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All Your Bayes - Uncertainty in xG. Part 2

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This is part 2 of an article on fitting a Bayesian partial pooling model to predict expected goals. It has the benefits of (a) quantifying aleatory and epistemic uncertainty, and (b) making both group-level (player-specific) and population-level (team-specific) probabilistic predictions. If you are interested in these ideas but not in statistical language, then you can also check out part 1. Expected Goals (or xG) is a metric that was developed to predict the probability of a football (soccer) player scoring a goal, conditional on some mathematical characterisation of the shooting opportunity. Since we have a binary outcome (he or she will either score or not score) we can use everyone’s favourite GLM - logistic regression. Unfortunately this causes some overlap with a previous blog post - ‘Bayesian Logistic Regression with Stan’, but don’t worry - the focus here is all about Partial Pooling. First let’s look at a non-Bayesian base case. StatsBomb have kindly made lots of football data

Uncertainty in xG. Part 2: Partial Pooling – All Your Bayes TLDR This is part 2 of an article on fitting a Bayesian partial pooling model to predict expected goals. It has the benefits of (a) quantifying aleatory and epistemic uncertainty, and (b) making both group-level (player-specific) and population-level (team-specific) probabilistic predictions. If you are interested in these ideas but not in statistical language, then you can also check out part 1 . Expected Goals Expected Goals (or xG ) is a metric that was developed to predict the probability of a football (soccer) player scoring a go

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