Quantifying congestion with player tracking data in Australian football.

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.

Team numerical advantage in Australian rules football: A missing piece of the scoring puzzle?

The primary aim of this study was to determine the relationship between a team numerical advantage during structured phases of play and match event outcomes in professional Australian football. The secondary aim was to quantify how players occupy different sub-areas of the playing field in match play, while accounting for match phase and ball location. Spatiotemporal player tracking data and play-by-play event data from professional players and teams were collected from the 2019 Australian Football League season played at a single stadium. Logistic regression analysed the relationship between total players and team numerical advantage during clearances and inside 50's. Total players and team numerical advantage were also quantified continuously throughout a match, which were separated into three match phases (offence, defence, and stoppage) and four field positions (defensive 50, defensive midfield, attacking midfield, and forward 50). Results identified an increased team numerical advantage produced a greater likelihood of gaining possession from clearances or generating a score from inside 50's. Although, an increased number of total players inside 50 was likely associated with a concomitant decrease in the probability of scoring, irrespective of a team numerical advantage. Teams were largely outnumbered when the ball was in their forward 50 but attained a numerical advantage when the ball was in the defensive 50.

Apples and oranges? Comparing player performances between the Australian Football League and second-tier leagues.

This study developed a model to determine the extent to which player performance objectively differs between various Australian football (AF) leagues. Champion Data (CD) ranking points were obtained during the 2016-2019 seasons, for all players across the Australian Football League (AFL) and the 10 main second-tier AF leagues. Data pertaining to each player's age, playing position and the AF leagues in which they competed in were also collected. Phase One investigated the difference between the AFL and the senior second-tier leagues in which AFL affiliate teams participate. Post-hoc tests indicated that objective player performance was substantially different between the AFL and each of the four senior second-tier leagues (effects ranging from 16.8 to 21.6 CD ranking points). Phase Two investigated the difference between the second-tier leagues from which players are traditionally drafted by an AFL club. Post-hoc tests indicated that objective player performance was substantially different between the four senior second-tier leagues as well as the under-18 national championships, in comparison to each of the reserve and under-18 state leagues. Professional sporting organisations may utilise the methods provided here as an example of what could be implemented to support decisions regarding player contracting, recruitment and team selection.