A data mining approach to solve the goal scoring problem

Authors:
Renato Oliveira;Paulo Adeodato;Arthur Carvalho;Icamaan Viegas;Christian Diêgo;Tsang Ing-Ren
Affiliations:
Center of Informatics, Federal University of Pernambuco, Brazil;Center of Informatics, Federal University of Pernambuco, Brazil;Center of Informatics, Federal University of Pernambuco, Brazil;Center of Informatics, Federal University of Pernambuco, Brazil;Center of Informatics, Federal University of Pernambuco, Brazil;Center of Informatics, Federal University of Pernambuco, Brazil
Venue:
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Year:
2009

Citing 5
Cited 0

Layered Learning in Multiagent Systems: A Winning Approach to Robotic Soccer

Layered Learning in Multiagent Systems: A Winning Approach to Robotic Soccer
Neural Networks: A Comprehensive Foundation

Neural Networks: A Comprehensive Foundation
Pattern Recognition and Neural Networks

Pattern Recognition and Neural Networks
An introduction to ROC analysis

Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
Collaborative Multiagent Reinforcement Learning by Payoff Propagation

The Journal of Machine Learning Research

Quantified Score

Hi-index	0.00

Visualization

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.