World Cup National Anthems, Emotions & Audio Features

Data Viz
World Cup
ggplot2
Author

Christina

Published

December 6, 2022

Outbursts of emotions in football are common and can be observed by watching players, managers, supporters, and even referees. The emotions revealed at tournaments such as the FIFA World Cup are visible in many situations. On this international stage, the playing of national anthems prior to kick-off frequently evokes strong expressions of emotions and, for some, may represent a unique affect driven experience.

The circumplex model of emotion developed by James Russell (Russell 1980) is a commonly cited framework for defining dimensions of affective states (Remington, Fabrigar, and Visser 2000).1 This model suggests that emotional states are processed and represented along the two dimensions of valence (horizontal axis) and arousal/activity (vertical axis) (Scott, Sauter, and McGettigan 2010) (Figure 1).

Figure 1. Russell's 2 dimensional model of emotions. Adapted from Russell (1980)

Research investigating the interaction of emotions and music is vast (Juslin 2013). Many models have been proposed (Eerola and Vuoskoski 2010) and interest in the study of emotions and music recommendation systems is growing (Schedl et al. 2018). Metrics similar to Russell’s dimensions are found in Spotify’s track audio features 2. Spotify defines their valence and energy (a proxy for arousal) measures as:

Valence:

“A measure from 0.0 to 1.0 describing the musical positiveness conveyed by a track. Tracks with high valence sound more positive (e.g. happy, cheerful, euphoric), while tracks with low valence sound more negative (e.g. sad, depressed, angry).”

Energy:

“Energy is a measure from 0.0 to 1.0 and represents a perceptual measure of intensity and activity. Typically, energetic tracks feel fast, loud, and noisy. For example, death metal has high energy, while a Bach prelude scores low on the scale. Perceptual features contributing to this attribute include dynamic range, perceived loudness, timbre, onset rate, and general entropy.”

Using the Spotifyr package (Thompson et al. 2021), Spotify audio features of valence and energy for the 32 national anthems of the participating countries at the 2022 FIFA Men’s World Cup were plotted with ggplot2 (Wickham 2016). This descriptive analysis is not without limitations. For example, results can vary depending on each artists’ interpretation of the song. That being said, the current analysis’ results are shown in Figure 2 below. Does Wales’ anthem strike a miserable nerve? Can we explain England’s bore draw tendencies via their low energy score? Conversely, did Costa Rica concede seven to Spain because their anthem is too energetic? To be clear, I don’t actually believe any of this relates to performance on the pitch, these are just some of my silly thoughts.

Figure 2. National Anthems of The 2022 World Cup: Emotions and Audio Features

References

Eerola, Tuomas, and Jonna K. Vuoskoski. 2010. “A Comparison of the Discrete and Dimensional Models of Emotion in Music.” Psychology of Music 39 (1): 18–49. https://doi.org/10.1177/0305735610362821.
Juslin, Patrik N. 2013. “What Does Music Express? Basic Emotions and Beyond.” Frontiers in Psychology 4. https://doi.org/10.3389/fpsyg.2013.00596.
Remington, Nancy A., Leandre R. Fabrigar, and Penny S. Visser. 2000. “Reexamining the Circumplex Model of Affect.” Journal of Personality and Social Psychology 79 (2): 286–300. https://doi.org/10.1037/0022-3514.79.2.286.
Russell, James A. 1980. “A Circumplex Model of Affect.” Journal of Personality and Social Psychology 39 (6): 1161–78. https://doi.org/10.1037/h0077714.
Schedl, Markus, Hamed Zamani, Ching-Wei Chen, Yashar Deldjoo, and Mehdi Elahi. 2018. “Current Challenges and Visions in Music Recommender Systems Research.” International Journal of Multimedia Information Retrieval 7 (2): 95–116. https://doi.org/10.1007/s13735-018-0154-2.
Scott, Sophie K., Disa Sauter, and Carolyn McGettigan. 2010. “Brain Mechanisms for Processing Perceived Emotional Vocalizations in Humans.” In, 187–97. Elsevier. https://doi.org/10.1016/b978-0-12-374593-4.00019-x.
Thompson, Charlie, Daniel Antal, Josiah Parry, Donal Phipps, and Tom Wolff. 2021. “Spotifyr: R Wrapper for the ’Spotify’ Web API.” https://CRAN.R-project.org/package=spotifyr.
Wickham, Hadley. 2016. “Ggplot2: Elegant Graphics for Data Analysis.” https://ggplot2.tidyverse.org.

Footnotes

  1. Please note that there are important limitations and criticisms to this model of emotions that go beyond the scope of this article.↩︎

  2. The validity and reliability of these metrics merits further investigation therefore they should be interpreted with caution.↩︎