Tracking player performance in team sports is difficult as games involve quick, agile movements, with many unpredictable changes in direction and frequent collisions between players. Previous approaches have partially solved the motion analysis problem by manually tracking player movements,
using observational sheets or computerised digitising pads and rods. However, manual tracking can be a subjective and laborious process which has arguably discouraged researchers from conducting more detailed analyses of multiple player interactions within games. The aim of this investigation
is to establish whether an automated, colour- recognition, motion detection system (A-Eye) is suitable as a tool for the analysis of multiple players in a sporting environment. Reliability tests show strong intra-operator reliability scores (2% TEM) and correlations. Validity tests comparing
manual and automatic tracking methods at court boundaries during short game sequences demonstrate moderate to strong correlations between the methods in most areas of the court. A comparison of time spent in each zone of the court also largely shows similarities between analysis methods (0.09%-
2.53%). This automated player tracking system provides a detailed multi-disciplinary analysis and has practical benefits for coaches, practitioners and researchers by enhancing understandings of sports performance and team work.