In this paper, we analyze the performance of the 2024 US presidential election forecasts published in the PS: Political Science & Politics special issue (online Oct 15, 2024). The variety of approaches used for forecasting – citizen forecasting, political-economy models, poll-based models, electronic markets, and machine learning - present us with the opportunity to assess forecasting accuracy, lessons learned, and avenues for future research. While vote intention polls and the media underestimated Trump’s performance and pointed to a very tight election, the state-level Electoral College vote forecasting models performed quite well. In particular, the Enns et al. (2024) state-level political economy model provided an accurate Electoral College forecast of 226 for Harris and 312 for Trump. Additionally, Mongrain and Stegmaier (2024) took the five state-level forecasts in the special issue and averaged them on a state-by-state basis, which also resulted in an accurate 226-312 Electoral College vote prediction. New approaches using AI demonstrated promise, while other methods, especially those that relied heavily on vote-intention polls or used survey questions based on a Biden-Trump rematch, offer lessons as the field moves forward.