How well can machine learning predict the outcome of a soccer game, given the most commonly and freely available match data? To help answer this question and to facilitate machine learning research in soccer, we have developed the Open International Soccer Database. Version v1.0 of the Database contains essential information from 216,743 league soccer matches from 52 leagues in 35 countries. The earliest entries in the Database are from the year 2000, which is when football leagues generally adopted the "three points for a win" rule. To demonstrate the use of the Database for machine learning research, we organized the 2017 Soccer Prediction Challenge. One of the goals of the Challenge was to estimate where the limits of predictability lie, given the type of match data contained in the Database. Another goal of the Challenge was to pose a real-world machine learning problem with a fixed time line and a genuine prediction task: to develop a predictive model from the Database and then to predict the outcome of the 206 future soccer matches taking place from 31 March 2017 to the end of the regular season. The Open International Soccer Database is released as an open science project, providing a valuable resource for soccer analysts and a unique benchmark for advanced machine learning methods. Here, we describe the Database and the 2017 Soccer Prediction Challenge and its results.

/pdf/the-open-international-soccer-database-for-machine-learning-a9nxmelh52.pdf

The Open International Soccer Database for machine learning

Soccer1 is the biggest global sport and a fast-growing,multi-billion dollar industry. Advanced data analytics are being more frequently employed on both the club and national levels to improve performance, equipment, marketing, scouting, etc. Soccer therefore offers interesting challenges for the machine learning community. This special issue solicited articles on all aspects of data analysis and machine learning for soccer. As part of the special issue, we posed the 2017 Soccer Prediction Challenge that revolved around predicting the outcomes of future soccer matches. This is an interesting task for the general public, researchers, clubs, media, news and advertising companies, and professional odds setters. Soccer outcome prediction has been the subject of research since at least the 1960s (Reep and Benjamin 1968; Hill 1974; Maher 1982; Dixon and Coles 1997; Angelini and Angelis 2017). Various statistical techniques have been used for outcome prediction, including Poisson models (Karlis and Ntzoufras 2003), Bayesian models (Baio and Blangiardo 2010; Rue and Salvesen 2000), rating systems (Hvattum and Arntzen 2010), and more recently alsomachine learningmethods, such as kernel-based relational learning (VanHaaren and Van den Broeck 2011). O’Donoghue et al. (2004) used machine learning and statistical methods to predict the results of the 2002 FIFA World Cup but achieved the best prediction with a simulation on a commercial game console.

/pdf/guest-editorial-special-issue-on-machine-learning-for-soccer-4kbecg8j11.pdf

Guest editorial: special issue on machine learning for soccer

Abstract The study focuses on proposing a solution for the 2023 Soccer Prediction Challenge organized in conjunction with the Machine Learning Journal's special issue on Machine Learning for Soccer. The challenge aimed to predict the outcomes of future matches from various leagues worldwide within a specific timeframe. In this paper, we examine the solution provided by our team "Friends of Elo" in detail. We experimented experimented with using Elo ratings and scoring indicators (goals) as arguments for the linear regression method. Poisson distribution was employed to predict the match results. The performance was evaluated based on the root mean squared error and the ranked probability score. Submitted predictions (714 matches) ranked 7th among 11 contestants in Task 1 and 5th among 13 contestants in Task 2, respectively. Futhermore, we conducted additional tests, where our model performed even greater on the expanded dataset of over 6800 matches to predict. The most significant advantage of our approach is that it does not require advanced match data, making it applicable worldwide. Overall, our study provides an in-depth analysis of the solution proposed by the "Friends of Elo" team, and we offer a superior alternative that performs well and is highly practical. 

Football prediction model based on the teams' Elo ratings and scoring indicators

The 2017 Soccer Prediction Challenge

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The 2017 Soccer Prediction Challenge

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The Open International Soccer Database for machine learning

Guest editorial: special issue on machine learning for soccer

Football prediction model based on the teams' Elo ratings and scoring indicators

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