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The aim of this study was to understand and describe the physiological and biomechanical demands of various tasks used in basketball training and, subsequently, to provide a practical application of these tasks in a typical training week. Twelve basketball players had their external load variables monitored across 179 training sessions (2896 samples) using local positioning system technology. These variables included total distance covered, distance covered at various intensity levels, accelerations, decelerations, PlayerLoad™, and explosive efforts. The analysis revealed significant differences in both physiological and biomechanical loads across various drills. Specifically, tasks with more space and fewer defenders, such as 3v0 full court, impose higher physiological loads compared to tasks with less space and more defenders, like 5v5 full court. The difference in physiological load between these tasks was statistically significant (p < 0.05) with a moderate effect size (ES: −0.60, 95% CI: [−0.99, −0.22]). In terms of biomechanical load, drills with increased defensive pressure, such as 5v5 full court, exhibited significantly higher values compared to less specific drills, such as 5v0 full court, with a very large effect size (ES: 1.37, 95% CI: [1.04, 1.70], p < 0.01). Additionally, comparisons between 5v5 full court and 3v0 full court for biomechanical load produced a very large effect size (ES: 1.67, 95% CI: [1.37, 1.97], p < 0.01), indicating a substantial difference in load demands. The results indicate that tasks with more space and fewer defenders impose higher physiological loads, while those with less space and more defenders increase the biomechanical load. For training design, it is recommended to schedule tasks with a higher biomechanical load at the beginning of the session and those with a physiological orientation toward the end. Understanding the distinct demands of different drills can help coaches structure training sessions more effectively to optimize player load and performance development throughout the week.

To quantify and compare the external peak demands (PD) encountered according to game result (win vs. loss), quarter result (win vs. tie vs. loss), and quarter point difference (± difference in score) in under-18 years (U18), male basketball players. Thirteen basketball players had external load variables monitored across 9 games using local positioning system technology, including distance covered, distance covered in different intensity zones, accelerations, decelerations, and PlayerLoad™. PD were calculated across 30-s, 1-min, and 5-min time windows for each variable. Linear mixed models were used to compare PD for each variable according to game result (win vs. loss), quarter result (win vs tie vs loss), and quarter point difference (high vs. low). External PD were comparable between games that were won and lost for all variables and between quarters that were won and lost for most variables (p > 0.05, trivial-small effects). In contrast, players produced higher (p < 0.05, small effects) 1-min high-speed running distance and 5-min PlayerLoad in quarters that were won compared to quarters that were lost. Additionally, high quarter point differences (7.51 ± 3.75 points) elicited greater (p < 0.05, small effects) external PD (30-s PlayerLoad , 30-s and 5-min decelerations, and 1-min and 5-min high-speed running distance) than low quarter point differences (-2.47 ± 2.67 points). External PD remain consistent (trivial-small effects) regardless of game result, quarter result, and quarter point difference in U18, male basketball players. Accordingly, external PD attained during games may not be a key indicator of team success.

This study aimed to compare the most demanding scenarios (MDS) of under-19 professional soccer players during official matches, controlling for contextual factors such as playing position, the level of opponent teams, playing venue, match status, playing surface, pitch size, and playing status of players. A total of 42 players were monitored across 27 games using Global Positioning System (GPS) technology to collect the external loads, including total distance covered, high-speed running, sprint distance, accelerations, and decelerations. MDS were calculated across 1-minute, 5-minute, and 10-minute time windows for each variable. Significant differences were found based on the contextual factors. (i) Opponent level did not show significant differences; (ii) full-backs covered more distance at 1 minute in home games; (iii) midfielders in winning vs. drawing scenarios exhibited higher decelerations at 1 minute but less distance at 10 minutes; (iv) in drawing vs. losing scenarios, central defenders had greater distance at 5 minutes and accelerations at 5 minutes, while midfielders showed greater distances at 1 minute, 5 minutes, and 10 minutes; (v) offensive midfielders and forwards displayed specific performance differences; (vi) no significant differences were found for the playing surface; (vii) regarding pitch size, full-backs covered more distance at 5 minutes on larger pitches, while midfielders covered more distance at 5 minutes and 10 minutes; and (viii) starters generally outperformed non-starter players across various variables depending on the playing positions. The findings suggest that contextual factors have a significant impact on soccer player performance across different playing positions.

This study aimed to compare the physical demands of playing opponents of different skill levels in basketball. Eighteen men's college basketball players wore accelerometers to measure the relative accumulated acceleration load (AAL), estimated equivalent distance, and frequencies of sprint, jump, and exertion events during games against professional teams (Pro), teams at the same competition level (Collegiate), and teams comprising intra-team members in practice games (Scrimmage). Internal responses were calculated using the relative rating of perceived exertion (sRPE). A repeated measures analysis of variance, Bonferroni post-hoc tests, and standardized Cohen's effect sizes were calculated to compare the physical demands and internal responses across matches played against different levels of opponents. The results showed that in the game against the Pro, AAL (arbitrary units), sprint events (cases per min), and exertion events (cases per min) were significantly ( < .05) higher than those in games against the Collegiate and Scrimmage teams. As the competitive level of the opponents increased, the relative external load of the participants also increased. Conversely, internal responses measured using sRPE were lower after games against the Pro than those against the Collegiate. Internal and external loads may vary from each other depending on contextual factors.

Introduction: Rugby union (RU) is characterized by being a decision-making-based, open-skilled, chaotic and unpredictable sport. The RU has an average game length of 80 minutes, with two halves of 40 minutes and a 15-minute half-time break. Objective: This study aimed to compare the mechanical and locomotive demands of specific playing positions and for the two halves of the game of Argentine male amateur rugby senior players. Methods: Thirty-two rugby players (n = 32) participated in this study. Wearable devices with embedded microtechnology (Catapult Sports, OptimEye S5) were used to examine movement patterns during eighteen (n=18) games. Players were divided into specific playing groups Forwards (Hooker, Prop, Lock and Back Row) vs Backs (Scrum Half, Fly Half, Centre, Wing and Fullback). The locomotive parameters analyzed were total distance (TD), relative total distance (TDr) (m/min), high-speed running >18km/h (HSR), relative high-speed running >18km/h (HSRr). Game-specific and mechanical parameters were the number of contacts, number of RHIE bouts, number of average efforts per RHIE bout (AVG Eff RHIE ) and maximum number of efforts per RHIE bout (MAX Eff RHIE). Results: The backs made a significantly higher locomotive effort (p < 0.001) and RHIE (p < 0.001) than the forwards. Reductions in each of the parameters measured were observed from the first to the second half for each and every of the positional groups. Conclusions: Significant differences were found between positional groups when compared with locomotive, mechanical, and game-specific parameters. Likewise, a deterioration was found in the parameters studied between the halves of the game for all the positional groups.

This study aimed to quantify the frequency of individual and team contact events during rugby union match play in top domestic and international men’s and women’s competitions. Analyst‐coded player individual and team contact event types (tackles, carries, attacking rucks and defensive rucks, lineouts, scrums and mauls) from the 2022/2023 rugby union season were analysed from top domestic and international competitions across the world using generalised linear mixed models. For both women’s and men’s rugby, competitions generally had similar numbers of contact events per playing position. Where differences were observed, most ranged between 0.5 and six per contact event per full game equivalent (FGE). Similar trends were observed when comparing women’s to men’s rugby. However, within‐game accumulation of these different contact events for certain positional groups may have a significant impact (e.g., a front five player called up from a Farah Palmer Cup team to play in WXV1 could be involved in as much as 6 more attacking rucks, 3 more tackles and 5 more mauls per game on average). Furthermore, the small differences between competitions per FGE may accrue across matches and thus result in far greater exposures across a season (e.g., a front five player in Premiership Rugby may make 48 more tackles over 20 matches than in Top 14 on average). Although a high proportion of contact events per FGE were similar between competitions and sexes per playing position, differences that were observed may have important implications for players transitioning between competitions and the long‐term exposure of players to higher‐risk contact events.

This study aimed at proposing a new heart rate (HR) method to track aerobic metabolism in soccer by: (i) validating a recently developed HR index (HRindex) in professional soccer players, (ii) comparing HRindex vs the most common HR parameters and (iii) testing the agreement between measured and estimated VO2 values using HRindex. cross-sectional. 184 professional soccer players performed a step incremental running test on a treadmill while VO2 and HR were recorded. HRindex was calculated (actual HR/resting HR) and its relationship with VO2 was compared with the relationships with the metabolism of actual HR, net HR, and % of HR reserve. Finally, HRindex was used to predict VO2=((HRindex·6)−5)·3.5) and measured and estimated VO2 were compared by 2W RM-ANOVA and Bland–Altman analysis. HRindex/VO2 relationship explained 85% of the variability in data, showing a higher performance than actual HR (77%) and similar values to the other parameters. Measured and estimated VO2 were not significantly different ≤14kmh−1, whereas at speeds ≥14kmh−1 measured VO2 was higher than estimated VO2. Finally, measured and estimated VO2 were highly correlated (R2=0.85, p=0.000), and showed no significant bias (bias=−1.03, z=−0.69, precision=3.75mlkgmin−1). We validated the HRindex/VO2 relationship in professional soccer players. HRindex showed better agreement with metabolism than actual HR and similar agreement to the other HR parameters. HRindex allowed to estimate VO2, but at very high-intensity HRindex underestimated VO2. Future studies should test this in real game conditions. HRindex could offer a time-efficient and easy-to-use “field” method to monitor aerobic metabolism in soccer.

The Nash bargaining solution (Nash in Econometrica 18(2):155–162, 1950) is the most used game theory tool for analyzing bargaining problems. Its validity is examined from an equilibrium analysis using a non-cooperative game such as Nash’s demand game (NDG). Since the NDG has multiple equilibria, we need an equilibrium selection. In this note, we apply the Harsanyi and Selten (A general theory of equilibrium selection in games. The MIT Press, Cambridge, 1988) risk-dominance criterion to the NDG. We show that in a wide class of utility functions, the risk-dominant equilibrium of the NDG coincides with the Nash bargaining solution.

Background: This study aimed to analyze and compare the internal and external training load responses in U16 female basketball players participating in a micro-cycle with the U18 team from the same club. Methods: Twelve U16 and six U18 female basketball players completed two U18-team training sessions (MD-3 and MD-1; 90 min each). The internal load (heart rate metrics) and external load (accelerations, decelerations, speed, and distance) were measured using Polar Team Pro sensors. Differences between groups were analyzed using t-tests and Cohen’s d effect sizes. Results: No significant differences (p > 0.05) were found between age categories for either the internal or external load variables. U16 players showed slightly higher maximum heart rate percentages (96.5% vs. 94.7%, ES = 0.29) but similar average heart rate and time in heart rate zones. For the external load, both groups exhibited comparable values in total distance, average speed, and movement across speed and acceleration/deceleration zones. Effect sizes were mostly small, with moderate differences found in specific acceleration and deceleration zones. Conclusions: U16 players training with the U18 team experienced similar internal and external loads, suggesting that they can cope with the physical and physiological demands of older-age-group training. These findings support the inclusion of younger players in higher-age-group training environments as part of their long-term athletic development.

This study aimed to compare the external peak physical demands (PDs) of under-19-year-old (U19) and professional male football players according to playing position. Positional data derived from Global Positioning System (GPS) tracking during 15 matches in the 2023/24 season for both groups were analyzed. The following variables were measured: total distance, high-intensity running distance, sprint distance, acceleration count, and high-intensity actions. A linear mixed-effects model was employed, with category and playing position included as fixed effects to compare these metrics at the player level. The results revealed only a few significant differences in physical demands between the U19 and professional players. Notably, central defenders and central midfielders exhibited lower performance in HSR distance compared to other positions, with the professional players registering higher values than their U19 counterparts. However, no significant differences were observed for total and relative sprint distances, the number of accelerations, high intensity and relative sprint running efforts. These findings highlight the overall similarity in physical demands between U19 players and professional players, suggesting that elite youth athletes may be adequately prepared to meet the physical challenges of professional competition, with the exception of HSR distance. These conclusions have practical implications for coaches and performance staff, supporting the development of position-specific training programs, optimizing workload management through GPS monitoring, improving microcycle planning, and enhancing injury prevention strategies.