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.

TLDR The Expected Goals (xG) metric is now widely recognised as numerical measure of the quality of a goal scoring opportunity in a football (soccer) match. In this article we consider how to deal with uncertainty in predicting xG, and how each players individual abilities can be accounted for.

TLDR We sometimes have the option to purchase additional information (like paying for an experiment to be performed, or for a report from an expert consultant) to help us in problems of decision making under uncertainty.

TLDR Maximum Likelihood Estimation (MLE) is one method of inferring model parameters. This post aims to give an intuitive explanation of MLE, discussing why it is so useful (simplicity and availability in software) as well as where it is limited (point estimates are not as informative as Bayesian estimates, which are also shown for comparison).

TLDR This post is intended to be a high-level discussion of the merits and challenges of applied Bayesian statistics. It is intended to help the reader answer: Is it worth me learning Bayesian statistics?

TLDR Logistic regression is a popular machine learning model. One application of it in an engineering context is quantifying the effectiveness of inspection technologies at detecting damage. This post describes the additional information provided by a Bayesian application of logistic regression (and how it can be implemented using the Stan probabilistic programming language).

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