Insights, research, and analysis on football analytics
My previous article introduced PlayerRatings, a mathematical model I’ve been working on over the past few months to quantify the ability of individual footballers. One of the nice characteristics of this approach is that player ratings can be aggregated together to create team...
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I originally submitted the idea behind this article to the recent Opta Pro Forum and although it was turned down I thought I’d write it up anyway incase anyone else was interested in the results...
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It's transfer window time so here's your mathematically optimised fantasy football team...
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In part one I introduced Massey Ratings and how they can be used to rank football teams in a way that accounts for their strength of schedule. Next, we’ll take a look at how Massey Ratings can be extended further to look at team’s attack and defence strength separately.
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We all know the league table can lie and one of the common causes of this is strength of schedule. Take Southampton, at the time of writing they are currently second in the Premier League twelve matches in yet still haven’t played...
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I’ve not posted this for a while so here is the latest Pythagorean for the English Premier League.
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I recently gave a presentation to the Manchester R Users' Group discussing how to predict football results using R. My presentation gave a brief overview of how to create a Poisson model in R and apply the Dixon and Coles adjustment to it to account for dependance in the scores.
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My last article on expected goals introduced the concept of using exponential decay to estimate the probability of scoring based on the shooter’s distance from the goal. The article received lots of feedback (thanks everyone!!), with a couple of common comments standing out th...
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The Premier League’s fantasy football is back ready for the new season so I thought I’d run through an example of how linear programming can help you mathematically select your team.
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In my last article on expected goals I showed how to incorporate the distance from goal along the Y axis into the expected goal model using Pythagoras’ Theorem. This all worked pretty well, giving us an r squared value of 0.95. However, while the r squared value was good there...
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