Selecting the Best Player Formation for Corner-Kick Situations Based on
  Bayes' Estimation
  In the domain of the Soccer simulation 2D league of the RoboCup project,
appropriate player positioning against a given opponent team is an important
factor of soccer team performance. This work proposes a model which decides the
strategy that should be applied regarding a particular opponent team. This task
can be realized by applying preliminary a learning phase where the model
determines the most effective strategies against clusters of opponent teams.
The model determines the best strategies by using sequential Bayes' estimators.
As a first trial of the system, the proposed model is used to determine the
association of player formations against opponent teams in the particular
situation of corner-kick. The implemented model shows satisfying abilities to
compare player formations that are similar to each other in terms of
performance and determines the right ranking even by running a decent number of
simulation games.
