# Matching Golfers' Movement Patterns during a Golf Swing
## 1 Introduction  
The golf swing is a whole-body multidimensional movement requiring the interaction between golfer and club to strike the golf ball a certain distance and direction towards a target. Golf biomechanics studies strive to explain differences in clubhead-ball impact parameters, which ultimately determine shot distance and direction, based on the analysis of golfers movement patterns [1]. Golfers, however, show inter-individual differences in their movement patterns even for similar clubhead-ball impact parameters [2]. The recommendation for future studies is that data analysis methods must provide interpretation and understanding of this individualized, multidimensional movement [3]. Analysis methods that can provide this type of interpretation are of interest to include in analysis systems aimed at providing quantitative feedback to coaches about the golfer's technique which help inform personalised coaching interventions for performance enhancement or injury prevention. A key stage of biomechanics analysis is to describe the salient patterns of a sporting movement [4]. Salient patterns in the golf swing are often referred to during phases of the swing, called the backswing, the downswing, and the follow-through, which are typically defined between the events takeaway to the top of the backswing, top of the backswing to impact, and impact to the end of the swing, respectively. Body rotation, typically described as axial rotation of the central body segments, is commonly investigated in golf biomechanics research based on coaches' insights [5] and regression analysis [1] linking rotation to overall driving distance. Discrete body rotation measures, such as the magnitude of pelvis and thorax axial rotation, the rate of change in pelvis and thorax axial displacement(i.e., axial rotation velocity), and the separation between pelvis and thorax axial rotation at top of the backswing or impact have been linked to clubhead and ball velocity, which are two important impact parameters determining overall driving distance [6; 7]. The choice of discrete measures in body rotation are often pre-defined by the researcher which raises the question of whether these are the key axial rotation features that distinguish golfers techniques. Data analysis methods, such as principal component analysis (PCA), can alleviate this limitation and could be used to identify the key movement patterns in axial rotation variables across the whole time-series. Principal component analysis has successfully identified distinct movement patterns in golfers centre of pressure, centre of gravity [2], and ground reaction force time-series data [8]. Graphical representation of PCA results highlighted differences between groups of golfers (i.e., amateurs and professionals) and between individual golfers. Notably, the linear relationship between the first principal components of medial-lateral centre of pressure and centre of gravity patterns identified extremes of movement in golfers (i.e., back foot style and front foot style) and was a proposed indicator of how golfers achieved dynamic balance, either with more lateral or rotational body movements [2]. Similarly, in baseball, hitters who aligned centre of pressure and centre of gravity evenly between their feet near impact emphasised rotational body movements which facilitated pelvis rotational acceleration [9]. The relationships between biomechanics variables and their ability to explain impact parameters are often explored using correlation or linear regression analysis methods that assume a change in outcome is directly proportional to the change in input. Human movement, however, has been described as non-linear and there is a recognized need for non-linear data analysis to study relationships in complex human movement [10]. Multiple correspondence analysis (MCA), the correspondence analysis of multivariate data, can be used to identify non-linear relationships in data.