I am excited to share our new publication in the International Journal of Sports Physiology and Performance! We wanted to explore the 'invisible monitoring' approach further by using a machine learning approach to build a fitness index from small sided game football data.
The study had 3 purposes:
(1) to develop an index using machine-learning techniques to predict the fitness status of soccer players,
(2) to explore the index’s validity and its relationship with a submaximal run test (SMFT), and
(3) to analyze the impact of weekly training load on the index and SMFT outcomes.
It was a pleasure to collaborate with Mauro Mandorino and Mathieu Lacome on this paper.
The study is available on IJSPP here.
You can request the full text for FREE on ResearchGate here.
Mandorino, M., Clubb, J., & Lacome, M. (2024). Predicting Soccer Players’ Fitness Status Through a Machine-Learning Approach. International Journal of Sports Physiology and Performance, 1(aop), 1-11.
