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Statistical football prediction is a method used in sports betting to predict the outcome of football matches

Statistical football prediction is a method used in sports betting to predict the outcome of football matches using statistical tools. The goal of the statistical game prediction is to surpass the predictions of the bookmakers who set odds for the outcome of football matches.

The most widely used statistical approach to forecasting is ranking. Football Ranking systems assign a rank to each team based on the results of past games, so that the highest rank is assigned to the strongest team. The outcome of the game can be predicted by comparing the opposing ranks. There are several football ranking systems, some of which are well-known: the FIFA World Ranking or the World Football Elo Ratings.

There are three major disadvantages to football game predictions based on ranking systems:

The ranks assigned to teams do not distinguish between their attack and defense strengths.

Ranks are accumulated averages that do not take into account skill changes in football teams.

The main objective of a ranking system is not to predict the results of football matches but to sort the teams by their average strength. Another approach to football prediction is known as rating system. While ranking only refers to team order, rating systems assign each team a continuously scaled strength indicator. In addition, the rating can be assigned not only to a team, but also their attack and defense strength, the home advantage or even the skills of each team player (according to Stern).

Publications on statistical models for football predictions appeared from the 1990s, but the first model was proposed much earlier by Moroney, who in 1956 published his first statistical analysis of the results of football matches. According to his analysis, both the Poisson distribution and the negative binomial distribution were given a reasonable fit to the results of football matches. The series of balls exchanged between players during a football game was successfully analyzed by Reep and Benjamin in 1968 using the negative binomial distribution. This method was improved in 1971, and in 1974 Hill noted that the results of football matches are predictable to a degree and not simply a matter of chance.

The first model, which predicts results of football matches between teams of different abilities, was proposed in 1982 by Michael Maher. According to his model, the goals scored by the opponents during the game are drawn from the Poisson distribution. The model parameters are defined by the difference between attack and defense abilities, which are adjusted by the home advance factor. Homesaving factor modeling methods were summarized in 1992 in an article by Caurneya and Carron. The time dependence of the team strengths was analyzed in 1999 by Knorr-Held. He used a recursive Bayesian estimate to rate football teams: this method was more realistic compared to football predictions based on general average statistics.

All prediction methods can be categorized by tournament type, time dependency, and regression algorithm. The forecasting methods for football vary between the round robin tournament and the knockout competition. The methods for the knockout competition are summarized in an article by Diego Kuonen.


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