Predicting golf ball trajectories from swing plane: An artificial neural networks approach

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Quantifying and validating descriptive heuristic rules that govern someone’s skills and expertise have been a known philosophical quest since the early Greek philosophers. Inherent to sport coaching is the qualitative assessment of complex human motion patterns, relying on subjective and ‘hard-to-quantify’ criteria that can be subject to experts/coaches disagreement. This paper presents an application of Artificial Neural Networks (ANN) for the discovery of predictive power of swing plane heuristic rules influencing golf ball trajectories. The golf data set (531 samples from 14 golfers) utilised in the experiments was captured via a ubiquitous computing device embedded in the handle of a driver club. Out of multiple swing performance factors influencing ball trajectory, the selected subset of features for subspace modelling was linked only to the swing plane concept. Quantitative evidence supporting empirical coaching rules for swing plane assessment were obtained by supervised learning of ANN models. Optimised ANN models Radial Basis Function (RBF) and Support Vector Machine (SVM), were able to draw inference from captured swing data linking ball trajectories with variations of swing plane (with overall classification of 87%). The obtained swing plane computer model inference, data analysis and implemented concept of generic data export utility support kinesiology, golf coaching, inform club fitting, golf manufacturing technology and demonstrate new cross- and multi-disciplinary integration of sport science, augmented coaching, ubiquitous computing, computational intelligence and the applications of expert systems for growing availability of sport, injury prevention/rehabilitation and golf related data sets.