This article argues that Agent-Based Modelling, owing to its capabilities and methodology, has a distinctive contribution to make to delivering coherent social science prediction. The argument has four parts. The first identifies key elements of social science prediction induced from real research across disciplines, thus avoiding a straw person approach to what prediction is. The second illustrates Agent-Based Modelling using an example, showing how it provides a framework for coherent prediction analysis. As well as introducing the method to general readers, argument by example minimises generic discussion of Agent-Based Modelling and encourages prediction relevance. The third deepens the analysis by combining concepts from the model example and prediction research to examine distinctive contributions Agent-Based Modelling offers regarding two important challenges: Predictive failure and prediction assessment. The fourth presents a novel approach -- predicting models using models -- illustrating again how Agent-Based Modelling adds value to social science prediction.