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The proliferation of observational and interventional research in the fields of sports medicine and orthopaedics has resulted in a rapid expansion of available knowledge. However, as the volume of evidence has increased, so has the number of independent investigators leading to the collection and storage of small, granular datasets by individual labs during routine research.1 There are growing concerns within sports medicine and related fields that the complex nature of the current scientific environment may limit the efficiency of data aggregation and that variation in the structure of individual datasets may limit the application of rigorous quantitative methods when answering challenging clinical or practical questions.2 3 Therefore, this editorial aims to introduce the concepts of long tail data and dark data in sports medicine research and describe the implications of both data types for advancing the field.