In recent years, athlete monitoring has become an important part in professional sports, with clubs tracking physical data, internal load, and wellbeing measures within athletes. The aim is to reduce injury risk, optimise performance, and provide coaches with a holistic understanding of their players. However, the relationship between these variables is still a subject of ongoing debate.

This thesis examines how self-reported athlete wellbeing variables and internal load are related within and between athletes over time. Using longitudinal data from an athlete monitoring system, the study applies Graphical Vector Autoregression to separate contemporaneous, temporal, and between-athlete network structures. 

The thesis specifically investigates whether variables such as mood, optimism, stress, sleep, recovery, and internal load form meaningful dynamic patterns, and whether team-level networks can provide useful insight into individual athletes. By separating between athlete- and team-level networks, the study aims to provide a more scientifically grounded and interpretable framework of self-report data in professional sports.