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Publications about statistical models for soccer predictions started appearing from the 90s, but the first model was proposed much earlier by Moroney, who published his first statistical analysis of soccer match results in 1956. According to his analysis, both Poisson distribution and negative binomial distribution provided an adequate fit to results of soccer games.

The series of ball passing between players during soccer matches was successfully analyzed using negative binomial distribution by Reep and Benjamin in 1968. They improved this method in 1971, and in 1974 Hill indicated that soccer game results are to some degree predictable and not simply a matter of chance.

The first model predicting outcomes of soccer matches between teams with different skills was proposed by Michael Maher in 1982. According to his model, the goals, which the opponents score during the game, are drawn from the Poisson distribution.

The model parameters are defined by the difference between attacking and defensive skills, adjusted by the home field advantage factor. The methods for modeling the home field advantage factor were summarized in an article by Caurneya and Carron in 1992.

Time-dependency of team strengths was analyzed by Knorr-Held in 1999. He used recursive Bayesian estimation to rate soccer teams: this method was more realistic in comparison to soccer prediction based on common average statistics.