Wiki article – a classic piece to start with.
Radford Neal’s Research – a good introduction to Bayesian methods for beginners.
Sceptical Sports Analysis – a good blog, “bucking the unconventional wisdom” on sports betting predictions.
Bayesian modelling of football outcomes: Using the Skellam’s distribution for the goal difference – a research paper by Dimitris Karlis and Ioannis Ntzoufras, Department of Statistics, Athens University of Economics and Business,Athens, Greece.
Tim Swartz’s Research – Simon Fraser University, Department of Mathematics, Burnaby, British Columbia, Canada.
Pinnacle Sports – Bayesian analysis and sports betting: How Bayesian analysis can help your betting.
BetFair – Betting strategy: How genius from the past can help us to profit from betting in the future.
Preprint of the Book Chapter: Bayesian Versus Frequentist Inference by Eric-Jan Wagenmakers, Michael Lee, Tom Lodewyckx and Geoff Iverson.
Bayesian hierarchical model for the prediction of football results by Gianluca Baio and Marta A. Blangiardo – The problem of modelling football data has become increasingly popular in the last few years and many different models have been proposed with the aim of estimating the characteristics that bring a team to lose or win a game, or to predict the score of a particular match. The authors propose a Bayesian hierarchical model to address both these aims and test its predictive strength on data about the Italian Serie A championship 1991-1992. To overcome the issue of over-shrinkage produced by the Bayesian hierarchical
model, the authors specify a more complex mixture model that results in better fit to the observed data. They test its performance using an example about the Italian Serie A championship 2007-2008.