Philosophy of Science, Mathematical Models in

Domotor, Zoltan

doi:10.1007/978-3-642-27737-5_407-3

Cited by 1 publication

(1 citation statement)

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“…The fundamental stages of mathematical modelling comprise 1) problem formulation; 2) determination and examination of solutions; and 3) model validation across diverse cases applicable in the real world [78]. Of the three phases, FFM research is most limited with regards to model validation.…”

Section: Model Validation: Integrating Practice Into Research Effortsmentioning

confidence: 99%

Traditional and contemporary approaches to mathematical fitness-fatigue models in exercise science: A practical guide with resources. Part I.

Hemingway¹,

Greig²,

Jovanovic³

et al. 2020

Preprint

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The fitness-fatigue model (FFM) has been around for more than 40 years and is one of most prominent conceptual models within exercise science. Translation from a purely conceptual form into a mathematical structure reveals there is no single model, but instead a collective of models with common properties. The greatest potential use of FFMs is to predict future performance of athletes with sufficient accuracy to assist with training program design. However, despite a long history and consistent study, there has been limited uptake in practice. This is most likely due to a lack of accessible resources explaining key concepts and processes required to fit models, and insufficient knowledge of predictive validity. The current review provides a comprehensive overview of FFMs and discusses three key aspects of fitting models in practice: 1) training load quantification; 2) criterion performance selection; and 3) parameter estimation. As the majority of athletes engage in sports where performance is complex and determined by a range of physical, psychological and technical factors, it is argued that greater focus should be placed on FFMs being used to quantify components of fitness (e.g. strength, power) targeted by an athletes training rather than competitive outcomes. Additionally, contemporary approaches to training monitoring (e.g. barbell velocity and repetitions in reserve) are recommended as tools to generate high frequency “performance” measurements to fit FFMs. Necessary further developments require collaboration between researchers and practitioners with larger data sets to establish conditions where suitable predictions to future training can be obtained.

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Section: Model Validation: Integrating Practice Into Research Effortsmentioning

confidence: 99%