Gambit Forecaster is a research and modeling project developed and operated by Sam Massey, a Political Science student at Purdue. This project contains a suite of Monte Carlo forecasting systems that incorporate structured inputs and posterior sampling to evaluate political outcomes. The election models rely on state level vote ranges, historical baselines, and uncertainty intervals to generate distributions of result paths. Each model produces thousands of simulations that reflect realistic variation in voter behavior, state elasticity, correlated state shifts, and the full range of competitive environments. The goal is to create forecasts that are transparent and reproducible while also staying grounded in data. This website also serves as a practical archive for various applied modeling projects. I use the same tools outside of electoral politics and running systems that explore sports performance and market behavior. Each model is built to showcase outcome shift swhen inputs change and to support a disciplined approach to thinking about uncertainty.
The project will continue to expand as each model is refined and as new data becomes available, and hopefully the website functions as a space to develop ideas, test assumptions, and refine the overall quality of forecasts.