Load monitoring has been identified as a valuable tool for optimizing training planning and minimizing injury risk. This study’s aim was divided into two main objectives: (1) to describe the physical demands during official competition through IMU (inertial movement unit) metrics and (2) to investigate the relationship between basketball statistics and these physical demands. Twelve female highly trained basketballers (26.5 ± 5.3 years, 180 ± 7.1 cm, and 73.6 ± 10.3 kg) were monitored during four official games. Our results indicate that games with more frequent possession changes, particularly those driven by steals and turnovers, exhibit higher physical demands. Additionally, longer game durations were associated with longer recovery time while maintaining similar active time and physical load. Players who assume prominent shooting roles face greater conditional demands, such as increased jumps and impacts, even with equal playing time. These findings suggest that IMUs provide valuable insights into high-intensity actions and patterns, indicating a direct association between physical load and player performance in professional female basketball. This study also highlights the potential for professionals to better manage workload and understand player demands using these insights, even in the absence of in-game sensor data. Our research underscores the importance of contextual analysis in sports performance studies, encouraging future investigations into game phases and their specific physical demands.

Background: This study aimed to analyze the effect of caffeine ingestion on basketball performance in semi-professional female players. Methods: A double-blind, placebo-controlled, randomized experimental design was conducted, in two different periods separated by a week. Twelve female basketball players ingested 3 mg of caffeine/kg of body mass or a placebo. After 60 min, participants completed two repetitions of the specified sequence: Abalakov jump, countermovement jump (CMJ), 5-0-5 (505) test, and 20 m sprint. Later, external loads and game statistics were recorded during a 30-min simulated basketball game. Results: Significant improvements were observed in CMJ height and 505 test with caffeine ingestion compared to the placebo. Significant moderate differences were evident between conditions in external load variables, specifically, an increased number of high-intensity changes of direction during simulated games with caffeine ingestion. Two-point shot efficiency significantly improved after pre-caffeine ingestion compared with placebo. Conclusions: Three mg of caffeine per kg of body mass may serve as an effective ergogenic aid to increase physical performance and some variables of performance index in highly trained basketball players.

Baena-Raya, A, Díez-Fernández, DM, Martínez-Rubio, C, Conceição, F, and López-Sagarra, A. Kinetic and kinematic characteristics underpinning change of direction performance in basketball: A comparative study between sexes and tests. J Strength Cond Res 38(4): e182–e188, 2024—This study (a) evaluated the sex-specific kinetic and kinematic outcomes underpinning change of direction (COD) performance, (b) compared these outcomes across 3 different COD tests, and (c) examined the sex-specific and test-specific outcome most strongly related to COD performance in basketball players. Thirty young basketball players (17 males and 13 females) competing at the national level were assessed for the modified 505 test, modified T test, and V-cut test. The Local Positioning System technology (WIMU PRO, Realtrack Systems S.L., Almería, Spain) was used to measure the maximum values of acceleration (ACC_max), deceleration (DEC_max), velocity (VEL_max), and centripetal force (CentF_max) in each COD test. Male players displayed greater ACC_max, VEL_max, DEC_max, and CentF_max outputs for each section of the 505 test, T test, and V-cut test (all p < 0.05), resulting in a faster COD performance than females across tests (all ES > −1.42; 95% CI = −3.45 to −0.57). The 505 test demanded significantly greater kinematic outputs than T test and V-cut test for both sexes (p < 0.01). The ACC_max explained ∼26–58% of the variability in COD time for males across tests (p < 0.05) and 40% in females' modified 505-test time (p < 0.05). Likewise, in females, DEC_max explained 38% of the modified T-test time variability, whereas VEL_max explained 53% in the V-cut test time (both p < 0.05). These findings suggest that (a) COD data should not be used interchangeably across sexes, (b) test specificities should be considered for designing COD training, and (c) the kinematic outcomes explaining COD performance might vary across sexes.

Background: Understanding the relationship between physical demands and game performance is essential to optimize player development and management in basketball. This study aimed to examine the association between game performance and physical demands in youth male basketball players. Methods: Fifteen players (16.3 ± 0.7 years) from a Spanish 4th division team were monitored over seven official games. Game performance variables were extracted from official statistics, including traditional and advanced metrics. Physical demands were monitored using an Electronic Performance Tracking System device, combining a positioning system and inertial sensors. Partial correlations, controlling for minutes played, were calculated to explore associations between physical demands and performance variables, both for the entire team and by playing position. Results: Significant correlations between physical demands and game performance were observed. Points scored correlated strongly with total distance and high-intensity accelerations, while assists correlated with high-intensity decelerations. Inertial metrics, such as player load and the number of jumps, showed large correlations with points, two-point attempts, and the efficiency rating. Positional analysis revealed stronger and more numerous correlations for centers compared to guards and forwards. Inertial sensor-derived metrics exhibited a greater number and strength of correlations than positioning metrics. Conclusions: Game performance and physical demands are intrinsically related, with specific patterns varying by playing position. Inertial sensors provide valuable complementary information to positioning systems for assessing physical demands in basketball. These findings can assist practitioners in tailoring monitoring and training strategies to optimize performance and manage player workload effectively.

Background/Objectives: understanding the relationship between physical demands and game performance is essential to optimize player development and management in basketball. This study aimed to examine the association between game performance and physical demands in youth male basketball players. Methods: Fifteen players (16.3 ± 0.7 years) from a Spanish 4th division team were monitored over seven official games. Game performance variables were extracted from official statistics, including traditional and advanced metrics. Physical demands were monitored using Electronic Performance Tracking System device, combining positioning system and inertial sensors. Partial correlations, controlling for minutes played, were calculated to explore associations between physical demands and performance variables, both for the entire team and by playing position. Results: Significant correlations between physical demands and game performance were observed. Points scored correlated strongly with total distance and high-intensity accelerations, while assists correlated with high-intensity decelerations. Inertial metrics, such as Player Load and the number of jumps, showed large correlations with points, two-point attempts, and efficiency rating. Positional analysis revealed stronger and more numerous correlations for centers compared to guards and forwards. Inertial sensor-derived metrics exhibited a greater number and strength of correlations than positioning metrics. Conclusions: Game performance and physical demands are intrinsically related, with specific patterns varying by playing position. Inertial sensors provide valuable complementary information to positioning systems for assessing physical demands in basketball. These findings can assist practitioners in tailoring monitoring and training strategies to optimize performance and manage player workload effectively.

Background: Maximum repetitions to failure (RTF) and repetitions in reserve (RIR) can be estimated through fastest mean velocity (MV _fastest ) and mean velocity (MV), respectively. However, the impact of inter-repetition intervals (IRI) on these relationships in free-weight back squat and bench press exercises is unclear. Hypothesis: The IRI would affect RTF-MV _fastest and RIR-MV relationships, with a higher goodness-of-fit using self-selected IRI (SSIRI) compared with 0 seconds (IRI0) and 3 seconds (IRI3). Study Design: Crossover study design. Level of Evidence: Level 3. Methods: Eighteen male participants completed 1 session per IRI configuration, consisting of 3 single sets of RTF (65%-75%-85% of the 1-repetition maximum) during the free-weight back squat and bench press exercises. Results: Individualized RTF-MV _fastest and RIR-MV relationships were stronger than generalized (median R ² = 0.98 vs 0.65 and 0.84 vs 0.40, respectively). The goodness-of-fit of the relationships was stronger for SSIRI than for IRI0 during back squat ( P < .01) and comparable between IRIs during bench press ( P ≥ .28). During back squat, MV _fastest values were higher for IRI0 than for IRI3 and SSIRI (eighth-fifteenth repetitions; P ≤ .07), whereas during the bench press, they were higher for IRI0 than for IRI3 (eleventh-fifteenth repetitions; P ≥ .28). Overall, MV values associated with each RIR were higher for IRI0 than for SSIRI (10 out of 18 comparisons) during back squat, and for IRI0 than for IRI3 and SSIRI (16 and 14 out of 18 comparisons) during bench press. Conclusion: These results highlight the importance of standardizing the IRI during set-to-failure to establish RTF-MV _fastest and RIR-MV relationships, with SSIRI recommended as a more accurate and effective procedure. Clinical Relevance: This information may provide practitioners with a valuable tool to objectively quantify the level of effort being exerted during resistance training sets by measuring movement velocity in free-weight exercises.