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

May 17th, 2024|General, Research, Sports Management|

Authors: George Terhanian1

Corresponding Author:

George Terhanian, PhD
200 Hoover Avenue, #2101
Las Vegas NV, 89101

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


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


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. 



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).

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

0-3 ft.6%1.290.86
3-6 ft.54%
6-9 ft.28%0.381.466.780.00.371.11
9+ ft.12%0.471.607.740.00.391.17
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
Off catch75%1.361.07
Off dribble25%-
22-24 ft31%1.381.13
24-25 ft.36%-0.090.91-
25-26 ft.20%-0.170.84-
26+ ft.13%-0.300.74-

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

Standard versus Non-Standard Interpretations

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

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

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

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

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

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

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

It would increase from 35% to:

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

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

Calculating Each Shot’s Make Probability

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

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

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

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

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

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

Table 2.

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

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

Predicting the Likely Effect of Multiple Changes to Multiple Predictor Variables

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

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

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

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

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

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

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

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

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

Figure 1.

Percentage of predicted makes by scenario 

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

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

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

Figure 2.

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

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

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  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 

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
0-2 secs.2%1.250.75
2-4 secs.3%
4+ secs.95%0.792.
Off catch46%1 .401.21
Off dribble54%
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-

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  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 


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. 


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. 


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.


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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An Analysis of the Geographic Distribution of Minor League Sports Teams

May 3rd, 2024|General, Research, Sports Management|

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

(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


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


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.


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.


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. 


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.


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).


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).


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.


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

April 20th, 2024|General, Research, Sport Training, Sports Exercise Science|

Authors: Dr. Mark Chen1, Dr. Andrew Richardson2

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

Corresponding Author:

Corresponding Author: Mark Chen
Campus Heart, Southfield Road, Middlesbrough
TS1 3BX, Tees Valley

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

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

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


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

Keywords: Powerlifting, Body Image, Weight Classed Sports


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

Background of Powerlifting

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

Theoretical models and frameworks

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

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

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

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

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

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

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

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

Aim and Objectives of the Study 


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


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

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


Participant Information

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

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


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


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

Data Analysis

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


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

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


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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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


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

April 8th, 2024|General, Research, Sports Management|

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

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


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


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.



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).          

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.


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.


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.


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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Comparing Public vs. Private High School Sports-Related Concussions from a Countywide Concussion Injury Surveillance System

April 5th, 2024|General, Research, Sport Training|

Authors: Gillian Hotz1, Jacob R. Griffin2, Hengyi Ke3, Raymond Crittenden IV4, Abraham Chileuitt5

1Department of Neurosurgery, University of Miami Miller School of Medicine, Miami, FL, USA
2KiDZ Neuroscience Center, The Miami Project to Cure Paralysis, University of Miami Miller School of Medicine, Miami, FL, USA
3Department of Public Health, Division of Biostatistics, University of Miami Miller School of Medicine, Miami, FL, USA
4Department of Neurology, University of Miami Miller School of Medicine, Miami, FL

Corresponding Author:

Gillian Hotz, Ph.D.
1095 NW 14th Ter
Miami, FL 33136

Gillian A. Hotz, PhD is a research professor at the University of Miami Miller School of Medicine and a nationally recognized behavioral neuroscientist and expert in pediatric and adult neurotrauma, concussion management, and neurorehabilitation. Dr. Hotz is the director of the KiDZ Neuroscience Center, WalkSafe, and BikeSafe programs.

Comparing Public vs. Private High School Sports-Related Concussions from a Countywide Concussion Injury Surveillance System


Largely, research on adolescent sports-related concussion (SRC) has focused on public school athletes. SRCs of private school athletes have been studied less and may differ due to differences between school types.

SRCs between Miami-Dade County high school athletes at trained public (n = 1088), trained private (n = 272), and untrained private (n = 79) were compared. Outcomes included days between date of injury (DOI) and clinic date, days between DOI and post-injury ImPACT retest, days withheld, return to play (RTP), ImPACT baseline and post-injury retest completion, and academic accommodation status.

Trained public and trained private groups had similar days between DOI and clinic date, days withheld, and percentage who RTP. Differences between the trained public and untrained private groups existed for RTP but not for days between DOI and clinic date or days withheld. Private group athletes were more likely to receive academic accommodations.

Public and private high schools trained on the same SRC protocol did not have significantly different outcomes. The untrained private schools, however, had worse outcomes compared to the public group.

Application In Sports
SRC outcomes in both public and private high schools may benefit from SRC education, training, an established protocol, and use of a management system.

Keywords: youth athletes, concussion recognition, concussion management, private schools, sports


Each year, an estimated 1.6 to 3.8 million sports-related concussions (SRCs) occur in the United States (1). While the nearly 8 million high school athletes participating in sports annually benefit from the improved social, psychological, and physical health gained from playing sports (2, 3), there is also an ongoing risk of injury due to consistent athlete-exposure (4). SRCs are understandably a concern for high school aged athletes due to the short-term and potentially lifelong behavioral, cognitive, emotional, physical, and psychological effects they can produce (1, 5). These consequences can be particularly worrisome as this population is already experiencing their own ongoing physical and cognitive development changes that can negatively be affected by an SRC (6). Understanding risk factors contributing to adolescent SRCs and what may lead to differences in outcomes is therefore imperative for identifying those most at risk and ensuring the proper management and treatment resources are in place.

Thus far, an overwhelming majority of research on SRCs has focused on or included samples of public high school athletes as opposed to private high school athletes. One example is the High School Sports-Related Injury Surveillance Study, Reporting Information Online (RIO) (7). The High School RIO is an internet-based data collection tool that captures athletic exposures and injury events through athletic trainers (ATs) that report data. It is often used as a source of SRC data for research (4). In the most recent report, nearly 80% of the participating high schools were public with the rest being private (7). Additionally, other studies on SRC incidence and trends have included only athletes from public high schools (8.) The lack of private high school inclusion in adolescent SRC research is an important consideration because known distinctions between public and private high schools possibly lead to differences in SRC incidence and outcomes (4). These include differences in school size, support services and resources, student racial/ethnic backgrounds, rigorous academic programs, and socioeconomics (9).

While there has been recent research that details private high school athlete SRC experiences and reporting behavior (4, 10), there is still a need for continued research into private high school SRC outcomes. Specifically, it would be important to examine how SRC outcomes differ between public and private high schools. Therefore, the purpose of this study was to compare SRC outcomes between public high schools who received specific concussion training and education to private high schools who received the same training and private high schools who did not receive training on the same SRC protocol. The goal of using these three distinct groups was to examine whether differences in SRC outcomes would be a result of differences in SRC education, training, and protocol.


Participants and Procedures

This study included Miami-Dade County (MDC) public and private high school athletes with an SRC that occurred in a practice or game between August 1st, 2012, and July 31st, 2022. All athletes were treated at the University of Miami Miller School of Medicine’s Concussion Clinic, UConcussion (UCC). Athletes that sustained an SRC outside of the study period were excluded as well as those with an SRC that did not occur during an MDC public or private high school practice or competition. If an athlete was treated at a provider other than the UCC, they were also excluded. 

 The UCC clinical team hosts an annual SRC training and educational workshop for MDC public high school ATs and athletic directors (ADs). In these workshops, ATs and ADs are trained on how to use the Six Steps to Play Safe protocol (11) and how to administer ImPACT (12) concussion tests. The UCC also makes available specialty concussion clinics where athletes with a suspected SRC can be referred to for management and treatment. The UCC similarly partners with and provides training and education to 8 private high schools within MDC. While athletes at other private high schools within MDC can still be referred to and receive treatment at the UCC, ATs and ADs at these high schools are not provided with the same educational workshops and training on the Six Steps to Play Safe protocol (11). In this study, there were 35 trained public, 8 trained private, and 29 untrained private high schools that were grouped as either “trained public,” “trained private,” or “untrained private,” respectively.              

The Six Steps to Play Safe (11) is a standardized protocol that can be used to manage an athlete’s SRC and safe return to play (RTP) and return to school during recovery (Figure 1). Included in this protocol are, in order, pre-season ImPACT (12) baseline testing, AT sideline testing, post-injury ImPACT testing, SRC clinic follow-up, gradual RTP and return to learn protocols, and SRC injury surveillance form completion.

Reported variables were collected during UCC visits and from surveillance reporting by ATs. Athlete information in the study included demographics and the sport played when the injury occurred. SRC specific information was also reported and included date of injury (DOI), days between DOI and first clinic date, days between DOI and post-injury ImPACT retest, RTP status (yes/no), and days between DOI and RTP (days withheld). To eliminate the few extreme outliers, athletes were only included in days between DOI and first clinic date as well as days withheld mean calculations if the value for these variables was < 120 days. For similar reasons, only athletes with days between DOI and post-injury ImPACT retest < 30 days were included in the calculation. Whether an athlete received academic accommodations was included as a variable because previous research (13) suggests that private high school students experience particularly high levels of stress due to concerns about academic performance and school requests, which potentially impacts whether academic accommodations are prescribed. The percentage of athletes who experienced loss of consciousness (LOC) was also reported because LOC indicates a potentially more severe SRC and is associated with longer recovery than SRCs without LOC (14). Athlete ImPACT (12) baseline testing and post-injury data from the ImPACT test online database was included and used to determine whether athletes had completed a baseline ImPACT test and/or a post-injury ImPACT retest. ImPACT testing comparisons were only included for the trained public and trained private high schools since untrained private high schools either did not use ImPACT or did not grant the UCC access to their records.

Data Analysis
Data analysis was performed using R 4.2.2. Athletes sustaining an SRC from MDC public high schools were compared with athletes from private schools between 2012-2022. The eight private schools were particularly selected because they followed a similar protocol and received the same SRC education as the public schools. The other 29 private schools did not receive the training or follow the protocol. For continuous data in the normal distribution like “Age”, mean and standard deviation were reported. For categorical data, such as “Gender”, data was presented as frequency and percentage. For those variables with important clinical significance, such as “Days withheld”, data was reported as median and interquartile range. Propensity score matching was performed to match the public schools with the eight private schools who received similar SRC training. SRC outcomes were therefore compared between trained public and trained private schools before and after matching. This was done to confirm whether one hypothesis, that public and private schools trained on the same SRC protocol would not differ in SRC outcomes, would be true when baseline covariates were and were not controlled for between the groups. Sample T-test was used to detect the significant difference for quantitative data in the normal distribution. The Wilcoxon test was used for quantitative data in non-normal distribution. The Chi-Square test was used to detect significant differences in categorical data. Statistical significance was set at < 0.05.


Participant Demographics
A total of 1,088 public, 272 trained private, and 79 untrained private athletes were treated at the UCC during the study period and are included in this study. The average age was similar for each group (16.5 and 16.2). While there were more male than female athletes in all three groups, the percentage of athletes that were female was greater in the trained (38.6%) and untrained (38.0%) private groups than the public group (25.9%). In both the trained and untrained private groups, a greater percentage of athletes were White (28.5% and 25.3%) or Hispanic (62.6% and 68.0%) compared to public athletes (8.0% White, 56.4% Hispanic). The public group instead had a greater percentage of Black athletes (30.9%) than the trained (24.7%) and untrained (6.7%) private groups. Across all three groups, football accounted for the greatest percentage of SRCs but was more prevalent in the public (58.3%) than both private groups (36.4% and 39.2%) (Table 1).

Comparing Trained Public and Trained Private High Schools SRCs
Data from trained public and trained private high schools was compared to determine if there were any differences in outcomes between public and private high schools that were trained using the same protocol and program. There were no differences between the groups for days between DOI and first clinic date (P = 0.1), days withheld (P = 0.83), post-injury retest completion (P = 0.06), and RTP (P = 0.30). The average days between DOI and post-injury ImPACT retesting was smaller (P < 0.001) for the public (3 days) than trained private (6 days) group. The public group also had a greater percentage of athletes who completed ImPACT baseline testing (88.5% vs. 80.1%; P < 0.001). The trained private group had a significantly greater percentage of athletes who had academic accommodations (P < 0.001) and experienced LOC (P < 0.001) (Table 2).

After matching, groups had similar demographic characteristics for age, sex, race, grade, and sport (Table 3). Outcomes between the matched groups were also compared, and there were no differences for days between DOI and first clinic date, days withheld, percentage of athletes who completed ImPACT baseline testing and post-injury retesting, and RTP (Table 4). However, average days between DOI and post-injury ImPACT retest was smaller for the public group (4 vs. 6 days, P < 0.001). The public-school group was also more likely to have experienced LOC (P < 0.001) and not receive academic accommodations (P < 0.001).

Comparing Trained Public and Untrained Private High School SRCs
Trained public and untrained private groups did not differ in average days between DOI and first clinic date (P = 0.40) or days withheld (P = 0.40). A significantly greater percentage of the public group did RTP (91.9% vs. 81.0%; P = 0.002). More of the athletes in the untrained private group received academic accommodations (P < 0.001) and experienced LOC (P < 0.001) than did the trained public group (Table 5).


Understanding risk factors, whether demographical (e.g., sex, age) or injury event-related (e.g., sport, mechanism of injury), that are associated with differences in SRC outcomes are important for ensuring that those most at risk receive proper SRC treatment and resources. One potential risk factor that was explored in this study was whether an athlete was from a public or private high school. Historically, most research on SRC risk and outcomes has been conducted using public high school athletes (4). This study provides further insight into how SRC outcomes between high school athletes differ based on the type of school attended and if a dedicated SRC protocol and education can help mitigate any differences.

While football accounted for the greatest percentage of SRCs in all three groups, its contribution was roughly 20% percent more in the public group than both private groups. Other sports, including soccer, basketball, and volleyball, were more prevalent in both private school groups. The distribution of sport played during the SRC injury event likely differed between public and private groups because private schools offer a variety of sport options, like crew and sailing, that were not available at public schools. This availability may have impacted the popularity of sports and participation numbers as private school athletes had a greater number of sports to choose from.

To our knowledge, there is only one other study (15) that directly compares SRC experiences between public and private schools. In that study, private school athletes were twice as likely to report a history of SRC compared to public school athletes, but there was no difference in RTP timelines between athletes at the different types of school (15). While the current study did not compare history of SRC between school types, analysis was performed to compare rates of RTP. There was no significant difference between the trained public and trained private school groups for RTP percentage or days withheld (Table 2), similar to the other study that concluded no difference in RTP. After matching, there was still no difference in RTP percentage or days withheld between these groups (Table 4). The untrained private group, however, had significantly less athletes RTP than the trained public group (Table 5). The UCC is a specialized concussion program that provides comprehensive SRC management and treatment, but the program also provides continuing education and a standardized protocol to the trained public and private high schools to better identify, manage, and treat athletes with an SRC (11). Athletes at these participating trained high schools potentially benefited from the coordinated and structured care they receive as a result of these trainings and partnerships, which may have led to better RTP outcomes compared to the untrained private group. These results also suggest that SRC outcomes do not necessarily depend on school type and the systematic differences between public and private schools (4, 9), but instead on AT and AD SRC education and if an SRC protocol is in place and being followed. Additionally, these results also indicate the positive effect an available and established SRC program and protocol with clinicians trained on SRC management and treatment can have on SRC outcomes.
Another finding was that the trained public and untrained private groups did not differ in average days between DOI and first clinic date (Table 5). Systematic differences in socioeconomics between public and private high schools (9) may explain why the trained public group did not have significantly fewer average days between DOI and first clinic date than the untrained private group, which was the initial hypothesized result. There is well established evidence (16) that supports a relationship between socioeconomics and access to healthcare, and socioeconomic differences between school type may have led to barriers, including transportation, time, and costs, that delayed public athletes from getting into the UCC (17). Yet, there was also no difference between trained public and trained private groups for average days between DOI and first clinic date in both unmatched and matched comparisons (Tables 2 and 4), suggesting that UCC’s partnership with these schools and the flexibility it provides by offering both on-site and virtual appointments may have alleviated any potential differences. These findings also indicate that educating ATs and ADs on the risks of SRCs leads to quicker identification and subsequent appointments.

The percentage of athletes who received academic accommodations after an SRC was significantly greater for both the trained (unmatched and matched) and untrained private school groups compared to the trained public school group. During recovery from an SRC, athletes may have post SRC symptoms that can interfere with their ability to participate and function in the classroom setting (18). Consequently, return to learn protocols and academic accommodations are often provided to the athlete to help reintegrate them into classes but also prevent worsening symptoms (19, 20). Previous research (13) shows that private school students face a particularly high level of academic pressure, potentially due to more rigorous academic programs (9), which could explain why a greater percentage of private groups in this study received more academic accommodations. These additional academic accommodations may have been provided to reduce the burden private group athletes felt about their academic responsibilities or at the request of academic advisors employed at these schools. However, it is important to ensure that all athletes with a sustained SRC receive any appropriate and necessary academic accommodation, regardless of school type attended, to prevent further symptom development.

This study is not without limitations. All participants in this study were athletes that attended a public or private high school in MDC. Results may not be generalizable to other playing levels, like youth, middle schools, and college, nor to public or private high schools in other counties. Additionally, while other counties may have their own SRC surveillance system, they may not have a program, such as the UConcussion program, that provides ATs with additional SRC training and encourages timely, accurate reporting. A larger sample population in all three groups would have also been beneficial and provided more evidence on the impact of SRC education and protocol on SRC outcomes in the high school setting.


Public and private high school groups trained on the same SRC protocol did not have significantly different SRC outcomes. The untrained private high school group, however, had worse SRC outcomes compared to the public school group, suggesting that SRC outcomes in the high school setting may benefit from education, training, and an established SRC protocol and program and not on whether the school is public or private.

Applications In Sport

An inherent risk of playing sports is injuries, and SRCs are a particularly concerning injury for high school athletes, especially those playing contact sports. Ensuring those responsible for helping to manage SRCs in high schools are educated about SRCs is important, and a collaborative approach to treating and managing SRCs has been recommended (20). As suggested by this study, all high school personnel involved with athletics should be offered SRC management training and education to help improve outcomes of those that sustain an SRC. Additionally, an SRC protocol, like the Six Steps to Play Safe (11), should be established and can include:

  • Pre-season baseline testing, using computer-based tests such as ImPACT (12)
  • Sideline testing after a potential SRC injury (SCAT5, Balance Error Scoring System (BESS), etc.)
  • Post-testing after a suspected SRC (to compare neurocognitive scores to pre-season baseline tests)
  • Clinic appointments with a healthcare professional trained in SRC who can evaluate tests and make recommendations
  • Gradual RTP and return to learn protocol after the athlete has been examined by a professional and is asymptomatic
  • Injury surveillance system reporting by ATs

The authors would like to thank: Dr. Kaplan and the UHealth Sports Medicine Clinic and Staff, the Division of Athletics and Activities for the Miami-Dade County Public Schools, all Miami-Dade County High School Certified Athletic Trainers, previous UConcussion team members, Dr Kester Nedd who served as medical director of the program from 2012 to 2019, current medical director Dr. Abraham Chileuitt, and The Miami Dolphin Foundation for supporting countywide ImPACT testing and educational workshops. We also want to thank David Goldstein and the Goldstein Family for the development of the Countywide Concussion Care Program and their initial and continued support. The project was supported by the University of Miami Clinical and Translational Science Institute.


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