Match predictions, statistics and tips for 2018-09-17

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Atlas - Club Tijuana. Banik Ostrava - Sparta Praha. Real Garcilaso - Sport Rosario. Hapoel Haifa - Beitar Jerusalem. Molde - Kristiansund BK. Oriente Petrolero - San Jose. ES Setif - Tadjenant. Difaa El Jadida - Olympique Khouribga.

Hacken - Trelleborgs FF. IK Sirius - Hammarby. Arema FC - Madura United. Pusamania Borneo - Persib Bandung. Mitra Kukar - Persipura Jayapura. Real Espana - Juticalpa. Petrojet - El Dakhleya. Al Mokawloon - Pyramids FC. El Entag El Harby. Manta FC - Fuerza Amarilla.

America de Quito - CD Olmedo. FC Gomel - Gorodeya. Aris Thessalonikis - Levadiakos. Osters IF - Jonkopings Sodra. IK Frej - Orgryte. Litex - CSKA Montana - Tsarsko Selo. Hapoel Petah Tikva - Hapoel R. Hapoel Akko - Hapoel Katamon. Beitar Tel Aviv - Hapoel Iksal. Hapoel Afula - Hapoel Marmorek. Hapoel Bney Lod FC. Publications about statistical models for football predictions started appearing from the 90s, but the first model was proposed much earlier by Moroney, [2] who published his first statistical analysis of soccer match results in 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 [3] in They improved this method in , and in Hill [4] 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 [5] in 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 [6] in Time-dependency of team strengths was analyzed by Knorr-Held [7] in He used recursive Bayesian estimation to rate football teams: All the prediction methods can be categorized according to tournament type, time-dependence and regression algorithm.

Football prediction methods vary between Round-robin tournament and Knockout competition. The methods for Knockout competition are summarized in an article by Diego Kuonen. The table below summarizes the methods related to Round-robin tournament. This method intends to assign to each team in the tournament a continuously scaled rating value, so that the strongest team will have the highest rating.

The method is based on the assumption that the rating assigned to the rival teams is proportional to the outcome of each match. Assume that the teams A, B, C and D are playing in a tournament and the match outcomes are as follows:. The same assumption can be made for all the matches in the tournament:.

Entries of the selection matrix can be either 1, 0 or -1, with 1 corresponding to home teams and -1 to away teams:. If not, one can use the Moore—Penrose pseudoinverse to get:.

Thus, the joint probability of the home team scoring x goals and the away team scoring y goals is a product of the two independent probabilities:. Improvements for this model were suggested by Mark Dixon statistician and Stuart Coles. Unlike the Poisson model that fits the distribution of scores, the Skellam model fits the difference between home and away scores. On the one hand, statistical models require a large number of observations to make an accurate estimation of its parameters.

And when there are not enough observations available during a season as is usually the situation , working with average statistics makes sense. On the other hand, it is well known that team skills change during the season, making model parameters time-dependent. Mark Dixon statistician and Coles [10] tried to solve this trade-off by assigning a larger weight to the latest match results. Rue and Salvesen [12] introduced a novel time-dependent rating method using the Markov Chain model.

Assuming that three teams A, B and C are playing in the tournament and the matches are played in the following order: B-C, the joint probability density can be expressed as:.