The goal of our research was to use simulation modelling for prediction of the Croatian First Football League seasonal ranking and analyse variation in teams’ performance during a season. We have developed a model of the number of goals scored by a team in a match based on the Poisson distribution. Parameters of the model were estimated from the data on consecutive matches in a season. Variation in a team’s performance was modelled as a moving parameter estimate. The final rankings were predicted from 1000 simulation runs of the second part of the season based on parameter estimates from the first part of the season. For each team the most frequent outcome of the simulation defined the team’s rank. The method was tested on seasons 2014/15 and 2015/16. Prediction was correct for six teams in the season 2014/15 and five teams in the season 2015/16. Proposed methods enable dynamic monitoring of a team’s performance and prediction of final rankings during the season. An advantage of the prediction method is that in addition to predicting the final ranking it also estimates probabilities of alternative positions.
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1. Arabzad A.C. (2014). Football Match Results Prediction Using Artificial eural Networks: The Case of Iran Pro League. International Journal of Applied Research on Industrial Engineering. Vol. 1 No. 3 pp 159-179.
2. Constantinou A.C. Fenton N.E. Neil M. (2012). pi-football: A Bayesian network model for forecasting Association Football match outcomes. Knowledge-Based Systems 36 pp. 322-339.
3. Dixon M.J. Coles S.G. (1997). Modelling Association Football Scores and Inefficiencies in the Football Betting Market. Journal of the Royal Statistical Society: Series C (Applied Statistics). Vol. 46 No. 2. pp. 265-280.
4. Dixon M.J. Robinson M. (1998). A birth process model for association football matches. Journal of the Royal Statistical Society: Series D (The Statistician) Vol. 47 No. 3 pp. 523-538.
5. Dobravec (2015). Forecasting the football world cup results using a matrix-factorization model. Elektrotehniški Vestnik Vol. 82 No 1 pp 61-65.
6. Hill I.D. (1974). Association football and statistical inference. Applied statistics Vol. 23 pp. 203-208.
7. Karlis D. Ntzoufras I. (2003). Analysis of sports data by using bivariate Poisson models. Journal of the Royal Statistical Society: Series D (The Statistician) Vol. 52 No. 3 pp. 381-393.
8. Koopman S.J. Lit R. (2015). A dynamic bivariate Poisson model for analysing and forecasting match results in the English Premier League Journal of the Royal Statistical Society: Series A (Statistics in Society) Vol. 178 No. 1 pp. 167-186.
9. Lee A. J. (1997). Modeling scores in Premier League: is Manchester United really the best. Chance Vol. 10 pp. 15-19.
10. Maher M.J. (1982). Modelling association football scores. Statistica Neerlandica Vol. 36 pp. 109-118.
11. Moneyball (2011). Directed by Bennett Miller [Film]. USA: Columbia Pictures.
12. Moroney M.J. (1951). Facts from figures. London: Penguin
13. Muller H-G. Stadtmuller U. (2005). Generalized Functional Linear Models. The Annals of Statistics Vol. 33 No. 2 pp. 774-805.
14. Munđar D. Šamarija D. (2016). Primjena simulacijskih modela za prognoziranje rezultata u bowlingu Poučak Vol. 64 pp. 73-79.
15. Munđar D. Šimić D. (2016) Croatian First Football League: Prediction of teams' ranking in the championship Proceedings of the ISCCRO - International Statistical Conference in Croatia. Zagreb: Croatian Statistical Association Vol 1 No. 1 pp. 211-217.
16. Owen A.J. (2011). Dynamic Bayesian forecasting models of football match outcomes with estimation of the evolution variance parameter. IMA Journal of Management Mathematics Vol. 22 No. 2 pp. 99-113.
17. Rotshtein A. Posner M. Rakytyanska H. (2005). Prediction of results of Football games base of Fuzzy model with genetic and neuro tuning. Cybernetics and Systems Analysis Vol. 41 No. 4 pp. 619-630.
18. Rue H. Salvesen O. (2000). Prediction and Retrospective Analysis of Soccer Matches in a League Journal of the Royal Statistical Society. Series D (Statistician) Vol. 49 No. 3 pp. 399-418.
19. Sports Analytics Group University of Toronto. Introduction to Sports Analytics http://sportsanalytics.sa.utoronto.ca/2014/12/11/introduction-to-sports-analytics/ [03 January 2016]