# Twitter thread for Eric's story - What do French pastries πŸ₯, baseball πŸ₯Ž, and marginal utility πŸ”§ have in common? Find out in our new story by Eric Daub on using bayesian modelling to estimate the replacement value of US baseball players. Read it here https://bit.ly/BaseballReplacementLevel. - Find out more by speaking to our baseball enthusiast and bayesian specialist author [Eric Daub](https://github.com/edaub). You can see the #code and run the #analysis yourself using the latest #data, and you can modify the model and its assumptions to see how this affects the predictions. - Eric's story uses Bayesian modelling, a technique in data science that uses incoming evidence to update the predictions of a model. We're interested in stories about a range of subjects and data science areas. - If you don’t know us yet, we are TuringDataStories, a mix of open data πŸ’Ύ code πŸ‘©β€πŸ’» narrative πŸ’¬ and visuals πŸ“Š to help understand the world around us. Read stories at https://bit.ly/TuringDataStories, or find out more https://github.com/alan-turing-institute/TuringDataStories - Stay tuned for more @TuringDStories, and if you have any feedback for us, or you want to get involved, get in touch!