Abstract
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. The authors 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.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Why Spain will win …, Engineering & Technology, 5 June–18 June 2010.
Andersson P, Memmert D, and Popowicz E, Forecasting outcomes of the World Cup 2006 in football: Performance and confidence of bettors and laypeople, Psychology of Sport & Exercise, 2009, 10(1): 116–123.
Schumaker R P, Solieman O K, and Chen H, Sports data mining, Integrated Series in Information Systems, 2010, 26: 55–63.
Vlastakis N, Dotsis G, and Markellos R, How efficient is the European football betting market? Evidence from arbitrage and trading strategies, Journal of Forecasting, 2009, 28(5): 426–444.
Ganter B and Wille R, Formal Concept Analysis - Mathematical Foundations, Springer, Berlin, Heidelberg, 1999.
Aranda-Corral G A, Borrego-Díaz J, and Galán J, Confidence-based reasoning with local temporal formal contexts, Lecture Notes in Computer Science, 2011, 6692: 461–468.
Carling C, Williams A M, and Reilly T, Handbook of Soccer Match Analysis, Routledge Press, New York, 2005.
Oberstone J, Differentiating the top English premier league football clubs from the rest of the pack: Identifying the keys to success, Journal of Quantitative Analysis in Sports, 2009, 5(3): Article 10.
Goldstein D G and Gigerenzer G, Fast and frugal forecasting, Int. J. of Forecasting, 2009, 25(4): 760–772.
Goldstein D G and Gigerenzer G, Models of ecological rationality: The recognition heuristic, Psychological Review, 2002, 109(1): 75–90.
Min B, Kim J, Choe C, Eom H, and McKay R I, A compound framework for sports results prediction: A football case study, Know. Based Syst., 2008, 21(7): 551–562.
Imberman S P, Domanskiand B, and Orchard R A, Using booleanized data to discover better relationships between metrics, Proc. Int. CMG Conference, 1999, 530–539.
Aranda-Corral G A, Borrego-Díaz J, and Galán-Páez J, Bounded rationality for data reasoning based on formal concept analysis, Proc. Int. Workshop on Database and Expert Systems Applications, 2011, 350–354.
Simon H A, Models of Bounded Rationality, MA: MIT Press, Cambridge, 1982.
Andersson P, Ekman M, and Edman J, Forecasting the fast and frugal way: A study of performance and information-processing strategies of experts and non-experts when predicting the World Cup 2002 in soccer, Working Paper Series in Business Administration, 2003, 9 Stockholm School of Economics.
Guigues V and Duquenne J L, Familles minimales d’ implications informatives resultant d’un tableau de donnees binaires, Math. Sci. Humaines, 1986, 95: 5–18.
Aranda-Corral G A and Borrego-Díaz J, Reconciling knowledge in social tagging web services, Lecture Notes in Artificial Intelligence, 2010, 6077: 383–390.
Balcázar J L, Redundancy, deduction schemes, and minimum-size bases for association rules, Logical Methods in Computer Science, 2010, 6(2): 1–23.
Giarratano J C and Riley G D, Expert Systems: Principles and Programming, Brooks/Cole Publishing Co, Pacific Grove, CA, 2005.
Motter A E, de Moura A P S, Lai Y, and Dasgupta P, Topology of the conceptual network of language, Physical Review E, 2002, 65(6): 065102.
Clauset A, Shalizi C R, and Newman M E J, Power-law distributions in empirical data, SIAM Review, 2009, 51(4): 661–703.
Albert R and Barabási A L, Statistical mechanics of complex networks, Reviews of Modern Physics, 2002, 74(1): 47–97.
Carmichael F, Thomas D, and Ward R, Team performance: The case of English premiership football, Managerial and Decision Economics, 2000, 21(1): 31–45.
Goldstein D G and Gigerenzer G, Reasoning the fast and frugal way: Models of bounded rationality, Psychological Review, 1996, 103(4): 650–669.
Brunswik E, Representative design and probabilistic theory in a functional psychology, Psychological Review, 1955, 62(3): 193–217.
DiFatta G, Haworth G, and Regan K, Skill rating by Bayesian inference, Proc. 2009 IEEE Symposium on Computational Intelligence and Data Mining, 2009, 89–94.
Author information
Authors and Affiliations
Corresponding author
Additional information
This work is supported by the TIN2009-09492 project of the Spanish Ministry of Science and Innovation, and Excellence project TIC-6064 of Junta de Andalucía co-financed by FEDER funds.
This paper was recommended for publication by Editors FENG Dexing and HAN Jing.
Rights and permissions
About this article
Cite this article
Aranda-Corral, G.A., Borrego-Díaz, J. & Galán-Páez, J. Complex concept lattices for simulating human prediction in sport. J Syst Sci Complex 26, 117–136 (2013). https://doi.org/10.1007/s11424-013-2288-x
Received:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11424-013-2288-x