In competitive team sports, individual player decisions significantly impact
overall team performance. Designing an appropriate evaluation function for scoring a
player’s action selection in complex games such as soccer is a challenging problem. It
is essential to reflect the supervisor’s instructions in decision making, but if a human is
assumed to be the supervisor, it is difficult for the supervisor to accurately score a large
number of trials. This paper employs a learning to rank method to obtain an evaluation
function for action selection, focusing on ball-chasing behavior in soccer. We employed
a gradient boosting decision tree as a learning to rank model. The RoboCup Soccer Sim-
ulator is used for experiments, deriving a ranking model from players’ action logs. The
results show a model with satisfactory performance can be learned when the number of
situations exceeds approximately 1,000, even with training data that is assumed to be
created by humans.
