Machine Learning to Support the Effectiveness of Physical Training

This study investigates the use of machine learning methods to assess and predict the level of athletes’ preparation for e-cycling competitions based on their training data. A three-month dataset of activities collected from selected competitors of the Polish Open E-cycling Championships was processed to extract key performance indicators describing individual training cycles. Three supervised learning models – linear regression, decision trees, and artificial neural networks – were evaluated, achieving average classification accuracy of approximately 0.6. Additionally, an unsupervised k-means clustering approach was applied to identify natural groupings of athletes based on multidimensional training characteristics. The findings indicate that the constructed dataset enables reasonable prediction of preparation level; however, broader and more diverse data, including contextual factors such as well-being, nutrition, and environmental conditions, may be required to significantly improve model performance. The results highlight both the potential and the limitations of applying machine learning techniques to support planning and evaluation of training in e-cycling.