Abstract
An understanding of basketball physical demands during official matches is fundamental for designing specific training, tactical, and strategic plans as well as recovery methods during congested fixture periods. Such assessments can be performed using wearable indoor time motion tracking systems. The purpose of this study was to analyze the time-motion profile of under 18-years of age (U’18) basketball players and compare their physical demands in relation to team ranking, playing position, match periods and consecutive matches during a 7-day tournament. Relative Distance (RD), percentage of High-Intensity Running (%HIR), Player Load (PL), Acceleration (Acc), Deceleration (Dec), Peak Speed (PSpeed), and Peak Acceleration (PAcc) were recorded from 94 players (13 centers, 47 forwards, and 34 guards) belonging to eight elite teams (age: 17.6 ± 0.8 years; height: 1.91 ± 0.08 m; body mass: 82.5 ± 8.8 kg). WIMU PROTM inertial measurement units with ultra-wide band (UWB) indoor-tracking technology recorded 13 matches during the Adidas Next Generation Tournament Finals in the 2016–2017 season. Paired t-tests and one-way analyses of variance with omega partial squared () and Cohen’s effect sizes (d) were used to analyze for differences between variables. According to team quality, the best teams had lower RD (p = 0.04; d = −0.14). Guards presented higher RD (p < 0.01; = 0.03), PSpeed (p < 0.01; = 0.01) and PAcc (p < 0.01; = 0.02) compared to forwards and centers. The first quarter showed differences with higher RD (p < 0.01; = 0.03), %HIR (p < 0.01; = 0.02), and PL (p < 0.01; = 0.04) compared to all other quarters. The third match of the tournament presented higher demands in RD (p < 0.01; = 0.03), HIR (p < 0.01; = 0.01) and PL (p < 0.01; = 0.02) compared with the first two matches. This study showed that team quality, playing position, match period, and consecutive matches throughout an U’18 basketball tournament influenced the kinematic demands experienced by players during official competition. Therefore, each of these contextual factors should be considered in managing the load and developing individualized strategies for players in tournament settings.
Highlights
Basketball is considered as a team sport involving intermittent efforts due to the elevated number of instances of high-intensity running alternating with low-intensity periods (Stojanovicet al., 2018)
Across sectional design with natural groups was employed in the current study (Ato et al, 2013) to analyze the intensity timemotion profile of elite U’18 basketball players during the Adidas Generation Tournament (ANGT 16-17) using an ultra-wide band (UWB) tracking system
There were no statistical differences in HIR (t = 0.42, p = 0.67, d = 0.03), PL (t = 0.48, p = 0.63, d = 0.03), Acc (t = −0.12, p = 0.90, d = 0.01), Dec (t = 0.01, p = 0.99, d = 0), PSpeed (t = 1.17, p = 0.24, d = 0.08) and PAcc (t = −0.74, p = 0.46, d = −0.05). the best teams had a significant lower RD
Summary
Basketball is considered as a team sport involving intermittent efforts due to the elevated number of instances of high-intensity running alternating with low-intensity periods (Stojanovicet al., 2018). Most elite team sport players are exposed to congested fixtures with a high number of matches or competitions within a few days (Ibáñez et al, 2009; Dellal et al, 2013; Rojas-Valverde et al, 2018), and this kind of situation could lead to an increase in fatigue and injury risk (McLean et al, 2018). In recent years this competitive dynamic has increased the interest of teams’ medical and technical staffs to analyze and better understand the internal and external physical load of players using objective methods during training and competition (Fox et al, 2017). Internal load is the physiological reaction and stress experienced when faced with a stimulus (Fox et al, 2018), and it can be measured by heart rate telemetry, rating of perceived exertion, fitness-wellness tests, as well as metabolically, using biochemical, hormonal, and immunological markers (Akubat et al, 2014). Load parameters vary among brands or device version (Aughey, 2011; Cummins et al, 2013; Dellaserra et al, 2014), most of them measure: (i) distance covered per minute (m/min), (ii) average speed as an indicator of intensity of movement (km/h); (iii) percentage of high-intensity actions (% HIA), (iv) accelerations and decelerations per minute (acc/min; dec/min), and (v) impacts at different intensities or specific formulas such as PlayerLoadTM (PLTM) (Edwards et al, 2018; Staunton et al, 2018; Svilar et al, 2018)
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