A Data Mining Approach to Solve the Goal Scoring Problem
This work addresses the specific problem of improving goal-scoring efficiency in simulated soccer matches, representing an incremental advancement over prior methods.
The paper tackled the problem of optimizing goal-scoring decisions in RoboCup soccer 2D-simulator by developing a data mining-based system to determine the best kick timing and direction, resulting in a 7.7% increase in kicks and a 78% increase in goals scored.
In soccer, scoring goals is a fundamental objective which depends on many conditions and constraints. Considering the RoboCup soccer 2D-simulator, this paper presents a data mining-based decision system to identify the best time and direction to kick the ball towards the goal to maximize the overall chances of scoring during a simulated soccer match. Following the CRISP-DM methodology, data for modeling were extracted from matches of major international tournaments (10691 kicks), knowledge about soccer was embedded via transformation of variables and a Multilayer Perceptron was used to estimate the scoring chance. Experimental performance assessment to compare this approach against previous LDA-based approach was conducted from 100 matches. Several statistical metrics were used to analyze the performance of the system and the results showed an increase of 7.7% in the number of kicks, producing an overall increase of 78% in the number of goals scored.