Abstract

Investigations of real-time feedback in performance analysis are increasing in order to support feedback in real-time situations. Real-time systems using performance analysis techniques have been used in recent years. However, academic research has not examined performance indicators relevant to realtime feedback systems and what distinguishes these from performance indicators used in lapsed-time systems. The difficulties for real-time analysis and feedback are the limited feedback time and analysis time, feasible equipment/technology and communication between analysts and coaches. The profiling of matches in performance analysis is a mechanism for deciding the numbers of matches or performances that are enough for a normative profile, in order to reduce the analysis time and only use a number of matches that are necessary. To further increase the efficiency of real-time analysis, the number of performance indicators to be used should be reduced to the most critical ones that distinguish between winning and losing performances. Performance indicators are major components of performances that are associated with outcomes of performances. The validity of performance indicators, however, has not often considered that they were often selected by inter-linked aspects of performance. Thus, the identifications of the optimal sets of performance indicators have to be clearly considered with the independency between variables used. Through the concept of the research idea, the Principle Components Analysis may be a possible method to identify the optimal sets of performance indicators for real-time analysis systems. The main aim of this study is, therefore, to present ways to identify the optimal set of performance indicators using Principle Components Analysis. Specifically, the process of the identification of the optimal set of performance indicators has been mainly considered for the real-time analysis systems. Aus der Einleitung