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

TL;DR

This paper introduces a Bayesian estimation-based model to select optimal player formations for corner-kicks in RoboCup 2D soccer simulations, enhancing team strategy against diverse opponents.

Contribution

It presents a novel approach using sequential Bayes' estimators to determine effective formations against opponent clusters, specifically applied to corner-kick scenarios.

Findings

Model effectively compares similar formations in performance.

System ranks formations accurately with limited simulations.

Demonstrates potential for strategic decision-making in RoboCup.

Abstract

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.