Machine Learning prediction of the expected performance of football player during training
Author(s)
Date Issued
2022
Type
conferenceObject
Start Page
574
End Page
578
Abstract
Predicting athletes’ performance is a fundamental
task for the evaluation of their physical status, specific skills and
quality of the training phase. This is even more important in
team sports such as football, where the performance of the
single athletes can significantly influence the whole team
performance. Thanks to the improvements of modern sensors,
which nowadays can be worn directly by the athletes within
their vests without limiting them, it is possible to accurately
measure several physiological parameters and movement
indices that may be useful to make performance prediction. A
growing number of studies is collecting these data and
investigating relations among each other, in order to uncover
possible correlations that can help in the prediction of sport
performance. In this work, we used multivariate regression to
attempt forecasting football players’ performance during
training sessions, starting from data about their movements and
physiological parameters, extracted through a sensors array
integrated in the vests worn by the athletes. The obtained results
showed that specific combinations of physiological parameters
can predict typical performance indicators, as acceleration and
deceleration distances, as well as distance run under high-
intensity effort, with an accuracy higher than 90%. The applied
methodology can thus be used profitably by football teams’ staff
to monitor their players, giving it the possibility to make tactical
decisions, design customized training sessions and orient market
choices.
Conference(s)
2022 IEEE International Conference on Metrology for Extended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE)