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.
The last post showed that using a fully Bayesian multi-level model of the match outcomes helped Predictaball achieve a 58% overall prediction accuracy on the four European leagues, up 8% from last season. This post will describe the betting system I used to try and profit by identifying value bets in the offered odds.
Betting system Before delving into the profit analysis I’ll firstly quickly summarise the staking model I used since I haven’t mentioned it anywhere before.
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.
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.
I’ve always been curious to know if any of the 4 major European leagues (Serie A, Bundesliga, Premiership, La Liga) are more predictable than others. La Liga certainly has a reputation as being dull and predictable, although this is due to the sheer dominance of Barcelona and Real Madrid in recent years. I’ve increased my database of football matches in order to improve my football prediction bot this summer, and so now have sufficient data to investigate.
This post summarises Predictaball’s performance in the 2015-2016 season. I’ll look at overall performance, accuracy per week, how it fared in terms of making profit, and finally the annual comparison with Lawro.
Compared to last year when it achieved 48% overall, Predictaball has fared less well this season with 43%. This isn’t largely surprising since this season has been full of surprises to say the least, with Leicester beating out the traditional top four for the title, and Spurs doing their best to break the monopoly (despite failing in typical Spurs fashion).
I’ve tinkered around with Predictaball a bit recently in an effort to increase its accuracy, with the overall goal of beating Paul Merson and Lawro so that I can claim ‘human competitiveness’. I’ve mentioned in previous posts that I envisage 2 potential ways to achieve this.
Include more player data Incorporate bookies odds Adding more player data (such as a variable for each player indicating whether they are in the squad or not) would allow the model to account for situations when a player who is strongly associated with the team winning is now injured - for an example see City’s abysmal record when Kompany isn’t playing.
It’s been a while since I’ve posted anything as I’ve spent my summer in a thesis related haze, which I’m starting to come out of now so expect more frequent updates - particularly as I work my way through the backlog of ideas I’ve been meaning to write about.
I’ll start with assessing Predictaball’s performance last season. Just to summarise, this was a classification task attempting to predict the outcome (W/L/D) of every premier league match from the end of September onwards.