昆士兰大学代写作业:性能分析

2017-03-14 01:23

性能分析是没有的，但是，一个完全没有风险的或完美的过程中，作为过程的经验当中，运动员可以导致“假”的分数在编译时间更新注册，或者通过自己的运动员得分太低以免失望，或得分过高保护自己的自我形象，例如。这可能会导致运动员和教练的分数的差异，导致执行过程中的性能分析过程中的问题，在使用过程作为一个诊断工具，提高培训和性能。整个表演过程完全依赖于诚实：运动员本身和教练和运动员之间的关系。这个过程将根本不工作，优化，作为一个工具，性能分析，如果运动员不诚实自己和教练是不诚实的运动员，反之亦然，关于他们如何看待他们目前的状态，他们的目标和他们的训练制度的影响。 休斯和巴特莱特（2002）认为，绩效指标可以利用游戏不同的结构定义和子分类不同运动的得分和终止规则应用到许多不同的运动，使不同的测量被定义作为诊断工具（休斯和弗兰克斯，2004）。他们的结论是，为了使一个全面、客观的从性能分析的数据，比较数据是至关重要的，与推荐，性能指标的标准化也应得到更广泛的应用在以数据分析等形式相结合，提供一系列的运动性能的致富机会（休斯巴特莱特（休斯，2002；弗兰克斯，2004）。例如，如酒糟（2002）表明，在性能分析的生物力学测量的掺入可以在技术分析方面提供了宝贵的见解，可能允许不同技术的有效性进行测试，对运动员的整体性能。

昆士兰大学代写作业:性能分析

Performance profiling is not, however, an entirely risk-free or faultless process, as inexperience of the process amongst athletes can lead to ‘false’ scores being registered at the time of compiling the profile, either through athletes scoring themselves too low to avoid disappointment, or scoring themselves too high to protect their self-image, for example. This can lead to discrepancies in the scores of the athlete and the coach, leading to problems in the implementation of the performance profiling process, in terms of using the process as a diagnostic tool for improving training and performance. The whole process of performance profiling relies entirely on honesty: of the athlete with themselves and of the relationship between the coach and the athlete. The process will simply not work, optimally, as a tool for performance analysis if the athlete is not honest with themselves and the coach is not honest with the athlete, and vice versa, with regards to how they feel about their current status, their goals and the effects of their training regime.
As Hughes and Bartlett (2002) argue, performance indicators can be applied to many different sports by using different structural definitions of games and by sub-categorising different sports by their rules of scoring and ending, allowing for different measurements to be defined and used as diagnostic tools (Hughes and Franks, 2004). Their conclusion was that in order to enable a full and objective interpretation of the data from the performance analysis, comparisons of data are vital, with the recommendation that normalizations of performance indicators should also be used more widely in conjunction with other forms of data analysis in order to provide opportunities for performance enrichment in a range of sports (Hughes and Bartlett, 2002; (Hughes and Franks, 2004). For example, as Lees (2002) suggests, the incorporation of biomechanical measurements in performance analysis can provide valuable insights in terms of technique analysis, potentially allowing the effectiveness of different techniques to be tested with regards to an athlete’s overall performance.

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