Estimación de parámetros del rating ELO para la liga de fútbol española 2009/2010

  1. Hernández-Díaz, Alfredo G. 1
  2. Sala Garrido, Ramón 2
  3. Caballero Fernández, Rafael 3
  1. 1 Universidad Pablo de Olavide, Sevilla
  2. 2 Universitat de València
    info

    Universitat de València

    Valencia, España

    ROR https://ror.org/043nxc105

  3. 3 Departamento de Economía Aplicada (Matemáticas) Universidad de Málaga
Mostrar afiliaciones +
Revista:
Anales de ASEPUMA

ISSN: 2171-892X

Año de publicación: 2010

Número: 18

Tipo: Artículo

DIALNET GOOGLE SCHOLAR lock_openDialnet editor

Otras publicaciones en: Anales de ASEPUMA

Resumen

The prediction of the results of sporting events has been of interest for a long time, and today remains a challenge, very difficult to achieve, and also a gainful activity. The ELO rating was used initially for the assessment of the chess players, and more recently other sporting events. Our intention in this work is to use the rating for predict of the results of football in the Spanish League following other preparatory work, but that we intend to use metaheuristics methods for estimating the parameters of this rating trying to improve the proportion of successful forecasts.

Referencias bibliográficas

  • Andersson, P., Edman, J. y Ekmanc M. (2005). “Predicting the World Cup 2002 in soccer: Performance and confidence of experts and non-experts”, International Journal of Forecasting 21(3), pp. 565-576.
  • Dobson, S. and Goddard, J. (2001): The Economics of football. Cambridge. Cambridge University Press.
  • Dobson, S. and Goddard, J. (2004): Revenue divergence and competitive balance in a divisional sports league Scottish Journal of Political Economy, 51(3), 359-376.
  • Eckard, E. W. (2001): Free agency, competitive balance and diminishing returns to pennant contention. Economic Inquiry. 39(3). 430-443.
  • Elo, A. E. (1978), The rating of chess players, past and present. Ed. Arco New York.
  • Hvattum, L.M., Arntzen, H. (2010). ”Using ELO ratings for match result prediction in association football”, International Journal of Forecasting 26(3), pp. 460-470.
  • Koning, R. H. (2000): Balance in competition in Dutch soccer. The Statistician. 49, 419-431
  • Laguna, M., Molina, J., Pérez, F., Caballero, R. y Hernández-Díaz, A. G. (2010). “The Challenge of Optimizing Expensive Black Boxes: A Scatter Search / Rough Set Theory Approach”, Journal of the Operational Research Society 61, pp. 53-67.
  • Leitner, C., Zeileisa, A. y Hornik, K. (2010). “Forecasting sports tournaments by ratings of (prob)abilities: A comparison for the EURO 2008”, International Journal of Forecasting 26(3), pp. 471-481.
  • Mocholí, M. y Sala, R. (2009). “La incertidumbre de los resultados en las ligas española, inglesa e italiana (2008/09) mediante el rating ELO”, Rect@ Vol Actas_17, 105.
  • Neale, W. (1964): The peculiar economics of professional sports. Quarterly Journal of Economics, 78, 1-14.
  • Peel, D. and Thomas, D. (1988):Outcome uncertainty and the demand for football: An analysis of match attendances in English Football League. Scottish Journal of Political Economy, 35(3), 242-249.
  • Rottemberg, S. (1956): The baseball players’ Labor Market. Journal of Political Economics. 64, 242-258.
  • Scully, G. W. (1989): The Business of Major League Baseball. Chicago. The Chicago University Press.
  • Scully, G. W. (1995): The Market Structure of Sports. Chicago. The Chicago University Press
  • Stefani, R. and Pollard, R. (2007), Football ratind system for top-level competition: A critical Survey. Journal of Quantitative Analysis in Sports. 3 (3), art. 3.
  • Szymanski, S. (2001): Income inequality, competitive imbalance and the attractiveness of team sports: some evidences and a natural experiment from English soccer. Economic Journal, 111, F59-F84.
  • Vrooman, J. (1995): A general theory of sports league. Southern Economic Journal. April, 971-990
Fuente de los datos: Dialnet