Measuring overachievement in football teams

eXpected Goals (xG) is a popular method of answering that age old question of which team ‘deserved’ to win a match. It does this by assigning a probability of a goal being scored from every opportunity based upon various metrics, such as the distance from goal, number of defenders nearby, and so on. By comparing a team’s actual standings with those from the output of an xG model we get a retrospective measure of how well a team is doing given their chances.

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.