ISSN: 1543-9518|Vol. 24

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

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


Corresponding Author:

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

269 Brehm Hall University of Tennessee at Martin

Martin, Tennessee 38238

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

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

ABSTRACT

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

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

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

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

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

Literature Review  

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

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

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

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

Methods 

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

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

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

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

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

Figure 1. Example Best-Worst Question

Survey

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

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

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

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

Table 2. Demographic Information

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

Table 2. Continued

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

Table 2. Continued

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

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

Non-Renewal of Referee License

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

Youth Referees

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

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

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

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

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

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

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

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

Adult Referees

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

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

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

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

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

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

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

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

Conclusions

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

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

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

Acknowledgements

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

Conflicts of Interest

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

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

Authors: George Terhanian1


Corresponding Author:

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

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

Making the most of basketball three-point shot data

ABSTRACT

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

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

INTRODUCTION

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

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

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

NBA Basketball: Basic Rules and Strategies

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

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

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

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

Clear Communication 

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

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

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

Methods

Data

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

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

Analysis Method 

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

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

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

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

Table 1.

Results of final logistic regression analysis

VariableFrequencyLogitOddszp>zProbEV
DefDist       
0-3 ft.6%–1––.290.86
3-6 ft.54%0.251.294.740.00.341.02
6-9 ft.28%0.381.466.780.00.371.11
9+ ft.12%0.471.607.740.00.391.17
ShotClock       
0-2 secs.5%–1––.210.62
2-4 secs.7%0.631.888.020.00.330.99
4+ secs.88%0.772.1711.870.00.361.08
Catch       
Off catch75%–1––.361.07
Off dribble25%-0.09.0.91-3.210.00.341.01
Period       
124%–1––.371.11
224%-0.110.89-3.340.00.341.03
325%-0.050.96-1.340.18.361.08
4+27%-0.150.86-4.540.00.341.01
ShotDist       
22-24 ft31%–1––.381.13
24-25 ft.36%-0.090.91-3.250.01.361.06
25-26 ft.20%-0.170.84-5.120.00.341.01
26+ ft.13%-0.300.74-7.130.00.310.92
Venue       
Away50%–1––.351.04
Home50%0.051.052.140.03.361.07
…Constant–-1.460.23-17.420.00.190.56

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

Standard versus Non-Standard Interpretations

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

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

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

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

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

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

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

It would increase from 35% to:

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

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

Calculating Each Shot’s Make Probability

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

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

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

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

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

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

Table 2.

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

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

Predicting the Likely Effect of Multiple Changes to Multiple Predictor Variables

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

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

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

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

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

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

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

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

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

Figure 1.

Percentage of predicted makes by scenario 

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

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

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

Figure 2.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Figure 3

All-or-nothing simulation 

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

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

Table 3.

Results of Steph Curry logistic regression analysis 

VariableFrequencyLogitOddszp>zProbEV
DefDist       
0-3 ft.11%–1––.240.72
3-6 ft.55%0.892.442.440.02.431.29
6-9 ft.24%0.972.652.480.01.451.35
9+ ft.10%1.263.512.750.00.521.55
ShotClock       
0-2 secs.2%–1––.250.75
2-4 secs.3%2.108.172.190.03.722.15
4+ secs.95%0.792.211.100.27.421.25
Catch       
Off catch46%–1– .401.21
Off dribble54%0.151.17.750.46..441.32
Period       
133%–1––.441.30
219%0.011.010.050.963.441.31
329%-0.030.97-0.120.902.431.28
4+19%-0.260.77-0.910.364.371.12
ShotDist       
22-24 ft16%–1––.551.65
24-25 ft.31%-0.750.47-2.460.01.371.11
25-26 ft.24%-0.510.60-1.580.11.431.28
26+ ft.28%-0.650.52-2.040.04.401.12
Venue       
Away54%–1––.411.23
Home46%0.111.12.560.58.441.31
…Constant–-1.530.22-1.80.07.190.56

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

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

Figure 4 

Steph Curry’s fine-tuning simulator 

Discussion

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

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

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

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

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

Application In Sports

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Conclusions

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

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

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

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

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

Appendix

Variables in the 2014-15 NBA shot dataset

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

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

ACKNOWLEDGEMENTS

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

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  21. Pina, M. (2021, June 20). Ben Simmons’s Flaws Laid Bare in Potential End of the Process. Sports Illustrated. https://www.si.com/nba/2021/06/21/sixers-hawks-game-7-ben-simmons-flaws-trae-young
  22. Ramsay, J. (1963). Pressure Basketball.
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  25. Stats LLC and NBA to make STATS SportVU Player Tracking data available to more fans than ever before. (2016, January 19). NBA.com: NBA Communications. https://pr.nba.com/stats-llc-nba-sportvu-player-tracking-data/
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2024-05-21T13:46:56-05:00May 17th, 2024|General, Research, Sports Management|Comments Off on Advice on making the most of basketball three-point shot data
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An Analysis of the Geographic Distribution of Minor League Sports Teams

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


Corresponding Author:

Mark Mitchell, DBA

Professor of Marketing

Associate Dean, Wall College of Business

NCAA Faculty Athletics Representative (FAR)

Coastal Carolina University

P. O. Box 261954

Conway, SC 29528

mmitchel@coastal.edu

(843) 349-2392

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

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

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

An Analysis of the Geographic Distribution of Minor League Sports Teams

ABSTRACT

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

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

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

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

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

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

INTRODUCTION

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

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

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

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

THE ORGANIZATION OF MAJOR LEAGUE AND MINOR LEAGUE SPORTS 

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

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

Men’s Baseball

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

Appendix A: Major League Baseball and Minor League Affiliates 

Major League Triple-A Double-A High-A Low-A 
Arizona Diamondbacks Reno Aces Amarillo Sod Poodles Hillsboro Hops Visalia Rawhide 
Atlanta Braves Gwinnett Stripers Mississippi Braves Rome Braves Augusta GreenJackets 
Baltimore Orioles Norfolk Tides Bowie Baysocks Aberdeen IronBirds Delmarva Shorebirds 
Boston Red Sox Worchester Red Sox Portland Sea Dogs Greenville Drive Salem Red Sox 
Chicago Cubs Iowa Cubs Tennessee Smokies South Bend Cubs Myrtle Beach Pelicans 
Chicago White Sox Charlotte Knights  Birmingham Barons Winston-Salem Dash Kannapolis Cannon Ballers 
Cincinnati Reds Louisville Bats Chattanooga Lookouts Dayton Dragons Daytona Tortugas 
Cleveland Guardians Columbus Clippers Akron RubberDucks Lake County Captains Lynchburg Hillcats 
Colorado Rockies Albuquerque Isotopes Hartford Yard Goats Spokane Indians Fresno Grizzlies 
Detroit Tigers Toledo Mud Hens Erie SeaWolves West Michigan Whitecaps Lakeland Flying Tigers 
Houston Astros Sugar Land Skeeters Corpus Christi Hooks Asheville Tourists Fayetteville Woodpeckers 
Kansas City Royals Omaha Storm Chasers Northwest Arkansas Naturals Quad Cities River Bandits Columbia Fireflies 
Los Angeles Angels Salt Lake Bees Rocket City Trash Pandas Tri-City Dust Devils Inland Empire 66ers 
Los Angeles Dodgers Oklahoma City Dodgers Tulsa Drillers Great Lakes Loons Rancho Cucamonga Quakes 
Miami Marlins Jacksonville Jumbo Shrimp Pensacola Blue Wahoos Beloit Snappers Jupiter Hammerheads 
Milwaukee Brewers Nashville Sounds Biloxi Shuckers Wisconsin Timber Rattlers Carolina Mudcats 
Minnesota Twins St. Paul Saints Wichita Wind Surge Cedar Rapids Kernels Fort Myers Mighty Mussels 
New York Mets Syracuse Mets Binghamton Rumble Ponies Brooklyn Cyclones St. Lucie Mets 
New York Yankees Scranton/Wilkes-Barre RailRiders Somerset Patriots Hudson Valley Renegades Tampa Tarpons 
Oakland Athletics Las Vegas Aviators Midland RockHounds Lansing Lugnuts Stockton Ports 
Major League Triple-A Double-A High-A Low-A 
Philadelphia Phillies Lehigh Valley IronPigs Reading Fightin Phils Jersey Shore BlueClaws Clearwater Threshers 
Pittsburgh Pirates Indianapolis Indians Altoona Curve Greensboro Grasshoppers Bradenton Marauders 
San Diego Padres El Paso Chihuahuas San Antonio Missions Fort Wayne TinCaps Lake Elsinore Storm 
San Francisco Giants Sacramento River Richmond Flying Squirrels Eugene Emeralds San Jose Giants 
Seattle Mariners Tacoma Rainiers Arkansas Travelers Everett AquaSox Modesto Nuts 
St. Louis Cardinals Memphis Redbirds Springfield Cardinals Peoria Chiefs Palm Beach Cardinals 
Tampa Bay Rays Durham Bulls Montgomery Biscuits Bowling Green Hot Rods Charleston RiverDogs 
Texas Rangers Round Rock Express Frisco RoughRiders Hickory Crawdads Down East Wood Ducks 
Toronto Blue Jays Buffalo Bisons New Hampshire Fisher Cats Vancouver Canadians Dunedin Blue Jays 
Washington Nationals Rochester Red Wings Harrisburg Senators Fredericksburg Nationals Fredericksburg Nationals 

Source: (20).  

Men’s Basketball

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

Appendix B: G-League Teams and NBA Affiliations 

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

Source: (27). 

Women’s Basketball

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

Men’s Hockey

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

Appendix C: American Hockey League Teams and Affiliated NHL Teams 

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

Source: (13). 

Men’s Soccer

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

Source: (36). 

Women’s Soccer

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

Men’s Football

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

New York/New Jersey Metro

New Orleans, LA

Philadelphia, PA

Pittsburgh, PA

Orlando, FL

Seattle, WA

Las Vegas, NA

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

Men’s Lacrosse

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

Women’s Professional Hockey

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

Miscellaneous: Athletes United

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

METHODOLOGY 

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

  1. States and/or provinces that host the most minor league teams; 
  1. Cities that host the most minor league teams; 
  1. Cities that are most likely to be considered for league expansion in the future. 

RESULTS 

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

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

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

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

State-by-State Analysis

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

California (26 teams in 17 different communities)

Texas (25 teams in 15 different communities)

Florida (23 teams in 16 different communities)

New York (19 teams in 12 different communities)

North Carolina (17 teams in 12 different communities)

Pennsylvania (12 teams in 9 different communities)

Ohio (10 teams in 7 different communities)

Georgia (9 teams in 8 different communities)

Iowa (8 teams in 5 different communities)

Michigan (8 teams in 5 different communities)

South Carolina (8 teams in 4 different communities)

Oklahoma (7 teams I 2 different communities)

Washington (7 teams in 4 different communities)

Arizona (7 teams in 3 different communities)

Indiana (7 teams in 3 different communities)

Virginia (7 teams in 5 different communities)


Province-by Province Analysis 

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

Ontario (6 teams in 3 communities)

British Columbia (3 teams in 2 communities)

Quebec (3 teams in 2 communities)

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

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

City-by-City Analysis 

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

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

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

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

DISCUSSION 

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

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

INSERT TBL3

A Cautionary Note – Minor League Baseball Relocations 

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

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

CONCLUSIONS

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

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

APPLICATION IN SPORT

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

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

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

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2024-05-01T12:50:45-05:00May 3rd, 2024|General, Research, Sports Management|Comments Off on An Analysis of the Geographic Distribution of Minor League Sports Teams
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Decision-making on injury prevention and rehabilitation in professional football – A coach, medical staff, and player perspective

Authors: Mads Røgen Noesgaard 1& Stig Arve Sæther2

1Department of Sociology and Political Science, Norwegian University of Science and Technology, Trondheim, Norway
2Department of Sociology and Political Science, Norwegian University of Science and Technology, Trondheim, Norway

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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2024-04-08T09:47:11-05:00April 8th, 2024|General, Research, Sports Management|Comments Off on Decision-making on injury prevention and rehabilitation in professional football – A coach, medical staff, and player perspective
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An analysis of the factors impacting win percentage and change in win percentage in women’s Division 1 college lacrosse

Authors: Christiana E. Hilmer1

1Department of Economics, San Diego State University, San Diego, CA

Corresponding Author:

Christiana Hilmer, PhD
5500 Campanile Drive
San Diego, CA 92182-4485
chilmer@sdsu.edu
619-301-9388

Christiana E. Hilmer, PhD, is a Professor of Economics at San Diego State University in San Diego, CA. Her research interests include the economics of sports, applied econometrics, labor economics, and resource and environmental economics.

An analysis of the factors impacting win percentage and change in win percentage in women’s Division 1 college lacrosse

ABSTRACT

What factors in women’s NCAA Division 1 college lacrosse led to an increase in win percentage in a single season and a change in win percentage across two consecutive seasons? Do these factors differ between teams at the top and the bottom ends of the win distributions? Using data from the 2023 and 2022 lacrosse seasons, we find that goals, assists, unassisted goals, and participation in the NCAA Championship tournament have a positive impact on win percentage, while opponent’s goals and if the team was new in 2023 have a negative impact on win percentage. The most crucial factor that explains the change in win percentage between the 2022 and 2023 lacrosse seasons is an improvement in the change in total shots ratio, while changes in attacking efficiency and defending efficiency are also important, all together explaining 58% of the variation. Teams at the bottom of the distributions have similar characteristics for both win percentage and change in win percentage as those teams in the middle and the top of the distributions, although there are some slight differences in the magnitudes of the statistically significant variables. These results suggest that lacrosse players and coaches should focus on obtaining additional goals and assists while concurrently minimizing the opponent’s goals to increase win percentage and changes in win percentage.

Keywords: distributional impacts, quantile regression, women’s college lacrosse

INTRODUCTION

Since the advent of sabermetrics pioneered by Bill James and the popularity of Lewis’s (5) Moneyball, the use of statistics to analyze sports has exploded in popularity. Reep and Benjamin (7) applied statistical analysis to team-wide factors in soccer where they investigated how the passing skill and position of a player on the field impacts goals. When analyzing a team’s performance, it is essential to determine which factors lead to a team’s success. Most research in this field has focused on professional sports. Busca et al. (1) examine eleven high-stakes international soccer tournaments to determine where a penalty kick is most likely to be struck. Pelechrinis and Winston (6) develop a framework that is comprised of publicly available data to determine the expected contribution of an individual professional soccer player to the probability of his team winning the game. Alberti et. al. (1) examine goal-scoring patterns in four different professional soccer leagues and find that the majority of goals are scored in the second half of the game with the most goals being scored in the last fifteen minutes of play. Castellano et. al. (3) analyze professional soccer match statistics to determine which factors impact winning, drawing, and losing a game and find that shots, shots on goal, and ball possession are important on the offensive end of the field, while total shots received and shots on target received are important on the defensive end of the field. A notable departure from research that focuses on professional soccer is Joslyn et al. (4), who examines the factors that improve the change in win percentage in men’s Division 1 (D1) college soccer. They find that improving shots, attacking, and defending positively impact the change in win percentage between two consecutive seasons.

This research utilizes the tools found in the team-focused literature from soccer and extends it to lacrosse. Soccer and lacrosse have many similarities, especially regarding possession, assists, goals, and defense. There are also marked differences between the two sports in addition to the obvious one: in soccer the ball is kicked while in lacrosse the ball is played with a net attached to a stick. Lacrosse is a higher-scoring game due to the presence of a 90-second shot clock and defending a women’s lacrosse player is more difficult in lacrosse than it is in soccer. One reason for this is that in lacrosse it is a foul to “move into the path of an opponent without giving the opponent a chance to stop or change direction, and causing contact” (page 51, 2022 and 2023 NCAA Women’s Lacrosse Rules Book (6)), while there is no such rule in soccer. Another reason is due to a rule in women’s lacrosse called shooting space (page 54, NCAA 2022 and 2023 Women’s Lacrosse Rules Book (6)), which states that “with any part of one’s body, guarding the goal outside or inside the goal circle so as to obstruct the free space to goal, between the ball and the goal circle, which denies the attack the opportunity to shoot safely and encourages shooting at a player” while soccer does not have a comparable rule. According to NCAA Statistics (7), the average number of goals per game scored in D1 women’s college lacrosse in 2023 was 12, while the average number of goals per game scored in D1 women’s college soccer in 2023 was 1.39. Another notable difference between lacrosse and soccer is that the offside rules are very different. The offsides rule in lacrosse states that there must be at least five defenders behind their defensive restraining line and at least four offensive players behind their offensive restraining line (page 61, NCAA 2022 and 2023 Women’s Lacrosse Rules Book (6)). The offsides rule in soccer is much less stringent and it states that when in the opponent’s half of the field “the player is not closer to the opponent’s end line than at least two opponents” (page 52, NCAA 2022 and 2023 Soccer Rules Book (7)). These disparities between lacrosse and soccer may result in differences in which factors impact win percentages and changes in win percentages.

This research examines which factors lead to an increase in win percentage and change in win percentage for women’s Division 1 college lacrosse teams. We also seek to determine if these factors differ among teams in the 25th, 50th, and 75th percentiles for win percentage and the change in win percentage. Using data from the 2023 women’s D1 college lacrosse season, we explain 86% of the variation in win percentage. Goals, unassisted goals, and participation in the NCAA Championship tournament have a statistically significant positive impact on win percentage, while opponent’s goals and if the team was new in 2023 have a statistically significant negative impact on win percentage. The most crucial factor explaining the change in win percentage between the 2022 and 2023 lacrosse seasons is an improvement in the change in total shots ratio, while changes in attacking efficiency and defending efficiency are also statistically significant, all together explaining 58% of the variation. The variables that explain both win percentage in a single season and the change in win percentage between seasons are similar between the 25th, 50th, and 75th percentiles. This suggests that teams at the bottom of the distributions should focus on the same factors as those at the top when they seek to improve during a season and between seasons.

METHODS

Data Source
Win percentage was collected from the National Collegiate Athletic Association (NCAA) archives for the 2023 and 2022 seasons. A win was awarded one point while a loss was awarded zero points. Offensive and defensive statistics for the 2023 and 2022 seasons were collected from each University’s women’s lacrosse website housed in the season’s cumulative statistics. It is important to note that these data are provided by individual institutions and therefore the statistical findings of this research is dependent on the accuracy of the information provided by each school. In addition to winning percentage, data was collected on goals, assists, shots, opponent’s goals, opponent’s shots, unassisted goals, ground balls, turnovers, caused turnovers, draw controls, whether the team was new to NCAA D1 lacrosse in the 2023 season, and if the team made the NCAA Championship tournament in 2023. Of the 126 D1 women’s lacrosse teams, 123 had information on every variable listed above.

Variables and Distributions

This analysis aims to determine what factors impact a single season winning percentage and which factors impact the change in win percentage across two consecutive seasons. Figure 1 is a histogram of win percentage for the 2023 women’s lacrosse season. The average win percentage was close to 50% at 48.27%; the minimum win percentage was 0 for the two teams that lost every game during the season, while the maximum win percentage was from a team that won 95.65% of their games. The team with the second-highest win percentage won the 2023 NCAA National Championship tournament.


Summary statistics for the 2023 D1 women’s lacrosse 2023 season are found in table 1. The average number of goals and opponent’s goals nearly offset each other at 211 and 210, respectively. There was an average of 495 shots with a large standard deviation of 105. Below half the goals were aided by an average of 92 assists, while over half of the goals resulted from an average of 119 unassisted goals. There were nearly twice as many turnovers as there were caused turnovers, 7% or a total of 8 teams were new D1 lacrosse teams in 2023, and 24% of the D1 lacrosse teams made the NCAA end-of-season tournament.


Figure 2 contains a histogram of win percentage change, which is constructed by taking the win percentage in the 2023 lacrosse season and subtracting the win percentage in the 2022 lacrosse season. There are fewer observations in the change in win percentage because the seven teams who were new in the 2023 season did not have any statistics for the 2022 season. On average, most teams had a similar win percentage in 2023 as they did in 2022, with an average change in the win percentage of .16. The team with the lowest change in win percentage between the two seasons of -51.47 had a win percentage of 75% in 2022, dropping to 24% in 2023. At the other end of the spectrum, the team with the highest change in win percentage won 12% of their games in 2022 and improved to winning 50% of their games in 2023.

Following Joyce et al. (4), we construct three measures of team success to explain the change in winning percentage: total shots ratio, attaching scoring efficiency, and defending scoring efficiency. The first measure, total shots ratio, is constructed as

The total shots ratio in both 2022 and 2023 is .5, which means, on average, teams are matching their opponent’s shots with their own shots with a range in values from .23 to .7 in 2023 and .3 to .63 in 2022.  This finding for lacrosse compares favorably to what Joyce et al. (4) found for D1 college soccer, where the total shots ratio ranged from .24 to .69 in D1 men’s soccer.

            The second measure of team success is attacking scoring efficiently or goals to shots ratio.

The average attaching scoring efficiency for 2023 and 2022 was .42. This measure had a relatively smaller variability than the total shots ratio, with a minimum of around .3 for both years and a maximum of .5 in 2023 to .58 in 2023. This maximum means that the teams with the highest attacking scoring efficiency earn an average of one goal for every two shots. Being able to convert shots into goals is an essential aspect of winning games. Lacrosse teams are much more likely to convert shots into goals, as Joyce et al. (4) found an average attacking scoring efficiency of .1 or 1 goal for every ten shots in D1 men’s soccer.

The third measure of team success is the defending scoring efficiency, which is contracted as

This final measure determines if teams can prevent opponents from turning shots into goals. The average values for defending scoring efficiency are slightly higher than attaching scoring efficiency, with an average of .43 in 2023 and .44 in 2022. The variability is higher for defending scoring efficiency than attacking scoring efficiency, with a minimum of .31 in 2023 and .34 in 2022 and a maximum of .66 in 2023 and .77 in 2022. Teams that are better at preventing shots from being converted into goals typically have a higher win percentage.

Regression Model
The first step in our regression analysis is to empirically estimate the degree to which offensive and defensive statistics impact the win percentage for the 2023 lacrosse season. The win percentage regression model takes the form:

where  is the error term and i is the individual women’s lacrosse team.  This model is estimated using ordinary least squares to obtain the average marginal impact of each of the 11 variables, as well as using quantile regression at the 50th, 25th, and the 75th percentiles of the win percentage.  Quantile regression is a statistical method that estimates the association between the explanatory variables for a conditional quantile of the dependent variable, see Walmann (8) for a more detailed explanation.  In this application, we use quantile regression to determine if teams at the lower end of the win percentage distributions display different characteristics than those at the median and the top end of the distributions.

            The second part of the analysis follows Joyce et. al. (4) to determine what factors impact the change in win percentage between the 2023 and 2022 lacrosse seasons.  The regression model is as follows

where ε_i is the error term and i is the individual women’s lacrosse team. As with the individual season analysis, this model is estimated using ordinary linear regression and quantile regression at the 50th, 25th, and 75th percentiles.

RESULTS

Table 3 contains the results for the estimation of equation (4) from the 2023 lacrosse season with robust standard errors in parentheses. Looking first at the results from the ordinary least squares model, 86% of the variation in win percentage is explained by the 11 independent variables. Turning to the variables that are statistically significant, each additional goal results in an increase of .18 in win percentage, while each opponent’s goal results in a decrease of .2 in win percentage, with goals and opponent’s goals nearly offsetting each other. On average, one additional unassisted goal results in an increase of .13 in win percentage. Being a new D1 women’s lacrosse team in 2023 results in a 9 point marginally statistically significant decrease in win percentage relative to teams that have been in the league in previous years. This result suggests that new D1 teams have a difficult time navigating their first year likely due to players and coaches lacking experience and chemistry, making obtaining wins more difficult. Women’s lacrosse teams who participated in the 2023 NCAA Championship Tournament have a statistically significant almost 5 point higher win percentage than those who did not participate in the tournament. This finding is not surprising given that the two ways to get a team into the tournament are to either receive an automatic bid by winning their conference tournament or earn an at-large bid by having a compelling enough record during the regular season and conference playoffs.


The last three columns of table 3 contain quantile regression results at the 50th, 25th, and 75th percentiles of the win percentage distribution. Opponent’s goals are the only statistically significant factor to explain wins across all three percentiles. The magnitude of opponent’s goals is largest at the 25th percentile at -.24 and is -.20 for both the 50th and 75th percentile. Teams at the 25th and 50th percentiles of the win percentage distribution that participates in the NCAA end-of-season tournament has a statistically significant 7 point and 6 point higher win percentage, respectively, relative to those who did not participate, while this variable is not statistically significant at the 75th percentile. This may be because most, 73%, of the tournament participants come from the teams at the top 25% of the win percentage distribution, while most teams at the middle and bottom of the distribution did not participate in the tournament. Aside from this difference, the results are similar between the models at the three points in the win percentage distribution.

Table 4 contains the second part of the regression analysis which estimates equation (5) that attempts to determine what factors impact the change in win percentage between the 2023 and 2022 seasons. The variables contained in this analysis mimic those in Joyce et. al. (4) for men’s D1 college soccer. Looking at the OLS results, teams that had a one unit increase in the change in total shots ratio between the two seasons had a 2.4 increase in the change in win percentage. Teams with a 1 unit increase in the change in attacking efficiency had a 1 unit increase in the change in win percentage, and teams with a one unit increase in the change in defending efficiency decreased the change in win percentage by 1.2 points. The statistical significance between these lacrosse results and those found for soccer by Joslyn et al. (4) are identical, suggesting that even though there are many differences between the two sports, the same factors are important in explaining the change in win percentage between consecutive years. Comparing magnitudes between the two applications is not possible because the estimation methods differed. The statistical significance of the variables included in the quantile regression evaluated at the 50th, 25th, and 75th percentiles were the same as in the OLS regression. The quantile regression performed at the 25th percentile of the change in win percentage had the highest impact for the change in total shots ratio and the change in attacking efficiency, while the change in defending efficiency had the smallest impact. The change in total shots ratio and the change in attacking efficiency had the smallest impact for those teams at the 75th percentile, while the change in defending efficiency had the largest impact for those teams at the 50th percentile. These results suggest that the factors that impact the change in win percentage are similar across teams at the bottom and the top of the change in win percentage distribution, although the marginal impacts differed slightly between the percentiles.

Discussion

It is not surprising that additional goals led to an increase in win percentage and an increase in opponent’s goals led to a decrease in win percentage. However, it was unanticipated that many of the other offensive and defensive statistics included in the regression were not statistically significant. It is likely that these other factors either lead to the team’s ability to score goals, such as shots, ground balls, and caused turnovers, or lead to the opponent’s goals, such as turnovers. One drawback of this research is that it does not investigate how these other factors impact goals and opponent’s goals. One adage in lacrosse is “win the draw, win the game.” Even though draw controls are not statistically significant in explaining win percentage, there was no information contained in the box scores on how many goals were obtained when the team won the draw control or how many goals were conceded when the team lost the draw control. More detailed information would be needed to investigate this relationship further. Other factors that likely explain win percentage and changes in win percentage such as team chemistry, the presence of a star player, the experience of the players and the coaches, and how different game management strategies, such as the usage of substitutes and quickness of play, are not included because they are difficult to measure, not included in the box scores, or both.

For a lacrosse coach or lacrosse player who is looking to improve win percentage between seasons, it is comforting to note that focusing on improving the changes in total shots ratio, attacking scoring efficiency, and becoming better at defending by decreasing the opponent’s goal-to-shot ratio will lead to an increase in the change in win percentage. One major drawback of this research is that it does not point to the factors that cause improvements in these variables and how they feed into additional goals or fewer conceded goals.

CONCLUSIONS

This study is the first to analyze which factors impact win percentage and changes in win percentage for NCAA D1 women’s lacrosse. The regression results suggest that goals, unassisted goals, and those who competed in the NCAA tournament had a positive impact on win percentage, while opponent’s goals and teams that were new in 2023 had a negative impact on win percentage. These factors were similar across the distribution of win percentage at the 25th, 50th, and 75th percentiles. Changes in win percentage between the 2023 and 2022 seasons are positively impacted by the change in the total shots ratio and attacking scoring efficiency and negatively impacted by the change in defending scoring efficiency. Even though there are many differences between lacrosse and soccer, the findings of this research and those of Joyce et. al. (4) that focus on college soccer suggest that the factors that explain changes in win percentage are similar between the two sports. These results also suggest that the statistics that explain win percentage and change in win percentage are similar between teams at the bottom, at the middle, and at the top of the distributions.

Applications In Sport

Women’s lacrosse programs at the collegiate level as well as at the national level can use these results to determine which factors to focus on when attempting to improve their win percentage within a specific year or over the course of several years. This research suggests that teams should emphasize their efforts in practice and in games on factors that increase goals as well as those factors that prevent goals. The lack of empirical analysis at the collegiate level, especially for women’s sports, can be rectified using available data. Additional publicly available information would make individual game analysis more informative such as how winning a draw control impacts goals as well as how focusing on specific factors such as caused turnovers or increasing assists increases goals and therefore positively impacts a team’s chances of winning.

REFERENCES

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2024-04-01T07:01:38-05:00March 22nd, 2024|General, Research, Sport Training, Sports Management|Comments Off on An analysis of the factors impacting win percentage and change in win percentage in women’s Division 1 college lacrosse
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