The impact of different training load quantification and modelling methodologies on performance predictions in elite swimmers
| Autor: | Philo U. Saunders, David B. Pyne, Lachlan J G Mitchell, John Fowlie, Ben Rattray |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Male
Quantification methods Computer science education Physical Exertion 030209 endocrinology & metabolism Physical Therapy Sports Therapy and Rehabilitation Athletic Performance Machine learning computer.software_genre 03 medical and health sciences Young Adult 0302 clinical medicine Humans Orthopedics and Sports Medicine Muscle Strength Training load Swimming Training set Artificial neural network business.industry 030229 sport sciences General Medicine Models Theoretical Elite Female Artificial intelligence Neural Networks Computer business computer |
| Zdroj: | European journal of sport science. 20(10) |
| ISSN: | 1536-7290 |
| Popis: | The use of rolling averages to analyse training data has been debated recently. We evaluated two training load quantification methods (five-zone, seven-zone) fitted to performances over two race distances (50 and 100 m) using four separate longitudinal models (Banister, Busso. rolling averages and exponentially weighted rolling averages) for three swimmers ranked in the top 8 in the world. A total of 1610 daily load measures and 108 performances were collected. Banister (standard error of the estimate (SEE) 0.64 and 0.62 s; five-zone and seven-zone quantification methods), Busso (SEE 0.73 and 0.70 s) and exponentially weighted rolling averages (SEE 0.57 and 0.63 s) models fitted more accurately ( |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|