Using performance data to identify styles of play in netball: An alternative to performance indicators

Abstract

The advent of sports technology has led to large, high-dimensional, performance data sets, which pose decision-making challenges for coaches and performance analysts. If large data sets are managed poorly inaccurate and biased decision-making may actually be enabled. This paper outlines a process for capturing, organising, and analysing a large performance data set in professional netball. 250 ANZ Championship matches, from the 2012–2015 seasons, where analysed. Self-organising maps and a k-means clustering algorithm were used to describe seven games-styles, which were used in a case study to devise a strategy for an upcoming opponent. The team implemented a centre-pass (CP) defence strategy based on the opponent’s previous successful and unsuccessful performances. This strategy involved allowing the oppositions Wing-attack to receive the CP while allowing their Goal-attack to take the second pass. The strategy was monitored live by the coaches on a tablet computer via a custom-built dashboard, which tracks each component of the strategy. The process provides an alternative to use of conventional performance indicators and demonstrates a method for handling large high-dimensional performance data sets. Further work is needed to identify an ecologically valid method for variable selection.