A Data Mining Approach to Solve the Goal Scoring Problem

TL;DR

This paper introduces a data mining decision system for soccer goal scoring in RoboCup simulations, using match data and neural networks to improve scoring chances and outperform previous methods.

Contribution

It presents a novel data mining approach with a neural network model to optimize goal scoring decisions in simulated soccer matches.

Findings

78% increase in goals scored compared to previous methods

7.7% increase in the number of kicks

Improved decision-making in goal attempts

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.