Show Notes
Fresh from pre-season camp in Australia, Richard Graves sits down with Stan Parker, Sports Scientist at the Western Bulldogs, to explore how tracking data, contextual analytics, and interdisciplinary collaboration shape performance in elite AFL.
Stan’s journey spans the Brisbane Broncos in the NRL, Queensland Rugby Sevens, and now five seasons embedded within the Bulldogs’ high-performance programme, including a PhD focused on contextualising tracking data beyond simple load metrics.
This episode moves beyond “distance covered” and “top speed” to examine how sports science actually influences coaching decisions, athlete development, injury management, and tactical execution. From peak demand analysis and real-world decision-making under fatigue, to AI readiness and workflow efficiency, this is a grounded, practitioner-led conversation about where elite sport is really heading.
In this episode you will learn:
Why contextualising tracking data matters more than collecting more of it
How to compare peak training demands to peak match demands
The role of vision and video in translating data for coaches and players
How to build buy-in when athletes aren’t “numbers learners”
The balance between availability and performance in weekly competition cycles
How interdisciplinary decision-making works inside an AFL club
Why isolated fatigue “flags” can be misleading
How gym-based physical development can be directly tied to on-field outcomes
The importance of soft skills in high-performance environments
Where AI realistically fits (and doesn’t fit) in elite sports science
About Stan Parker
Stan Parker is a Sports Scientist with the Western Bulldogs in the AFL. He has previously worked with the Brisbane Broncos and Queensland Rugby Sevens, building experience across multiple elite sporting codes in Australia.
Stan is also completing a PhD focused on the contextualisation of tracking data in team sports, exploring how movement patterns and peak demands can better explain performance impact rather than serving purely as load monitoring tools.
He is particularly interested in bridging the gap between data analytics, coaching vision, and real-world performance application.
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