The aim of this study was to identify the dynamics of tactical behaviour emerging on different timescales in football small-sided games and to quantify short- and long-term exploratory behaviour according to the number of opponents. Two teams of four professional male footballers played small-sided games against two different teams with a variable number of opponents (3, 5 and 7). Data were collected using a combination of systematic observation and a non-differential global positioning system (15 Hz). The temporal diversity and structural flexibility of the players were determined by calculating the dynamic overlap order parameter q, entropy and trapping strength. Analysis of the exploratory dynamics revealed two different timescales, forming a different metastable landscape of action for each constraint. Fast dynamics lasted on average a few seconds and consisted of changes in tactical patterns. The long timescale corresponded to the shared tasks of offence and defence lasting tens of seconds. The players' tactical diversity decreased with an increasing number of opponents, especially in defence. Manipulating numerical imbalance is likely to promote changes in the diversity, unpredictability and flexibility of tactical solutions. The fact that the temporally nested structure of constraints shaped the emergence of tactical behaviour provides a new rationale for practice task design. The manipulation of numerical imbalance on the timescale of a few tens of seconds, on which the exploratory behaviour of players saturates, may help coaches to optimise the exploratory efficiency of the small-sided games.

Basketball can be described as a moderate-to-long duration exercise including repeated bouts of high-intensity activity interspersed with periods of low to moderate active recovery or passive rest. A match is characterized by repeated explosive activities, such as sprints, jumps, shuffles and rapid changes in direction. In top-level modern basketball, players are frequently required to play consecutive matches with limited time to recover. To ensure adequate recovery after any basketball activity (i.e., match or training), it is necessary to know the type of fatigue induced and, if possible, its underlying mechanisms. Despite limited scientific evidence to support their effectiveness in facilitating optimal recovery, certain recovery strategies are commonly utilized in basketball. It is particularly important to optimize recovery because players spend a much greater proportion of their time recovering than they do in training. Therefore, the main aim of this report is to facilitate useful information that may lead to practical application, based on the scientific evidence and applied knowledge specifically in basketball.

This study investigated age-related differences in maturity, physical and functional characteristics and playing performance in youth Australian Football (AF). Young male players (n = 156) were recruited from 12 teams across 6 age groups (U10-U15) of a recreational AF club. All players were tested for body size, maturity and fitness. Player performance was assessed during a match in which disposals (kicks and handballs) and their effectiveness were coded from a video recording and match running performance measured using Global Positioning System. Significant main effects (P < 0.01) for age group were observed for age, years to peak height velocity, body mass, height, 20 m sprint, maximal speed over 20 m, vertical jump, 20 m multistage shuttle run, match distance, high-speed running distance, peak speed, number of effective disposals and percentage of effective disposals. Age-related differences in fitness characteristics (speed, lower body power and endurance) appeared to transfer to match running performance. The frequency in which players disposed of the football did not differ between age groups, however the effectiveness of each disposal (i.e., % effective disposals) improved with age. Match statistics, particularly those that evaluate skill execution outcome (i.e., effectiveness), are useful to assess performance and to track player development over time. Differences between age groups, and probably variability within age groups, are strongly associated with chronological age and maturity.