Bayesian Analysis

Predicting football results in 2016-2017 with machine learning - Bayesian hierarchical modelling

And so we come to the end of another season of football, and more importantly, Predictaball! This season has seen several large updates that I was meaning to detail these at the start of the season but life got in the way. The predictive model is now fully Bayesian I’ve added a betting system that identifies value bets I’ve expanded it to include the 3 other main European leagues: La liga Serie A Bundesliga Rather than detailing these new aspects as well as summarising the season’s performance in one massive blog, I’ll split this into two parts.

Predicting AFL results with hierarchical Bayesian models using JAGS

I’ve recently expanded my hierarchical Bayesian football (aka soccer) prediction football prediction framework to predict the results of Australian Rules Football (AFL) matches. I have no personal interest in AFL, instead I got involved through an email sent to a statistics mailing list advertising a competition that’s held by Monash University in Melbourne. Sensing an opportunity to quickly adapt my soccer prediction method to AFL results and to compare my technique to others, I decided to get involved.