In order to address the study of complex systems, the detection of patterns in their dynamics could play a key role in understanding their evolution. In particular, global patterns are required to detect emergent concepts and trends, some of them of a qualitative nature. Formal Concept Analysis (FCA) is a theory whose goal is to discover and extract Knowledge from qualitative data (organized in Concept Lattices). In complex environments, such as sport competitions, the large amount of information currently available turns Concept Lattices into complex networks. We analyze how to apply FCA reasoning in order to increase confidence in sports predictions by means of detecting regularities from data through the management of intuitive and natural attributes extracted from publicly available information. The complexity of concept lattices -considered as networks with complex topological structure- is analyzed. It is applied to building a Knowledge based system for confidence-based reasoning, which simulates how humans tend to avoid the complexity of concept networks by means of bounded reasoning skills.