The use of K-means clustering for the organisation of training groups in Australian football

This study investigated the potential use of k-means clustering for organising training groups in Australian Football (AF) based upon match performance data. Global positioning system (e.g., distance per minute) and technical action (e.g., kicks) data were collected on 38 AF players from 22 matches during the 2021 season. Each player was assigned their season average for all metrics, as well as their primary playing position (the position they occupied the most during the season). Following the application of the elbow method, the data was grouped into four clusters; cluster one could be defined as the low technical action players, cluster two as the low physical output players, cluster three as the high technical action players, and cluster four as the high physical output players. The results of the k-means clustering revealed that each group had unique match performance characteristics compared to each other. The clustering revealed that the majority of players within each cluster shared common playing positions, similar to traditional AF groupings. However, it was also able to highlight that some players were grouped away from their traditional positions based upon their match performance data, indicating that they may require an alternative training stimulus to successfully prepare for competitive matches. Training recommendations for each of the four groups are provided. The study indicated that k-means clustering could provide a useful additional tool for the organisation of training groups within AF populations, and potentially for other similar team sports.