Changes in Locomotor Ratio During Basketball Game Quarters From Elite Under-18 Teams

Quantifying game and training demands in basketball allows to determine player’s readiness and optimizes preparation to perform and reduce injury risks. Available research is using tracking technology to perform general descriptions of the game activities at professional levels, but somehow, is not exploring the possibilities of gathering data from new variables that can contribute with complementary information for the coaching staffs. The aim of this study was to identify changes in locomotor ratio, at higher and lower speeds, during the game quarters from elite under-18 basketball teams. Ninety-four male players participated in the study (age: 17.4 ± 0.74 years; height: 199.0 ± 0.1 cm; body mass: 87.1 ± 13.1 kg) from different playing positions, Guards (n = 35), Forwards (n = 42), and Centers (n = 17). Data were gathered from an international tournament and players’ movements were measured using a portable ultra-wide band position-tracking system (WIMU PRO®, Realtrack Systems, Almeria, Spain). The following variables were measured: (1) relative distance in different speed zones: walking (24.1 km·h−1); and (2) player load, vector magnitude expressed as the square root of the sum of the squared instantaneous rates of change in acceleration in each of the three planes divided by 100. Afterward, these variables were used to calculate players’ locomotor ratio (player load per meter covered) at higher (running, high-intensity running, and sprinting) and lower speeds (walking and jogging). Results from the locomotor ratio at both lower and higher speeds presented a significant effect for the quarter (F = 7.3, p < 0.001 and F = 7.1, p < 0.001, respectively) and player position (F = 3.1, p = 0.04, F = 9.2, p < 0.001, respectively). There was an increase in the locomotor ratio from game quarter (Q) Q1 to Q4 at lower speeds, but contrary trends at higher speeds, i.e., the values have decreased from Q1 to Q4. Also, forwards and centers of the best teams presented lower values at higher speeds. Altogether, the findings may be used by coaching staffs as a first baseline to elaborate normative behavior models from the players’ performance and also to induce variability and adaptation in specific practice planning.