Bayesian methods are becoming increasingly popular in sports analytics. Identified
advantages of the Bayesian approach include the ability to model complex problems, obtain …

The sports domain presents a number of significant computational challenges for artificial
intelligence (AI) and machine learning (ML). In this paper, we explore the techniques that …

Online multiplayer games, such as Gears of War and Halo, use skill-based matchmaking to
give players fair and enjoyable matches. They depend on a skill rating system to infer …

In wake of the Covid-19 pandemic, 2019–2020 soccer seasons across the world were
postponed and eventually made up during the summer months of 2020. Researchers from a …

We investigate the state-of-the-art in score-based soccer match outcome modelling to
identify the top-performing methods across diverse classes of existing approaches to the …

A Bayesian network is a graphical probabilistic model that represents the conditional
dependencies among uncertain variables, which can be both objective and subjective. We …

Many efforts has been made in order to predict football matches result and selecting
significant variables in football. Prediction is very useful in hel** managers and clubs …

The paper describes Dolores, a model designed to predict football match outcomes in one
country by observing football matches in multiple other countries. The model is a mixture of …

Home advantage in professional sports is a widely accepted phenomenon despite the lack
of any controlled experiments at the professional level. The return to play of professional …

We describe our winning solution to the 2017's Soccer Prediction Challenge organized in
conjunction with the MLJ's special issue on Machine Learning for Soccer. The goal of the …