@article{LudwigAsterothRascheetal.2019,
  author    = {Melanie Ludwig and Alexander Asteroth and Christian Rasche and Mark Pfeiffer},
  title     = {Including the Past: Performance Modeling Using a Preload Concept by Means of the Fitness-Fatigue Model},
  series   = {International Journal of Computer Science in Sport},
  volume    = {18},
  number    = {1},
  publisher = {Sciendo},
  issn      = {1684-4769},
  doi       = {10.2478/ijcss-2019-0007},
  url       = {https://nbn-resolving.org/urn:nbn:de:hbz:1044-opus-46626},
  pages     = {115 -- 134},
  year      = {2019},
  abstract  = {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.},
  language  = {en}
}