A Data Mining Approach to Solve the Goal Scoring Problem
Abstract
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.
Keywords
Cite
@article{arxiv.1305.4955,
title = {A Data Mining Approach to Solve the Goal Scoring Problem},
author = {Renato Oliveira and Paulo Adeodato and Arthur Carvalho and Icamaan Viegas and Christian Diego and Tsang Ing-Ren},
journal= {arXiv preprint arXiv:1305.4955},
year = {2013}
}