The relationship between training load and injury risk in basketball is a critical factor in performance optimization and injury prevention. Evidence accumulated over the past 25 years indicates a complex, non-linear association: while excessive training may increase injury risk, appropriately managed workloads can provide protective benefits, whereas undertraining or sudden workload spikes may also elevate risk. This review synthesizes findings from studies examining professional and youth basketball players, incorporating both observational and retrospective cohort research. A systematic search of PubMed, Scopus, Web of Science, and Google Scholar up to September 2025 identified six studies that met the inclusion criteria. Observational studies using session rating of perceived exertion (s-RPE) demonstrated that most injuries occurred in the lower extremities and were frequently associated with periods of peak physical and psychological fatigue. These monitoring approaches represent practical and cost-effective tools for tailoring workloads and preventing overtraining. Retrospective cohort analyses of professional players indicated that athletes with lower in-game activity, such as fewer decelerations or reduced distance covered, exhibited higher injury incidence, suggesting that under-loaded players may be at greater risk. In youth basketball, wearable technologies and neuromuscular metrics such as weighted jump height revealed that overuse injuries often occur following periods of low chronic workload combined with high acute load highlighting the impact of sudden workload spikes. Collectively, these findings underscore the importance of individualized workload management, progressive training strategies, and continuous monitoring to reduce injury risk while optimizing performance. Integrating session rating of perceived exertion and biomechanical metrics into training planning offers an evidence-based approach for safeguarding athletes’ health and optimizing outcomes.
Artificial intelligence and computer vision have made significant progress in recent decades, profoundly impacting many scientific and professional disciplines, kinesiology included. The development of markerless motion capture technologies has enabled precise tracking of kinematic and dynamic parameters of the human body movements without the need for physical markers or complex equipment. These technologies use advanced computer vision and deep learning algorithms to analyze human movements in real time, allowing the quantification of biomechanical parameters such as joint angles, movement speed, stride length, gait asymmetries, and complex movements such as jumping or running. Markerless technologies reduce preparation and recording time and allow movement analysis in natural conditions, making them useful not only in laboratory but also in clinical, sports, rehabilitation, and everyday settings. The use of smartphone video recordings further facilitates the availability and implementation of these systems. However, the application of markerless technologies to complex three-dimensional movements, such as trunk rotations or upper limb activities, remains a challenge. The accuracy of these systems depends on various factors, including movement type, number of cameras, recording quality, and lighting conditions. Advances in deep learning and computer vision allow continuous improvement in reliability, making these systems more competitive with the traditional marker-based methods. Markerless technologies have significant potential in rehabilitation and sports performance optimization, but further development is needed regarding validation standardization and algorithmic robustness. This paper aims to show how markerless technologies enable new approaches in the analysis of human movement, exploring their advantages, challenges, and potential for further development in kinesiology.
Although evidence suggests that physical fitness level may play a contributing role in injury development in athletes, available literature yields conflicting results with positive, null or even negative associations between them. Therefore, the main purpose of the study was to examine cross-sectional associations between baseline physical fitness and injury occurrence. In this study, participants were 60 male 1st Division basketball players (mean age: 22.1 ± 1.4 years, height: 193.3 ± 6.2, body mass: 89.3 ± 12.1 kg). Physical fitness was assessed through musculoskeletal (squat jump, countermovement jump, and countermovement jump with the arm swing) and cardiorespiratory fitness (maximal oxygen uptake). Injury registration included a question regarding any musculoskeletal complaint or injuries during the previous seven days with ‘yes’ and ‘no’ answers. The least fit group of participants in squat jump (OR = 1.25, 95% CI 1.10 to 2.55, p<.001), countermovement jump (OR = 1.14, 95% CI 1.02 to 1.96, p=.036), and maximal oxygen uptake (OR = 1.32, 95% CI 1.10 to 2.10, p<.001) exhibited the highest likelihood of injury occurrence, compared with the high fit group. The medium fit group in squat jump (OR = 1.05, 95% CI 1.01 to 4.05, p=.045) was at the significant and increased risk for injury occurrence comparing with the high fit group. This study indicates that lower levels of musculoskeletal and cardiorespiratory physical fitness may lead to higher likelihood of injury occurrence in male basketball players. Thus, physical fitness may be considered as a protective factor against injuries incurred.
Soccer is an aerobic sport with intermittent characteristics, requiring load management strategies. The physical efficiency index (PEI) is commonly used for this purpose, calculated as the ratio between external and internal load (distance per minute / %HRmax). Recently, a new PEI was proposed, incorporating the number of accelerations [(number of accelerations x distance per minute) / %HRmax] due to their impact on match performance. Therefore, the aim of this study was to compare the traditional PEI with the new PEI in professional soccer matches. This study compared both indices in 14 matches played by a professional team in Brazil’s Série B, with 11 athletes (25.4 ± 3.24 years; 74.17 ± 4.60 kg; 179.10 ± 7.94 cm; VO₂ max: 54.76 ± 12.24 ml/kg/min). Variables analyzed included total distance covered, sprints, accelerations, and decelerations. The average total distance was 9712.00 ± 490.90 m, with 496.80 ± 88.28 m in high-speed running and 173.98 ± 40.20 m in sprints. The mean number of accelerations was 103.4 ± 8.91, and decelerations 99.72 ± 9.06. The new PEI showed higher values than the traditional PEI (1.35 vs. 1.18; p<.001). Results suggest that the new PEI, incorporating accelerations, is a more suitable tool for load monitoring, as it better reflects the biomechanical and physiological demands of the game. This can help optimize training strategies, minimize athletes’ fatigue, and improve their post-match recovery.
The aim of this study was to compare the performance of the Abalakov jump (AJ) test and the countermovement jump (CMJ) test in youth football players, and to examine the effects of these tests on jump-related outputs. Thirty-two male football players, aged between 10 and 14 years with an average of three years of football experience, participated in the study. Jump tests were performed using the ForceDecks system (VALD, Brisbane, Australia). Force-time derived performance variables obtained during CMJ and AJ were analyzed and compared. The findings revealed that the AJ test produced significantly higher values in jump height, peak force, peak power, and take-off velocity compared to the CMJ (p<.05). While the AJ test provides an advantage in sport-specific and dynamic performance measurements, the CMJ can be used as a more standardized and controlled assessment tool. Therefore, it is recommended that both tests be used complementarily in training monitoring and performance evaluation processes in football players within this age group.
