  Basketball players' performance measurement is of critical importance for a
broad spectrum of decisions related to training and game strategy. Despite this
recognized central role, the main part of the studies on this topic focus on
performance level measurement, neglecting other important characteristics, such
as variability. In this paper, shooting performance variability is modeled with
a Markov Switching dynamic, assuming the existence of two alternating
performance regimes. Then, the relationships between each player's variability
and the lineup composition is modeled as an ARIMA process with covariates and
described with network analysis tools, in order to extrapolate positive and
negative interactions between teammates
