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A model for competitiveness level analysis in sports competitions: Application to basketball

Author

Listed:
  • de Saá Guerra, Y.
  • Martín González, J.M.
  • Sarmiento Montesdeoca, S.
  • Rodríguez Ruiz, D.
  • García-Rodríguez, A.
  • García-Manso, J.M.

Abstract

The degree of overall competitiveness of a sport league is a complex phenomenon. It is difficult to assess and quantify all elements that yield the final standing. In this paper, we analyze the general behavior of the result matrices of each season and we use the corresponding results as a probably density. Thus, the results of previous seasons are a way to investigate the probability that each team has to reach a certain number of victories. We developed a model based on Shannon entropy using two extreme competitive structures (a hierarchical structure and a random structure), and applied this model to investigate the competitiveness of two of the best professional basketball leagues: the NBA (USA) and the ACB (Spain). Both leagues’ entropy levels are high (NBA mean 0.983; ACB mean 0.980), indicating high competitiveness, although the entropy of the ACB (from 0.986 to 0.972) demonstrated more seasonal variability than that of the NBA (from 0.985 to 0.990), a possible result of greater sporting gradients in the ACB. The use of this methodology has proven useful for investigating the competitiveness of sports leagues as well as their underlying variability across time.

Suggested Citation

  • de Saá Guerra, Y. & Martín González, J.M. & Sarmiento Montesdeoca, S. & Rodríguez Ruiz, D. & García-Rodríguez, A. & García-Manso, J.M., 2012. "A model for competitiveness level analysis in sports competitions: Application to basketball," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(10), pages 2997-3004.
    • Handle: RePEc:eee:phsmap:v:391:y:2012:i:10:p:2997-3004
    DOI: 10.1016/j.physa.2012.01.014
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    References listed on IDEAS

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    1. Martin B. Schmidt & David J. Berri, 2001. "Competitive Balance and Attendance," Journal of Sports Economics, , vol. 2(2), pages 145-167, May.
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    Cited by:

    1. Schaigorodsky, Ana L. & Perotti, Juan I. & Billoni, Orlando V., 2014. "Memory and long-range correlations in chess games," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 394(C), pages 304-311.
    2. Martín-González, Juan Manuel & de Saá Guerra, Yves & García-Manso, Juan Manuel & Arriaza, Enrique & Valverde-Estévez, Teresa, 2016. "The Poisson model limits in NBA basketball: Complexity in team sports," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 464(C), pages 182-190.
    3. Marc Garnica-Caparrós & Daniel Memmert & Fabian Wunderlich, 2022. "Artificial data in sports forecasting: a simulation framework for analysing predictive models in sports," Information Systems and e-Business Management, Springer, vol. 20(3), pages 551-580, September.
    4. Mukherjee, Satyam, 2012. "Identifying the greatest team and captain—A complex network approach to cricket matches," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(23), pages 6066-6076.

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