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Estimating Carb & Calorie Burn and Machine Learning for Talent Prediction
Manage episode 460382815 series 3556407
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This week’s episode saw multiple listener questions answered including a discussion on prediction of caloric and carbohydrate burn from training data. There’s some quick fire talking points including the new TGL golf league, a women’s 5K world record and Netflix’s content strategy in sport. We then touch on the use of machine learning for talent prediction in football. Finally we look at injury and illness rates for the USA Olympic and Paralympic teams from the Tokyo games.
(00:00) Introduction
(03:41) David’s Update and Training
(06:23) Xylon’s Update
(17:30) Community Shoutouts
(33:30) Listener Question - Training on Tired Legs
(36:10) Listener Question - Heart Rate’s Relationship to Calories
(41:10) Predicting Carbohydrate and Caloric Burn
(46:50) Netflix Secures Women’s Football World Cup
(48:32) TGL Golf League
(51:08) Women’s 5K World Record Broken
(51:44) Ilona Maher Debuts for the Bristol Bears
(54:04) The Coaching Roundabout
(60:38) Machine Learning Prediction of Football Talent Selection
(64:20) Doctor’s Office - USA Tokyo Olympic & Paralympic Team Illness and Injuries
Thanks to Precision Fuel and Hydration for their sponsorship, use the link below to get 15% off your first order and support the podcast:
https://visit.pfandh.com/theonepercentbetterpodcast
If you’re unsure how which products to use or how to plan your race nutrition check out Precision’s Fuel & Hydration Planner (if you follow this you will have your discount applied automatically on your first order too):
https://visit.pfandh.com/onepercentfuelplanner
David’s Newsletter Article on Calories
Article on New Golf League
Article on Machine Learning and Youth Football Academy Success Prediction
https://www.tandfonline.com/doi/full/10.1080/02640414.2024.2442850
Article on Predicting Carbohydrate & Energy Expenditure
https://link.springer.com/article/10.1007/s40279-024-02131-z
Article on Injury & Illness at Tokyo Olympics from the USA
https://journals.sagepub.com/doi/10.1177/23259671241304417
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