Bayesian network learning for winning structure and losing structure in basketball games

This paper proposes a learning model of basketball players structure based on the Bayesian network. By analyzing the data of NBA 's players, we complete the structural learning of basketball players network based on the +/- values of 5 resident players in the Portland Trail Blazers team. We finally obtain a winning and losing models for the five resident players of the Portland Trail Blazers team, and we make suggestions for coaches about player rotation based on the analysis of the models.