Journal of Sports Science and Medicine
Journal of Sports Science and Medicine


Journal of Sports Science and Medicine
ISSN: 1303 - 2968
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JCReports 2016
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©Journal of Sports Science and Medicine (2017) 16, 480 - 488
Research article
The Use of External Workload to Quantify Injury Risk during Professional Male Basketball Games
Toni Caparrós1,2,, Martí Casals2,3, Javier Peña2,4, Eduard Alentorn-Geli5-7, Kristian Samuelsson8,9, Álvaro Solana, Jim Scholler, Tim J. Gabbett10,11

1 Institut Nacional d’Educació Física de Catalunya (INEFC), Barcelona, Spain
10 Gabbett Performance Solutions, Brisbane, Australia
11 University of Southern Queensland, Institute for Resilient Regions, Ipswich, Australia
2 port Performance Analysis Research Group (SPARG), Universitat de Vic, Vic, Spain
3 Research Centre Network for Epidemiology and Public Health (CIBERESP), Barcelona, Spain
4 Universitat de Vic, Vic, Spain
5 Artroscopia GC, SL, Hospital Quirón, Barcelona, Spain
6 Fundación García-Cugat, Barcelona, Spain
7 Mutualidad Catalana de Futbolistas – Federación Española de Fútbol, Barcelona, Spain
8 Department of Orthopaedics, Sahlgrenska University Hospital, Mo¨ lndal, Sweden
9 Department of Orthopaedics, Institute of Clinical Sciences, Gothenburg, Sweden

Toni Caparrós
✉ c/Sant Joan Bosco 7-9, 08338, Premià de Dalt, Barcelona, Spain

03-05-2017 -- Accepted: 27-09-2017 --
Published (online): 01-12-2017


The main purpose of this study was to identify possible risk factors for injury in professional basketball using game tracking data. An observational prospective cohort study involving a professional basketball team of the National Basketball Association (NBA) of USA was conducted during three consecutive seasons. Twenty-six professional basketball players took part in this study. The team had a mean of 87.7 ± 2.9 games played per season. A total of 32 injuries were recorded, accounting for 301 total missed games with a mean of 8.9 ± 3.1 per player and season. Tracking data included the following variables: minutes played, physiological load, physiological intensity, mechanical load, mechanical intensity, distance covered, walking maximal speed, running maximal speed, sprinting maximal speed, maximal speed, offensive average speed, defensive average speed, level one acceleration, level two acceleration, level three acceleration, level four acceleration, level one deceleration, level two deceleration, level three deceleration, level four deceleration, player efficiency rating and usage percentage. The influence of demographic characteristics, tracking data and performance factors on the risk of injury was investigated using a multivariate analysis with their incidence rate ratios (IRRs). Athletes with less than 16 accelerations per game (IRR, 6.01; 95% CI, 1.37-20.63) and those running less than 2 miles per game (lower workload) (IRR, 2.94; 95% CI, 1.24-6.94) had a higher risk of injury during games (p = 0.01 in both cases). Therefore, unloaded players have a greater risk of injury. Adequate management of training loads might be a relevant factor to reduce the likelihood of injury according to individual profiles.

Key words: Game tracking, multivariate analysis, accelerations, distance, injury prevention
Key Points
The number of accelerations and the total distance can be considered risk factors for injuries in professional basketball players.
Unloaded players have greater risk of injury compared to players with higher accumulated external workload.
Workload management should be considered a major factor in injury prevention programs.

Injuries are a major issue in professional sports such as basketball (Deitch, 2006). A detrimental effect of performance (Busfield et al., 2009) and a significant number of games missed due to injury at the end of the season have been highlighted in the NBA (National Basketball Association) (Podlog et al., 2015). Additionally, a significant inverse association between games missed due to injury and percentage of won games has also been confirmed in the past in professional male basketball (Podlog et al., 2015). In the NBA, patellofemoral inflammation is the most significant injury in terms of days lost in competition, while ankle sprain is the most common injury among players (Drakos et al., 2010). The monetary impact that sports injuries have on professional teams and franchises is not negligible either. In the NBA, during the 2000-2015 period, losses between 10 and 50 million dollars per team and season due to injuries have been reported (Talukder et al., 2016). Nevertheless, despite obvious interest and enormous research and practical efforts to prevent injuries, there is still no effective solution to decrease injury incidence in this family of sports.

A relationship between competitive schedule congestion and the occurrence of sports injuries has also been observed previously (Teramoto et al., 2016; Drew and Finch, 2016). Increased incidence of sports injuries in specific periods within a season has been reported in some sports (Carling et al., 2016; Folgado et al., 2015). Level of competition and gender seem to play a significant role in the epidemiological incidence, too (Anderson et al., 2003; Deitch, 2006; Gabbett and Domrow, 2007). High-level athletes are more prone to injuries due to competing demands. Thus, NBA players chances to suffer a game-related injury are twofold when compared to their collegiate counterparts (Deitch, 2006). Nevertheless, an increase in the number of sports injuries is also observed at a sub-elite level (Gabbett and Domrow, 2007).

A significant number of initiatives have been presented to study the incidence of injuries in team sports (Drew and Finch, 2016; Ullah et al., 2012). Novel approaches to prevent and manage sports injuries tend to use technology consistently during competition. Player tracking (Sampaio et al., 2015), accelerometry (Colby et al., 2014) and global positioning systems (GPS) (Casamichana et al., 2013; Dellaserra et al., 2014; Rossi et al., 2017) are today’s sports standard tools. Although the use of these technologies can be useful in all disciplines, they have spread mainly in team sports: basketball (Caparrós et al., 2016; Sampaio et al., 2015), soccer (Casamichana et al., 2013; Osgnach et al., 2010), Australian football (Carey et al., 2016; Colby et al., 2014) and rugby (Gabbett and Domrow, 2007; Gabbett and Jenkins, 2011). In this process, studies take advantage of technological innovations by using data obtained in stadiums and competition venues (Mangine et al., 2014; Sampaio et al., 2015). Average and peak speeds, accelerations or decelerations values (Casamichana et al., 2013), total distance traveled or the total number of high-intensity efforts performed while in training or competition (Carling et al., 2010) can be determined by the use of electronic devices. That is also the case of player tracking, a technology that provides kinematic variables, while also enabling a better understanding of physiological, technical and tactical variables (Mangine et al., 2014; Sampaio et al., 2015).

This data can be important in the field of injury prevention because different measurements of external and internal training loads can be employed to establish ratio values that would place the athlete at risk of injury (Gabbett, 2016). A great body of literature has previously used external loads to find a consistent association between ratio values and the risk of injury (Colby et al., 2014; Drew and Finch, 2016; Gabbett and Jenkins, 2011; McNamara et al., 2017). The use of the acute:chronic workload ratio (ACWR) ratio has allowed a better understanding of the relationship of workload and risk of injury (Hulin, 2013; Hulin et al., 2016; Murray et al., 2017; Gabbett and Jenkins, 2011) in sports such as cricket (McNamara et al., 2017), football (Bowen et al., 2017) and rugby (Gabbett and Jenkins, 2011; Hulin et al., 2016). However, to our knowledge, only one study has been performed in professional basketball (Weiss et al., 2017). While providing an interesting starting point for workload-injury research in this sport, it was limited to one playing season. Given the popularity of this discipline, it seems of interest to establish whether a relationship exists between player workloads and injury risk in professional basketball by using data from more than one competitive season to reinforce the strength of statistical models.

The primary purpose of this study was to identify possible risk factors for injury, related to variables from game tracking datain professional basketball, to improve specific preventive strategies.


An observational, prospective cohort study was conducted between October and April during three consecutive seasons of NBA, obtaining data from a total of 1799 observations and 272 games from 26 players of a professional male basketball team. This study received Institutional approval by the coaching staff and a board of trustees of the professional franchise. The team allows the use of these data attending to the standards of the Declaration of Helsinki, revised in Fortaleza (World Medical Association, 2013). Players were assigned an individual identifier code with the identity concealed, ensuring player anonymity was maintained.

Data collection was based on the methodology of the UEFA consensus statement for epidemiological studies (Hägglund et al., 2005). A time-loss injury was defined as any injury (contact and non-contact) occurring during a practice session or game which caused an absence for at least the next practice session or competition. Time-loss from associated injuries was retrospectively determined by the number of days of absence from participation.

Study data included the demographic characteristics of the players, information about tracking and performance. Archival data were obtained from the company responsible for the player tracking process (SportsVU, Northbrook, IL, USA) (Embiricos and Poon, 2014; Hu et al., 2011; Lofti et al., 2011; Maymin, 2013; Siegle et al., 2013; Tamir and Oz, 2006) and performance from the open-access websites available ( and (Gesbert et al., 2016; Yonggangniu and Zhao, 2014; Maheswaran et al., 2012). These records contained both non-tracking and tracking data. The different databases were then collated to assign the specific information about each game to each specific player. All 23 tracking and non-tracking variables presented by the companies designing the software were selected. Tracking variables were categorized into four main groups: physiological variables, speed and distance variables, mechanical load variables, and motor variables (Table 1).

There were not exclusion criteria. Minutes and games played by every player were considered on the unbalanced study design with repeated measures. Given that not all of the players were observed for the same number of seasons, and that the number of games per season varied from one player to another. The possible risk variables for injuries considered were height, mass, age, season year, season month, won/lost game and home/away venue, minutes played, and the additional variables shown in Table 1. Medical staff was responsible for recording all time-loss injuries included in the current investigation.

Statistical analysis

A descriptive analysis of all variables of interest was carried out. In the case of categorical variables, absolute and relative frequencies were presented. For quantitative variables, measures of central tendency (mean and median) and statistical dispersion (standard deviation, percentiles 25th (P25), percentiles 75th (P75), and range) were calculated. To study the risk factors from the games tracking data variables, a generalized linear mixed model (GLMM) was conducted assuming the frequency of the injuries followed a Poisson’s distribution. The same statistical approaches have been previously applied (Casals et al., 2015). Following the studies of Bolker (2009), Vanderbogaerde (2010) and Casals (2014), a list of relevant information and basic characteristics of the GLMM model were reported. The model expression for player i in his jth games is the following: log (λij) = log(mij)+ Xij β+ ui where Yij~Po(λij). λij is the number of injuries, mij is the number of minutes exposures of player, which is the offset of this model, and Xij includes all independent variables of interest. The vector β contains the fixed effects, whereas ui is the random effect corresponding to player i. The random effects are assumed to be independent and normally distributed: ui ~N(0;σ2), where σu2 is the variance of random effect. The model accounted for repeated measures and the fact that the values of Xij could change from one game to the next.

The simplification of the model was performed by backward selection of variables from the full model, and models were compared using the likelihood ratio test (LRT) until a minimal adequate model was obtained. Model selection was based on the Akaike Information Criterion (AIC). To estimate the model variables we used the Gauss-Hermite quadrature (GHQ) with 5 points (Bolker, 2009). The statistical significance of the fixed effects associated with the covariates included in the model was assessed using the Wald test. The correlation and the main possible interactions among the covariates were checked in the final model. A possible over-dispersion in the model was studied using Pearson’s dispersion parameter (Bolker, 2009). Measures of association were calculated using incidence rate ratios (IRR) with 95% confidence intervals (CI). To prevent overfitting, a cross-validation procedure was completed using leave-one-out cross-validation (LOOCV). In the LOOCV, the prediction model is trained on data from all of the participants except one, which is “held out” and used as the test dataset. The process is repeated until all participants have served as the test data set. Moreover, the performances of estimation models were evaluated by the commonly used measures of goodness-of-fit: RMSE (Root Mean Square Error) and MAE (Mean Absolute Error). Finally, in order to assess the predictive or discriminatory ability of the model we performed the area under the curve (AUC) (Lopez-Raton et al., 2014).

Statistical significance was set at p < 0.05. To improve the interpretability of the clinical finding, suggestions made by Cook (2016) were followed, and continuous data were divided into categories (a dichotomized variable based on exploring residuals versus covariates). Also, facilitation of data interpretation was achieved by providing the IRR.

All statistical analyses were performed with the statistical package R (The R Foundation for Statistical Computing, Vienna, Austria), version 3.3.3. In particular, the R package lme4 (Bates, 2014) was used to fit the GLMM.


The demographic characteristics of the 26 professional basketball players included in this study were: mean ± SD age of 29.3 ± 3.7 years, height of 2.01 ± 0.03m. and weight of 99.8 ± 1.5 kg. The team had a mean ± SD of 87.7 ± 2.9 games played per season, with a total exposure of 46687 minutes during all 7-month seasons (3 consecutive seasons). The team played an average of 3.4 games weekly during the competition and won 56.8% of them (49.6% at home court). Data on tracking and non-tracking (performance) factors are shown in Table 2.

A total of 32 injuries were recorded throughout the study involving all 26 players, and accounting for 301 total missed games (MG) with a mean ± SD of 8.9 ± 3.1 per player and season. Of these, six were in season 1, 14 were in season 2, and 12 were in the third season (Table 3).

The multivariate analysis using the GLMM for risk factors of injuries in professional basketball is shown in Table 4. The variables that remained significantly associated with risk of injury in the final model were lower number of accelerations, less distance and slower average defensive speed (Table 4). Although the latter was not significant, this variable is important because the AIC of the model was better than the simpler model without this variable. The player-level variance was 0.416. Based on RMSE and MAE values, the calibration model (0.13 and 0.03, respectively) was similar to the measure of the validation model (0.13 and 0.01). Given that these measures are comparable, we can conclude that there is no overfitting. The AUC value of the model showed a satisfactory performance (AUC: 0.72; 95% CI: 0.63-0.82), exhibiting modest to good discrimination. Table 5 illustrates the classification confusion matrix. As can be seen, the percentage of instances well classified was (1634+13)/1799 = 91.55%, where no-injuries are classified as acceptable. But a low injury rate causes sensitivity to drop to lower values (40%).


The present study investigated the relationship between tracking and non-tracking (performance) data and injuries in professional basketball. The main finding of this study was that a lower number of accelerations, less distance covered, and slower average defensive speed were significantly associated with injury during professional basketball games.

Related to external load (Mendez-Villanueva, 2012; Soligard et al., 2017) a few of the variables analyzed here were strongly related to injury (Ullah et al., 2012) on competition (Carling et al., 2012; Cross et al., 2016; Hulin et al., 2016; Murphy et al., 2012; Talukder et al., 2016), the variables that were significant in the multivariate analysis are quantitative. However, the correct interpretation of the statistical analysis has to be done in a multifactorial dimension (Carey et al., 2016; Ullah et al., 2012) and consider correlated data. The strength of the model is the association established within the variables presented. Acceleration is one of the main variables that define basketball (Abdelkrim et al., 2007; Chaouachi et al., 2009; Maymin, 2013; Scanlan et al., 2014). Third level accelerations were found to be a protective factor: players who achieved fewer than 16 accelerations, and covered less than 2 miles were at greater risk of injury. Players undergoing lower workloads had a higher risk of injury than the rest of the roster (Blanch and Gabbett, 2016; Gabbett, 2016; Gabbett and Jenkins, 2011). Our findings of lower workloads increasing the risk of injury are in agreement with recent findings. Despite the fact that many previous studies have analyzed the impact in the number of injuries of excessive training loads imposed on players (Caparros et al., 2016, Gabbett and Ullah, 2012) the protective effect of proper load management, as well as a negative effect of excessively diminished training loads, have also been observed in the past (Gabbett, 2016). Adequate levels of training might have a protective effect on the athlete, also decreasing their risk of injury. The development of a minimum amount of quantitative chronic workload (distance) and qualitative acute workload (accelerations) seems an important factor to prevent injuries (Soligard et al., 2017; Talukder et al., 2016). Regarding intensities as risk factor, a minimum amount of high-intensity accelerations per game are needed to keep the player on optimal performance (Gabbett, 2016). It might be argued that its total number can be related to the minutes that a player is on the court during the game. However, the capability to maintain higher intensities might be associated with other factors as readiness, performance, freshness, fatigue (Soligard et al., 2017) or the opponents match up. A player can achieve an amount of accelerations, but the risk factor might be related to how intensely the acceleration is performed (Schelling and Torres, 2016), and to how player’s muscles can recover from those repeated efforts. In terms of speed parameters, same conclusions are described using GPS technology (Gabbett et al., 2014; Rossi et al., 2017).

Failure to perform high-intensity accelerations, to provide the players with a minimum cumulative distance, or to achieve adequate in-game or between-games recovery increases the chances of injury. Workload between-games (Gabbett, 2016; Gabbett and Jenkin, 2011; Gabbet et al., 2013; Purdam et al., 2015; Scanlan et al., 2014) could be also managed with this model. According to the tracking data, acceleration thresholds can be customized on the accelerometer software. This allows modifying individual practice plans according to previously accumulated workloads.

In most sports, players are not involved in more than two games per week. However, in the professional basketball competitions observed in this study, an average of 3.4 matches were played per week for 24 weeks (Podlog et al., 2015; Teremoto et al., 2016). Therefore, the right management of player workloads is a critical strategy to avoid injuries (Drew and Finch, 2016; Gabbett, 2016; Gabbett and Jenkin, 2011) at certain periods of the season (Windt et al., 2017), and according to individual profiles. For certain players, increasing their participation in the competition is needed (Carey et al., 2016), or they may have higher chances of injury later during critical stages of the season. Acute workloads have to be specifically considered according to the players’ age (Gabbett, 2016) and the period of the season. “Spikes” in workload, which are sometimes unavoidable, should be carefully controlled using individualized recovery protocols (Hulin et al., 2013).

The absence of variables related to intensity could be considered a limitation of this model, especially given that “spikes” in workload from higher intensity activities are associated with greater risks (Gabbett and Ullah, 2012). However, this does not reduce the applicability of the model. The average defensive speed can be retrospectively used to identify these intensity peaks once the game concludes (Bengtsoon et al., 2013; Gabbett and Ullah, 2012), especially in periods of high chronic workload already accumulated. Defense is more unpredictable because it is related to the opponent matchups, but scouting reports may be used as a tool to manage the fatigue arising from “spikes” in acute load (Gabbett et al., 2014; Hullin et al, 2016; Rossi et al., 2017).

Finally, at present no variables could be related to performance. Future studies investigating the relationship between player performance metrics and the overall team performance (wins or losses) are warranted.

The present study has some limitations. First, all data were obtained from the same professional franchise, potentially limiting the external validity of the results. Therefore, studies with involvement of multiple teams are needed. Second, tracking and non-tracking data were obtained during games. Data from practice sessions should be incorporated to adequately apply in-season workload plans, as suggested by Carey et al., (2017). It might offer cause-effect relationship that could be potentially established between workload and injury on experimental designs. This limitation needs to be highlighted from the results obtained in our study. This is an observational study which has no an experimental design. In experimental designs, we can control factors and, thus, conclusions could establish causality, but in our current context, this is not possible. Regarding the validity of the model built, we can observe strong performance metrics (AUC = 0.85) in other basketball studies (Talukder et al., 2016), even if not based on tracking parameters. Recent research on these specific parameters (Carey et al., 2017) offers similar model performance (AUC = 0.76), even is related to Australian football. Therefore, this model identifies risk factors but some limitations are suggested in a predictive level. This fact is probably due to a low injury rate and the lack of better injury definition. However, our effort tries to achieve a deeper understanding of injury prediction to several other recent studies (Bahr, 2016; Carey et al., 2017; Hewett 2017; Jovanovic 2017; Rossi et al., 2017).

The strengths of this study are its specificity for professional basketball and the fact that it used well-established technology to identify risk factors for injury (Carl et al., 2016). However, use and applicability of technology is in some aspects sports-specific (Bangsbo et al., 2006; Gabbett and Jenkins, 2011; Hagglund et al., 2010; Hopkins et al., 2009; Hugues and Franks, 2004). Therefore, conclusions from technology-based investigations should take into account the context in which the research is conducted (Fuller, 2007; Ullah et al., 2012). Regarding methodology, a positive aspect of this study is that the model used (GLMM) tries to control for repeated measures (correlated data among the same players). Ignoring correlation of data when fitting the model may lead to biased estimates and misinterpretation of results (Casals, 2015). The study highlights the need for a correct balance between competitive schedule, team workload design and in-season recovery process. Further research should be conducted to determine how internal and external factors may be related to injury risk and performance.

Practical applications

Tracking systems, which can be easily incorporated into regular practice sessions and games, can provide useful information for the coaching staff to prevent injuries in professional basketball players. Athletes with lower external workload should be identified so that appropriate prevention strategies can be individually applied to avoid injuries.


Unloaded players, regarding the number of accelerations and total distance covered, have greater risk of injury. Increasing external workload may likely reduce the risk of injury in professional basketball. More studies are needed to confirm these findings so that adequate prevention programs can be implemented to decrease the number of injuries in professional basketball and other sports.


None of the authors have any conflict of interest to declare. No funding was received for the present investigation.


Toni Caparrós
Employment: Strength and Conditioning coach Male Basketball Spanish National Team; Institut Nacional d’Educacio Fisica, (INEFC), University of Barcelona
Degree: PhD, BSc, MSc, MBA, MSo
Research interests: Sports performance, injury prevention, load management, elite basketball players

Martí Casals
Employment: Biostatistician of the National Research Centre Network for Epidemiology and Public Health (CIBERESP); Public Health Agency of Barcelona
Degree: PhD Biostatistics, Degree MS
Research interests: injury prevention, Sports medicine and Sports biostatistics

Javier Peña
Employment: Strength and Conditioning coach. Professional Basketball & Volleyball. University of Vic
Degree: PhD, BSc, MSc
Research interests: Sports performance, injury prevention, load management

Eduard Alentorn-Geli
Employment: Artroscopia GC, SL, Hospital Quirón; Mutualidad Catalana de Futbolistas, Federación Española de Fútbol, Barcelona, Spain
Degree: BSc, MD, MSc, PhD, FEBOT, FACGME
Research interests: Shoulder and elbow injuries and treatment options. Knee surgery and sports traumatology

Kristian Samuelsson
Employment: Department of Orthopedic Surgery, Sahlgrenska Academy, University of Gothenburg, Mölndal, Sweden
Degree: MD, PhD
Research interests: Knee injuries, Sports traumatology, Sports performance

Álvaro Solana
Employment: Data analyst
Degree: Bachelor on Mathemathics.
Research interests: Data mining

Jim Scholler
Employment: ACT
Degree: ACT
Research interests: Injury preventi

Tim J. Gabbett
Employment: Institute for Resilient Regions, Univ. of Southern Queensland, Ipswich, Australia
Degree: PhD, BHSc (Hons)
Research interests: Sports performance, injury prevention, load management, elite athletes

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