Clustering Offensive Strategies in Australian-Rules Football Using Social Network Analysis

Abstract

Sports teams aim to understand the tactical behaviour of their opposition to gain a competitive advantage. Prior research of tactical behaviour in team sports has predominantly focused on the relationship between key performance indicators and match outcomes. However, key performance indicators fail to capture the patterns of ball movement deployed by teams, which provide deeper insight into a team’s playing style. The purpose of this study was to quantify existing ball movement strategies in Australian-rules Football (AF) using detailed descriptions of possession types from 396 matches of the 2019 season. Ball movement patterns were measured by social network analysis for each team during offensive phases of play. K-means clustering identified four unique offensive strategies. The most successful offensive strategy, defined by the number of matches won (83/396), achieved a win/loss ratio of 1.69 and was characterised by low ball movement predictability, low reliance on well-connected athletes, and a high number of passes. This study’s insights into offensive strategy are instructional to AF coaches and high-performance support staff. The outcomes of this study can be used to support the design of tactical training and inform match-day decisions surrounding optimal offensive strategies.

Keywords

• machine learning
• performance analysis
• team sport
• tactics
• ball movement patterns