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Implementing injury prediction models in practice and machine learning methods with Sam Robertson (Professor of Sports Analytics at Victoria University)

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Archived series ("Inactive feed" status)

When? This feed was archived on May 26, 2022 08:28 (2y ago). Last successful fetch was on April 22, 2022 06:07 (2y ago)

Why? Inactive feed status. Our servers were unable to retrieve a valid podcast feed for a sustained period.

What now? You might be able to find a more up-to-date version using the search function. This series will no longer be checked for updates. If you believe this to be in error, please check if the publisher's feed link below is valid and contact support to request the feed be restored or if you have any other concerns about this.

Manage episode 242429789 series 2364515
Content provided by Robert Pacey. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Robert Pacey or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://player.fm/legal.

In this episode of the Pacey Performance Podcast, I am speaking to Professor of Sports Analytics at Victoria University, Sam Robertson. This is Sam's second time on the podcast but this episode was sparked by a thread on Twitter which Sam started around injury prediction. This thread went viral among the sports science community so it seemed the perfect time to get Sam on and expand on the 14 tweet explanation. Since Sam's last appearance on the podcast, a few things have changed but what hasn't changed is Sam's ability to communicate very complex topics in a very simple and digestible way.

Hope you enjoy this episode with Sam Robertson.

In this episode we discussed...

  • Who is Sam Robertson (background, education and current role)
  • True positives and false positives
    • Risk being a strategy
      • Pulling players out of sessions based on data
    • Determining the performance of a model
  • Implementing injury prediction models in practice
    • Data quality
    • Data volume
    • Relationships between variables and injury
  • Machine learning methods

Sam can be found on Twitter @Robertson_SJ

Keep up to date with everything that is going on with the podcast by following on Twitter @strengthofsci or visiting strengthofscience.com.

Enjoy

PP

  continue reading

421 episodes

Artwork
iconShare
 

Archived series ("Inactive feed" status)

When? This feed was archived on May 26, 2022 08:28 (2y ago). Last successful fetch was on April 22, 2022 06:07 (2y ago)

Why? Inactive feed status. Our servers were unable to retrieve a valid podcast feed for a sustained period.

What now? You might be able to find a more up-to-date version using the search function. This series will no longer be checked for updates. If you believe this to be in error, please check if the publisher's feed link below is valid and contact support to request the feed be restored or if you have any other concerns about this.

Manage episode 242429789 series 2364515
Content provided by Robert Pacey. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Robert Pacey or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://player.fm/legal.

In this episode of the Pacey Performance Podcast, I am speaking to Professor of Sports Analytics at Victoria University, Sam Robertson. This is Sam's second time on the podcast but this episode was sparked by a thread on Twitter which Sam started around injury prediction. This thread went viral among the sports science community so it seemed the perfect time to get Sam on and expand on the 14 tweet explanation. Since Sam's last appearance on the podcast, a few things have changed but what hasn't changed is Sam's ability to communicate very complex topics in a very simple and digestible way.

Hope you enjoy this episode with Sam Robertson.

In this episode we discussed...

  • Who is Sam Robertson (background, education and current role)
  • True positives and false positives
    • Risk being a strategy
      • Pulling players out of sessions based on data
    • Determining the performance of a model
  • Implementing injury prediction models in practice
    • Data quality
    • Data volume
    • Relationships between variables and injury
  • Machine learning methods

Sam can be found on Twitter @Robertson_SJ

Keep up to date with everything that is going on with the podcast by following on Twitter @strengthofsci or visiting strengthofscience.com.

Enjoy

PP

  continue reading

421 episodes

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