Nowadays, wearables are a must-have tool for athletes and coaches. Wearables can provide real-time feedback to athletes on their athletic performance and other training details as training load, for example. The aim of this study was to systematically review studies that assessed the accuracy of wearables providing real-time feedback in swimming. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were selected to identify relevant studies. After screening, 283 articles were analyzed and 18 related to the assessment of the accuracy of wearables providing real-time feedback in swimming were retained for qualitative synthesis. The quality index was 12.44 ± 2.71 in a range from 0 (lowest quality) to 16 (highest quality). Most articles assessed in-house built (n = 15; 83.3%) wearables in front-crawl stroke (n = 8; 44.4%), eleven articles (61.1%) analyzed the accuracy of measuring swimming kinematics, eight (44.4%) were placed on the lower back, and seven were placed on the head (38.9%). A limited number of studies analyzed wearables that are commercially available (n = 3, 16.7%). Eleven articles (61.1%) reported on the accuracy, measurement error, or consistency. From those eleven, nine (81.8%) noted that wearables are accurate.

(1) Background: Tennis has changed toward power-driven gameplay, demanding a nuanced understanding of performance factors. This review explores the role of machine learning in enhancing tennis performance. (2) Methods: A systematic search identified articles utilizing machine learning in tennis performance analysis. (3) Results: Machine learning applications show promise in psychological state monitoring, talent identification, match outcome prediction, spatial and tactical analysis, and injury prevention. Coaches can leverage wearable technologies for personalized psychological state monitoring, data-driven talent identification, and tactical insights for informed decision-making. (4) Conclusions: Machine learning offers coaches insights to refine coaching methodologies and optimize player performance in tennis. By integrating these insights, coaches can adapt to the demands of the sport by improving the players’ outcomes. As technology progresses, continued exploration of machine learning’s potential in tennis is warranted for further advancements in performance optimization.

Futsal carries a high risk of injury for players. This systematic review aimed to assess the existing literature on injury prevention strategies for futsal players. The literature was searched using PubMed, Web of Science, and Scopus databases from inception to 20 March 2024. Relevant articles were searched using the terms “futsal” AND “injury” AND “prevention”. Fourteen studies were included in the review. The review identified several injury prevention strategies with potential benefits for futsal players. Structured warm-up routines were shown to improve balance and eccentric strength and to reduce total, acute, and lower limb injuries. Proprioceptive training methods were suggested to improve joint stability and landing mechanics, which may reduce the risk of injury. Furthermore, multicomponent methods that include components such as core stability and flexibility have shown potential for reducing injury rates in futsal players. Finally, by reducing fatigue and improving movement control, strength training procedures designed to correct muscular imbalances may improve performance, which may ultimately minimize the risk of injury. This systematic review demonstrates the potential benefits of different injury prevention strategies for futsal players. The combination of several strategies, such as proprioceptive training, multicomponent programs, warm-up routines, and strength training specifically designed to address muscular imbalances, appears promising.

Understanding the difference in each upper limb between age groups can provide deeper insights into swimmers’ propulsion. This study aimed to: (1) compare swimming velocity and a set of kinematical variables between junior and juvenile swimmers and (2) compare the propulsion outputs through discrete and continuous analyses (Statistical Parametric Mapping—SPM) between junior and juvenile swimmers for each upper limb (i.e., dominant and non-dominant). The sample was composed of 22 male swimmers (12 juniors with 16.35 ± 0.74 years; 10 juveniles with 15.40 ± 0.32 years). A set of kinematic and propulsion variables was measured at maximum swimming velocity. Statistical Parametric Mapping was used as a continuous analysis approach to identify differences in the propulsion of both upper limbs between junior and juvenile swimmers. Junior swimmers were significantly faster than juveniles (p = 0.04, d = 0.86). Although juniors showed higher propulsion values, the SPM did not reveal significant differences (p < 0.05) for dominant and non-dominant upper limbs between the two age groups. This indicates that other factors (such as drag) may be responsible for the difference in swimming velocity. Coaches and swimmers should be aware that an increase in propulsion alone may not immediately lead to an increase in swimming velocity.

Evidence on the role of propulsion compared to drag in swimming, based on experimental settings, is still lacking. However, higher levels of propulsion seem to lead to faster swimming velocities. The aim of this study was to understand the variation in a set of kinematic and kinetic variables between two swimming sections and their relationship to swimming velocity. The sample consisted of 15 young adult recreational swimmers (8 males: 20.84 ± 2.03 years; 7 females: 20.13 ± 1.90 years). Maximum swimming velocity and a set of kinematic and kinetic variables were measured during two consecutive sections of the swimming pool. Differences between sections were measured and the determinants of swimming velocity were analyzed. Swimming velocity, propulsive force, and the other kinematic and kinetic variables did not change significantly (p < 0.05) between sections (only the intra-cyclic fluctuation of swimming velocity decreased significantly, p = 0.005). The modeling identified the propulsive force, stroke length, and active drag coefficient as the determinants of swimming velocity. Swimming velocity was determined by the interaction of kinematic and kinetic variables, specifically propulsive force and active drag coefficient.

Besides recurrently assessed water-based parameters, there are also some individual characteristics that affect swimming performance that are not water related. In the past few years, dynamic balance has been associated with land sports performance. Conversely, evidence on this topic in swimming is scarce. The purpose of this study was to assess the association between on-land dynamic balance and swimming performance. Sixteen young adults and recreational swimmers were recruited for the present study (8 males 20.8 ± 2.0 years, and 8 females 20.1 ± 1.9 years). A set of anthropometric features were measured. The upper quarter Y-balance test was selected as a dynamic balance outcome, and swimming speed as the swimming performance indicator. The results showed a moderate and positive correlation between dynamic balance and swimming performance (p < 0.05). Speed fluctuation was highly and negatively related to swimming speed (p < 0.001), i.e., swimmers who had higher scores in the dynamic balance were more likely to deliver better performances. This suggests that in recreational swimmers, the stability and mobility of the upper extremity had a greater influence on swimming performance. Therefore, swimming instructors are advised to include dynamic balance exercises in their land-based training sessions to improve their swimmers’ performance.