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Statistical Football prediction is a method that predicts the outcome of football matches using statistical tools. The goal of statistical prediction is to outperform predictions made by bookmakers [citation needed][dubious-to-discuss], who use them for betting on the outcome of football matches. The most widely used statistical approach to prediction is ranking. Ranking is the most widely used statistical method for predicting the outcome of football matches. Each team is assigned a rank based on past results. The strongest team gets the highest rank. Comparing the ranks of your opponents can help predict the outcome of the match. There are many football ranking systems, such as the FIFA World Rankings and the World Football Elo Ratings. There are three main drawbacks to football match predictions that are based on ranking systems: * Ranks assigned to the teams do not differentiate between their attacking and defensive strengths. * Ranks are accumulated averages which do not account for skill changes in football teams. * A ranking system's main purpose is not to predict the outcome of football games but to classify teams according to their average strength. Rating systems are another method of football prediction. While ranking refers only to team order, rating systems assign to each team a continuously scaled strength indicator. Moreover, rating can be assigned not only to a team but to its attacking and defensive strengths, home field advantage or even to the skills of each team player (according to betting tips Stern).

History

Publications about statistical models for football 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 football games. The series of ball passing between players during football matches was successfully analyzed using negative binomial distribution by Reep and Benjamin in 1968. This method was improved in 1971 by Hill, who in 1974 stated that soccer game results can be predicted and not just random. Michael Maher, in 1982, proposed the first model that could predict the outcome of football matches between teams with differing skills. His model predicts the outcome of football matches between teams with different skills. The Poisson distribution determines the goals that the opponents score during the game. The home field advantage factor adjusts the parameters to determine the difference between defensive and attacking skills. Caurneya & Carron outlined the methods used to model the home field advantage factor in 1992 in an article. Time-dependency of team strengths was analyzed by Knorr-Held in 1999. To rate football teams, he used recursive Bayesian estim to calculate their strengths. This method was more accurate than soccer prediction based upon common average statistics.