AUTHOR=Pino-Ortega José , Rojas-Valverde Daniel , Gómez-Carmona Carlos David , Bastida-Castillo Alejandro , Hernández-Belmonte Alejandro , García-Rubio Javier , Nakamura Fábio Yuzo , Ibáñez Sergio José 

TITLE=Impact of Contextual Factors on External Load During a Congested-Fixture Tournament in Elite U’18 Basketball Players

JOURNAL=Frontiers in Psychology

VOLUME=10

YEAR=2019

URL=https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2019.01100

DOI=10.3389/fpsyg.2019.01100

ISSN=1664-1078

ABSTRACT=<p>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 PRO<sup>TM</sup> inertial measurement units with ultra-wide band (UWB) indoor-tracking technology recorded 13 matches during the <italic>Adidas Next Generation Tournament Finals</italic> in the 2016–2017 season. Paired <italic>t</italic>-tests and one-way analyses of variance with omega partial squared (<inline-formula><mml:math id="M1" xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:msubsup><mml:mi mathvariant="normal">ω</mml:mi><mml:mi mathvariant="normal">p</mml:mi><mml:mn>2</mml:mn></mml:msubsup></mml:mrow></mml:math></inline-formula>) and Cohen’s effect sizes (<italic>d</italic>) were used to analyze for differences between variables. According to team quality, the best teams had lower RD (<italic>p</italic> = 0.04; <italic>d</italic> = −0.14). Guards presented higher RD (<italic>p</italic> &lt; 0.01; <inline-formula><mml:math id="M2" xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:msubsup><mml:mi mathvariant="normal">ω</mml:mi><mml:mi mathvariant="normal">p</mml:mi><mml:mn>2</mml:mn></mml:msubsup></mml:mrow></mml:math></inline-formula> = 0.03), PSpeed (<italic>p</italic> &lt; 0.01; <inline-formula><mml:math id="M3" xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:msubsup><mml:mi mathvariant="normal">ω</mml:mi><mml:mi mathvariant="normal">p</mml:mi><mml:mn>2</mml:mn></mml:msubsup></mml:mrow></mml:math></inline-formula> = 0.01) and PAcc (<italic>p</italic> &lt; 0.01; <inline-formula><mml:math id="M4" xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:msubsup><mml:mi mathvariant="normal">ω</mml:mi><mml:mi mathvariant="normal">p</mml:mi><mml:mn>2</mml:mn></mml:msubsup></mml:mrow></mml:math></inline-formula> = 0.02) compared to forwards and centers. The first quarter showed differences with higher RD (<italic>p</italic> &lt; 0.01; <inline-formula><mml:math id="M5" xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:msubsup><mml:mi mathvariant="normal">ω</mml:mi><mml:mi mathvariant="normal">p</mml:mi><mml:mn>2</mml:mn></mml:msubsup></mml:mrow></mml:math></inline-formula> = 0.03), %HIR (<italic>p</italic> &lt; 0.01; <inline-formula><mml:math id="M6" xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:msubsup><mml:mi mathvariant="normal">ω</mml:mi><mml:mi mathvariant="normal">p</mml:mi><mml:mn>2</mml:mn></mml:msubsup></mml:mrow></mml:math></inline-formula> = 0.02), and PL (<italic>p</italic> &lt; 0.01; <inline-formula><mml:math id="M7" xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:msubsup><mml:mi mathvariant="normal">ω</mml:mi><mml:mi mathvariant="normal">p</mml:mi><mml:mn>2</mml:mn></mml:msubsup></mml:mrow></mml:math></inline-formula> = 0.04) compared to all other quarters. The third match of the tournament presented higher demands in RD (<italic>p</italic> &lt; 0.01; <inline-formula><mml:math id="M8" xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:msubsup><mml:mi mathvariant="normal">ω</mml:mi><mml:mi mathvariant="normal">p</mml:mi><mml:mn>2</mml:mn></mml:msubsup></mml:mrow></mml:math></inline-formula> = 0.03), HIR (<italic>p</italic> &lt; 0.01; <inline-formula><mml:math id="M9" xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:msubsup><mml:mi mathvariant="normal">ω</mml:mi><mml:mi mathvariant="normal">p</mml:mi><mml:mn>2</mml:mn></mml:msubsup></mml:mrow></mml:math></inline-formula> = 0.01) and PL (<italic>p</italic> &lt; 0.01; <inline-formula><mml:math id="M10" xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:msubsup><mml:mi mathvariant="normal">ω</mml:mi><mml:mi mathvariant="normal">p</mml:mi><mml:mn>2</mml:mn></mml:msubsup></mml:mrow></mml:math></inline-formula> = 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.</p>