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Including the Past: Performance Modeling Using a Preload Concept by Means of the Fitness-Fatigue Model

  • In mathematical modeling by means of performance models, the Fitness-Fatigue Model (FF-Model) is a common approach in sport and exercise science to study the training performance relationship. The FF-Model uses an initial basic level of performance and two antagonistic terms (for fitness and fatigue). By model calibration, parameters are adapted to the subject’s individual physical response to training load. Although the simulation of the recorded training data in most cases shows useful results when the model is calibrated and all parameters are adjusted, this method has two major difficulties. First, a fitted value as basic performance will usually be too high. Second, without modification, the model cannot be simply used for prediction. By rewriting the FF-Model such that effects of former training history can be analyzed separately – we call those terms preload – it is possible to close the gap between a more realistic initial performance level and an athlete's actual performance level without distorting other model parameters and increase model accuracy substantially. Fitting error of the preload-extended FF-Model is less than 32% compared to the error of the FF-Model without preloads. Prediction error of the preload-extended FF-Model is around 54% of the error of the FF-Model without preloads.

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Document Type:Article
Author:Melanie Ludwig, Alexander Asteroth, Christian Rasche, Mark Pfeiffer
Parent Title (English):International Journal of Computer Science in Sport
First Page:115
Last Page:134
Publishing Institution:Hochschule Bonn-Rhein-Sieg
Date of first publication:2019/08/21
Copyright:© 2019 Melanie Ludwig et al., published by Sciendo. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.
Funding Information:We are very grateful for financial support given by the Ministry of Culture and Science (MKW) of the North Rhine-Westphalia state within the program FH-STRUKTUR 2017 (AZ: 322- 2017/07), and by the Equal Opportunity Commission at Bonn-Rhein-Sieg University o.a.S.
Keyword:fitness-fatigue model; mathematical modeling; performance modeling; performance prediction; training performance relationship
Departments, institutes and facilities:Fachbereich Informatik
Institut für Technik, Ressourcenschonung und Energieeffizienz (TREE)
Projects:eTa - effiziente Transportalternativen (322-
Dewey Decimal Classification (DDC):0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 006 Spezielle Computerverfahren
Entry in this database:2019/11/09
Licence (German):License LogoCreative Commons - CC BY-NC-ND - Namensnennung-Nicht kommerziell-Keine Bearbeitung 3.0