Background: Decision-making during late-game possessions in basketball is considered one of the most essential aspects of winning a game. This study examined key determinants of late-game shot-making success in closely contested EuroLeague basketball matches, aiming to optimize offensive decision-making in clutch situations. Material and Methods: An observational analysis from the last two minutes of 83 EuroLeague games recorded 471 clutch shot attempts. Logistic regression and chi-square analyses assessed the impact of offense time, shot range, defensive pressure, game status, and offensive tactical strategies on shot success. Results: The results showed that paint clutch shots (63.9% success) were significantly more effective than mid-range (30.0%) and three-point attempts (33.5%). Defensive pressure was a critical factor, with uncontested shots (58.6%) outperforming contested attempts (36.4%). Quick offensive execution and big player position also influenced the shot outcome. The primary key determinants in predicting clutch shot success included shot range, defensive pressure, offense time, and game status. Conclusions: By prioritizing high-percentage shot opportunities, leveraging cooperative movement-based offensive strategies, and utilizing big players effectively, teams can enhance their performance in high-pressure moments. These findings provide actionable insights for coaches and players, emphasizing the need for data-driven decision-making to optimize late-game performance.

Home advantage (HA) remains a persistent phenomenon in elite basketball. This study quantified HA across eight EuroLeague seasons (2016–2025) and examined how team quality (TQ) and competitive balance (CB) influence it. Data from 2,154 regular-season games were obtained from the official EuroLeague website. HA% and home win percentage (HW%) were calculated, while CB was measured using the Standard Deviation Ratio (SDR). TQ was determined using a Two-Step Cluster Analysis based on win percentage (W%), classifying teams into high, medium, or low-quality groups. One-way ANOVA examined seasonal and TQ variation, while Spearman's correlation and multiple regression assessed relationships among the variables. The results showed that EuroLeague teams won 62.9% of home games on average. HA% peaked in 2021–2022 and dropped in 2020–2021, reflecting the impact of pandemic-related spectator restrictions. CB fluctuated moderately, with a notable imbalance in 2018–2019 (CB = 2.17). Regression results showed that TQ significantly predicted HA% (p < .01), with lower-quality teams exhibiting greater HA% effects (B = 10.06, SE = 2.15), whereas CB was not a significant predictor (p = .088). HA remains a consistent structural phenomenon in EuroLeague basketball, more likely due to psychological and situational factors, resilient to changes in league parity.

Background/Objectives: Insufficient physical activity remains a major public health concern among adult women, highlighting the need to identify structured activity contexts that can contribute meaningfully to recommended weekly physical activity levels. Official masters basketball may represent one such context; however, the amount of physical activity accumulated during female masters basketball match play remains insufficiently quantified. This study quantified the physical activity profile of official tournament match play among female masters basketball athletes and described the associated external physical demands. Methods: This observational study included 52 female master basketball athletes aged 37–63 years who competed in a three-day national masters tournament. Match demands were monitored using tri-axial microsensors. Physical activity was classified from processed raw tri-axial acceleration data into intensity zones, and differences in time spent across zones were examined using one-way repeated-measures ANOVA. External load during active play was quantified using total distance, distance across speed zones, accumulated acceleration load (AAL), mechanical load (ML), jump load (JL), and Physio Load. Results: Significant differences were observed across physical-activity intensity zones, with more time accumulated in light physical activity (LPA) and vigorous physical activity (VPA) than in moderate physical activity (MPA), whereas MPA accounted for the least time overall [F (1.98, 101.16) = 47.57, p < 0.001, ηp2 = 0.48]. Descriptively, moderate-to-vigorous physical activity (MVPA) amounted to 42.78 min, calculated as the sum of MPA (9.41 ± 3.82 min) and VPA (33.37 ± 14.49 min). During active play, athletes covered 59.19 ± 17.26 m·min−1, with most distance accumulated in the low- and medium-speed zones and limited very-high-speed running; AAL, ML, and JL averaged 8.32 ± 2.31 AU·min−1, 22.35 ± 5.53 AU·min−1, and 31.26 ± 28.35 J·min−1, respectively. Conclusions: Official female masters basketball appears to provide a meaningful intermittent physical-activity stimulus within a single monitored match exposure and may contribute substantially to weekly aerobic physical-activity accumulation in adult women.

Objectives: Local positioning systems (LPSs) used in indoor team sports generate a large number of external load variables, often exceeding practical monitoring capacity. The redundancy and overlap among these variables make it difficult to identify the most informative metrics for performance analysis and load management. This study aimed to reduce the dimensionality of external load variables derived from LPS data and to identify data-driven external-load observation profiles using principal component analysis and clustering techniques. Methods: A total of 188 observations from indoor team sports (basketball, handball, and futsal) were analyzed. Continuous external load variables were standardized and subjected to principal component analysis (PCA), with component retention based on a ≥90% cumulative explained variance threshold. K-means clustering was applied in both the full standardized feature space and the PCA-reduced space. The optimal number of clusters was determined using silhouette analysis and the elbow method. Agreement between clustering solutions was assessed using Adjusted Rand Index (ARI) and Normalized Mutual Information (NMI). Cluster characteristics were further examined using descriptive statistics and variable separation analysis. Results: The first two principal components explained 53.7% of the total variance, representing high-intensity external load and neuromuscular load dimensions, while 12 components were required to exceed 90% cumulative explained variance. Clustering analysis consistently identified three moderately separated clusters in both the full and PCA-reduced spaces. The PCA-based solution demonstrated improved separation (silhouette = 0.362) compared to the full-space solution (silhouette = 0.319). Agreement between clustering approaches was high (ARI = 0.981; NMI = 0.971), indicating that dimensionality reduction largely preserved the main clustering structure within the analyzed dataset. The most discriminative variables included jump load, acceleration load, metabolic power, and anaerobic activity distance. Conclusions: A large set of external load variables can be reduced into interpretable latent dimensions that support exploratory external-load profile identification. The combination of PCA and clustering provides an exploratory and structure-preserving framework for summarizing complex external-load datasets and identifying latent load dimensions. These findings may assist future monitoring strategies; however, the practical utility of the identified profiles requires prospective validation before implementation in training-load management.

Purpose: The primary aim of the present study was to examine the extent to which participation in organized youth basketball training contributes to physical activity across intensity zones during training sessions in relation to biological maturation status. Methods: Participants were classified into three maturity groups based on predicted age at peak height velocity (PHV): -2.5 to -1.5, -1.5 to -0.5, and >=-0.5 to 0.83 years from PHV. Data from two training sessions per participant were averaged to obtain representative individual values. One-way analyses of variance (ANOVAs) were used to examine differences in anthropometric, physical performance, and field performance variables between PHV groups. Physical activity patterns were analyzed using two-way mixed-design ANOVAs with PHV stage as the between-subject factor and intensity zone (MET- and HRR-based) as the within-subject factor. Results: Across all maturity groups, approximately 10-17% of total training time was spent in light-intensity activity, while the majority of time was accumulated in moderate-to-vigorous intensity zones (approximately 35-50%, depending on the classification method). Significant maturity-related differences were observed in anthropometric variables and physical performance measures, with more mature players demonstrating superior sprint performance, jumping ability, and grip strength. Field performance indicators also differed between PHV groups, with more mature athletes exhibiting higher external and internal training loads. In contrast, no significant interactions or main effects of PHV stage were observed for physical activity intensity distribution. Conclusions: Organized basketball training contributes substantially to moderate-to-vigorous physical activity accumulated during training sessions. However, these findings reflect training-specific activity and should not be interpreted as representing total daily physical activity. No differences in activity intensity distribution were observed between maturation groups, although this finding should be interpreted with caution, given methodological limitations. These results highlight the need to consider biological maturation when designing youth training programs.

Background: Strength and the strength-power continuum may increase athletic performance, although data are scarce regarding the effects of long-term periodized training on the athletic performance of adolescent track and field athletes. The purpose of this study was to investigate performance modifications following 8 weeks of strength and strength-power resistance training, focusing on the athletic performance of adolescent track and field athletes. Methods: Following an equivalent single-arm pre-post intervention design, 16 adolescent athletes (age: 16.3 +/- 0.5 years; mass: 56.5 +/- 10.4 kg; height: 1.67 +/- 0.07 m) participated in the study. Athletes followed an 8-week periodized resistance training program aiming to increase strength and strength-power. Measurements were performed before (T1), at the middle (T2) and at the end of the training period (T3) and included the standing long jump, single-leg standing long jump, five-step long jump, seated medicine ball throw, 0-80 m sprint and 1RM in the bench press and parallel squat. Results: The standing long jump (F-(2,F-14) = 109.564; eta(2) = 0.940; p = 0.001), single-leg long jump (F-(2,F-14) > 41.801; eta(2) = 0.857; p = 0.001) and five-step long jump (F-(2,F-14) = 148.564; eta(2) = 0.955; p = 0.001) improved significantly from T1 to T2 (p < 0.001) and from T2 to T3 (p < 0.001). The seated medicine ball throw (F-(2,F-14) = 124.305; eta(2) = 0.947; p = 0.001) and sprinting performance (F-(2,F-14) = 51.581; eta(2) = 0.828; p = 0.001) were significantly enhanced from T1 to T2 (p < 0.001) and from T2 to T3 (p < 0.001). The 1RM in the bench press (F-(2,F-14) = 36.280; eta(2) = 0.838, p = 0.001) and in the parallel squat (F-(2,F-14) = 48.165; eta(2) = 0.873, p = 0.001) increased significantly from T1 to T2 (p < 0.001) and from T2 to T3 (p < 0.01). Conclusions: Strength and the strength-power continuum appear to have a positive effect on the physical fitness of adolescent track and field athletes, which highlights the importance of strength-based resistance training programs in adolescent athletes.

Background/Objectives: Multifrequency bioelectrical impedance analysis (MF-BIA) is increasingly used for practical body composition assessment when dual-energy X-ray absorptiometry (DXA) is unavailable or impractical. However, MF-BIA estimates are device-, population-, and outcome-specific, and therefore require validation against reference methods under standardized conditions. This study examined the agreement, concordance, and systematic bias between a standing 8-point MF-BIA device and DXA-derived body composition estimates in apparently healthy Greek adults. Methods: A total of 1250 adults aged 18 to 80 years completed same-day DXA and MF-BIA (Charder MA801) assessments. Fat mass (FM), fat-free mass (FFM), body fat percentage (BF%), and appendicular skeletal muscle mass estimate (ASM) were compared between methods. Analyses were performed by sex and BMI category. Pearson correlations described association, whereas Bland-Altman analysis, Lin's concordance correlation coefficient (CCC), mean absolute error (MAE), root mean square error (RMSE), and proportional bias testing evaluated agreement and error magnitude. Results: MF-BIA showed strong associations with DXA-derived outcomes, but systematic bias was observed. When BMI categories were considered collectively, MF-BIA underestimated BF% by 3.59 percentage points in men and 4.25 percentage points in women, underestimated FM by 2.89 kg and 2.58 kg, and overestimated FFM by 3.09 kg and 3.29 kg, respectively. CCC was highest for FM (men: 0.913; women: 0.949) and lower for FFM and ASM in women (0.642 and 0.714, respectively). Proportional bias was observed for BF%, FM, and ASM in both sexes, and for FFM in women. Conclusions: The MA801 showed strong associations and outcome-specific concordance with DXA, but systematic bias and individual-level error limit interchangeability. Under standardized conditions, MF-BIA may support group-level or repeated same-device assessments but not precise individual-level assessment, clinical classification, or monitoring of small longitudinal changes.

ABSTRACT:Poulios, A, Grammenos, N, Fatouros, IG, Avloniti, A, Tsimeas, P, Papanikolaou, K, Syrou, N, Rosvoglou, A, Tsaousidis, I, Chatzinikolaou, A, Tsiokanos, A, Mohr, M, Jamurtas, AZ, and Draganidis, D. Recovery kinetics after repeated sprint training with directional changes in soccer: It is a matter of angle. J Strength Cond Res XX(X): 000-000, 2026-This study determined the recovery kinetics of performance, delay onset muscle soreness (DOMS), and neuromuscular fatigue after repeated sprint training using 2 angles of changes of direction (COD) in soccer. Ten male players randomly completed 3 conditions using a randomized cross-over, repeated measures design: control, COD45 (COD of 45°) and COD90 (COD of 90°). Training load was monitored using global positioning system with accelerometers and heart-rate monitors. Blood count, maximal voluntary isometric contraction, countermovement jump (CMJ), DOMS, speed, and agility were measured at baseline and at 24-, 48-, and 72-h postexercise. Maximal voluntary isometric contraction, CMJ, and DOMS were also evaluated at 1-, 2- and 3-h postexercise. Agility, DOMS, and blood count levels remained unaltered in all conditions ( p > 0.05). Blood lactate increased (COD45:91%; COD90:89.5%, p < 0.05) postexercise. COD45 was characterized by a higher average (31%) and maximum speed (25%) and lower decelerations (58-77%) and accelerations (66-77%) than COD90 ( p < 0.05). The 10- and 30-m speed in COD45 decreased ( p < 0.05) by 20 and 9% postexercise and remained lower (4-12%) than that in COD90 for as long as 24 h. Countermovement jump declined ( p < 0.05) for 3 h in COD45 (8%) and 24 h in COD90 (5-7%). Maximal voluntary isometric contraction of knee extensors in COD45 declined (8%) and was lower than COD90 (9%) at 24 h in both limbs. Maximal voluntary isometric contraction of knee flexors decreased (COD45:14%) for 24 h in dominant and 3 h (COD45:2%, COD90:14%) in nondominant limb with COD45 inducing a greater decline than COD90 ( p < 0.05). Although COD protocols do not increase DOMS, it seems that COD training at lower angles may be associated with a slower recovery (24 h) than that at greater angles.

Background: Monitoring external load in team sports is essential for performance optimization, injury prevention, and individualized training prescription. Although Local Positioning Systems (LPS) are widely used for indoor athlete tracking, they require wearable devices and specialized infrastructure. Recent advances in artificial intelligence and computer vision allow markerless athlete tracking; however, their validity for basketball remains insufficiently explored. Objective: To evaluate the validity of a deep-learning multi-camera computer-vision system for quantifying external-load variables in basketball compared with a commercial LPS. Methods: The framework integrated fisheye video acquisition, player detection, and pose estimation using YOLOv11x-Pose and player re-identification through ResNet-50 and FAISS similarity search. Positional data were transformed into real-world court coordinates to derive distance, acceleration, deceleration, player load, and average speed metrics. Outputs were compared with measurements obtained from Kinexon LPS. Results: Strong correlations were observed for total distance (r = 0.92), acceleration counts (r = 0.90), deceleration counts (r = 0.92), and player load (r = 0.81), while average speed showed a moderate-to-strong correlation (r = 0.66). ICC and Bland-Altman analyses indicated agreement between systems. Conclusions: The proposed computer-vision system demonstrated high agreement with LPS, supporting its use as a valid, non-invasive, and scalable solution for external load monitoring in basketball.

Background/Objectives: Comprehensive knowledge of body composition and bone status across the lifespan is critical for clinical evaluation and public health initiatives. This study aimed to develop age- and sex-specific reference curves for body composition and bone status in a physically active Greek population aged 18–80 using dual-energy X-ray absorptiometry (DXA). A secondary objective was to examine age- and sex-related trends in fat distribution, lean mass (LM), and bone status. Methods: A cross-sectional analysis was conducted on 637 participants (275 men and 362 women). Physical activity was assessed through structured interviews evaluating type, frequency, and intensity, categorized using established guidelines from organizations such as the American Heart Association and World Health Organization. Anthropometric data and DXA scans were utilized to measure parameters including fat mass (FM), LM, and BMD. Participants were stratified into age categories, and percentile curves were generated using generalized additive models for location, scale, and shape (GAMLSS). Results: Among women, body mass increased by 20.9% and body fat percentage rose by 38.3% from the youngest to the oldest age group, accompanied by a 5.7% reduction in bone mineral density (BMD) and an 11.5% decline in bone mineral content (BMC). Men exhibited a 49.1% increase in body fat percentage, with LM remaining stable across age groups. In men, BMD decreased by 1.7%, while BMC showed minimal variation. Notable sex differences were observed in fat redistribution, with android fat (AF) increasing significantly in older individuals, particularly among women, highlighting distinct age-related patterns. Conclusions: This study provides essential reference data on body composition and bone status, emphasizing the need for tailored interventions to address sex- and age-related changes, particularly in fat distribution and bone density, to support improved health outcomes in aging populations.

Sports-related injuries remain a major challenge in team sports, with important consequences for athlete health, performance, and team success. Recent advances in artificial intelligence (AI) and sensor-based monitoring technologies have enabled the integration of large volumes of training, competition, and physiological data to support injury prediction and risk modelling. However, the literature is characterised by substantial methodological diversity, limiting the ability to draw consistent conclusions. Hence, this scoping review aimed to map the existing evidence on the use of AI and sensor-based monitoring technologies for injury prediction and risk modelling in team sports, and to identify key methodological trends and research gaps. The scoping review was conducted in accordance with the PRISMA-ScR guidelines. Systematic searches were performed in PubMed and Scopus. Eligible studies included team-sport athletes and applied AI or machine learning approaches to predict injury occurrence, injury risk, or related outcomes using data derived from wearable or monitoring systems. Data were charted on study characteristics, sports and competition level, data sources, modelling techniques, validation strategies, and performance metrics. The database search yielded 123 records (PubMed: n = 37; Scopus: n = 86). After screening and eligibility assessment, 11 studies met the inclusion criteria. Most studies focused on football and rugby and relied primarily on wearable-derived data, particularly GPS and inertial sensor outputs. Common predictors included external workload variables, training exposure, previous injury history, and, in some studies, wellness or physiological markers. A wide range of models was reported, including logistic regression, decision trees, random forests, support vector machines, and neural networks. Validation strategies and reported performance varied markedly, and external validation was rarely undertaken. Across the included studies, injury risk was most consistently associated with external workload metrics, previous injury history, and internal or physiological indicators of recovery and readiness. However, current models remain limited by heterogeneous methodologies, single-team datasets, and the lack of external validation. Future research should emphasise multimodal data integration and multi-centre validation to develop reliable, interpretable, and practically applicable AI-based injury prediction systems.

3 & times; 3 basketball is a high-intensity intermittent sport practiced by both professional and recreational athletes. However, the use of predefined absolute thresholds to quantify external load may overlook meaningful inter-individual differences in movement intensity. This study examined internal and external load demands during official 3 & times; 3 match play using individualized, performance-based load zones. Seventeen male players were monitored across 38 valid match observations during a two-day tournament. External load was collected via inertial measurement units, while internal load was assessed through continuous heart-rate monitoring. Raw triaxial accelerometer data were processed in Python to remove gravitational components and reconstruct speed-acceleration profiles, allowing identification of individual acceleration, deceleration, and jump events. Statistical analyses were conducted using linear mixed-effects models with Bonferroni-adjusted post hoc comparisons to evaluate differences between absolute and individualized zones. Players sustained high physiological strain, operating at approximately 85-90% of HRmax, and performed frequent high-intensity mechanical actions. Individualized acceleration, deceleration, and jump zones yielded a more even dispersion of events across low-, moderate-, and high-intensity categories. In contrast, predefined absolute thresholds classified over 90% of events as low intensity, masking meaningful variability. These findings highlight substantial inter-individual differences in 3 & times; 3 match demands and support the use of individualized load profiling for accurate monitoring, performance evaluation, and training prescription.

Objectives: The analysis of basketball performance has increasingly incorporated advanced analytics and machine learning methods to better understand the factors that influence offensive efficiency and match dynamics. The present study aimed to identify performance profiles in basketball using unsupervised machine learning techniques and to examine the physical load and performance indicators that differentiate these profiles. Methods: Team-quarter observations from the Final 8 phase of the Greek U16 Basketball Championship were stratified into quarters with large score differences and quarters with small score differences according to the quarter-specific score differential and the sample median of 5 points. K-means clustering was applied separately to each dataset to identify latent performance patterns. Candidate solutions were evaluated using the Elbow method, Silhouette coefficient, Calinski-Harabasz Index, and Davies-Bouldin Index. Based on their combined interpretation, together with considerations of parsimony and practical interpretability, two-cluster solutions were retained for both datasets. Cluster stability was assessed using the Adjusted Rand Index (ARI), while t-distributed stochastic neighbor embedding (t-SNE) was used exclusively for visualization of the identified clusters. Results: Welch's independent-samples t-tests with Benjamini-Hochberg false discovery rate (FDR) correction identified significant differences between clusters across several external load variables, including jump load, total distance covered, accumulated acceleration load, and distance covered in different speed zones (pFDR < 0.001). Clusters characterized by higher movement intensity also exhibited higher values for basketball performance and offensive-efficiency indicators. Although higher-performance clusters showed numerically higher winning proportions in both contexts (large score differences: 70.0% vs. 45.7%; small score differences: 56.7% vs. 40.6%), chi-square analyses indicated that cluster membership was not significantly associated with quarter outcomes. Conclusions: Overall, the findings suggest that performance profiles in basketball are primarily differentiated by external-load characteristics, particularly movement intensity, and offensive-performance indicators, highlighting the importance of integrating both physical and technical performance indicators in basketball performance analysis.

Home advantage (HA) remains a persistent phenomenon in elite basketball. This study quantified HA across eight EuroLeague seasons (2016-2025) and examined how team quality (TQ) and competitive balance (CB) influence it. Data from 2,154 regular-season games were obtained from the official EuroLeague website. HA% and home win percentage (HW%) were calculated, while CB was measured using the Standard Deviation Ratio (SDR). TQ was determined using a Two-Step Cluster Analysis based on win percentage (W%), classifying teams into high, medium, or low-quality groups. One-way ANOVA examined seasonal and TQ variation, while Spearman's correlation and multiple regression assessed relationships among the variables. The results showed that EuroLeague teams won 62.9% of home games on average. HA% peaked in 2021-2022 and dropped in 2020-2021, reflecting the impact of pandemic-related spectator restrictions. CB fluctuated moderately, with a notable imbalance in 2018-2019 (CB = 2.17). Regression results showed that TQ significantly predicted HA% (p < .01), with lower-quality teams exhibiting greater HA% effects (B = 10.06, SE = 2.15), whereas CB was not a significant predictor (p = .088). HA remains a consistent structural phenomenon in EuroLeague basketball, more likely due to psychological and situational factors, resilient to changes in league parity.

ABSTRACT:Kyriacou-Rossi, A, Ieronymides, D, Hadjipantelis, A, Stampoulis, T, Hadjicharalambous, M, Avloniti, A, Chatzinikolaou, A, and Zaras, N. Effect of concurrent power and sprint training on physical fitness in well-trained youth soccer players: A pilot study. J Strength Cond Res XX(X): 000-000, 2026-The purpose of the study was to investigate the effect of 5-week concurrent power-sprint training on power, repeated sprint ability (RSA), and aerobic capacity in well-trained youth soccer players. Sixteen male players (15.9 ± 0.5 years; height: 173.8 ± 5.0 m; mass: 65.3 ± 8.2 kg) participated in the study. After baseline evaluation, players were matched and divided into the concurrent (Conc) and the compound (Comp) groups. Players in the Conc group performed power-sprint training in the same training session, while the Comp group performed power and sprint training the following day. At the beginning and end of the 5-week training program, measurements included body composition, flexibility, countermovement (CMJ) and drop jumps (DJ), isometric mid-thigh pull (IMTP), 5-step long-jump, 0-30 m linear sprint, t test agility, RSA, and 30-15 intermittent fitness test (IFT). No changes were found for body composition and flexibility, although both groups improved CMJ height (Conc: 6.4%, p < 0.000; Comp: 5.6%, p < 0.001) and DJ reactive strength index (Conc: 38.2-42.5%, p < 0.000; Comp: 27.5-44.7%, p = 0.001), but only Conc increased IMTP (8.6%, p = 0.014). Five-step long-jump increased for both groups (Conc: 2.8 ± 2.6%, p = 0.027; Comp: 3.3 ± 3.6%, p = 0.012) but no chances were observed for 0-30 m linear sprint and agility. Repeated sprint ability increased in both groups, but Conc induced greater increases than Comp (p < 0.001). Significant increases were found for 30-15 IFT V̇o2max (Conc: 4.8%; Comp: 4.0%) for both groups. In conclusion, concurrent power-sprint training improves strength, power, and aerobic fitness in youth soccer players. Implementing both modalities within the same training session yields superior gains particularly in RSA, a key factor in soccer performance.

Hyperbaric Oxygen Treatment (HBOT) is a therapeutic method that combines the effects of hyperoxia and increased pressure. This clinical trial aimed to assess how ten HBOT sessions would influence body composition and physical performance of healthy young men, and whether the frequency of treatment would affect these outcomes. Healthy adult males were enrolled and randomized into three groups: a control group (n = 15), frequent HBOT users (six sessions per week; n = 20), and rare HBOT users (three sessions per week; n = 19). Participants in the intervention groups received ten 60-min sessions of 100% oxygen at a pressure of 2.5 atmospheres absolute (ATA). Participants underwent body composition evaluation and performed an incremental treadmill test before and after the intervention. Marginal but statistically significant reductions in body mass and the body mass index (BMI) were observed only in the rare HBOT users. In both intervention groups, but not in the control group, a significant increase was noted in the maximal speed and distance covered during the treadmill test. Additionally, rare HBOT users showed a significant increase in relative maximal oxygen uptake, while absolute VO₂max remained unchanged. The main finding of our study was that ten hyperbaric oxygen treatments did not significantly enhance body composition or physical capacity in healthy young men. Furthermore, our study showed no indications for the daily HBOT administration in healthy individuals; instead, the findings suggested potential benefits from a less frequent, extended treatment protocol.

Predicting basketball game outcomes in elite competitions is a complex task influenced by multiple interacting performance factors. This study applied a supervised machine learning (ML) framework to predict EuroLeague game outcomes using team-level game-related statistics. Four algorithms—Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), and Naïve Bayes (NB)—were trained and compared following recursive feature elimination (RFE) to identify the most informative predictors. The dataset comprised comprehensive in-game statistics describing shooting efficiency, rebounding, ball security, and spatial shot distribution. Model performance was evaluated using accuracy, area under the receiver operating characteristic curve (AUC), precision, recall, and F1-score, ensuring both discrimination and calibration assessment. Among the four classifiers, SVM (AUC = 0.922, Accuracy = 0.841) and LR (AUC = 0.933, Accuracy = 0.818) achieved the highest predictive performance, outperforming RF and NB. Feature importance analysis using Shapley Additive Explanations (SHAP) on the best-performing SVM classifier revealed that true shooting percentage (TS%), defensive rebounds (DR), steals (ST), and turnovers (TO) were the most influential predictors of game outcomes. Teams that demonstrated higher shooting efficiency, greater rebounding control, and fewer turnovers showed a significantly higher probability of winning. These results confirm that well-validated and interpretable ML models can accurately predict game outcomes in professional basketball using readily available box-score statistics. The integration of RFE-based feature selection and SHAP interpretability provides transparent, evidence-based insights that can inform tactical decisions, enhance scouting accuracy, and support coaches in developing data-driven performance strategies within elite basketball environments.

ABSTRACT:Chatzikamagianni, E, Poulios, A, Avloniti, A, Rosvoglou, A, Liakou, C, Papanikolaou, K, Stampoulis, T, Tsimeas, P, Batrakoulis, A, Chatzinikolaou, A, Draganidis, D, Jamurtas, AZ, and Fatouros, IG. Evaluation of the physiological responses and energy expenditure induced by suspension training exercises. J Strength Cond Res 39(9): 933-944, 2025-This study determined the energy expenditure and physiological responses of 6 beneficial suspension training (ST) exercises (overheads [OS], single-leg squat [SLS], torso rotations [TR], back row [BR], chest press [CP], and plank). Ten healthy young adults randomly completed both of 2 trials of ST exercises, for 30 (T30) or 45 (T45) seconds. The training load was monitored using a mobile gas analyzer, heart rate monitors, and blood lactate measurements. The total energy expenditure (TEE) (classified as the sum of oxidative [OES], glycolytic [GC], and excess postexercise energy cost [EPOC]) was estimated using the V̇ o2 consumption (at rest, during exercise, and postexercise) and blood lactate (La) concentration (at rest and postexercise). The level of significance was set at p ≤ 0.05. All exercises were associated with a low-to-moderate physiological strain (rate of perceived exertion: 7.3-12.3; % of maximal heart rate: 52.3-61.7%; METs: 2.6-3.7; La: 2.2-3.9 mM; EPOC duration: 5.7-7.8 minutes), with the OS and the SLS associated with the greatest physiological/metabolic load in both trials. T45 induced a greater heart rate (53.7-61.7 %HRmax), with SLS and OS inducing the highest values. Minimal differences were noted between T30 and T45 for TEE, MET, and La values for most. EPOC had a greater contribution to TEE compared with OES and GC. Low-to moderate physiological effort during ST results in a TEE of 16.6-24.7 and 20.1-30 kcal·min -1 when performed for 30 and 45 seconds, respectively. There is a variation in TEE and physiological overload among ST exercises. Weight management exercise programs and dietary regimens need to take these finding into account.

Background: Basketball is a high-intensity, multidirectional sport involving frequent jumping, sprinting, and rapid changes of direction, which may expose the musculoskeletal system to varying and potentially asymmetric mechanical demands. The mechanical loading associated with basketball-specific movements may also serve as a consistent osteogenic stimulus, potentially leading to side-specific adaptations in body composition and bone characteristics. Long-term participation in basketball may lead to functional and structural asymmetries between the lower and upper limbs, potentially increasing the risk of injury and impacting performance. This study aimed to investigate structural and functional asymmetries in male basketball players using body composition, health, and performance-related measures. Methods: Thirty-eight right-handed basketball players (age: 21.1 ± 2.8 years; body mass: 86.2 ± 9.2 kg; height: 1.91 ± 8.3 cm) were assessed in a single testing session. The evaluation included bioelectrical impedance analysis (BIA), dual-energy X-ray absorptiometry (DXA), single-leg countermovement rebound jumps (CMRJs), and handgrip strength testing. Results: Significant interlimb differences were observed in lean mass and the phase angle for both the arms and legs. Performance differences favored the left leg in terms of maximum jump height (12.0 ± 17.5%, p = 0.001) and reactive strength index (RSI), whereas the right arm exhibited greater grip strength than the left (6.4 ± 5.9%, p = 0.001). DXA analysis revealed significant asymmetries in bone parameters, including bone mineral density (BMD) of the trochanter (1.81 ± 5.51%, p = 0.031, dz = 0.37), total hip (1.41 ± 4.11%, p = 0.033, dz = 0.36), and total arms (–1.21 ± 2.71%, p = 0.010, dz = 0.43), as well as bone mineral content (BMC) in total arms (–2.16 ± 5.09%, p = 0.012) and total legs (1.71 ± 3.36%, p = 0.002, 0.54). Conclusions: These findings suggest that basketball may induce both functional and structural adaptations, likely due to repetitive unilateral loading and sport-specific movement patterns. However, individual variability and the use of diverse assessment methods may complicate the detection and interpretation of asymmetries. Coaches and practitioners should monitor and address such asymmetries to reduce injury risk and optimize performance.

Resting metabolic rate (RMR) significantly impacts total daily energy expenditure, particularly on training days, and varies among trained individuals. Studies estimating RMR in this population show notable discrepancies. This study aimed to develop and validate new bioelectrical impedance analysis-based (BIA) RMR equations for young athletes, using a calibration and a validation group of 219 and 51 participants, respectively. RMR was measured via indirect calorimetry, while body composition was assessed through DXA and BIA. Correlation and agreement were evaluated by using Pearson’s correlation coefficients and Bland–Altman analysis. Multiple linear regression was applied for the estimation of RMR and a one-way ANOVA was used to compare the new BIA-based equations with other specific formulas. A significant correlation was noted between the BIA and DXA measurements. The final equation, applicable to both genders, was significantly correlated with intracellular water (ICW) and trunk fat, predicting 71.1% of RMR variance. When analyzed separately, body weight and protein displayed a moderate correlation with RMR in men (r = 0.616, p < 0.001), while ICW was correlated with the percentage of body fat in women (r = 0.579, p < 0.001). In the validation group, the values obtained through the three BIA-based equations were similar to the measured RMR, but differed significantly from those obtained through the four existing equations for trained individuals. In conclusion, the developed equations based on BIA-mediated body composition analysis provide a reliable method for estimating RMR in trained populations daily.