• R. Anderson
  • I. Kenny
  • C. Tucker
  • J. O'Halloran
Keywords: golf, outcome measure prediction, pseudo-random data, simulation


Recently, a large amount of research has been focused on the effect of movement variability on human performance in sport. It is now generally accepted that specific amounts of variability are essential to attain a high level of performance (Davids et al., 2003). When studying the effect of movement variability on outcome performance, the usual method involves collecting numerous data sets from an individual and, assuming that these data sets will all be different (i.e. contain variability), attempt to connect the amount of variability to the change in outcome or performance measure using a number of statistical techniques. The aim of this study is to remove the requirement to collect a large amount of data which, by chance, may contain the level of variability required and shorten the data collection phase significantly by using the proposed process to create theoretical data sets containing alterable variability content while still exhibiting major characteristics of the actual data. When these theoretical data sets are used in conjunction with a full-body 3D computer model operating inverse and forward dynamics simulations a change in outcome or performance measure can be predicted. The advantages this process offers over traditional techniques is the ability to directly control and quantify the amount of variability introduced into the test data and a significant reduction in data collection time.