  Professional sports are developing towards increasingly scientific training
methods with increasing amounts of data being collected from laboratory tests,
training sessions and competitions. In cycling, it is standard to equip
bicycles with small computers recording data from sensors such as power-meters,
in addition to heart-rate, speed, altitude etc. Recently, machine learning
techniques have provided huge success in a wide variety of areas where large
amounts of data (big data) is available. In this paper, we perform a pilot
experiment on machine learning to model physical response in elite cyclists. As
a first experiment, we show that it is possible to train a LSTM machine
learning algorithm to predict the heart-rate response of a cyclist during a
training session. This work is a promising first step towards developing more
elaborate models based on big data and machine learning to capture performance
aspects of athletes.
