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Predicting the success of football players using machine learning methods
 Janeček, Jan ; Filipenská, Marina (referee) ; Ředina, Richard (advisor)
This bachelor thesis focuses on the implementation of an artificial neural network in the Python programming language using the Keras library. The aim of the work is the numerical prediction of a football player’s match readiness on a scale from 0 to 1. The prediction is based on five physiological-kinematic data obtained from three training sessions preceding a given match. The reference data for training the artificial neural network includes technical data on the number of successful and total actions during the match. The data used in this work was collected from Sigma Olomouc U19 football club players using Polar Team Pro and Wyscout software. The lowest recorded model error, which was 0.1046, was achieved using a single hidden layer containing 15 perceptrons.
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