Stories have been shown to be potentially valuable for language model pretraining because their semantics are largely self-grounded. Existing human storytelling datasets are relatively small, and existing synthetic datasets lack realism. Here we describe the guided synthesis of a story corpus that is both large and realistic. We evaluate a set of metrics for measuring the quality of individual stories, including coherence, surprise, complexity, as well for quality of the dataset as a whole. Using the metrics we extensively evaluate multiple prompting approaches and three small models (3-8B). Based on the results, we steer dataset generation to yield a high-quality ’synthetic twin’ of a Dutch children’s storytelling collection, similar in terms of lexical and semantic diversity, but much larger, which we call Stories for the Win (StoriesFTW). We validate our metrics and evaluate samples from our corpus with crowd, expert, and LLM judgements.