Injuries are a significant issue in professional sports such as basketball (Deitch, 2006). A detrimental effect in 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 several studies on different professional male basketball leagues (Caparros et al., 2016; Podlog et al., 2015). Moreover, a significant inverse association between games missed due to injury and the percentage of won games at the end of the competitive season has also been confirmed in the past in professional male basketball (Podlog et al., 2015). Patellofemoral inflammation is the most significant injury regarding lost competition days, while ankle sprain is the most common injury among professional male basketball players (Drakos et al., 2010). The monetary impact that these sports injuries have on professional teams and franchises is not negligible. In the NBA, during the 2000-2015 period, losses between 10 and 50 million dollars per team and season due to injuries were reported (Talukder et al., 2016). Nevertheless, despite apparent interest and enormous research and practical efforts to prevent injuries, there is still no practical solution to decrease the injury incidence significantly in this family of sports.
To address this issue, a relationship between competitive schedule congestion and the occurrence of sports injuries has been studied previously (Drew and Finch, 2016; Teramoto et al., 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 also seem to play a significant role in the epidemiological incidence (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 observed at a subelite level too (Gabbett and Domrow, 2007).
A substantial 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 such as basketball (Caparros 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 processes, professional teams and researchers take advantage of technological innovations by using data obtained in stadiums and competition venues (Mangine et al., 2014; Sampaio et al., 2015). For instance, top football teams seem to rely on external training load variables such as acceleration, total distance, distance covered above specific speeds and metabolic power to make their decisions when managing training and competition loads (Nassis and Gabbet, 2017). The advantage is that 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 different electronic devices and systems, fairly frequent in elite sport. That is the case of player tracking, a technology that provides kinematic variables, while also enables a better understanding of physiological, technical and tactical variables (Mangine et al., 2014; Sampaio et al., 2015).
This data is essential in the field of injury prevention because different values of external and internal training loads can be employed to establish ratios that may indicate that the athlete is has been situated in a risk area (Gabbett, 2016). A vast 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) has allowed a better understanding of the relationship of workload and risk of injury (Gabbett and Jenkins, 2011; Hulin et al., 2013; 2016; Murray et al., 2017) 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 using this methodology has been conducted 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 data in professional basketball to improve specific preventive strategies.
An observational, retrospective cohort study was conducted between October and April during three consecutive regular seasons, obtaining data from a total of 2613 observations and 246 games from 33 different players of a professional male basketball team. The use of these data attended 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 (Hagglund 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.
All study data were available to the general public in open-access websites and included the demographic characteristics of the players, player tracking, injury and performance values. Tracking data were obtained from the website of the company STATS (http://stats.com/), responsible of the game tracking process in the competition (SportsVU, Northbrook, IL, USA) following the trend of previous studies (Embiricos and Poon, 2014; Hu et al., 2011; Lofti et al., 2011; Maymin, 2013; Siegle et al., 2013; Tamir and Oz, 2006). Injury information was obtained from public resources (www.rotoworld.com, www.cbs.com and http://www.basketball-reference.com/), following the same procedure with the performance data (http://stats.com/ and http://www.basketball-reference.com/). Again, several studies in the past have used this information showing its reliability (Gesbert et al., 2016; Maheswaran et al., 2012; Yonggangniu and Zhao, 2014). 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).
Time-loss injuries suffered during regular season games were included in the study. There are no other 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.
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 ([[lambda].sub.ij]) = log([m.sub.ij])+ [X.sub.ij] [beta] + [u.sub.i] where [Y.sub.ij] ~Po([[lambda].sub.ij]j). [[lambda].sub.ij] is the number of injuries, [m.sub.ij] is the number of minutes exposures of player, which is the offset of this model, and [X.sub.ij] includes all independent variables of interest. The vector [beta] contains the fixed effects, whereas [u.sub.i] is the random effect corresponding to player i. The random effects are assumed to be independent and normally distributed: [u.sub.i] ~N(0; [[sigma].sup.2])...