Previsão da eficácia ofensiva do futebol profissional: Um caso Português

Caiado, Jorge and Vieira, Aníbal and Bonito, Ana and Reis, Carlos and Fernandes, Francisco (2006): Previsão da eficácia ofensiva do futebol profissional: Um caso Português. Forthcoming in: Gestin (2006)

Abstract

The forecast plays an important role in the planning, the decision-making and control in any domain of activity, including the sportive phenomenon of the soccer. The experience has shown that the extrapolative or not casual models (univariate models), that use only the information of its past values to forecast the future, can often predict future with more accuracy than causal or multivariate models. In this paper, we model and forecast the offensive effectiveness of the soccer team Sport Lisbon and Benfica, in Portuguese soccer league, by using deterministic methods (linear trend, moving average, exponential smoothing, holt, naïve) and stochastic models (ARMA models, random walk). The model selection criteria used in our study were the mean squared error, the mean absolute error and the mean absolute percentage error based in a one-step forecast of the last three observations. Keywords: Exponential smoothing, soccer, moving average, ARMA model, forecast.

• Item Type: MPRA Paper
• Language: Portuguese
• Keywords: Exponential smoothing; Soccer; Moving average; ARMA model; Forecast
• Subjects: C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C53 - Forecasting and Prediction Methods ; Simulation Methods; L - Industrial Organization > L8 - Industry Studies: Services > L83 - Sports ; Gambling ; Restaurants ; Recreation ; Tourism

References

Albert, J. (1994), “Exploring baseball hitting data: What about those breakdown statistics?, Journal of the American Statistical Association, 89, 1066-1074. Anderson, P., Ekman, M. e Edman, J. (2003), “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 No 2003:9. Ayton, P. (1992). “On the competence and incompetence of experts”. In: F. Bolger e G. Wright (Eds.), Expertise and Decision Support, Plenum Press, 77-105. Bird, P. (1982), “The demand for league football”, Applied Economics, 14, 637-649. Box, G. E. P. e Jenkins, G. (1970), Time Series Analysis, Forecasting and Control, Holden-Day, San Francisco. Box, G. E. P., Jenkins, G. e Reinsel, G. C. (1994), Time Series Analysis, Forecasting and Control, 3.ª ed., Prentice-Hall, New York. Brailsford, T. J., Easton, S. A., Gray, P. K. e Gray, S. F. (1995), “The efficiency of Australian football betting markets”, Australian Journal of Management, 20, 167-195. Caiado, J. (2002), “Métodos de previsão em economia e gestão: desenvolvimentos recentes”, Inforbanca, XIV, n.º 53, 18-19 e 44-47. Clarke, S. R. (1993), “Computer forecasting of Australian rules football for a daily newspaper”, Journal of the Operational Research Society, 44, 753-759. Craig, L. A. e Hall, A. R. (1994), “Trying out for the team: Do exhibitions matter? Evidence from the National Football League”, Journal of the American Statistical Association, 89, 1091-1099. Crowder, M., Dixon, M., Ledford, A. e Robinson, M. (2002), “Dynamic modeling and prediction of English Football League matches for betting”, Journal of the Royal Statistical Society Series D (The Statistician), 51, 157-168. Dobson, S. e Goddard, J. (1992), “The demand for standing and seated viewing accommodation in the English Football League”, Applied Economics, 24, 1155-1163. Gandar, J. M., Zuber, R. A. e Lamb, R. P. (2001), “The home field advantage revisited: a search for the bias in other sports betting markets”, Journal of Economics and Business, 53, 439-453. Glickman, M. E. e Stern, H. S. (1998), “A state-space model for National Football League scores”, Journal of the American Statistical Association, 93, 25-35. Gray, P. K. e Gray, S. F. (1997), “Testing market efficiency: Evidence from the NFL sports betting market”, Journal of Finance, 52, 1725-1737. Hart, S., Hutton, J. e Sharot, T. (1975), “A statistical analysis of association football attendances”, Journal of Royal Statistical Society Series C, 24, 17-27. Lee, A. J. (1997), “Modelling scores in the premier league: Is Manchester United really the best?”, Chance, 10, 15-19. Lucifora, C. e Simmons, R. (2003), “Superstar effects in sport: Evidence from Italian soccer”, Journal of Sports Economics, 3, 169-203. Makridakis, S., Wheelwright, S. e Hyndman, R. (1998), Forecasting: Methods and Applications, 3.ª ed., John Wiley & Sons, New York. Schall, T. e Smith, G. (2000), “Do baseball players regress toward the mean?”, American Statistician, 54, 231-235. Stefani, R. T. e Clarke, S. R. (1992), “Predictions and home advantage for Australian Rules football”, Journal of Applied Statistics, 9, 251-261. Szymanski, S. e Smith, R. (1997), “The English football industry: performance, profit and industrial structure”, International Review of Applied Economics, 11, 135-154. Vergin, R. C. (2001), “Overreaction in the NFL point spread market”, Applied Financial Economics, 11, 497-509. Wei, W. W. S. (1990), Time Series Analysis: Univariate and Multivariate Methods, Addison-Wesley Publishing Company, New York.