elo

Introduction Following on from Part 1 in this series of posts looking back at the history of Predictaball, I’ll be reexamining the Elo models that were used from 2017-2019. One obvious flaw with the hierarchical Bayesian regression model was that there was no acknowledgement of time - a team’s skill was modelled at the point of training and kept fixed from them on. The model could be retrained after each match, but MCMC is time-consuming, and the resultant skill would still be an average over the full training period rather than the value at the current time.

This post continues on from the mid-season review of the Elo system and looks at my Bayesian football prediction model, Predictaball, up to and including matchday 20 of the Premier League (29th December). I’ll go over the overall predictive accuracy and compare my model to others, including bookies, expected goals (xG), and a compilation of football models. Overall accuracy So far, across the top 4 European leagues, there have been 696 matches with 379 (54%) of these outcomes being correctly predicted.

This is going to be the first of 2 posts looking at the mid-season performance of my football prediction and rating system, Predictaball. In this post I’m going to focus on the Elo rating system. Premier league standings I’ll firstly look at how the teams in the Premiership stand, both in terms of their Elo rating and their accumulated points, as displayed in the table below, ordered by Elo. Over-performing teams, as defined by being at least 3 ranks higher in points than in Elo, are coloured in green, while under-performing teams, the opposite, are highlighted in red.

My football prediction has previously relied upon a Bayesian approach to quantify a team’s skill level, by modelling it as a random intercept in a hierarchical model of the outcome of a match. While this model performed very well (62% accuracy last season), I was never fully satisfied since this measure of skill is an average across the last ten seasons that I had data for, rather than being updated to reflect the time-varying nature of form.