The relationship of practice exposure and injury rate on game performance and season success in professional male basketball.

Author:Caparros, Toni
Position:Research article - Report
 
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Introduction

Winning in professional sport requires the optimal mix of sport specific training to optimize conditioning while limiting exposure to injury risk scenarios (Fuller et al., 2012). In this context, to objectively measure the impact of training and practice models relative to injury incidence require the systematic aggregation of both healthand performance-related parameters integrated into all aspects of training and competition (Carling and Court, 2012; Hughes and Franks, 2004). Ultimately, the goal to monitor training response is to allow for the assessment and adjustment of planification and, thus, optimize team gains. An effective season planning should prevent training load error (Claudino et al., 2012; Gabbett and Ullah, 2012; Strudwick, 2012), optimize recovery, minimize load-related risk factors (Bangsbo et al., 2006; Fuller et al., 2012; McGill et al., 2012; Hagglund et al., 2013) and decrease rates of injury (Raysmith and Drew, 2016). This will provide the best opportunity for injury-free season success.

The success of a basketball team may be influenced by many factors (Kazimierz et al., 2013), requiring a multifactorial assessment that allows every game success and performance to be evaluated in a translatable and objective way (Hughes and Franks, 2004; Gomez Ruano et al., 2008). Hence, in professional basketball numerous forms of performance data are routinely collected, precisely and objectively evaluating player and team performance variables (Hughes and Franks, 2004; Strudwick, 2012). Similarly, epidemiological methods for capturing both injury incidence and exposure are well established (Hagglund et al., 2005).

Epidemiological research has been performed in basketball (Caine and Maffulli, 2005; Borowski et al., 2008; Newman and Newberg, 2010), and some studies have investigated relationships between training, competition and injury rates (Manzi et al., 2010; McGill et al., 2012). However, currently one study has integrated performance data, exposure and injury rates at the professional level (Podlog et al., 2015). In addition, there is limited integration of game data and injury epidemiological research into real competition and training periods (Caparros et al., 2014). Therefore, the purpose of this investigation was to determine the relationship among game performance, injury rate, and practice exposure in professional male basketball.

Methods

The local research ethics committee (Consell Catala de l'Esport, Barcelona, Generalitat de Catalunya N[degrees] 162015CEICGC) approved the study. Study participants were informed of the purpose and nature of the study and were given the opportunity to decline inclusion of their data. Players were assigned an individual identifier code with the identity concealed, ensuring player anonymity was maintained.

Design

A retrospective study of prospectively collected data during seven consecutive seasons, 2007/2008 to 2013/2014, from an professional basketball team (F.C. Barcelona) that played three main competitions every season: Liga ACB (Spanish Division 1 Championship), Spanish Cup (Play-off competition) and Euroleague (European top division Championship). Players were evaluated at the beginning of each season using the Futbol Club Barcelona (FCB) periodic health examination protocol. The Team Physician (Gil Rodas) was responsible for diagnosis, rehabilitation and return-to-play for each injury, as well as recording of all injuries included in the current investigation.

Data collection

Data collected for this study included 3 main parameters:

1) Exposure time: Exposure for individual players was measured as both the number and hours (h) for games and number and hours for total practice sessions. Exposure aggregation was considered from the beginning until the end of the season. Game time was defined as the hours that each player played competitive games, and practice time refers to specific team practice on court, conditioning and injury prevention workouts.

2) Injury: This data collection was based on the methodology of the Union of European Football Association (UEFA) consensus statement for injury incidence collection (Hagglund et al. 2005). A time-loss injury (TLI) was defined as any injury occurring during a practice season or matches which caused an absence for at least the next practice session or match. Each individual data was recorded daily after every practice and game by the Team Physician (Gil Rodas). Time-loss from associated injuries were retrospectively categorized based on severity, as determined by the number of days of absence from participation. Incidence was calculated as the number of injuries per 1000 player hours (X injuries/X exposure h*1000), depending whether the event occurred in practice or in competition. Every injury was considered as an independent case. Each player could have more than one injury.

3) Performance statistics were collected from the official game statisitics for both the team and player. At the end of each game the official ranking (RKG) quantified for each player the scored points (SP), missed shots (MS: 2 points, 3 points and free throws), total rebounds (R) (offensive plus defensive), assists (A), steal balls (ST), turnovers (T), blocks received (BA), blocks made (BM), faults incurred (FC) and faults received (FR). From these values a positive or negative ranking number is obtained based on the following formula (Gomez Ruano et al. 2008): RKG= (SP + R + A + ST + T + BM) - (MS + T+ FC). For each season, the total team ranking, mean game ranking, mean player ranking and the mean player scored points were calculated. Championships achieved and performance outcomes were also recorded.

Statistical analysis

Data was recorded for each player and season and was accumulated to provide mean team values. Those mean team values were correlated with an outcome parameter (sport performance, injury rate or exposure time) for each season using a Pearson's correlation (r). This coefficient ranges between -1 and +1. Data analysis was performed with IBM SPSS Statistics for Windows (Version 20.0, IBM Inc., Armonk, NY, USA). The alpha level was set to p

Results

Participants

The sample consisted of 44 players: mean [+ or -] SD age of 27.6 [+ or -] 4.1 years, height of 2.00 [+ or -] 0.09 m, and mass of 98.5 [+ or -] 12.6 kg) from a Spanish basketball club (F.C. Barcelona). Two players played at the team during six seasons; one during five; five players over four seasons; four over three, eleven over two, and forty four of...

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