Using Poisson model for goal prediction in European football




Poisson Model, Goal, Football, Soccer, Prediction, European leagues


Predicting the features of behaviour of big data and multivariable systems has been a research subject in various fields of science. When it comes to football, as it is a field of sports followed by the whole world, the number of studies carried out aiming at predicting the results of football games has been increasing in the field of football science. Although the result of a football match depends on various variables, it is mainly determined over the offensive and defensive strengths of the teams. Different variables have so far been determined in the literature to figure out these strengths of the teams. In this study, it was aimed to predict correctly how many goals a team could score or concede in the last 5 weeks based on the average number of goals they scored and conceded since the beginning of the season in 6 European leagues. For this reason, a Poisson distribution model was established based on these offensive and defensive strengths. A total of 4264 matches and 5938 goals were analysed in the study and the established model yielded affirmative results at the level of 50% in the leagues analysed.


Download data is not yet available.


Carron, A.V., &. Hausenblas, H.A (1998). Group Dynamics in Sport. 3rd Edn., Morgantown, WV: Fitness Information Technology.

Crowder, M., Dixon, M., Ledford, A., & Robinson, M., (2002). Dynamic Modeling and Prediction of English Football League Matches for Betting. The Statistician, 51(2), 157-168.

Deloitte, Football Money League 2020. Sports Business Group. (2020, accessed 05 March 2020).

Koopman, S. J., & Lit, R. (2012). A Dynamic Bivariate Poisson Model for Analysing and Forecasting Match Results in the English Premier League. Tinbergen Institute Discussion Paper. Amsterdam.

Leitner, C., Zeileis, A.,& Hornik, K. (2010). Forecasting Sports Tournaments by Ratings of (prob) Abilities: A Comparison for the Euro 2008, International Journal of Forecasting, 26(3), 471–481.

Moroney, M. J. (1956). Facts From Figures. 3rd edition, Penguin Books , London.

Moura, F. A., Santiago, P. R. P., Misuta, M. S., Barros, R. M. L., & Cunha, S. A. (2007). Analysis of the Shots to Goal Strategies of First Division Brazilian Professional Soccer Teams, in ‘ISBS-Conference Proceedings Archive’, Vol. 1. Retrieved from

Mwembe, D., Sibanda, L., & Mupondo, C. N. (2015). Application of a Bivariate Poisson Model in Devising a Profitable Betting Strategy of the Zimbabwe Premier Soccer League Match Results. American Journal of Theoretical and Applied Statistics. 4(3),99-111.

Pollard, R., (1986). Home Advantage in Aoccer: A Retrospective Analysis. Journal of Sports Sciences, 4(3), 237-248.

Pollard, R., & Pollard, G. (2005). Home Advantage in Soccer: A Review of Its Existence and Causes. International Journal of Soccer and Science Journal, 3(1), 28-38.

Williams, T., &Walters, C. (2011), The Effects of Altitude on Soccer Match Outcomes, In Proceedings of the MIT Sloan Sports Analytics Conference Boston. Retrieved from


Statistics RUA



How to Cite

Inan, T. (2021). Using Poisson model for goal prediction in European football. Journal of Human Sport and Exercise, 16(4), 942–955.



Performance Analysis of Sport