Football Charts puts two numbers side by side for every match: what the historical data implies, and what the betting market prices. Where they diverge is the interesting part \u2014 we chart it, rank it, and publish how it turns out, across 90+ leagues.<\/p>\n
Interesting charts, data, and tools. An interactive Dixon-Coles & fair-odds calculator<\/a> with R and Python code is also available.<\/p>\n Navigate website<\/a><\/p>\n Leonardo Egidi<\/a>,\u00a0Francesco Pauli<\/a>,\u00a0Nicola Torelli<\/a><\/p>\n The plots provided in the links below are derived from a draft of paper in which a hierarchical Bayesian model is proposed for the home and the away goals, respectively modeled through two conditionally independent Poisson distributions. The Poisson rates are convex combinations of two parameters weighted with a certain probability, and they refer to two separate sources of information: the historical data and the bookmakers’ betting odds. Preliminary simulations and predictions show a good predictive accuracy on hold-out data and good efficiency in terms of betting strategies.<\/p>\n See the graphs<\/a><\/p>\n Andreas Groll, Christophe Ley, Gunther Schauberger, Hans Van Eetvelde<\/p>\n In this work, we compare three different modeling approaches for the scores of soccer matches with regard to their predictive performances based on all matches from the four previous FIFA World Cups 2002 – 2014: Poisson regression models, random forests and ranking methods. While the former two are based on the teams’ covariate information, the latter method estimates adequate ability parameters that reflect the current strength of the teams best. Within this comparison the best-performing prediction methods on the training data turn out to be the ranking methods and the random forests. However, we show that by combining the random forest with the team ability parameters from the ranking methods as an additional covariate we can improve the predictive power substantially. Finally, this combination of methods is chosen as the final model and based on its estimates, the FIFA World Cup 2018 is simulated repeatedly and winning probabilities are obtained for all teams. The model slightly favors Spain before the defending champion Germany. Additionally, we provide survival probabilities for all teams and at all tournament stages as well as the most probable tournament outcome.<\/p>\n Scientific paper<\/a><\/p>\n Leonardo Egidi, Nicola Torelli<\/p>\n In this document a Bayesian multinomial logistic model for the soccer Russia FIFA World Cup 2018 is presented. Non-statisticians may skip model characteristics and focus their attention on predictions, whereas statisticians may enjoy the model formulation and the fitting steps outlined in the Model section.<\/p>\n Read the document<\/a><\/p>\n Luca Malfatti<\/a>,\u00a0Gianluca Rosso<\/a><\/p>\n The aim of this document is to analyze, by means of descriptive statistics, group B of the Piedmontese regional championship 2017\/2018 – category “Giovanissimi” FB – where 26 games have been played. The study has been devoleped within the project\u00a0Quant4Sport<\/span><\/a>.<\/p>\n
\nA Bayesian Model for Football Scores using the Bookmakers Odds<\/h4>\n
\nPREDICTION OF THE FIFA WORLD CUP 2018<\/h4>\n
\nRUSSIA WORLD CUP 2018: a multinomial logistic model: Who wins the world cup?<\/h4>\n
\nANALISI STATISTICA DEL CAMPIONATO REGIONALE PIEMONTESE 2017\/2018 (GIRONE B) CATEGORIA GIOVANISSIMI FB (In Italian)<\/h4>\n