Journal of Systems Science and Complexity

, Volume 26, Issue 1, pp 117–136 | Cite as

Complex concept lattices for simulating human prediction in sport

  • Gonzalo A. Aranda-Corral
  • Joaquín Borrego-Díaz
  • Juan Galán-Páez
Article

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.

Key words

Bounded rationality complex networks formal concept analysis sport forecasting 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. [1]
    Why Spain will win …, Engineering & Technology, 5 June–18 June 2010.Google Scholar
  2. [2]
    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.CrossRefGoogle Scholar
  3. [3]
    Schumaker R P, Solieman O K, and Chen H, Sports data mining, Integrated Series in Information Systems, 2010, 26: 55–63.CrossRefGoogle Scholar
  4. [4]
    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.MathSciNetCrossRefGoogle Scholar
  5. [5]
    Ganter B and Wille R, Formal Concept Analysis - Mathematical Foundations, Springer, Berlin, Heidelberg, 1999.MATHCrossRefGoogle Scholar
  6. [6]
    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.CrossRefGoogle Scholar
  7. [7]
    Carling C, Williams A M, and Reilly T, Handbook of Soccer Match Analysis, Routledge Press, New York, 2005.CrossRefGoogle Scholar
  8. [8]
    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.Google Scholar
  9. [9]
    Goldstein D G and Gigerenzer G, Fast and frugal forecasting, Int. J. of Forecasting, 2009, 25(4): 760–772.CrossRefGoogle Scholar
  10. [10]
    Goldstein D G and Gigerenzer G, Models of ecological rationality: The recognition heuristic, Psychological Review, 2002, 109(1): 75–90.CrossRefGoogle Scholar
  11. [11]
    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.CrossRefGoogle Scholar
  12. [12]
    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.Google Scholar
  13. [13]
    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.Google Scholar
  14. [14]
    Simon H A, Models of Bounded Rationality, MA: MIT Press, Cambridge, 1982.Google Scholar
  15. [15]
    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.Google Scholar
  16. [16]
    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.MathSciNetGoogle Scholar
  17. [17]
    Aranda-Corral G A and Borrego-Díaz J, Reconciling knowledge in social tagging web services, Lecture Notes in Artificial Intelligence, 2010, 6077: 383–390.Google Scholar
  18. [18]
    Balcázar J L, Redundancy, deduction schemes, and minimum-size bases for association rules, Logical Methods in Computer Science, 2010, 6(2): 1–23.CrossRefGoogle Scholar
  19. [19]
    Giarratano J C and Riley G D, Expert Systems: Principles and Programming, Brooks/Cole Publishing Co, Pacific Grove, CA, 2005.Google Scholar
  20. [20]
    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.Google Scholar
  21. [21]
    Clauset A, Shalizi C R, and Newman M E J, Power-law distributions in empirical data, SIAM Review, 2009, 51(4): 661–703.MathSciNetMATHCrossRefGoogle Scholar
  22. [22]
    Albert R and Barabási A L, Statistical mechanics of complex networks, Reviews of Modern Physics, 2002, 74(1): 47–97.MathSciNetMATHCrossRefGoogle Scholar
  23. [23]
    Carmichael F, Thomas D, and Ward R, Team performance: The case of English premiership football, Managerial and Decision Economics, 2000, 21(1): 31–45.CrossRefGoogle Scholar
  24. [24]
    Goldstein D G and Gigerenzer G, Reasoning the fast and frugal way: Models of bounded rationality, Psychological Review, 1996, 103(4): 650–669.CrossRefGoogle Scholar
  25. [25]
    Brunswik E, Representative design and probabilistic theory in a functional psychology, Psychological Review, 1955, 62(3): 193–217.CrossRefGoogle Scholar
  26. [26]
    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.Google Scholar

Copyright information

© Institute of Systems Science, Academy of Mathematics and Systems Science, CAS and Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Gonzalo A. Aranda-Corral
    • 1
  • Joaquín Borrego-Díaz
    • 2
  • Juan Galán-Páez
    • 2
  1. 1.Department of Information TechnologyUniversidad de HuelvaPalos de La FronteraSpain
  2. 2.Department of Computer Science and Artificial IntelligenceUniversidad de SevillaSevillaSpain

Personalised recommendations