Measuring the Eurovision Song Contest: A Living Dataset for Real-World MIR

Every year, several dozen, primarily European, countries, send performers to compete on live television at the Eurovision Song Contest, with the goal of entertaining an international audience of more than 150 million viewers. Each participating country is able to evaluate every other country's performance via a combination of rankings from professional jurors and telephone votes from viewers. Between fan sites and the official Song Contest organisation, a complete historical record of musical performances and country-to-country contest scores is available, back to the very first edition in 1956, and for the most recent contests, there is also information about each individual juror's rankings. In this paper, we introduce MIRoVision, a set of scripts which collates the data from these sources into a single, easy-to-use dataset, and a discrete-choice model to convert the raw contest scores into a stable, interval-scale measure of the quality of Eurovision Song Contest entries across the years. We use this model to simulate contest outcomes from previous editions and compare the results to the implied win probabilities from bookmakers at various online betting markets. We also assess how successful content-based MIR could be at predicting Eurovision outcomes, using state-of-the-art music foundation models. Given its annual recurrence, emphasis on new music and lesser-known artists, and sophisticated voting structure, the Eurovision Song Contest is an outstanding testing ground for MIR algorithms, and we hope that this paper will inspire the community to use the contest as a regular assessment of the strength of modern MIR.