Creating a framework to maximise jumping and landing training with athletes with Dana Agar-Newman and Jeremy Sheppard

Creating a framework to maximise jumping and landing training with athletes with Dana Agar-Newman and Jeremy Sheppard

This week’s guests on the Pacey Performance Podcast are Dana Agar-Newman and Jeremy Sheppard. Dana is a senior practitioner at the Canadian Sport Institute Pacific, and a head strength conditioning coordinator at the University of Victoria. He has also worked in rowing and rugby, including with the Canadian women’s rugby sevens team at Rio 2016. Jeremy is a strength conditioning coach with Canada Snowboard, previously having also worked with the Canadian Sport Institute.

The duo have recently written the jumping and landing training chapter in High Performance Training for Sports (second edition). Here, they discuss aspects of their research such as jump testing analysis and performance metrics, explaining the metrics to measure between differing sports and athlete levels. They also talk about tools to avoid, and whether to pursue variations in training.

In addition, the pair also talk about jump sustainability and what works in different sports and for different athletes. This includes what to consider when developing jumping exercises, force vectors, and landing evaluations. When it comes to jumps training, performance development and analysis, this week’s guests quite literally wrote the book on it, so hit the play button now for all this insight and much, much more.

This week’s topics:

  • Jump testing analysis
  • Tools to avoid due to their unreliability
  • Differing sport-specific analysis metrics
  • Differing metrics based on athlete experience and level
  • The process for developing jumping exercises
  • Arguments for and against lots of variation in training
  • Force vectors and choosing exercises based on vertical and horizontal
  • Jump sustainability, and the programming that influences it
  • Landing evaluations and differences between sports
  • What landings can tell us about other training variables, e.g., deceleration

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