Quantifying the difficulty of playing songs has recently gained traction in the MIR community. While previous work has mostly focused on piano, this paper concentrates on rhythm guitar, which is especially popular with amateur musicians and has a broad skill spectrum. This paper proposes a rubric-based ‘playability’ metric to formalise this spectrum. The rubric comprises seven criteria that contribute to a single playability score, representing the overall difficulty of a song. The rubric was created through interviewing and incorporating feedback from guitar teachers and experts. Additionally, we introduce the playability prediction task by adding annotations to a subset of 200 songs from the McGill Billboard dataset, labelled by a guitar expert using the proposed rubric. We use this dataset to weight each rubric criterion for maximal reliability. Finally, we create a rule-based baseline to score each rubric criterion automatically from chord annotations and timings, and compare this baseline against simple deep learning models trained on chord symbols and textual representations of guitar tablature. The rubric, dataset, and baselines lay a foundation for understanding what makes songs easy or difficult for guitar players and how we can use MIR tools to match amateurs with songs closer to their skill level.

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