A while ago, the popular data journalism site 538 posted a challenging probability puzzle:
On the table in front of you are two coins. They look and feel identical, but you know one of them has been doctored. The fair coin comes up heads half the time while the doctored coin comes up heads 60 percent of the time. How many flips — you must flip both coins at once, one with each hand — would you need to give yourself a 95 percent chance of correctly identifying the doctored coin?
In this post, I describe how I used Emprirical Bayesian methods to estimate the accuracy of NBA three-point shooters. This analysis closely follows the process outlined by David Robinson in his excellent book Introduction to Empirical Bayes: Examples from Baseball Statistics, and is performed using his ebbr package in R.^ The goal is to make a reasoned ranking of the top sharp shooters, despite insonsistent and imperfect records of how often players make the shots they attempt.
Somerville, MA has been fighting a war against rats for months, and now we have the data to show that it’s working: reported sightings have dropped 66% year-to-date; some of that is due to weather patterns and random fluctuation, but a Bayesian model of the data estimates that the City’s policies have reduced calls by 40%.
Three years ago, the city where I work was dealing with an onslaught of rats.