Team sports are in continuous evolution, and physical performance is acquiring greater importance in the game. The assessment of physical fitness is the most reliable method for knowing if the athlete’s physical fitness is appropriate. Therefore, the objectives of this research were to identify profiles of physical-physiological demands with different specific tests of physical fitness related to the maximum strength of the lower body and speed. Moreover, some differences based on the sex and age of the players were identified. One hundred and forty-nine basketball players were analyzed (men n = 103 vs. women n = 46, weight: 74.74 ± 11.65 kg vs. 56.89 ± 3.71 kg, height: 184.66 ± 11.67 vs. 164 ± 4 and BMI: 21.7 ± 0.83 vs. 21.6 ± 0.90). The players performed an Abalakov test, a multi-jump test, and a repeat sprint ability test. Each player was equipped with a WIMU PRO device, and photoelectric cells were used. A MANOVA was performed to discover the differences between ages, and a t-test was used regarding sex. The results showed significant differences based on age and sex in variables related to time and Player Load/min (p < 0.001). The variables related to steps (contact, step, flight) also showed significant differences as a function of age (p < 0.001) and sex (p < 0.05). The multi-jump test showed differences based on age (p < 0.05 except in between jumps), but not on sex. These results confirm the importance of working together on lower body strength and speed skills. In addition, the planning of the work on these abilities must be individualized and according to the characteristics of the athlete.

In previous studies found in the literature speed (SP), acceleration (ACC), deceleration (DEC), and impact (IMP) zones have been created according to arbitrary thresholds without considering the specific workload profile of the players (e.g., sex, competitive level, sport discipline). The use of statistical methods based on raw data could be considered as an alternative to be able to individualize these thresholds. The study purposes were to: (a) individualize SP, ACC, DEC, and IMP zones in two female professional basketball teams; (b) characterize the external workload profile of 5 vs. 5 during training sessions; and (c) compare the external workload according to the competitive level (first vs. second division). Two basketball teams were recorded during a 15-day preseason microcycle using inertial devices with ultra-wideband indoor tracking technology and microsensors. The zones of external workload variables (speed, acceleration, deceleration, impacts) were categorized through k-means clusters. Competitive level differences were analyzed with Mann–Whitney’s U test and with Cohen’s d effect size. Five zones were categorized in speed (<2.31, 2.31–5.33, 5.34–9.32, 9.33–13.12, 13.13–17.08 km/h), acceleration (<0.50, 0.50–1.60, 1.61–2.87, 2.88–4.25, 4.26–6.71 m/s²), deceleration (<0.37, 0.37–1.13, 1.14–2.07, 2.08–3.23, 3.24–4.77 m/s²), and impacts (<1, 1–2.99, 3–4.99, 5–6.99, 7–10 g). The women’s basketball players covered 60–51 m/min, performed 27–25 ACC-DEC/min, and experienced 134–120 IMP/min. Differences were found between the first and second division teams, with higher values in SP, ACC, DEC, and IMP in the first division team (p < 0.03; d = 0.21–0.56). In conclusion, k-means clustering can be considered as an optimal tool to categorize intensity zones in team sports. The individualization of external workload demands according to the competitive level is fundamental for designing training plans that optimize sports performance and reduce injury risk in sport.

Basketball is a sport in continuous evolution, being one of these key aspects of the players’ physical fitness that has an impact on the game. Therefore, this study aimed to characterize and identify the physical fitness level and profiles of basketball players according to sex. Total of 26 semi-professional basketball players were assessed (13 male, 13 female) through inertial devices in different previously validated fitness tests. T-test for independent samples and principal component analysis were used to analyze sex-related differences and to identify physical fitness profiles. The results showed differences according to sex in all physical fitness indexes (p < 0.01; d > 1.04) with higher values in males, except in accelerometer load during small-sided games (p = 0.17; d < 0.20). Four principal components were identified in male and female basketball players, being two common ([PC1] aerobic capacity and in-game physical conditioning, [PC4 male, PC3 female] unipodal jump performance) and two different profiles (male: [PC2] bipodal jump capacity and acceleration, [PC3] curvilinear displacement; female: [PC2] bipodal jump capacity and curvilinear displacement, [PC4] deceleration). In conclusion, training design must be different and individualized according to different variables, including physical fitness profiles between them. For practical applications, these results will allow knowing the advantages and weaknesses of each athlete to adapt training tasks and game systems based on the skills and capabilities of the players in basketball.

La Revista Internacional de Medicina y Ciencias de la Actividad Física y del Deporte es una revista multidisciplinar, científico-técnica, en torno a las Ciencias de la Actividad Física y del Deporte. Todos los temas son publicables siempre que estén relacionados con la actividad física y el deporte y sea explícita esta relación en el artículo.

Introduction: Currently, knowing how to conduct training activities is essential to optimize player training. Therefore, one of the strategies most used by coaches to optimize the training process is to modify the number of players (Game Situation). Purpose: The objective of the study was to analyze the teaching-learning process of a school-age basketball coach, as well as to know the association between the Game Situation and the Pedagogical, External Load, and Organizational variables used that influence the design of tasks. Method: For this, a total of 20 training sessions consisting of 100 tasks, recorded through the Comprehensive System for the Analysis of Training Tasks, were analyzed. A descriptive and inferential analysis was conducted between the variables raised in the research in relation to the Game Situation. Results: The results demonstrated a significant relationship between the variables analyzed with the Game Situation. The way the players are grouped during the development of the training tasks conditions the results of the rest of the variables that define a training task at a pedagogical, organizational, and external load level. Conclusion: These pieces of evidence vindicate the importance of knowing the factors that influence training tasks. In addition, the results show that there are differences in the use of game situations depending on the rest of the variables used.

The external workload measured in one anatomical location does not determine the total load supported by the human body. Therefore, the purpose of the present study was to characterize the multi-location external workload through PlayerLoad_RT of 13 semi-professional women’s basketball players, as well as to analyze differences among anatomical locations (inter-scapulae line, lumbar region, 2× knee, 2× ankle) and laterality (left vs. right) during five tests that represent the most common movements in basketball—(a) linear locomotion, 30-15 IFT; (b) acceleration and deceleration, 16.25-m RSA (c) curvilinear locomotion, 6.75-m arc (d) jump, Abalakov test (e) small-sided game, 10’ 3 vs. 3 10 × 15-m. Statistical analysis was composed of a repeated-measures t-test and eta partial squared effect size. Regarding laterality, differences were found only in curvilinear locomotion, with a higher workload in the outer leg (p < 0.01; η_p² = 0.33–0.63). In the vertical profile, differences among anatomical locations were found in all tests (p < 0.01; η_p² = 0.56–0.98). The nearer location to ground contact showed higher values except between the scapulae and lumbar region during jumps (p = 0.83; η_p² = 0.00). In conclusion, the multi-location assessment of external workload through a previously validated test battery will make it possible to understand the individual effect of external workload in each anatomical location that depends on the type of locomotion. These results should be considered when designing specific strategies for training and injury prevention.