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title: Twitter thread for Eric's story

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# Twitter thread for Eric's story

- What do French pastries 🥐, baseball 🥎, and marginal utility 🔧 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 💾 code 👩‍💻 narrative 💬 and visuals 📊 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!
