Money Predicted Spain as Football World Champion

2014

JOURNAL OF APPLIED SCIENCE AND TECHNOLOGY TRENDS, 2021

During the past decades, sport in general has become one of the most powerful competitions and the most popular in the world. As well as, everyone is waiting for the winner, and who will be the champion in the end in different tournaments. Among these sports, football popularity is more than all other sports. Football matches results predicting, as well as the champion in various competitions, has been seriously studied in recent years. Moreover, it has become an interest field for many researchers. In this work, the Poisson model has been presented to predict the winner, draw and loser from the football matches. The method is applied to the Spanish Primera División (First Division) in 2016-2017; the data has been downloaded from the football-data.co.uk website, which will be used to find the predict accuracy.

The UEFA Champions League is the most prestigious football club competition in Europe and hence there is major interest, among fans and experts alike, in forecasting the winner of this tournament. To investigate this issue, a class of linear mixed-effects models for quoted winning odds from various bookmakers is explored. Based on this "prospective" data reflecting the expectations of the bookmakers (as opposed to past performances used in many other forecasting methods) different models for the "true" odds of winning the tournament can be established, capturing both team-specific effects (along with effects for the team's tournament group and national association) and bookmaker-specific variations. A selection among various model specifications yields a model with a fixed team effect plus a random bookmaker-specific deviation. It forecasts team FC BATE Borisov with a probability of 0.10% as the winner of the tournament; the runner-up is Anaethosis Famagusta FC with a winning probability of 0.11%. In addition to the forecast of the winning probability, information about the groups of the preliminaries and the European football associations can be obtained from the model. This is a preprint of an article published in R Koning and P Scarf (eds.),

International Journal of Sports Science, 2014

An analysis is done on soccer games played by three top European soccer leagues: England, Spain, and Italy during the first 33 rounds of soccer during the 2011-2012 year. Each league has 20 teams playing two games with each other. Two regression models are developed in an effort to predict the point spread of a game between two teams (Team A and Team B) based on the following variables: sum of differences in the number of cards received by Team A and their opposing teams for the last k rounds, sum of differences in the number of cards received by Team B and their opposing teams for the last k rounds, sum of differences in the number of goals received by Team A and their opposing teams for the last k rounds, and sum of differences in the number of goals received by Team B and their opposing teams for the last k rounds, with the value of k always being even. The models developed were used to predict winners of games for the last five rounds of the 2011-2012 season. The models correctly predicted the winner of a game at 73% to 80% of the time. Of particular interest in this research is whether the sum of the differences in the number of cards received by each team and their opponents in the last k rounds of soccer has a significant effect on which team will win the soccer match.

Social Science Research Network, 2019

This study analyses point forecasts of exact scoreline outcomes for football matches in the English Premier League. These forecasts were made for distinct competitions and originally judged differently. We compare these with implied probability forecasts using bookmaker odds and a crowd of tipsters, as well as point and probability forecasts generated from a statistical model. From evaluating these sources and types of forecast, using various methods, we argue that regression encompassing is the most appropriate way to compare point and probability forecasts, and find that both these types of forecasts for football match scorelines generally add information to one another.

Journal of Quantitative Analysis in Sports, 2000

The objective of this research is to provide a statistical measurement to predict the likely winners of international football tournaments with particular reference to the Euro 2008 football tournament. This research argues that there exists a positive relationship between the competitive balance and success in international football tournaments. The level of the competitive balance in a domestic football league is measured by the seasonal coefficients of variation (CV) of the end-of-season points. The CV values are employed as ranking indicators between the contesting countries in international football tournaments to predict the likely winners. The seasonal CV values are computed from the top divison football leagues of participating countries of Euro 1996, Euro 2000, Euro 2004, and Euro 2008 order to rank and to predict the likely winner of these tournaments. The results indicate that the higher the level of domestic competition the better chance of winning an international football tournament. The CV ranking is also compared to UEFA ranking and to the odds of some well known bookmakers in predicting the likely winners of the Euro 2008 football tournament. On the basis of the overall results, this research predicts that the likely winners of the Euro 2008 football tournament would be France, Spain or Germany.

2021

Master's thesis in Information systems (IS501)Arguably, soccer is a more fluid sport than other popular sports such as baseball, American football, and cricket, which explicitly require more discrete ‘plays.’ Moreover, with twenty-two players always committed, it has more moving elements than basketball or ice hockey. From a mathematical perspective, this signifies that soccer has more degrees of freedom than these other sports, making it difficult to evaluate the game using one or a small number of metrics. Over time, this challenge has been made exceedingly more difficult within the soccer community. The availability of data has increased much more rapidly than the scientific advancements required to valorize these data. In the aftermath, most recent research papers have elaborated on specific in-game data separately and how to approach them -not how soccer clubs can assemble and utilize them through big data analytics. In accordance, an exploratory case study conducted in col...

Pesquisa Operacional, 2015

In this paper we propose a simulation-based method for predicting the 2010 and 2014 Football World Cup. Adopting a bayesian perspective, we modeled the number of goals of two opposing teams as a Poisson distribution whose mean is proportional to the relative technical level of opponents. FIFA ratings were taken as the measure of technical level of teams and experts' opinions about scores of matches were taken to construct prior distribution of parameters. Just before each round, tournament simulations were performed in order to estimate probabilities of events of main interest for audience and bettors such as qualifying to the knockout stage, reaching semifinals , reaching the final match, winning the tournament, among others.

Physica A: Statistical Mechanics and its Applications

In this work, we extended a stochastic model for football leagues based on the team's potential [R. da Silva et al. Comput. Phys. Commun. 184 661-670 (2013)] for making predictions instead of only performing a successful characterization of the statistics on the punctuation of the real leagues. Our adaptation considers the advantage of playing at home when considering the potential of the home and away teams. The algorithm predicts the tournament's outcome by using the market value or/and the ongoing team's performance as initial conditions in the context of Monte Carlo simulations. We present and compare our results to the worldwide known SPI predictions performed by the "FiveThirtyEight" project. The results show that the algorithm can deliver good predictions even with a few ingredients and in more complicated seasons like the 2020 editions where the matches were played without fans in the stadiums.

RePEc: Research Papers in Economics, 2013

Corruption is hidden action aimed at influencing the outcome of an event away from its competitive outcome. It is likely common in all walks of life yet its hidden nature makes it difficult to detect, while its distortionary influence on resource allocation ensures the importance of trying to detect it both practically and economically. This paper further develops methods to detect corrupt activity contained in Olmo et al. (2011) and Reade (2013) that make use of different forecasting methods and their information sets to detect corruption. We collect data from 63 bookmakers covering over 9,000 international football matches since 2004 and assess a claim made in early 2013 by Europol that the outcomes of almost 300 international matches since 2009 were fixed. Our collected data consists of match outcomes and prematch bookmaker odds, which we use to explore the divergence between two kinds of forecasts of match outcomes: those by bookmakers, and those constructed by econometric models. We argue that in the absence of corrupt activity to fix outcomes these two forecasts should be indistinguishable as they are based on the same information sets, and hence any divergence between the two may be indicative of corrupt activity to fix matches. Such an assertion is conditional on the quality of the econometric model and in this paper we discuss the peculiarities of modelling international football match outcomes. In the absence of corroborating evidence we cannot declare any evidence procured in our manner as conclusive regarding the existence or otherwise of corruption, but nonetheless we argue that is it indicative. We conclude that there is mild evidence regarding potentially corrupt outcomes, and we also point towards yet more advanced strategies for its detection.

Royal Society Open Science, 2021

In recent years, excessive monetization of football and professionalism among the players have been argued to have affected the quality of the match in different ways. On the one hand, playing football has become a high-income profession and the players are highly motivated; on the other hand, stronger teams have higher incomes and therefore afford better players leading to an even stronger appearance in tournaments that can make the game more imbalanced and hence predictable. To quantify and document this observation, in this work, we take a minimalist network science approach to measure the predictability of football over 26 years in major European leagues. We show that over time, the games in major leagues have indeed become more predictable. We provide further support for this observation by showing that inequality between teams has increased and the home-field advantage has been vanishing ubiquitously. We do not include any direct analysis on the effects of monetization on foot...

Zenodo (CERN European Organization for Nuclear Research), 2023

Journal of Forecasting, 2009

This article compares the forecast accuracy of different methods, namely prediction markets, tipsters and betting odds, and assesses the ability of prediction markets and tipsters to generate profi ts systematically in a betting market. We present the results of an empirical study that uses data from 678-837 games of three seasons of the German premier soccer league. Prediction markets and betting odds perform equally well in terms of forecasting accuracy, but both methods strongly outperform tipsters. A weighting-based combination of the forecasts of these methods leads to a slightly higher forecast accuracy, whereas a rule-based combination improves forecast accuracy substantially. However, none of the forecasts leads to systematic monetary gains in betting markets because of the high fees (25%) charged by the state-owned bookmaker in Germany. Lower fees (e.g., approximately 12% or 0%) would provide systematic profi ts if punters exploited the information from prediction markets and bet only on a selected number of games.

Prediction has been successfully applied in sports like football and basketball. Although in certain aspects, this application has been of very small limits. It is very necessary to look into the application of Machine Learning in these instances and see if its application can yield better results in the analysis of soccer. Machine learning which an area of Intelligent Systems (IS) is will be used in this report to help provide solution to problems faced by bookmakers in aspect of predicting the results of the football league. Although, it is necessary to note that taking into account all the features that have great influence on the results of football league matches is a difficult fit but this study has been able to find most features that are significant. By making use of machine learning and database that is more precise, this research aims to proffer a solution that will help to make predictions more accurate and precise. Being aware of what the future holds is something that will be a lot easier by making use of this IS.

2012

Despite the massive popularity of probabilistic (association) football forecasting models, and the relative simplicity of the outcome of such forecasts (they require only three probability values corresponding to home win, draw, and away win) there is no agreed scoring rule to determine their forecast accuracy. Moreover, the various scoring rules used for validation in previous studies are inadequate since they fail to recognise that football outcomes represent a ranked (ordinal) scale. This raises severe concerns about the validity of conclusions from previous studies. There is a wellestablished generic scoring rule, the Rank Probability Score (RPS), which has been missed by previous researchers, but which properly assesses football forecasting models.