Abstract: In this paper, I introduce a Bayesian model for detecting changepoints in a time-series of contributions to candidates over the course of a campaign. This game-changers model is ideal for campaign contributions data because it allows for overdispersion, a key feature of contributions data. Furthermore, while many extant changepoint models force researchers to choose the number of changepoint ex ante, the game-changers model incorporates a Dirichlet process prior in order to estimate the number of changepoints along with their location. I demonstrate the usefulness of the model in data from the 2012 Republican primary and the 2008 U.S. Senate elections.