greater chances you are willing to take with the athlete’s health”.

The decision to deny a player to train or play a match based on the risk of injury, was considered difficult for the medical staff because of uncertainty of the outcome. The coach describes how they in some cases start the player and see how it goes. Even though this was described as happening seldom and especially since this could be considered treating the players differently, which potentially could impact the team dynamic:

Lars: “If you and I play in the same position, and you train 3 times a week but you are a little better than me. I’m training every single day, and then you get to play matches. I train more than you, twice a week, and then arrangements will be made for you to play. That could become a conflict.”

The medical staff points out how this load-management strategy is potentially positive for RTP, the coach argument furthermore how this might add pressure for the next matches both for the player and the medical staff. If the team loses, one could consider that being in minus and that means that the next match must be won. This adds on to the earlier statement that an injury might be a heavy process for a player:

David: “From the moment you feel that you are a part of something, then you will show up the day after you have been injured, then you show up for work. You eat breakfast, you go to the locker room and then the rest of the team go out on field and do what you love the most, they play football. But you wander into a dark gym alone and do what all footballers think is the most boring job, cycling and doing rehab training. As boring as it gets. But you have to do it. You go into such a lonely and confined, empty mental phase, it’s really hard.”

What was considered the “right” decision depended on the perspective, even though obviously the most impacted part is the player:

Niels: “Perhaps I have been lucky in that I have not had so many major injuries, but at the same time the one injury I have had, where it was done the way it was done, that was enough for me to think: “yes, I lost some good matches that year”, then you can think of those who have been injured longer and have had more injuries, how much it has affected them.”

Injuries are however also described by all actors as a natural part of professional football, and that this often means taking risks to be able to perform on the highest level. One of the players, David, describes it as following:

David: “At the top level, you are balancing on a knife’s edge much more often, because you are pushing boundaries all the time and then the need for medical help is all the greater than when you operate at a not so fully professional level.”

It could seem from a professional players perspective that the players consider their everyday life as a footballer as finding the optimal balance to be able to stay fit and avoid injuries, and that this situation is difficult and that they need help from the medical staff to be able to keep staying “in the game”. Even so, the physical coach Lars highlights the difference between pain and injury:

Lars: “I think when you play football and it’s one-on-one, it’s dueling, you can get a knee in the side, you can get hit by an elbow, so after a football match, you might have a bruise here and a little bit of swelling there and you can have, stiffness in generel. That doesn’t mean you need 2-3 days to recover because that pain you feel”.

Protecting the players

The coach stated that it was important to protect the players and not introduce them for unnecessary risk, even though he pointed out that there is a limit in terms of how much consideration one could do for each player. In this regard did the physical coach acknowledges that there had not been a reduction in the number of injuries despite the heavy number of added resources to prevent them. The injuries have changed but one has not been able to eliminate the incident rate:

Thomas: “There is much less ankel rolls, but there are more hamstring injuries and groin injuries because there is more sprinting in the matches and the matches are closer schedueled. And you can’t quite solve that. Even with sufficient sleep, enough nutrition, tablets in the fusion of plasma, i.e. “you name it”, game ready – the player still breaks down and then you see that if you train very well, then maybe you will go through the season with very little damage.”

This was also something the players describes as problematic in certain situations, as stated by Niels: “Coach, physio and they, they really push you back in and then it’s difficult as a player to sit there and say: “I’m not healthy”, it’s difficult!”

The physical coach recons it is all about the time spent on the pitch to improve RTP and the high amount of matches impact on the possibilities for the medical staff to schedule and complete the injury preventions and rehabilitation. One example mentioned are an away match where the travel time is the reason for the player not attending enough training sessions, even though he is ready to train.  Furthermore, the game importance is an important factor because of the impact on the results sportingly and economically and has been found to be the reason as to why players play partly injured, or at least adding on to the pressure on the medical staff and their decision on every player potentially injured.

          Also, one of the players described how he perceived that the players are at their best when the get to train and play matches as much as possible:

David: “All footballers perform at their best when they get the opportunity to play football every day. Play every match. That’s when you get into a rhythm, where you act on intuition in battle and in that moment. In order to do that, you have to have continuity in your training and to have that, you have to be good at taking care of your body, to manage and last through a tough week of training, to perform in every match. So it’s definitely important. You profit from doing a good job (ed. injury prevention) in order to be able to perform in the best possible way. It is absolutely indisputable.”

Both the players and the medical staff highlights that the injury prevention is important for the players to be able to train more.  The physical coach highlights that this injury prevention training has a direct impact on the player opportunity to run faster and develop more power.

One of the players mentions how each club and their culture try to maximise the development and that the club culture is impacting the performance. This was also mentioned by the coach who stated that building the club is one of the most important tasks for the club, which is considered difficult since both players and coaches comes and goes. Another challenge is the impact the head coaches have on how the club perceive injury and development. The physiotherapist describes how the many changes also impact on the medical staff and their way of working:

Hans: “I think that, the biggest challenge in all of this is the constant change in player material, the constant change, at least as it has been in X, that coaches change, and therefore you constantly have different routines. It is natural that a coach who comes in and is boss wants to have it his way, and then a new coach comes in who wants it his way. Then there will always be changes and that means that what you tested on last year will be tested in a different way this year.”

Both players and the physical coach add on to this position, even though they also see positive outputs when new people are trying to collaborate:

Thomas: “Things that work well can also be diluted by poor execution. I think we make it work. I think so. that’s how it is when you bring new things to the table. Basically, it should be a good thing and if you manage to get best out of it, then it will be beneficial.”

The injury situation as an opportunity for development

All the actors thought of the injury period as a period for potential development of performance level of the player. So even though the players considered it as a tough and challenging period, it also contains opportunities. The coach highlighted that this motivation and opportunity had to come from within, and that he medical staff and the coach’s role was to facilitate and further motivate. In that way the injury period can be effective and also an opportunity, which could be considered a win-win situation both for the player and the team. 

Still, at times the players felt pressured to play, and sometimes felt alone and “naked” in the discussion between them, the medical staff and the coaches. This was partly confirmed by the physiotherapist, who described football as being black or white at times, and that he felt the need to protect the player:

Hans: “A player who is out several times and often… It can very quickly become black and white in a football club, “This player is always injured. No, we’ll give up on him a little”, and then it’s challenging to say: “You mustn’t give up on him, even if he’s a bit injured now. There are several factors that cause him to be injured and we have to look at ourselves as well, all of us.” What we have often done is to look at the coach and say: “If we are going to get him out of this, we’ll have to make a change. What we are doing now is not good enough. So we have to take him out of training and have to do this instead of that. He can’t play every game and at the moment”.

However, at other times the medical staff also feel the need to push the players to return to ordinary training or playing matches. They feel the need to be careful since they might misstep. Some players might get pushed back to soon, while others need a push.

Lars: “Sometimes where you have to push a little, and we really do that for the sake of the player, not because we absolutely have to. We don’t take any chances with players, that is. But if we see that he has done what he is supposed to and at the same time it is a player who is a bit more careful with himself. Because that too, you have to know the group, you have to know the player, because there are some who can be too tough too early, and then there are some who are actually ready, but holding back. So you can say that sometimes we have to try and push them in a positive way too, I think. Without us doing anything wrong.”

One of the players Niels stated that for some of the players, they need to be more included in the decision-making-process. One example mentioned by one of the players was the importance to get into the pre-season together with the squad, to be able to compete about his playing position.

The medical staff clearly stated that they did not consider themselves having the definitive solution in every case. They also mentioned the fact that holding a player back from a match based on the fear of being injured might deprive the player from development and potentially economic gain (e.g. club transfer, bonuses etc.) or the team’s performance or the club’s economic gains. Many of the actors highlighted that if the player felt ready to play, and the coaches meant that he would have an impact on the game, the medical staff would take that into consideration. This position of taking a decision which is good for all the actors both in a short-term and long-term perspective was considered a difficult dilemma for the medical staff, since they feel an extra responsibility related to the players health.

Keeping the players on their toes but still together

The coach also highlighted that the competition between players could challenge the individuals in the club. Internal competition is essential and when a player is injured, that could create an opportunity for other players. This competition was also highlighted by the two players, however as a stressor for the injured player. The coach however stated that this type of competition must be present and that it makes the players push each other, and fight for a place on the team. This type of pressure, trying to withhold your place on the team, having the right attitudes, frequent changes in the coaching staff, and short-term results, was describes from all the actors as impacting the medical staff’s opportunity to impact the decision for players to play matches and their development. Both the coach and the medical staff highlighted that this might impact the decision, but never determined the RTP, while the players could consider this as a weighty stressor

The players point out a potential isolation of the injured players by dividing the players into two groups: those who are injured and those who are not, but this division is described differently based on the perspective. They also describe the rehabilitation as lonely, heavy, and boring, especially the acute phase, and experience that the injured players not to be a part of the community in the club, which the player Niels described in the following: “But I want to put it this way, you are down in hell and then you start the ascent from there, and then it becomes a bit like tunnel vision. You don’t see the light at the start, but you see it eventually”. The coach, however, does not describe this as an isolation or division of the team, but rather a natural part of the everyday life in a club, but highlight the importance of joint meals and meeting schedules. The medical staff have another nuance of this division, since an injury might be challenging and create a sense of exclusion, while this could also be good for the team, since the negativity which often comes with an injury does not get spread among the other team members. The physical coach highlights the same and furthermore that it should be attractive not to be injured.

All the actors describe the deprivation from matches in times of doubt about a player’s availability have both sportingly and economic negative impact on the player’s career:

David: “Football can be so simpel that if you, how should I put it , score a hat-trick in the right match against the right team, you can be like… And the salaries are so high, so if you end up in the right place then you, then you can in a way support the whole family for the rest of your life. So it’s quite clear that injuries affect the course of a career.”

Injuries means less time to train, and the actors agree that the time for the specific football training and matches are essential for a player’s individual development. Both the coach and the physiotherapist highlighted however the importance of making the most of the injury period, which could be considered as a window of opportunity to focus on individual skill development, which normally one does not have time for. The physical coach stated however that it might be difficult for a player to develop largely during the rehabilitation process. And this could be related to the somewhat black-white perspective the medical staff and the coach has on injuries. The physiotherapist meant that this approach might have a positive consequence for a player who have experienced an injury. They often work harder than before to be able to get back to football. At the same time Hans also pointed to the fact that the players could be “forgotten” by the coaches if they achieve a “bad” reputation: “But if you first get a reputation of being.. that the coach gets the feeling that he is not available, then it can often be difficult. A fight really. That is my experience”. The coach Lars partly confirmed this by stating that the coaches are aware of players who have a history of injuries, which often mean that they cannot play all matches during a season:

Lars: “In other words, injury follows injury. It’s a bit like that. So there are certain players that you know more or less that he is not going to play 100% of the games. Let’s say there is an exclusive player who often gets hamstring issues, then you know that during the season he will play 70% of the games. It may happen that we have players, who we know are like that.”

In a long-term perspective and focusing on the players career, the coach also highlighted that the players are screened and assessed by clubs if a club transfer is in motion, that a player with a large injury history would be considered as less interesting to recruit:

Lars: “[…] But the more players who don’t have an injury history.. So if you’re going to build a team then you have to get as few players as possible with an injury history, because often you see that those type of issues, especially if it’s the groin or hamstring or those types of injuries, they often come back.”

The coach described players’ injury history as essential when clubs assess which player they could recruit, and that injured players must convince the coaches to become relevant for a club transfer. These types of assessment are important for coaches in their process of building a squad both in a short-term and long-term perspective.

Discussion

The aim of this study is to research how the decision-making on RTP from the medical staff impact on the perceived short- and long-term performance of the player and the team, from a coach, medical staff, and player perspective. The decision-making process on RTP in this professional football club were partly based on the hierarchy in the club (40). So, despite that the actor´s in the present study describes the process as a natural dynamic, and felt a shared responsibility in the process, their different roles impact on the decisions. The coaches were described highest in the hierarchy and related to them being responsible for the sportingly results and the performance of the team. The players were described as having a say in the decision of his availability, even though they often highlighted an experience of being pressured to play in certain situations (9). The medical staff was considered to have a two-sided role, since they were employed and a part of the coaching team and naturally felt a responsibility on behalf of the coaches and the club, they also felt the need to protect the players and their health as professional health workers (20). Their decisions would often mean that they had to “disappoint” the coaches or the player, by denying the player to play or the availability of a player in a match.

Responsibility was a term especially the medical staff used to describe how they felt about their role, but also when taking part in the final decision in the RTP process. This responsibility became important in the process of making “the right” call based on the information available while trying to account for the interests of all the actors. This might mean that they let a player play, with a “let´s see how it goes” approach, and that the outcome of the decision was described as “right” if the player played the whole game. A dilemma in the process was also related to the natural part of pain and injury as part of professional football described by all the actors in the process (31). So even if protecting the players was important, time spent on the pitch is the main goal for both the individual players and the team’s development and performance. Even so, earlier research (41) has indicated that elite sports have a pain culture where pain is a natural and expected part of elite sports, which could have a negative impact on the players development, if this means that the players do not communicate when feeling injured or unavailable for training and matches.

Professional football is all about results and performance (32). So, a characteristic off successful environments is their constant search of areas to develop further (14). This seemed to be the case in this club as well since a period of injury was considered an opportunity for the player to develop. The players are competing about a place in the starting line-up and need to pick up the glow to get back into the team. Still, there was also a mutual understanding that each RTP case might be different and had to be considered individually. So, in some cases both the medical staff and the coaches felt that some players needed a push to get back. This may in many cases also be in the best interest of the player since it could mean that they in example get identified by scouts, impacting their career by a club transfer. Furthermore, this pressure could mean that the players are willing to take a higher risk by playing while injured. The players in this study described being injured as lonely and feeling isolated from the team, as found in earlier studies (32), which could be perceived as an increased motivation to RTP potentially even before the mind or body are ready.

In accordance with the focus on results and performance in professional football are also the high degree of uncertainty in this professional context (15). This could be related to the small margins between success and failure. This is also related to the RTP process, since all actors in the process of RTP must make the best decision for both the individual and teams’ performance. Still, there is a lack of knowledge related to the potential outcome of the decision. This means that the actors must “take risks” to be able to maximize the opportunity to succeed. While it was not a part of the study, the obvious economically benefits of decreasing time loss in training and competition on both an individually (players, medical team, and coaching team) and club level (potential sale of players), also makes both the rehabilitation and preventive strategies important. The club perspective might conflict with the individual actors in the RTP process, with the example of the club winning the league, while a player got injured because of the overload and hereby potentially ending his career.

CONCLUSIONS

All the actors in this study highlight that football is a sport where you must expect to feel pain regularly and that injury is a part of being a professional football player. So even though the medical staff and the injury prevention could mean that the player could have a longer career, the choices made in the process of RTP is often based on short term player and team performance. Professional football players have competition as a living and are expected to enjoy and embrace competing against both other teams related to winning trophies and teammates related to a place on the team in matches. This degree of competition was also seen as a part of the RTP process since the competition with teammates gave the players motivation to overcome their injury situation and get back to compete for their “spot” on the team. Even though this study only includes experiences from one professional football club, it gives insight into howe the RTP process is done in a professional football context. Future studies should consider recruiting representatives from the club management, which also could give insight on how the macro aspects of a club impact on the RTP decisions in the coaching team of a professional football club.

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