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Optimizing collective behaviour helps to increase performance in mutual tasks. In team sports settings, the small-sided games (SSG) have been used as key context tools to stress out the players' awareness about their in-game required behaviours. Research has mostly described these behaviours when confronting teams have the same number of players, disregarding the frequent situations of low and high inequality. This study compared the players' positioning dynamics when manipulating the number of opponents and teammates during professional and amateur football SSG. The participants played 4v3, 4v5 and 4v7 games, where one team was confronted with low-superiority, low- and high-inferiority situations, and their opponents with low-, medium- and high-cooperation situations. Positional data were used to calculate effective playing space and distances from each player to team centroid, opponent team centroid and nearest opponent. Outcomes suggested that increasing the number of opponents in professional teams resulted in moderate/large decrease in approximate entropy (ApEn) values to both distance to team and opponent team centroid (i.e., the variables present higher regularity/predictability pattern). In low-cooperation game scenarios, the ApEn in amateurs' tactical variables presented a moderate/large increase. The professional teams presented an increase in the distance to nearest opponent with the increase of the cooperation level. Increasing the number of opponents was effective to overemphasise the need to use local information in the positioning decision-making process from professionals. Conversely, amateur still rely on external informational feedback. Increasing the cooperation promoted more regularity in spatial organisation in amateurs and emphasise their players' local perceptions.

Purpose The aim of this study was to quantify and predict relationships between RPE and GPS training load variables in professional Australian Football (AF) players using group and individualised modelling approaches. Methods Training load data (GPS and RPE) for 41 professional AF players was obtained over a period of 27 weeks. A total of 2711 training observations were analysed with a total of 66 13 sessions per player (range; 39 to 89).Separate generalised estimating equations (GEE) and artificial neural network analyses (ANN) were conducted to determine the ability to predict RPE from training load variables (i.e. session distance, high-speed running (HSR), high-speed running %, m·min Further, importance plots generated from the ANN revealed session distance was most predictive of RPE in 36 of the 41 players, whereas, HSR was predictive of RPE in just 3 players and m . min -1 as predictive as session distance in just 2 players. Conclusions This study demonstrates that machine learning approaches may outperform more traditional methodologies with respect to predicting athlete responses to training load. These approaches enable further individualisation of load monitoring, leading to more accurate training prescription and evaluation.

This study aimed to determine the intra- and inter-device accuracy and reliability of wearable athletic tracking devices, under controlled laboratory conditions. A total of nineteen portable accelerometers (Catapult OptimEye S5) were mounted to an aluminum bracket, bolted directly to an Unholtz Dickie 20K electrodynamic shaker table, and subjected to a series of oscillations in each of three orthogonal directions (front-back, side to side, and up-down), at four levels of peak acceleration (0.1g, 0.5g, 1.0g, and 3.0g), each repeated five times resulting in a total of 60 tests per unit, for a total of 1140 records. Data from each accelerometer was recorded at a sampling frequency of 100Hz. Peak accelerations recorded by the devices, Catapult PlayerLoad™, and calculated player load (using Catapult’s Cartesian formula) were used for the analysis. The devices demonstrated excellent intradevice reliability and mixed interdevice reliability. Differences were found between devices for mean peak accelerations and PlayerLoad™ for each direction and level of acceleration. Interdevice effect sizes ranged from a mean of 0.54 (95% CI: 0.34–0.74) (small) to 1.20 (95% CI: 1.08–1.30) (large) and ICCs ranged from 0.77 (95% CI: 0.62–0.89) (very large) to 1.0 (95% CI: 0.99–1.0) (nearly perfect) depending upon the magnitude and direction of the applied motion. When compared to the player load determined using the Cartesian formula, the Catapult reported PlayerLoad™ was consistently lower by approximately 15%. These results emphasize the need for industry wide standards in reporting validity, reliability and the magnitude of measurement errors. It is recommended that device reliability and accuracy are periodically quantified.

The purpose of this study was to measure differences in the cardiovascular workload (heart rate [HR]) and time-motion demands between positional groups, during numerous basketball training drills, and compare the results with in-game competition demands. A convenience sample of 14 top-level professional basketball players from the same club (Spanish First Division, ACB) participated in the study. A total of 146 basketball exercises per player (performed over an 8-week period in 32 team training sessions throughout the competitive season) and 7 friendly matches (FM) played during the preparatory phase were analyzed. The results reveal that HRavg and HRpeak were the highest in FM (158 ± 10; 198 ± 9 b · min(-1), respectively). Time-motion analysis showed 1v1 to be the most demanding drill (53 ± 8 and 46 ± 12 movements per minute for full and half court, respectively). During FM, players performed 33 ± 7 movements per minute. Positional differences exist for both HR and time-motion demands, ranging from moderate to very large for all basketball drills compared with FM. Constraints such as number of players, court size, work-to-rest ratios, and coach intervention are key factors influencing cardiovascular responses and time-motion demands during basketball training sessions. These results demonstrate that systematic monitoring of the physical demands and physiological responses during training and competition can inform and potentially improve coaching strategy, basketball-specific training drills, and ultimately, match performance.

The biological effects of immersion in water, which are related to the fundamental principles of hydrodynamics, may be beneficial in certain training contexts. The effects and physical properties of water, such as density, hydrostatic pressure and buoyancy are highly useful resources for training, when used as a counterbalance to gravity, resistance, a compressor and a thermal conductor. Not only does the aquatic medium enable a wider range of activities to be used in a context of low joint impact, but it also constitutes a useful tool in relation to sports rehabilitation, since it allows the athlete to return to training earlier or to continue with high-intensity exercise while ensuring both low joint impact and greater comfort for the individual concerned. Moreover, this medium enables the stimulation of metabolic and neuromuscular systems, followed by their corresponding physiological adaptations allowing both to maintain and improve athletic performance. Hydrotherapy can also play a beneficial role in an athlete’s recovery, helping to prevent as well as treat muscle damage and soreness following exercise.

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Training load monitoring is a core aspect of modern-day sport science practice. Collecting, cleaning, analysing, interpreting, and disseminating load data is usually undertaken with a view to improve player performance and/or manage injury risk. To target these outcomes, practitioners attempt to optimise load at different stages throughout the training process, like adjusting individual sessions, planning day-to-day, periodising the season, and managing athletes with a long-term view. With greater investment in training load monitoring comes greater expectations, as stakeholders count on practitioners to transform data into informed, meaningful decisions. In this editorial we highlight how training load monitoring has many potential applications and cannot be simply reduced to one metric and/or calculation. With experience across a variety of sporting backgrounds, this editorial details the challenges and contextual factors that must be considered when interpreting such data. It further demonstrates the need for those working with athletes to develop strong communication channels with all stakeholders in the decision-making process. Importantly, this editorial highlights the complexity associated with using training load for managing injury risk and explores the potential for framing training load with a performance and training progression mindset.