|
Statistical Football prediction is a method used in sports betting, to predict the outcome of football (soccer) matches by means of statistical tools. The goal of statistical match prediction is to outperform the predictions of bookmakers, who use them to set odds on the outcome of football matches. The most widely used statistical approach to prediction is ranking. Football ranking systems assign a rank to each team based on their past game results, so that the highest rank is assigned to the strongest team. The outcome of the match can be predicted by comparing the opponents’ ranks. Today, eight different football ranking systems exist: FIFA World Rankings, World Football Elo Ratings, AQB Sports Ratings, The Roon Ba, InternationalMark, Rsoccer, Mondfoot and Chance de Gol. 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. # The main goal of a ranking system is not to predict the results of football games, but to sort the teams according to their average strength. Another approach to football prediction is known as rating systems.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 Stern 〔). An example of a football rating system is the pi-rating system which provides relative measures of superiority between football teams (also applicable to other sports), and which is said to outperform considerably (in terms of profitability against the betting market) the widely accepted Elo rating system. == 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. 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 football 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 football teams: this method was more realistic in comparison to soccer prediction based on common average statistics. 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「Statistical association football predictions」の詳細全文を読む スポンサード リンク
|