  Determining the value of basketball players through analyzing the players'
behavior is important for the managers of modern basketball teams. However,
conventional methods always utilize isolated statistical data, leading to
ineffective and inaccurate evaluations. Existing models based on dynamic
network theory offer major improvements to the results of such evaluations, but
said models remain imprecise because they focus merely on evaluating the values
of individual players rather than considering them within their current teams.
To solve this problem, we propose an analysis and evaluation model based on
networks and a hidden Markov model. To the best of our knowledge, we are the
first to combine a network form representing the players who are playing with
the use of a hidden Markov model to mine the network and generate the desired
results. Applying our approach to SportVU data collected from the National
Basketball Association shows that this analysis and evaluation model can
effectively analyze the performance of each player in a game and provides an
assistive tool for team managers.
