Selecting the Best Player Formation for Corner-Kick Situations Based on Bayes' Estimation

In the domain of RoboCup 2D soccer simulation league, 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 cornerkick. 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.