Quantifying congestion with player tracking data in Australian football

• Published in: PloS one 2022-08, Vol.17 (8), p.e0272657
• Main Authors: Alexander, Jeremy P, Jackson, Karl B, Bedin, Timothy, Gloster, Matthew A, Robertson, Sam
• Format: Article
• Language: English
• Subjects: Algorithms; Athletic Performance; Australia; Australian football; Australian rules football; Biology and Life Sciences; Clustering; Coders; Computer and Information Sciences; Congestion; Density; Engineering and Technology; Football; Football players; Humans; Information management; Male; Neighborhoods; People and Places; Physical Sciences; Players; Recall; Research and Analysis Methods; Social Sciences; Team Sports; Tracking
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0272657

With 36 players on the field, congestion in Australian football is an important consideration in identifying passing capacity, assessing fan enjoyment, and evaluating the effect of rule changes. However, no current method of objectively measuring congestion has been reported. This study developed two methods to measure congestion in Australian football. The first continuously determined the number of players situated within various regions of density at successive time intervals during a match using density-based clustering to group players as 'primary', 'secondary', or 'outside'. The second method aimed to classify the level of congestion a player experiences (high, nearby, or low) when disposing of the ball using the Random Forest algorithm. Both approaches were developed using data from the 2019 and 2021 Australian Football League (AFL) regular seasons, considering contextual variables, such as field position and quarter. Player tracking data and match event data from professional male players were collected from 56 matches performed at a single stadium. The random forest model correctly classified disposals in high congestion (0.89 precision, 0.86 recall, 0.96 AUC) and low congestion (0.98 precision, 0.86 recall, 0.96 AUC) at a higher rate compared to disposals nearby congestion (0.72 precision, 0.88 recall, 0.88 AUC). Overall, both approaches enable a more efficient method to quantify the characteristics of congestion more effectively, thereby eliminating manual input from human coders and allowing for a future comparison between additional contextual variables, such as, seasons, rounds, and teams.