Selecting Attributes for Sport Forecasting using Formal Concept Analysis

TL;DR

This paper explores the use of Formal Concept Analysis to identify key attributes and temporal patterns in sports data, aiming to enhance confidence in sports betting decisions through a knowledge-based reasoning system.

Contribution

It introduces a novel application of FCA for attribute selection and pattern detection in sports forecasting, developing a system to improve betting confidence.

Findings

FCA effectively detects temporal regularities in sports data.

The system enhances confidence in sports betting decisions.

FCA provides a formal foundation for reasoning with qualitative sports data.

Abstract

In order to address complex systems, apply pattern recongnition on their evolution could play an key role to understand their dynamics. Global patterns are required to detect emergent concepts and trends, some of them with qualitative nature. Formal Concept Analysis (FCA) is a theory whose goal is to discover and to extract Knowledge from qualitative data. It provides tools for reasoning with implication basis (and association rules). Implications and association rules are usefull to reasoning on previously selected attributes, providing a formal foundation for logical reasoning. In this paper we analyse how to apply FCA reasoning to increase confidence in sports betting, by means of detecting temporal regularities from data. It is applied to build a Knowledge-Based system for confidence reasoning.