In soccer, the assessment of the load imposed by training and a match is recognized as a fundamental task at any competitive level. The objective of this study is to carry out a systematic review on internal and external load monitoring during training and/or a match, identifying the measures used. In addition, we wish to make recommendations that make it possible to standardize the classification and use of the different measures. The systematic review was carried out according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The search was conducted through the electronic database Web of Science, using the keywords “soccer” and “football”, each one with the terms “internal load”, “external load”, and “workload”. Of the 1223 studies initially identified, 82 were thoroughly analyzed and are part of this systematic review. Of these, 25 articles only report internal load data, 20 report only external load data, and 37 studies report both internal and external load measures. There is a huge number of load measures, which requires that soccer coaches select and focus their attention on the most useful and specific measures. Standardizing the classification of the different measures is vital in the organization of this task, as well as when it is intended to compare the results obtained in different investigations.

The aim of the present study was to identify basketball game performance indicators which best discriminate winners and losers in regular season and playoffs. The sample used was composed by 323 games of ACB Spanish Basketball League from the regular season (n=306) and from the playoffs (n=17). A previous cluster analysis allowed splitting the sample in balanced (equal or below 12 points), unbalanced (between 13 and 28 points) and very unbalanced games (above 28 points). A discriminant analysis was used to identify the performance indicators either in regular season and playoff games. In regular season games, the winning teams dominated in assists, defensive rebounds, successful 2 and 3-point field-goals. However, in playoff games the winning teams’ superiority was only in defensive rebounding. In practical applications, these results may help the coaches to accurately design training programs to reflect the importance of having different offensive set plays and also have specific conditioning programs to prepare for defensive rebounding.

Physical fitness is a performance factor that is characterized by its ability to be assessed using closed tests. Among the existing tests today, there are no data on which are the most optimal ones for the sport for which they are applied. Tests of a generic nature are widely used regardless of the sample of players or the sport to which is being referred as they allow to compare the performance among athletes. In spite of this, it is necessary to identify and develop specific tests of physical fitness for each sport. Thus, the main purpose of this review on physical fitness in basketball players was to identify and group the different tests described in the literature up until November 2018. A literature search was conducted in the different data base to systematically ascertain the most commonly used tests for assessing physical fitness in basketball players. A total of 40 articles were selected, after passing the selection and exclusion criteria. Finally, they were classified according to the tests assessed in each document. The capacities more frequently studied in literature are jump (N.=21) aerobic capacity (N.=17) and anaerobic capacity (N.=16). On the contrary, the least common evaluated capacities are speed and agility (N.=14). The results and discussion showed that few specific tests are used to assess this quality in athletes. The analyzed bibliography reveals the lack of design and use of specific tests to highlight the qualities involved in the targeted sport.

The policy of school organisation for grouping students in the same academic year is based on date of birth. The differences in the experiences and maturation of older students involve a relatively better performance in academic settings, which is known as the relative age effect (RAE). This effect is more important the younger the student is. The goal of this study is to identify the connections of influence that RAE, socioeconomic status (SES), and type of institution have on academic performance in a school population of eighth graders. The study is based on a population-based, representative sample of 15,234 8th graders (50.4% female; average age = 13.61 years) in the 2011 National System of Quality Assessment in Education Survey (SIMCE) from Chile. The SIMCE for global academic performance consists of 4 tests: reading, mathematics, social studies, and science. All tests consist of multiple-choice and closed questions. In addition, in order to have the information of general academic performance, an extra variable expressing the average score of each student was created. Also, the SIMCE includes additional variables for the evaluation process such as SES or type of school. Students were assigned to one of five age groups in terms of date of birth (G1, G2, G3, G4, and G5), in which students belonging to G1 are the oldest and students belonging to G5 are the youngest. The results achieved in the structural equation modelling indicate a good global fit. Individual relationships show significant effects of the three variables observed on academic performance, although SES received the highest values. The influence of RAE took place both in the full sample and sub-samples composed according to the SES and academic performance, showing higher values for students with lower scores. Although the influence of RAE decreases when SES is controlled, its effect is still significant and contributes to additionally explain the performance. The RAE remains, even with residual values, an explanatory factor in academic performance even in eighth graders. Since the RAE decreases as the influence of schooling increases, the potential adverse effects for some students would be placed in previous and initial moments of formal schooling. These findings may be useful into taking steps towards flexibilisation on age of entry in compulsory schooling. Moreover, the need to implement early, comprehensive evaluation systems which include aspects related to neurodevelopment in order to provide maximum information to parents and educators is also drawn.

The Relative Age Effect (RAE) in sport is defined as the age difference in the same sport group. This chronological difference implies a different level of sport experience and performance due to developmental and maturational processes. The aim of the present study was to analyze the relative age effect in elite u-18 basketball according to playing positions. The variables analyzed were: date of birth, playing position and technical-tactical performance indicators in basketball (TTPI). A descriptive analysis was carried out to characterize the sample, a lineal regression was used to analyze the influence of the date of birth on basketball performance and finally an ANOVA and discriminant analysis were performed to identify the differences among different playing positions. The results show the existence of the RAE in the top European Under-18 basketball competition (S1 = 67%; S2 = 33%). Performance indicators which are predictors of the birth quarter (p< .05) were only found in the small forwards (defensive rebounds (β: -.463) and fouls received (β: -.140)) and in the centers (three point throws scored (β: -.321) and tried (β: .342)). These results may be of use for coaches and clubs when recruiting players for each playing position. Older forwards and centers are capable of performing at a higher level. Therefore, clubs have to sign up those players born at the beginning of the year.

An understanding of basketball physical demands during official matches is fundamental for designing specific training, tactical and strategic plans as well as recovery methods during congested fixture periods. Such assessments can be performed using wearable indoor time motion tracking systems. The purpose of this study was to analyze the time-motion profile of under 18-years of age (U’18) basketball players and compare their physical demands in relation to team ranking, playing position, match periods and consecutive matches during a seven-day tournament. Relative Distance (RD), percentage of High-Intensity Running (%HIR), Player Load (PL), Acceleration (Acc), Deceleration (Dec), Peak Speed (PSpeed) and Peak Acceleration (PAcc) were recorded from 94 players (13 centers, 47 forwards and 34 guards) belonging to eight elite teams (age: 17.6±0.8 years; height: 1.91±0.08 m; body mass: 82.5±8.8 kg). WIMU PROTM inertial measurement units with ultra-wide band (UWB) indoor-tracking technology recorded 13 matches during the Adidas Next Generation Tournament Finals in the 2016-2017 season. Paired t-tests and one-way analyses of variance with omega partial squared (ωp²) and Cohen's effect sizes (d) were used to analyze for differences between variables According to team quality, the best teams had lower RD (p= .04; d= -0.14). Guards presented higher RD (p< .01; ωp²= 0.03), PSpeed (p< .01; ωp²= 0.01) and PAcc (p< .01; ωp²= 0.02) compared to forwards and centers. The first quarter showed differences with higher RD (p< .01; ωp²= 0.03), %HIR (p< .01; ωp²= 0.02), and PL (p< .01; ωp²= 0.04) compared to all other quarters. The third match of the tournament presented higher demands in RD (p< .01; ωp²= 0.03), HIR (p< .01; ωp²= 0.01) and PL (p< .01; ωp²= 0.02) compared with the first two matches. This study showed that team quality, playing position, match period and consecutive matches throughout an U'18 basketball tournament influenced the kinematic demands experienced by players during official competition. Therefore, each of these contextual factors should be considered in managing the load and developing individualized strategies for players in tournament settings.