## Bayesian analysis of Premier League football

In this post we are going to look at some football statistics. In particular, we will examine English football, the Premier League, using Bayesian statistics with Stan. If you have no idea what Bayesian statistics is, you can read my introductory post on it. Otherwise this post shouldn’t be a difficult read. All right, let’s get to it. First, we need some data. I will use all the matches from the Premier League seasons 16/17 and 17/18 (which is still ongoing at the time of the writing). [Read More]

## Summer Olympics: the countries that beat the expectations

In this post we take a look at the summer Olympics and try to see which countries performed substantially differently than was expected of them. We will look at the Olympics from 1964 through to 2008. For each year, we will run a predictive model, trying to predict the number of medals a country wins, using selected datasets that are available before each of the Olympics. We will see that this model performs well out of sample and this model will be what we expect. [Read More]

## Analysis of calving of JH Dorrington Farm Part II

This is the second part of the analysis for the data from JH Dorrington Farm. You might want to read the first part before reading this one. Before we put on our science hats, let us make an outline for what we will do. Previously we split the data into training and test sets 80/20. We will fit all of our models and calibrate them on the training set. Decisions about keeping/dropping predictors, transforming predictors and which model to chose will be left to the test set. [Read More]

## Analysis of calving of JH Dorrington Farm Part I

Here I will analyse a real life problem. My friend Chris at JH Dorrington Farm has kindly provided me with the data and allowed me to make this post. This will be several parts as I explore the data and try to fit various models. I’m going to stop milking this introduction and get right to it. My friend Chris has been collecting various forms of data about his cows. [Read More]