Up to now systematic game observations have insufficiently been used to describe basketball's tactical structures in detail. Thus, it seems to be urgently necessary to evaluate literatures and coaches' recommendations by objective game data. Basketball literature has been analyzed to build a process-orientated state-event model that represents players' offensive-defensive interactions, especially within group-tactical plays in set offense against man-to-man defense. Based on this model, a specific observation system has been introduced to describe game reality by using the method of systematic game observation. Furthermore, the so-called inter-rater reliability of two separate observations has been calculated to guarantee a sufficient quality of the observation system (Cohen's kappa [κ] for each observational category: coefficients between 0.685 to 1.000). Sixty games of international elite-level basketball were analyzed by using the interactive video computer system VIDEO AS. Results show a surprisingly wider variety of offensive-defensive interactions (as sequences of opening action, defensive constellation, and following action within grouptactical plays like screen actions) than described in literature. Suggestions for basketball training can be made considering also rates of success of the different interaction sequences. We conclude that a wider spectrum of group-tactical action patterns, especially within screen actions, has to be developed when practicing with junior basketball players.

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This short review is based on general knowledge and guidelines about creatine metabolism and supplementation (CS). These principles provide the starting point for an attempt at inferring the theoretical effects in ballgames with their specific workload profiles. The self-reported prevalence of creatine use in game players ranges from 2% in female volleyball players to 71% in male American football players. A search on the PubMed database for relevant articles related to ballgames resulted in 18 hits, all published in the last 5 years (soccer 6, American football 5, tennis 2, handball 2, ice hockey 1, squash 1, softball 1). It is critical that the authors of those articles measure basic conditional aspects in the first place. Only 5 articles try to investigate the effects of CS under test conditions specifically designed for ballgames. Six of 10 studies showed that a short-term creatine loading results in an improvement of the intermittent sprint performance, while 4 studies failed to measure any shortterm effects at all. Longitudinal training studies with game players uniquely came to the conclusion that CS combined with resistance and sprint conditioning improves strength and power at a higher rate than the respective training routine without supplementation. We conclude that CS increases the basic conditional performance of game players when combined with a specific training period. Nevertheless, a careless use of creatine on a regular basis is not advisable, since the transfer on the ballgame competition performance of these effects has not yet been clarified sufficiently. It also varies depending on the workload profile of the game and the player's individual physical conditions and demands.

INTRODUCTION The nationwide implementation of physical performance test batteries for youth squad players can be valuable for compiling individual physical performance profiles based on age- and gender-specific norm values. This approach is frequently used for optimizing training prescription and thus athletic development. The aim of this study was to introduce a distribution-based approach to derive an effect size scale for assessing athletic development from normative testing data in youth players, which can then be translated to setting performance goals for athletic development.METHODS Secondary analysis of norm values (mixed longitudinal and cross-sectional data [1]). In the age-groups under 12 to under 17, a maximum number of 1,172 and 846 tests were available for male and female basketball squad players, respectively. Biannual testing was conducted as part of a federal research project (20-m sprint, 20-m change of direction sprints with/without basketball, jump & reach, standing long jump, chest pass, mid-range jump shot, multistage fitness test). An effect size scale was derived from norm values which were available as quintile scores (five categories). Trivial changes were defined as the age-related mean annual performance development which was estimated as the average age-group-to-age-group change for the quintiles. Threshold values for small, medium, and large changes were calculated as average changes that were required to increase performance classification by one, two or three categories, respectively. These thresholds were additionally compared to the default effect size scale commonly used for interpreting standardized mean differences (between-player standard deviation: small 0.2, medium 0.6, large 1.2 [2]).RESULTS For example, the age-related mean annual development in the jump & reach for male players was 4 cm (trivial change). To reach one, two or three higher performance categories, jump height must improve by 8, 12 and 15 cm, respectively (i.e., small, medium, large). Compared with the default standardized effect size scale, these quintile-based thresholds were larger.CONCLUSION The quintile-based analysis presents a simple and practical approach to derive effect size thresholds based on norm values created from regular physical performance testing. These effect size scales can be easily visualized and communicated to players and coaches, as they are typically familiar with percentile-based performance classification of testing data. A limitation of this study was that only norm values in the form of quintile scores were used for analysis. Future research should attempt to model longitudinal datasets while accountingfor within- and between-player effects. Furthermore, the choice of appropriate and realistic percentilebased thresholds clearly remains up for debate and requires adequate analysis of original longitudinal data.REFERENCES 1. Stadtmann (2012) PhD thesis, Ruhr University Bochum. 2. Hopkins et al. (2009) MSSE,41,3-12.

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