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Understanding the statistical properties of performance indicators in Australian Football
Dave Matteo1, Matthew C Varley1, Joshua D Ruddy2
1Discipline of Sport and Exercise Science, School of Allied Health, Human Services & Sport, La Trobe University, Melbourne, Australia.
Journal of Sports Sciences
|July 19, 2026
Summary
Count-based performance metrics like kicks and goals are reliable for Australian Football (AF) players. Advanced metrics also show stability, but percentage-based stats are less consistent, especially for teams.
Area of Science:
- Sports Science
- Performance Analytics
- Statistical Modeling
Background:
- Evaluating player and team performance in Australian Football (AF) relies on various metrics.
- The statistical properties of these metrics, specifically their discrimination and stability, are crucial for accurate performance evaluation.
- Understanding metric utility is essential for data-driven decision-making in professional sports.
Purpose of the Study:
- To statistically evaluate common performance metrics in Australian Football (AF) using discrimination and stability.
- To assess the utility of 33 player and team-level metrics across 9 seasons.
- To establish reference values for metric selection in AF performance analysis.
Main Methods:
- Utilized a meta-metric framework focusing on discrimination and stability.
- Analyzed data from 1,383 players across 1,823 matches (2015-2023).
- Assessed 33 player and team-level metrics, including count-based, percentage-based, Ranking Points, and Rating Points.
Main Results:
- Count-based metrics (kicks, handballs, disposals, goals) demonstrated high discrimination and stability.
- Percentage-based metrics showed lower discrimination and stability, particularly at the team level.
- Advanced metrics (Ranking Points, Rating Points) exhibited high discrimination and stability across positional groups.
Conclusions:
- Count-based and advanced metrics are robust indicators of player performance in AF.
- Contextual variability affects the reliability of percentage-based metrics.
- Metric selection should prioritize stability and discrimination, with further research needed on contextual data integration.
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