Greatest Hits Versus Deep Cuts: Exploring Variety in
Set-lists Across Artists and Musical Genres
Edward Abel1,∗ , Andrew Goddard2
1
University of Southern Denmark, Denmark
2
Independent Researcher
Abstract
Live music concert analysis provides an opportunity to explore cultural and historical trends. The art
of set-list construction, of which songs to play, has many considerations for an artist, and the notion
of how much variety different artists play is an interesting topic. Online communities provide rich
crowd-sourced encyclopaedic data repositories of live concert set-list data, facilitating the potential for
quantitative analysis of live music concerts. In this paper, we explore data acquisition and processing of
musical artists’ tour histories and propose an approach to analyse and explore the notion of variety, at
individual tour level, at artist career level, and for comparisons between a corpus of artists from different
musical genres. We propose notions of a shelf and a tail as a means to help explore tour variety and
explore how they can be utilised to help define a single metric of variety at tour level, and artist level.
Our analysis highlights the wide diversity among artists in terms of their inclinations toward variety,
whilst correlation analysis demonstrates how our measure of variety remains robust across differing
artist attributes, such as the number of tours and show lengths.
Keywords
computational musicology, statistical music analysis, set-list composition, music information retrieval
1. Introduction
Live music experiences offer a unique glimpse into society and have significant cultural impact
[6]. They have also become a crucial source of revenue for artists in the streaming era [17]. Con-
structing live music set-lists involves several considerations for an artist, such as catering to
different types of fans with varying expectations and managing the trade-offs between these
expectations [26]. Artists must also consider how performing specific songs or covers could at-
tract more media attention than the concert might otherwise receive [16, 24]. The implications
of a set-list now extend beyond just the audience in attendance in the venue, with artists like
Bruce Springsteen offering the ability to buy and stream every single show from current and
previous tours [15], while bands like Metallica1 and Pearl Jam2 provide numerous ofÏcial live
recordings of their performances. In addition, live shows’ set-lists have been shown to poten-
CHR 2024: Computational Humanities Research Conference, December 4 – 6, 2024, Aarhus, Denmark.
∗
Corresponding author.
£ edabelcs@gmail.com (E. Abel); bossfansheff@gmail.com (A. Goddard)
ç www.edabel.co.uk (E. Abel)
ȉ 0000-0002-3694-5116 (E. Abel); 0000-0001-7384-0252 (A. Goddard)
© 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
1
https://www.livemetallica.com/
2
https://pearljam.com/music/bootlegs
802
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
tially impact and influence listening behaviour of an artist’s fans regarding non-live material
[30].
The process of set-list creation varies, with some artists meticulously planning their sets in
advance, while others prefer to make more spontaneous decisions based on the energy and
reactions of the crowd during the show. Getting the right balance of set-list variety is crucial.
Artists may face backlash if their set-lists are perceived as too formulaic and lacking in variety.
For example, during Bruce Springsteen’s 2023 tour, fans criticised performances for being too
similar night after night, leading band members to defend their choices on social media [25].
Conversely, there is a risk of alienating fans by including too many unexpected songs at the
expense of beloved greatest hits, as seen by some fan’s reactions to recent Bob Dylan shows
[13], or by altering hit songs too much from their album versions, as seen by fan frustrations at
recent Arctic Monkeys’ shows [7]. Constructing a set list involves navigating diverse expecta-
tions, and the degree of variety in an artist’s performances is an intriguing topic with broader
applications, such as historical live performance recommender systems [1].
In this paper, we explore the data acquisition and processing of musical artists’ tour histories,
and propose an approach to explore variety, at individual tour level, artist career level, and for
comparisons between a corpus of artists. For this, we propose notions of a shelf and a tail, as
a means to investigate and explore tours’ variety and properties, and explore how they can be
used to quantify variety at tour level and artist level. Additionally, our approach explores the
impact of cover songs upon variety, and explores variety comparisons across musical genres.
Through analysing many artists, we highlight the wide range of artist variety levels, from
those who tend to focus more on greatest hits to those who perform more diverse shows fea-
turing deep cuts, and everything in between. Additional correlation analysis of our concept of
variety explores how our measure remains robust across artists with differing characteristics,
such as the number of tours or average show lengths. For more information about the project
and its data, see the project’s GitHub repository,3 and to interactively explore the data and the
proposed approach, see the project’s Interactive Web App4 .
The rest of the paper is structured as follows: Section 2 covers background literature, Section
3 details our approach, and Section 4 provides conclusions.
2. Background
Work has explored live music performance exploring performer or audience psychology and
touching on how set-lists impact this, such as in terms of presence and representation within
shows and set-lists [27], through measuring value of live music as a motivation scale [23],
and exploring how the set-list and beyond has a bearing on an artist’s impact as part of their
stage success [10]. Other work has explored live music performances from the perspective of
music theory, such as work defining performance parameters in relation to how performances
bring compositions to life through variations in timing and dynamics and its impact on listener
perception [19], and work exploring how composers’ choices in tones, intervals, and harmonies
influence stylistic music changes over time [22].
3
https://github.com/EdAbel/setlist-variety
4
www.edabel.co.uk/setlist-variety
803
Various work exploring live music performance taking a more quantitative approach have
focused on an in-depth analysis of a single artist. Work has explored the band The Grateful
Dead, examining the band’s live recordings over three decades to analyse the performances in
relation to cultural trends in music and to investigate how the performances of the band change
over time [33]. Others have analysed The Grateful Dead’s live concerts from 1972 to 1995 in
comparison to listening habits outside of concerts [30], highlighting how there are correlations
between live set-lists and home listening. Work focusing on the artist Bruce Springsteen, has
explored his live performances and set-lists from the perspective of examining how set-list
analysis can provide inclinations about tensions between commercial considerations for play-
ing new album material and playing expected but older hits [2]. Bob Dylan is another artist
work has focused upon, such as a study of Bob Dylan’s set-lists from the 1960s to the 2020 that
investigates his approach to performing, exploring how he curates a show and how this in turn
creates a meta-narrative [8].
Limited studies have looked to compare tour and set-lists for multiple artists. The Music &
Entertainment publication Consequence set out and discussed the ”25 Best Rock Acts with the
most Unique set-lists” [31]. Within such articles, although there is an implication of a focus,
here on the acts whose shows define the word unique, the methodology involved in compiling
such a list is obscured, and so therefore it is difÏcult to assess.
Some recent discourse has explored how the make up of fan communities of artists such
as Taylor Swift and The Grateful Dead may share surprising similarities, despite their differ-
ent musical styles, and the impact this can have on further similarities between performance
set-lists [5]. The notion of a comparison between Taylor Swift and The Grateful Dead per-
formances has been further explored, along with a limited number of other artists, in terms
of unique songs [11]. Focusing on considerations of the notion of special songs, analysis of
set-lists from individual tours for different artists is carried out, to explore the prominence
of unique (and quite unique) song occurrence rates. Comparisons highlight how The Grate-
ful Dead is seen as a very varied artist compared to Taylor Swift, when considering variety
only from the perspective of unique special ”surprise” songs. In this analysis, only individual
tours are considered, which may result in an unrepresentative view of an artist’s overall career.
Additionally, the methodology favours artists with longer tours and longer sets.
The metric Consecutive Set Similarity (CSS) has been proposed to look to measure variety
for an artist [20]. Here, an artist’s career of set-lists are arranged in sequence and each set-list is
compared to the previous set-list in terms of the amount of different songs, resulting in a value
of 1 if the the set-list is identical to the previous set, and -1 if it is completely different. From this,
an overall average is derived for each artist, from which comparisons and clustering of different
artists can be performed [28]. The measure has a very narrow focus due to only considering
two shows at a time, and so will consider oscillating changes of songs that are played frequently
but not every night as signalling high variety. Moreover, it does not consider information such
as when one tour ends and another begins (likely to signal a significant change in set-lists,
which may in turn favour artists with many small tours over a long period).
Some coverage has highlighted how artists themselves explore the art of set-list curation,
and the considerations they have for variety. During a tour that contained over 800 shows the
band Radiohead made a conscious choice to ensure every show was unique and to never have a
repeating set-list. The band would curate the set-lists daily and emphasised the importance of
804
Figure 1: Data Pipeline Stages of our Approach
variety, contrasting their approach with other artist’s that play identical sets nightly [21]. More
recently, it has been claimed that the band Metallica look to customise concert set-lists based
on local Spotify data (and local radio trends), to look to cater to localised fans’ preferences, and
to help increase show diversity [29].
3. Our Approach
In our approach, raw artist tour data is first collected and processed, then utilized in various
stages of analysis, as illustrated in Figure 1. Communities such as MusicBrainz5 and Setlist.fm6
provide extensive crowd-sourced encyclopaedic data on musical artists and live concert set-lists.
The following sub-section outlines our data acquisition process, detailing how these sources
were leveraged to obtain the data used for our analysis.
3.1. Data Acquisition
Given a set of music artist names, we acquire tour details, and data for each tour of each artist,
and store the data as depicted in the data model in Figure 2. For each artist name, they can
be uniquely identified via MusicBrainz Identifiers (MBIDs), which is a universal unambiguous
standard artist identification.7 Through calls to the Music Brainz API,8 the MBID for each artist
name is acquired, along with additional Music Brainz artist information including their Gen-
5
https://musicbrainz.org/
6
https://www.setlist.fm
7
https://musicbrainz.org/doc/MusicBrainz_Identifier
8
https://musicbrainz.org/doc/MusicBrainz_API (utilising the musicbrainz API wrapper R package -
https://github.com/dmi3kno/musicbrainz
805
Table 1
Genre Mappings
General Genre (Specific) Genre
Rock Rock, Hard Rock, Pop Rock, Southern Rock, Gothic Rock, Comedy
Rock, Blues Rock, Rap Rock
Alternative Alternative Rock, Indie Rock, Post-Rock, Dance-Punk, Garage Rock, In-
die Pop, Britpop, Emo, Grunge, Alternative Hip Hop
Metal Heavy Metal, Thrash Metal, Power Metal, Gothic Metal, Melodic Death
Metal, Progressive Metal, Nu Metal, Symphonic Metal, Alternative
Metal, Groove Metal, Metalcore, Death Metal, Glam Metal
Punk Pop Punk, Punk Rock, Hardcore Punk, Post-Hardcore, Gypsy Punk
ElectronicAndDance Synthpop, Electronic, Industrial Metal, Industrial Rock, Dance
Folk Folk Rock, Indie Folk, Folk Punk
Progressive Progressive Rock, Progressive Metal, Experimental Rock, Post,
Psychedelic Rock
Pop Pop, Pop Rock
der (one of Male, Female, or Group), and their Start Date and EndDate.9 Additionally, a single
musical genre considered most representative was curated for each artist using MusicBrainz’s
genre information. As this data often assigns multiple genre tags to an artist, a manual selec-
tion of a single tag was performed where necessary. Next, using each artist’s MBIDs, each’s
corresponding setlist.fm ID (a separate setlist.fm unique identifier for each artist) is obtained
via calls to the setlist.fm API.10 This data is stored as depicted in the Artist table in Figure 2.
The determined single genres for each artist result in a large number of different genre values,
many representing similar sub-genres of a more general genre. To make genre analysis more
tractable, the set of genres can be mapped onto a smaller set of generalized genres. Such a
mapping of dozens of genres onto a set of generalized genres is shown in Table 1. This data is
stored as depicted in the Genre table in Figure 2.11
Given a set of artist setlist.fm IDs, a list of each artist’s tours can be obtained, from which
data associated with each tour of each artist can be subsequently acquired. For each tour,
overall information of the tour name, the total number of shows on the tour, and date ranges
are acquired, and stored as depicted in the Tour table in Figure 2. For each tour’s songs, the list
of songs played on the tour, along with each song’s number of plays on the tour are acquired.
Additionally, for each song, whether the song is denoted as a cover song (with respect to the
artist the tour is for) is recorded.12 The song information is stored as depicted in the Song table
9
For groups this represents their formation date and disband dates, for solo artists it holds just their birth dates and
retirement or death dates. For ongoing artists EndDate will be ”present”.
10
https://api.setlist.fm/docs/1.0/index.html (utilising the SetListR wrapper R package -
https://github.com/fusionet24/SetListR
11
The assignment of a single genre to each artist, followed by the mapping of these genres to a broader set of gener-
alized categories, has been curated as a proof of concept. However, genre classification is a complex and expansive
subject in its own right, with numerous studies addressing the challenges associated with genre categorisation
[9] and the phenomenon of genre crossover [32]. Given such complexities, the automation of genre classification
represents a compelling area for future exploration.
12
Additionally, tours that are empty (made up of only empty shows) are removed, as are any artists for which all
806
Figure 2: Data Model of our Approach
in Figure 2.
From this, full artist tour history data for over 200 artists was acquired, chosen for their
prominence within popular music history and culture. This number is progressively expanding,
and up-to-date figures, and access to the data, can be found at the project’s GitHub repository.13
All of the data acquired is stored as raw data, as depicted in Figure 1, to then be utilised within
the analysis stage.
3.2. Data Pre-Processing
Before beginning analysis, pre-processing of the raw dataset is performed. Within our analy-
sis, we are interested in artists who have sufÏciently substantial touring histories. Therefore,
we define thresholds to utilise only artists that have a minimum number of tours and a mini-
mum overall number of shows, and only keep tours that have a minimum number of shows,
a minimum number of unique songs played on the tour, and a minimum show length of the
tours are empty. Further, tours identified by their name as a set of Promotional publicity media/private shows,
are removed.
13
https://github.com/EdAbel/setlist-variety
807
Table 2
Raw Data Threshold Parameters
Variable Value
Artist Minimum No. of Tours 5
Artist Minimum Total No. of shows 200
Tour Minimum No. of shows 20
Tour Minimum No. of songs 10
Tour Minimum Average Show Length 10
shows from the tour.14 These threshold values are inherently subjective and context dependent;
therefore, we conduct the analysis on the raw data, preserving its integrity so that alternative
thresholds could be applied if needed. Within our analysis that follows, the threshold param-
eter values utilised are shown in Table 2. Following the pre-processing stage, we begin the
analysis at individual tour level.
3.3. Tour Analysis
For a tour, each (unique) song has a Play Count (𝑃𝐶), denoting how many times it was played
on that tour. Tours can vary in terms of how many shows they are made up of, therefore, for
comparisons between tours, a tour’s (absolute) 𝑃𝐶 values can be normalised with respect to
the number of shows in the tour. For a tour, a Relative Play Count (𝑅𝑃𝐶) for each song played
on the tour can be computed via:
PC𝑖
𝑅𝑃𝐶 𝑖 = ( ) ∗ 100 (1)
𝑡𝑁
Where 𝑃𝐶 𝑖 is the 𝑃𝐶 value of the 𝑖-th song and 𝑡𝑁 is the number of shows in the tour. A
𝑅𝑃𝐶 value of 100 represents a song being played every single show of a tour, whilst a value
of 50 represents a song being played at exactly half of the tour’s shows. We can visualise a
tour and its songs in terms of their 𝑅𝑃𝐶 values, where the y-axis denotes 𝑅𝑃𝐶 value, and the
x-axis denotes song number where songs are sorted with respect to 𝑅𝑃𝐶 values high to low.
For example, Bruce Springsteen (and the E-Street Band’s) 2023 tour, is shown in Figure 3a, and
Coldplay’s Music of the Spheres 2023 Tour is shown in Figure 3b. Such tour visualisations
highlight a generality for many tours to map out an s shaped sigmoidal like function shape.
From a tour’s dataset, notions of Shelf, Tail, 100%’ers, Uniques and Covers can be calculated and
subsequently highlighted within such visualisations. Each of these notions are defined and
explained next.
Shelf - The notion of a tour’s Shelf is a measure of the significance of a tour to have a set of
of songs that are played at most of the tour’s shows, and outline a shelf like shape in the top
left of the plots in Figure 3. Given a Shelf Size (𝑆𝑆) value, denoting what top percentile of tour
songs are to be considered part of the shelf, a Shelf Value 𝑆 can be calculated as the ratio of a
14
Such thresholds are beneficial for identifying and removing tours with missing data issues, such as tours which
only a few of the shows have been added for, or tours that have many shows added but lack song information for
many of the shows. Such tours, if left in the data, can unduly impact and bias analysis.
808
100
100
80
80
Relative Play Count
Relative Play Count
60
60
40
40
20
20
0
0
0 10 20 30 40 50 60 70 0 20 40 60 80 100 120 140
Song No. Song No.
(a) Bruce Springsteen (and The E-Street Band) - (b) Coldplay - Music of the Spheres 2023 Tour (Just
2023 Tour (Just Data Points). Data Points)
Figure 3: Tours Visualisation Just Data Points
tour’s songs that are in its shelf. Given the tour’s set of X songs, of length 𝑛, sorted as, 𝑥1 to 𝑥𝑛 ,
from high to low with respect to their 𝑅𝑃𝐶 values:
X = {𝑥1 , 𝑥2 , … , 𝑥𝑛 } where 𝑥1 ≥ 𝑥2 ≥ ⋯ ≥ 𝑥𝑛 (2)
The Shelf Songs are selected as the top 𝑆𝑆 percentile of X:
Shelf Songs = Top𝑆𝑆 Percentile = {𝑥 ∈ X|𝑥 ≥ 𝑃𝑆𝑆 (X)} (3)
and 𝑆 is calculated via:
|Shelf Songs|
𝑆= (4)
𝑛
An 𝑆𝑆 value of 10% would select all the songs that have been played at 90% or more of a
tour’s shows, and the corresponding 𝑆 value represents the ratio of the tour’s songs that are
played at 90+% of the tour’s shows. So, an 𝑆 value of 0.25 would represent that 25% of the tour’s
songs are played at 90% or more of its shows.
Tail - The notion of a tour’s Tail is a measure of the significance of a tour to have a set of songs
that are played only rarely on the tour, and outlines a tail like shape in the bottom right of the
plots in Figure 3. Given a Tail Size (𝑇 𝑆) value, denoting what bottom percentile of tour songs
are to be considered a part of the tail, a Tail Value 𝑇 can be calculated as the ratio of a tour’s
songs that are in its tail. The Tail Songs are selected as the bottom 𝑇 𝑆 percentile of X:
Tail Songs = Bottom𝑆𝑆 Percentile = {𝑥 ∈ X|𝑥 ≤ 𝑃𝑇 𝑆 (X)} (5)
and 𝑇 is calculated via:
|Tail Songs|
𝑇 = (6)
𝑛
809
A 𝑇 𝑆 value of 10% would select the songs that are played at most at 10% of a tour’s shows,
and the corresponding 𝑇 value represents the ratio of the tour’s songs that are played at 10%
or less of the tour’s shows. So, a 𝑇 value of 0.4 would represent that 40% of the tour’s songs
are played at 10% or less of its shows.
100%’ers - In the set of songs making up the tour’s shelf, there exists a subset of 0 or more
songs that are played at 100% of the tour’s shows. This subset of songs (100%′ 𝑒𝑟 𝑆𝑜𝑛𝑔𝑠) can
be identified as the set of songs that have 𝑅𝑃𝐶 = 100. A 100%’ers Value 𝐻 is calculated as the
ratio of a tour’s songs that are in this subset.
|100%’er Songs|
𝐻 = (7)
𝑛
Uniques - In the set of songs making up a tour’s tail, there exists a subset of 0 or more songs
that are played only once during the whole tour. This subset of songs (𝑈 𝑛𝑖𝑞𝑢𝑒𝑆𝑜𝑛𝑔𝑠) can be
identified as the set of songs that have a 𝑃𝐶 = 1. A Uniques Value 𝑈 is calculated as the ratio
of a tour’s songs that are in this subset.
|Unique Songs|
𝑈 = (8)
𝑛
Covers - For each song played on the tour we have information denoting which are cover songs
(with respect to the artist), from which the shelf songs that are cover songs, and the tail songs
that are cover songs, can be determined. From this, the set of shelf songs minus those that are
covers can be determined and a Shelf Minus Covers Ratio Value 𝑆𝑀𝐶 calculated. Similarity,
the set of tail songs minus those that are covers can be determined and a Tail Minus Covers
Ratio Value 𝑇 𝑀𝐶 calculated.
These calculated notions of Shelf, Tail, 100%’ers, Uniques and Covers can be highlighted
visually within our tour plots, as shown for the tours introduced earlier of Bruce Springsteen
(and the E-Street Band’s) 2023 tour in Figure 4a, and Coldplay’s Music of the Spheres 2023
Tour in Figure 4b. In these plots, the shelf lower edge is denoted via a dotted green line and
the 100%s’ers are those songs that sit on the solid green line. The tail upper edge is denoted via
a dotted orange line and the uniques are those songs that sit on the solid orange line. Cover
songs are denoted by filled red data points, and the shelf and tail covers can be identified as
those filled data points above the shelf’s lower edge and below the tail’s upper edge respectively.
Here, and within subsequent analysis, 𝑆𝑆 and 𝑇 𝑆 parameters of 10% are utilised. However, these
parameters can be altered to any desired numbers. For experimentation exploring sensitively
analysis and impacts of these parameters see Appendix A.
Such analysis can be utilised to explore and compare different tours from the same artist and
to compare tours of different artists. For example, regarding other Bruce Springsteen tours, the
plot for the Wrecking ball tour is shown in Figure 4c and the plot for the Bruce Springsteen on
Broadway tour shown in Figure 4d. These plots highlight how, as in The Wrecking ball tour’s
case, a tour can have quite a small and sharp shelf, or, as in the Bruce Springsteen on Broadway
tour, a tour can alternatively be predominantly made up of a shelf. The tour for Taylor Swift’s
Speak Now World Tour is shown in Figure 4e, highlighting a tour which has a large long tail
that is made up of many uniques that are cover songs. The tour for Pink Floyd’s The Wall tour
810
100
100
Cover Cover
80
80
Relative Play Count
Relative Play Count
60
60
40
40
20
20
0
0
0 10 20 30 40 50 60 70 0 20 40 60 80 100 120 140
Song No. Song No.
(a) Bruce Springsteen - 2023 Tour (b) Coldplay - Music of the Spheres Tour
100
100
Cover Cover
80
80
Relative Play Count
Relative Play Count
60
60
40
40
20
20
0
0
0 50 100 150 200 250 5 10 15
Song No. Song No.
(c) Bruce Springsteen - Wrecking Ball Tour (d) Bruce Springsteen - Broadway Tour
100
100
Cover
80
80
Relative Play Count
Relative Play Count
60
60
Cover
40
40
20
20
0
0
0 20 40 60 80 0 5 10 15 20 25
Song No. Song No.
(e) Taylor Swift - Speak Now World Tour (f) Pink Floyd - The Wall Tour
Figure 4: Tour Visualisations with Shelf, Tail, and Covers Identified
is shown in Figure 4f, highlighting a tour where all of the tour’s songs are contained in the
shelf and in fact are all 100%’ers.
Building on this analysis of single tours, next, we explore analysis of an artist’s whole career
of tours.
811
3.4. Artist Career of Tours Analysis
For a single tour, its Shelf Value 𝑆 and Tail Value 𝑇 can be calculated. With the calculation of a
pair of such values for every tour of an artist’s career, their whole career can be visualised, in
chronological order, in a bar plot. Such a visualisation, for every tour for Bruce Springsteen is
shown in Figure 5a. Here, for each tour, tail values are shown as negative blue bars and each
tour’s corresponding shelf values are shown as green bars.
Additionally, for each tour, its 100%’ers Value 𝐻 and Uniques Value 𝑈 can be calculated.
These pair of values represent values equal to or less than the tour’s 𝑆 and 𝑇 values. Therefore,
an artist’s whole career can be visualised in a bar plot where the amount of each tour’s tail
that is made up of uniques, and the amount of each tour’s shelf that is made up of 100%’ers is
highlighted. For Bruce Springsteen, every tour with this information is shown in Figure 5b.
Moreover, for each tour the amount of its shelf that is made up of covers, and the amount
of its tail that is made up of covers can be calculated. Then, an artist’s whole career can be
visualised with the amount of each tour’s tail that is made up of covers, and the amount of
each tour’s shelf that is made up of covers highlighted. Every tour for Bruce Springsteen with
this information is shown in Figure 5c. Alternatively, we could visualise the impact of cover
songs on each tour’s shelf and tail through calculating each tour’s Shelf Minus Covers Value
𝑆𝑀𝐶 and Tail Minus Covers Value 𝑇 𝑀𝐶. Then, an artist’s whole career can be visualised,
highlighting how shelves or tails that are made up of a substantial amount of cover songs will
become smaller. Every tour for Bruce Springsteen with this information is shown in Figure 5d.
Such analysis can be utilised to explore and compare the careers of different artists. For
example, Figure 6a shows the career of Iron Maiden, and Figure 6b shows the career of Slipknot,
each showing Shelf and Tail values and how much of them are taken up by 100%s and Uniques.
These plots highlight how these artists have a clear leaning towards playing more conformity
and greatest hits like sets, and highlight how over their career this has only become more
pronounced. The career of Taylor swift, showing each tour’s Shelf and Tail values and how
much of them are taken up by covers, is shown in Figure 6c. Here, we observe how after her
first tour, a similar size of shelves and tails is observed. However, whereas the Speak Now
World Tour’s tail is made up almost entirely of cover songs, we observe the inverse for the
tail of the most recent Eras tour. The range of different shelf and tail values for the tour’s of
Pink Floyd are shown in Figure 6d. From this plot we observe stark differences between early
tours which have little or no shelves, and later tours, that coincide with their commercial peak,
having little or no tails and large shelves.
Building on this analysis of the whole touring career of a single artist, next, we explore
comparison analysis between a corpus of artists.
3.5. Comparing Artists
For an artist, Shelf 𝑆 and Tail 𝑇 values for each tour can be calculated, denoting the size of
shelf and tail for each tour. From the set of shelf values, a single average shelf value 𝑆 ̄ can be
calculated via:
812
Tail Tail_Minus_Uniques Shelf_Minus_The100s
Shelf Uniques The100s
Greetings From Asbury Park, N.J. Greetings From Asbury Park, N.J.
The Wild, The Innocent & The E Street Shuffle The Wild, The Innocent & The E Street Shuffle
Chicken Scratch Tour Chicken Scratch Tour
Born to Run Born to Run
Lawsuit Tour Lawsuit Tour
Darkness Darkness
The River The River
Born in the U.S.A. Born in the U.S.A.
Tunnel of Love Express Tunnel of Love Express
Tour Name
Tour Name
Human Rights Now! Human Rights Now!
Bruce Springsteen 1992–1993 World Tour Bruce Springsteen 1992–1993 World Tour
The Ghost of Tom Joad The Ghost of Tom Joad
Reunion Tour Reunion Tour
The Rising The Rising
Devils & Dust Devils & Dust
Seeger Sessions Seeger Sessions
Magic Magic
Working on a Dream Working on a Dream
Wrecking Ball Wrecking Ball
High Hopes High Hopes
The River Tour 2016 The River Tour 2016
Springsteen On Broadway Springsteen On Broadway
Springsteen On Broadway 2021 Springsteen On Broadway 2021
Springsteen & E Street Band 2023 Tour Springsteen & E Street Band 2023 Tour
−100 −50 0 50 100 −100 −50 0 50 100
Relative Size Relative Size
(a) All Tours Shelf and Tail (b) All Tours Shelf & 100%’ers and Tail & Uniques
Tail_Minus_TailCovers Shelf_Minus_ShelfCovers Tail_Minus_TailCovers
TailCovers ShelfCovers Shelf_Minus_ShelfCovers
Greetings From Asbury Park, N.J. Greetings From Asbury Park, N.J.
The Wild, The Innocent & The E Street Shuffle The Wild, The Innocent & The E Street Shuffle
Chicken Scratch Tour Chicken Scratch Tour
Born to Run Born to Run
Lawsuit Tour Lawsuit Tour
Darkness Darkness
The River The River
Born in the U.S.A. Born in the U.S.A.
Tunnel of Love Express Tunnel of Love Express
Tour Name
Tour Name
Human Rights Now! Human Rights Now!
Bruce Springsteen 1992–1993 World Tour Bruce Springsteen 1992–1993 World Tour
The Ghost of Tom Joad The Ghost of Tom Joad
Reunion Tour Reunion Tour
The Rising The Rising
Devils & Dust Devils & Dust
Seeger Sessions Seeger Sessions
Magic Magic
Working on a Dream Working on a Dream
Wrecking Ball Wrecking Ball
High Hopes High Hopes
The River Tour 2016 The River Tour 2016
Springsteen On Broadway Springsteen On Broadway
Springsteen On Broadway 2021 Springsteen On Broadway 2021
Springsteen & E Street Band 2023 Tour Springsteen & E Street Band 2023 Tour
−100 −50 0 50 100 −100 −50 0 50 100
Relative Size Relative Size
(c) All Tours Shelf & Covers and Tail & Covers (d) All Tours Shelf And Tail Without Covers
Figure 5: All Tours Analysis - Bruce Springsteen
𝑛
1
𝑆̄ = ∑𝑆 (9)
𝑛 𝑖=1 𝑖
where 𝑆 ̄ is the average of the individual Shelf values, 𝑆𝑖 represents the Shelf value for Tour 𝑖,
and 𝑛 is the number of Tours for the artist.
Similarly, an average tail value 𝑇 ̄ for the artist can be calculated via:
𝑛
1
𝑇 ̄ = ∑ 𝑇𝑖 (10)
𝑛 𝑖=1
where 𝑇 ̄ is the average of the individual Tail values, 𝑇𝑖 represents the Tail value for Tour 𝑖, and
𝑛 is the number of Tours for the artist.
From these calculations, a pair of 𝑆 ̄ and 𝑇 ̄ values can be calculated for every artist, and the
whole set of artists can be shown within a single scatter plot, as shown in Figure 7. Here, the
x-axis denotes mean tail values (𝑇 ̄ ) and the y-axis denotes mean shelf values (𝑆),̄ with the shelf
axis scale inverted to highlight how, in variety terms, the larger the shelf the less variety, and
the larger the tail the more variety. Each data point in the plot represents an artist, coloured
with respect to their generalized genre value. The solid blue diagonal line, running from the
bottom left to the top right, signifies the vector of values where the sum of 𝑆 ̄ and 𝑇 ̄ is 100, which
813
Tail_Minus_Uniques Shelf_Minus_The100s Tail_Minus_Uniques Shelf_Minus_The100s
Uniques The100s Uniques The100s
Metal for Muthas Ozzfest 1999
Iron Maiden Tour 1980 Livin La Vida Loco
Killer World Tour World Domination Tour
The Beast on the Road Ozzfest 2001
World Piece Pledge of Allegiance
World Slavery Tour European Iowa Tour 2K2
Somewhere On Tour
Seventh Tour of a Seventh Tour Jägermeister Music Tour (Spring 2004)
No Prayer on the Road European Open Air Tour 2004
Fear of the Dark Ozzfest 2004
Tour Name
Tour Name
A Real Live Tour The Unholy Alliance Tour 2004 2005
The X Factour Subliminal Verses World Tour 2005
Virtual XI World Tour Mayhem Festival 2008
The Ed Hunter Tour All Hope is Gone
Brave New World Mayhem Festival 2012
Give Me Ed... 'til I'm Dead Memorial World Tour
Dance of Death Summer's Last Stand Tour
Eddie Rips Up the World Prepare For Hell Tour
A Matter of Life and Death North American Summer Tour 2016
Somewhere Back in Time
The Final Frontier World Tour Knotfest Roadshow
Maiden England We Are Not Your Kind
The Book of Souls World Tour Knotfest Roadshow 2021
Legacy of the Beast Knotfest Roadshow 2022
The Future Past The End, So Far
−100 −50 0 50 100 −100 −50 0 50 100
Relative Size Relative Size
(a) Iron Maiden – All Tours Shelf & 100%’ers and (b) Slipknot – All Tours Shelf & 100%’ers and Tail
Tail & Uniques & Uniques
Tail_Minus_TailCovers Shelf_Minus_ShelfCovers Tail_Minus_Uniques Shelf_Minus_The100s
TailCovers ShelfCovers Uniques The100s
Pink Floyd World Tour 1968
Fearless
The Man and The Journey
Atom Heart Mother World Tour
Speak Now World Tour
Meddle U.S. Tour 1971
Dark Side of the Moon
Tour Name
Tour Name
The Red Tour
British Winter Tour 1974
Wish You Were Here
The 1989 World Tour
In the Flesh
The Wall
reputation Stadium Tour
A Momentary Lapse of Reason
Another Lapse
The Eras Tour
The Division Bell
−100 −50 0 50 100 −100 −50 0 50 100
Relative Size Relative Size
(c) Taylor Swift – All Tours Shelf & Covers and Tail (d) Pink Floyd – All Tours Shelf & 100%’ers and Tail
& Covers & Uniques
Figure 6: All Tours Analysis - Various Artists
would signify that 100% of songs (in all the artist’s tours) are contained within shelves and tails.
Therefore, each artist’s distance to this line signifies their average combined shelves and tail
size. The solid red diagonal line, running from the top left to the the bottom right of the plot,
signifies the set of pairs of equal 𝑆 ̄ and 𝑇 ̄ values. Artists sitting on this line have equally sized
average shelf (𝑆)̄ and tail (𝑇 ̄ ) values, artists that sit above the line have a greater average tail
than shelf suggesting more variety, and artists that sit below the line have a greater average
shelf then tail suggesting less variety. The distance each artist is from this line represents the
strength of this property. Artists further towards the bottom left of the plot represent those
with much larger shelves and smaller tails on average, suggesting they are the artists with the
least variety. Artists further towards the top right of the plot represent those with smaller
shelves and larger tails on average, suggesting they are the artists with the most variety. Such
a visualisation, which preserves the dimensions of the shelf and the tail separately, enables
nuanced comparisons within this multi-dimensional space [3]. This facilitates highlighting,
for example, differences between artists who are equidistant from the solid red diagonal line
but vary in their distance from the solid blue line.
Further analysis can consider shelves and tails not including the songs in them that are cover
songs, through computing average shelf and tail values for each artist in relation to this, and
814
umphrey's−mcgee
0
u.d.o. grateful−dead frank−zappa
céline−dion nofx pearl−jam red−hot−chili−peppers yo−la−tengo
iced−earth
jethro−tull cheap−trick frank−turner
two−door−cinema−club
elton−john−and−billy−joel
primus
faith−no−more billy−joel
you−me−at−six queens−of−the−stone−age r.e.m.
wilco beck
pj−harvey elton−john foo−fighters
foals limp−bizkit
kaiser−chiefs aerosmith eric−clapton
him trivium radiohead bon−jovi the−killers
the−national
kings−of−leonbiffy−clyro deftones coldplay
pixies rise−against bruce−springsteen
heart imagine−dragons
yellowcard
thirty−seconds−to−mars taylor−swift
−25 lacuna−coil epica
kasabian arcade−fire green−day
black−sabbath dream−theater u2
bob−dylan
interpol incubus
the−offspring
the−cult journey
whitesnake
judas−priest exodus oasis
new−found−glory
rammstein kiss
metallica weird−al−yankovic
Mean Shelf
in−flames
rob−zombie the−rolling−stones
zz−top tool
nickelback yes
−50 avenged−sevenfold
velvet−revolver acdc disturbed fall−out−boy
linkin−park van−halen queen
slayer paramore opeth
lady−gaga
tina−turner korn paul−mccartney
cher def−leppard
sigur−rós eagles
iron−maiden depeche−mode
slipknot
beyoncé
−75 pink−floyd madonna
roger−waters
GeneralizedGenre
ghost a Alternative
britney−spears a ElectronicAndDance
a Folk
rush
a Metal
a Pop
a Progressive
a Punk
−100 a Rock
0 25 50 75 100
Mean Tail
Figure 7: All Artists Comparisons - Average Tail Vs Average Shelf
creating a scatter plot of these results, as shown in Figure 8. In this plot, we see how some
artists, that play a lot of covers within their tail, move further away from the top right of
the plot, highlighting the importance, for some artists, of playing cover songs as part of their
attainment of variety.15
From Figures 7 and 8’s data, additional analysis can compute overall averages for each gen-
eralized genre. Calculated genre averages, for Tail and Shelf values are shown in Figure 9a.
The plot highlights how genres, such as Electronic and Dance, and Pop, on average exhibit
less variety that other genres, such as Folk, Alternative, and Punk. Calculated genre averages,
when shelf and tail cover songs are not considered are shown in Figure 9b. Here, we observe
the impacts removing covers has on the genre averages, and how the impacts are greater for
some genres, such Rock and Alternative, than others, such as Punk.
Further analysis from Figure 7 and 8’s data can, through division of the 2-dimensional plot
15
Similarity we could explore utilising data pertaining to the amount of uniques and 100%’ers within shelves and
tails to, for example, use a weighting system to give these songs more impact.
815
u.d.o. umphrey's−mcgee
yo−la−tengo frank−zappa
iced−earth céline−dion
0
red−hot−chili−peppers nofx frank−turner
two−door−cinema−clubgrateful−dead
pearl−jam
eric−clapton primus
jethro−tull cheap−trick
you−me−at−six r.e.m.
billy−joel beck wilco
pj−harvey elton−john
foo−fighters
him aerosmith
the−national
pixies
imagine−dragons
biffy−clyro
green−day
−25
tina−turner taylor−swift
whitesnake
interpol the−offspring
velvet−revolver
oasis
Mean Shelf Minus Covers
zz−top cher
rob−zombie
avenged−sevenfold acdc yes
lady−gaga fall−out−boy
−50
opeth
linkin−park slayer lamb−of−god
korn
def−leppard paul−mccartney
sigur−rós
beyoncé
iron−maiden depeche−mode
slipknot
pink−floyd
−75 ghost
madonna
britney−spears roger−waters
GeneralizedGenre
a Alternative
a ElectronicAndDance
a Folk
rush a Metal
a Pop
a Progressive
a Punk
−100 a Rock
0 25 50 75 100
Mean Tail Minus Covers
Figure 8: All Artists Comparisons - Average Tail Minus Tail Covers Vs Average Shelf Minus Shelf Covers
space, cluster the artists into a set of ordinal clusters from very high variety, to very low variety.
For discussions and results from such clustering analysis see Appendix B.
Finally, in the pursuit of a single measure of variety for each artist, the shelf and tail values
of a tour are combined, to derive a single measure of Variety 𝑉 for each tour. For tour 𝑖, its
Variety measure 𝑉𝑖 can be calculated via:
𝑉𝑖 = 𝑇𝑖 − 𝑆𝑖 (11)
where 𝑇𝑖 is the Tail value of tour 𝑖 and 𝑆𝑖 is the Shelf value of tour 𝑖. A positive value represents
a tour with a tail larger than its shelf, suggesting more variety, and a negative value represents
a tour with a tail smaller than its shelf, suggesting less variety. From this, an average overall
variety 𝑉 ̄ value of an artist’s tours can be calculated via:
𝑛
1
𝑉̄ = ∑𝑉 (12)
𝑛 𝑖=1 𝑖
816
0 0
Folk Folk
Alternative
Alternative
−25 −25 Punk
Punk
Rock
Metal Rock
Metal
Progressive Progressive
Mean Shelf
Mean Shelf
ElectronicAndDance
ElectronicAndDance
Pop
−50 −50
Pop
−75 −75
GeneralizedGenre GeneralizedGenre
a Alternative a Alternative
a ElectronicAndDance a ElectronicAndDance
a Folk a Folk
a Metal a Metal
a Pop a Pop
a Progressive a Progressive
a Punk a Punk
−100 a Rock −100 a Rock
0 25 50 75 100 0 25 50 75 100
Mean Tail Mean Tail
(a) All Artists - Tail and Shelf analysis with Genre (b) All Artists - Tail without covers and Shelf with-
Averages out covers analysis with Genre Averages
Figure 9: Genre Averages Analysis
where 𝑉 ̄ is the average of the individual tour 𝑉 values, 𝑉𝑖 represents the Shelf value for Tour 𝑖,
and 𝑛 is the number of Tours for the artist. The set of all artists and their 𝑉 ̄ values, ordered with
respect to 𝑉 ̄ , and coloured with respect to generalized genre, is shown in Figure 10, providing
an overall visualisation of a corpus of artists with respect to variety.
3.6. Correlation Analysis of Variety with Other Features
To examine the robustness of our notion of variety for comparing different artists, despite their
varying characteristics, such as the number of tours, the length of performances, and activity
during different time periods, we conducted a correlation analysis between our 𝑉 ̄ Variety mea-
sure and such properties. Table 3 shows the correlation results for seven artist properties, along
with definitions of each property. The Correlation values are the correlation levels found be-
tween each of the seven properties and our 𝑉 ̄ measure. Here, correlation is calculated with
respect to Pearson Correlation CoefÏcient. For fuller descriptions and discussions of each of
these properties see Appendix C, which also contains visualisation scatter plots of each prop-
erty against our 𝑉 ̄ measure. Table 3 highlights how our measure has only very weak correlation
to these properties,16 suggesting our measure is robust for analysis between artists, and will
not be unduly bias by, for example, different artists having more tours or longer shows.
16
Where the semantics of correlation strength can be classified as - Very Weak Correlation: |𝑟| < 0.2, Weak Cor-
relation: 0.2 ≤ |𝑟| < 0.4, Moderate Correlation: 0.4 ≤ |𝑟| < 0.6, Strong Correlation: 0.6 ≤ |𝑟| < 0.8, Very Strong
Correlation: |𝑟| ≥ 0.8 [12]
817
umphrey's−mcgee
yo−la−tengo
frank−turner
red−hot−chili−peppers
billy−joel
taylor−swift
r.e.m.
pearl−jam
cheap−trick
frank−zappa
coldplay
the−killers
foo−fighters
beck
nofx
wilco
eric−clapton
grateful−dead
bon−jovi
bruce−springsteen
green−day
faith−no−more
céline−dion
imagine−dragons
the−national
aerosmith
queens−of−the−stone−age
jethro−tull
u.d.o.
tom−petty−and−the−heartbreakers
rise−against
primus
deftones
limp−bizkit
arcade−fire
thirty−seconds−to−mars
bob−dylan
iced−earth
elton−john−and−billy−joel
elton−john
kasabian
kaiser−chiefs
radiohead
foals
the−flaming−lips
biffy−clyro
nick−cave−&−the−bad−seeds
heart
the−offspring
trivium
jimmy−eat−world
epica
u2
halestorm
pj−harvey
death−cab−for−cutie
two−door−cinema−club
kings−of−leon
you−me−at−six
arctic−monkeys
journey
whitesnake
him
soundgarden
system−of−a−down
the−smashing−pumpkins
alice−in−chains
yellowcard
florence−+−the−machine
dream−theater
incubus
papa−roach
oasis
GeneralizedGenre
pixies
jane's−addiction Alternative
Artist Name
the−who
black−sabbath
editors ElectronicAndDance
new−found−glory
bullet−for−my−valentine Folk
franz−ferdinand
deep−purple
anthrax
Metal
volbeat
neil−young Pop
enter−shikari
testament Progressive
placebo
weird−al−yankovic
lacuna−coil
Punk
blink−182 Rock
peter−gabriel
weezer
the−used
helloween
david−bowie
five−finger−death−punch
children−of−bodom
twenty−one−pilots
the−black−keys
within−temptation
garbage
scorpions
yes
queensrÿche
the−cult
muse
the−rolling−stones
megadeth
exodus
motörhead
marilyn−manson
noel−gallagher's−high−flying−birds
interpol
ozzy−osbourne
kiss
killswitch−engage
fall−out−boy
metallica
nine−inch−nails
nickelback
mastodon
lynyrd−skynyrd
tool
nightwish
the−cure
queen
in−flames
rammstein
disturbed
rob−zombie
judas−priest
acdc
van−halen
lady−gaga
opeth
guns−n'−roses
zz−top
avenged−sevenfold
velvet−revolver
paul−mccartney
lamb−of−god
mötley−crüe
slayer
paramore
linkin−park
korn
def−leppard
eagles
tina−turner
cher
depeche−mode
beyoncé
sigur−rós
iron−maiden
slipknot
madonna
roger−waters
pink−floyd
ghost
britney−spears
rush
−100
−50
0
50
100
Average Variety
Figure 10: All Artists, Variety analysis, coloured by Generalized Genre
4. Conclusions
In this paper, we explored data acquisition and processing of musical artists’ touring histories,
and proposed an approach to explore set-list variety, at tour level, artist career level, and for
818
Table 3
Correlation Analysis
Property Name Description Correlation
Number of Tours The total number of tours -0.1786
Total Number of Shows The total number of shows from all tours -0.0725
Length of Tours The average number of shows per tour 0.0908
Average Show Length: The average show legnth in terms of number of songs 0.0952
H-Index The careear H-Index, where an artist has a h-index 0.0630
of h if they have played h songs at least h times each
Artist Start Date The formation incarnation date of the artist (for 0.0933
(Groups Only) groups only)
Amount Time Period The amount time active in terms of years (for groups -0.0952
(Groups Only) only)
comparisons between artists. Our approach proposed the notions of a shelf and a tail, to aid
explorations of, and to quantify, variety at tour level and artist level. Furthermore, the ap-
proach explores the impact of cover songs on these notions of variety, and explores variety
comparisons between different musical genres. The analysis of variety highlighted the diver-
sity among artists, in terms of a prevalence to lean towards playing more conformative or more
diverse shows. Additional correlation analysis explored the robustness of the proposed notion
of variety, with respect to differing artist properties, such as the number of tours or the average
lengths of shows.
From our data processing, some data quality issues were uncovered, such as incomplete or
empty data, for which such instances can be flagged, and filtering thresholds utilised. Gen-
erally, we found more setlist.fm data issues for older tours and shows, and for less popular
artists. Additionally, setlist.fm provides set-list data without details on other potential set-list
semantics, such as variations in how a song is performed or the inclusion of special elements
like artist monologues or other forms of communication during a show. Therefore,future work
will explore integrating additional data sources, such as artist fan community databases, to en-
rich our dataset and model, offering potential for incremental improvements. Additionally,
future work will investigate incorporating our analysis into live music recommender systems,
which suggest items based on user preferences [4]. Given that factors such as variety and diver-
sity have become increasingly important in this field [18], our analysis may provide valuable
insights.
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over and over? Tech. rep. Tableau, 2018. url: https://www.tableau.com/blog/data-music
-which-artists-use-same-old-setlists-gig-after-gig.
[29] A. Rodriguez. Metallica bases its setlist on what fans listen to on Spotify. 2018. url: https:
//qz.com/1340887/metallica-bases-its-setlist-on-what-fans-listen-to-on-spotify.
[30] M. Rodriguez, V. Gintautas, and A. Pepe. “A Grateful Dead Analysis: The Relationship
Between Concert and Listening Behavior”. In: First Monday 14 (2008). doi: 10.5210/fm.v
14i1.2273.
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[31] M. Roffman. The 25 Best Rock Acts with Unique Setlists. 2015. url: https://consequence.n
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[32] D. Silver, M. Lee, and C. C. Childress. “Genre Complexes in Popular Music”. In: Plos One
11.5 (2016), e0155471. url: https://doi.org/10.1371/journal.pone.0155471.
[33] F. Thalmann, E. Nakamura, and K. Yoshii. “Tracking The Evolution Of A Band’s Live Per-
formances Over Decades”. In: Proc. of the 23rd Int. Society for Music Information Retrieval
Conf. 2022, pp. 850–857. url: https://madmom.readthedocs.io.
A. Exploration of Shelf and Tail Parameter Values
To aid selection of pertinent 𝑆𝑆 and 𝑇 𝑆 parameters, and to aid understanding our of dataset,
sensitively analysis experimentation was performed exploring the average percentage of tour
songs that are contained within different combined shelf and tail size parameter values. The
impact of experimentation with different sized shelf and tail values is shown in Figure 11. The
x-axis denotes different combined sizes of shelf and tail values (so 20 represents where the shelf
and tail values are both 10) and the y-axis denotes the overall average percentage of songs that
are contained within the combined shelf and tail.
Figure 11 highlights how, due to the general trend observed for tours to map out an s shaped
sigmoidal like function, there is a pattern that the percentage of songs contained within the
shelf and tail is greater than the percentile values denoting the shelf and tail size. From example,
a shelf and tail percentile size both of 10% (20% combined value) results in over 50% on average
of tours’ songs being contained within this 20% percentile space. Moreover, these values denote
a point in the plot where the decrease of the gradient of the line is levelling off, suggesting their
suitability as shelf and tail percentile sizes.
Further analysis could explore additional experimentation such as, breaking the results down
to see the separate contributions of the shelf and tail to the total, using unequal shelf and tail
size values, and exploring differences within the results when the data is subsetted for features
such as genre.
B. Ordinal Clustering Analysis
From calculations of a pair of 𝑆 ̄ and 𝑇 ̄ values for every artist, the whole set of artists can be
visualised within a single scatter plot, as shown in Figure 7, and from calculations of shelves and
tails not including cover songs, the set of artists can be visualised within a single scatter plot, as
shown in Figure 8. Further analysis of the data in Figures 7 and 8 can cluster artists into ordinal
groups by dividing the 2-dimensional plot space. Variety can be viewed as a combination of
levels of shelves and tails, with the plot space divided accordingly, as shown in Figure 12a, here,
for average shelf and tail values for each artist. In this plot, the dotted red lines divide the space
into 8 levels of variety, representing 8 ordinal clusters. Each artist belongs to only one cluster,
as shown by the data point colours in Figure 12a. The membership constraints of each of the 8
clusters can be defined in terms of mean shelf (𝑆)̄ and mean tail (𝑇 ̄ ) value ranges, and assigned
semantic ordinal names such as:
822
100
100
90.53
90
80.29
80 74.76
68.67
Mean Tail And Shelf Percent
70 65.08
61.39
60
52.99
50
46.79
40.57
40
30 26.44
16.32
20 12.13
10
0
0
0 10 20 30 40 50 60 70 80 90 100
Combined Shelf and Tail Values
Figure 11: Shelf and Tail Parameter Size Impact on Overall Percentage of Songs Included
1. Very High Variety: Where the difference between the mean tail and mean shelf value
is greater than or equal to 75.
2. High Variety: Where the difference between the mean tail and mean shelf value is
greater than or equal to 50 and less than 75.
3. Moderate Variety: Where the difference between the mean tail and mean shelf value
is greater than or equal to 25 and less than 50.
4. Low Variety: Where the difference between the mean tail and mean shelf value is
greater than or equal to 0 and less than 25.
5. Low Uniformity: Where the difference between the mean tail and mean shelf value is
greater than or equal to -25 and less than 0.
6. Moderate Uniformity: Where the difference between the mean tail and mean shelf
value is greater than or equal to -50 and less than -25.
7. High Uniformity: Where the difference between the mean tail and mean shelf value is
greater than or equal to -75 and less than -50.
8. Very High Uniformity: Where the difference between the mean tail and mean shelf
value is less than -75.
Similar analysis can be conducted for data calculations of shelves and tails not including
any songs that are cover songs, as shown in Figure 12b. Here, we observe that when covers
are excluded, some artists shift in the plot to the extent that they belong to a different cluster,
invariably one with less variety. Within such cluster analysis, the % of artists in each cluster can
be computed. The breakdown of each cluster artist %, for both data including cover songs, and
data not considering cover songs, is shown in Table 4. The Table highlights how when cover
songs are not considered, cluster memberships exhibit a general trend for the distribution to
move from variety to uniformity.
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u.d.o. umphrey's−mcgee u.d.o.
0 frank−zappa yo−la−tengo frank−zappa
iced−earth céline−dion
grateful−dead 0 umphrey's−mcgee
iced−earth
céline−dion
pearl−jam red−hot−chili−peppers yo−la−tengo red−hot−chili−peppers nofx frank−turner
elton−john−and−billy−joel grateful−dead
two−door−cinema−club
nofx cheap−trick frank−turner pearl−jam
two−door−cinema−club jethro−tull r.e.m. eric−clapton primus
primus faith−no−more billy−joel jethro−tull cheap−trick
you−me−at−six elton−john you−me−at−six r.e.m.
queens−of−the−stone−age wilco beck beck
kaiser−chiefs billy−joel wilco
eric−clapton foo−fighters pj−harvey elton−john
pj−harvey limp−bizkit
foals foo−fighters
radiohead aerosmith bon−jovi the−killers him aerosmith
him trivium
deftones the−national the−national
coldplay pixies
pixies kings−of−leon biffy−clyro bruce−springsteen imagine−dragons
heart rise−against imagine−dragons biffy−clyro
yellowcard
thirty−seconds−to−mars green−day green−day
−25 lacuna−coil −25
epica arcade−fire taylor−swift taylor−swift
kasabian tina−turner
interpol u2 whitesnake
bob−dylan
incubus interpol the−offspring
the−offspring
the−cult journey velvet−revolver
oasis
judas−priest
in−flames kiss the−rolling−stones weird−al−yankovic zz−top cher
zz−top yes rob−zombie
tool rob−zombie
Mean Shelf
yes
Mean Shelf
avenged−sevenfold acdc nickelback avenged−sevenfold acdc
lady−gaga fall−out−boy
−50 fall−out−boy −50
velvet−revolver disturbed opeth
linkin−park van−halen queen linkin−park
slayer opeth slayer lamb−of−god
lady−gaga korn
paramore korn
paul−mccartney
tina−turner
cher def−leppard def−leppard paul−mccartney
sigur−rós sigur−rós
eagles beyoncé
iron−maiden depeche−mode iron−maiden depeche−mode
slipknot slipknot
beyoncé
pink−floyd
−75 pink−floyd madonna −75 ghost
madonna
ghost
roger−waters Cluster britney−spears roger−waters Cluster
a High Variety a High Variety
britney−spears
a Moderate Variety rush
a Moderate Variety
a Low Variety a Low Variety
rush a Low Uniformity a Low Uniformity
a Moderate Uniformity a Moderate Uniformity
a High Uniformity a High Uniformity
−100
a Very High Uniformity −100
a Very High Uniformity
0 25 50 75 100 0 25 50 75 100
Mean Tail Mean Tail
(a) All Artists Average Tail Vs Average Shelf Clus- (b) All Artists Average Tail Without Covers Vs Av-
ter Membership erage Shelf Without Covers Cluster Member-
ship
Figure 12: Cluster Membership Analysis
Table 4
Cluster Membership Breakdown Percentages
Cluster Name Artist % (Including Covers) Artist % (Not Including Covers)
Very High Variety 0.00 0.00
High Variety 1.85 0.62
Moderate Variety 12.96 2.47
Low Variety 19.14 25.31
Low Uniformity 40.74 43.21
Moderate Uniformity 17.90 21.60
High Uniformity 5.56 6.17
Very High Uniformity 1.85 0.62
C. Correlation Investigation and Discussions of Variety
Our analysis of artist variety explores comparisons of shelves and tails, and our single artist
level measure of variety, for comparisons between artists. The robustness of our notion of
variety can be explored for its capability to compare different tours and different artists, even
though different artists have different career characteristics, in terms of properties such as the
number of tours, tour show count sizes, show lengths, being active and touring within different
time periods and more. We explore the presence of correlation between our 𝑉 ̄ variety measure
and such properties, with the Pearson Correlation CoefÏcient calculated via:
𝑛(∑ 𝑥𝑦) − (∑ 𝑥)(∑ 𝑦)
𝑟= (13)
2 2 2 2
√[𝑛 ∑ 𝑥 − (∑ 𝑥) ][𝑛 ∑ 𝑦 − (∑ 𝑦) ]
824
The explored properties and results are shown in Table 5, and visualisation scatter plots of each
property against our 𝑉 ̄ measure are shown in Figures 13 – 16. Next, each property is outlined
and discussed.
Number of Tours: Our dataset includes artists with varying numbers of tours. Figure 13a
compares each artist’s tour count with their 𝑉 ̄ , showing no strong relationship between the
variables. The Pearson Correlation CoefÏcient value for these two variables is -0.1786.
Total Number of Shows: For the artists in our dataset, the overall number of shows varies.
Comparisons between artists’ total show count and their 𝑉 ̄ is shown in Figure 13b. Here, the
plot highlights there is no strong relationship between these variables; the Pearson Correlation
CoefÏcient value for these two variables is -0.0725.
Length of Tours: Within our dataset, the number of shows within our artists’ tours differs.
Comparisons between each artist’s average tour show count and their 𝑉 ̄ is shown in Figure 14a,
and the correlation value between these variables is 0.0908. The outlier in the plot is due to the
fact Bob Dylan’s ”Never Ending Tour” is ofÏcially billed as a single continuous tour spanning
from 1988 to present day and contains around 4000 shows. A plot with Bob Dylan excluded to
aid readability is shown in Figure 14b.
Average Show Length: For our set of artists, their average show length, in terms of the num-
ber of songs in the set-lists, varies. Comparisons of each artist’s average show Length and their
𝑉 ̄ is shown in Figure 15a, and the correlation value between these variables is 0.0952.
H-Index: The H-index is a scientific output metric that quantifies both the productivity and
citation impact of a researcher’s publications, where a scholar has an index of h if h of their
papers have been cited at least h times each [14]. We could consider this notion within the set-
list domain as a measure for an artist (instead of a researcher) and songs (instead of papers),
where an artist has a h-index of h if they have played h songs at least h times each. To calculate
this for each artist, the set of tours for an artist is combined and overall songs counts calculated
from which the H-Index can then be derived from. The plot comparing artists’ H-Index against
𝑉 ̄ is shown in Figure 15b, and the correlation value between these variables is 0.0630.
Artist Start Dates and Amount of Active Years (Groups Only): For the groups within our
artist dataset, information pertaining to the groups formation start date and, if applicable, dis-
bandment date (denoted as present day for still going groups) is retrieved from MusicBrainz.
Comparisons of each group’s start date and their 𝑉 ̄ is shown in Figure 16a, and the correlation
value between these variables is 0.0933. From this timeline data, the length of time each group
were/have been active for can be calculated (with groups that are still active measured up to
present day). Comparisons of each group’s Years Active and their 𝑉 ̄ is shown in Figure 16b,
and the correlation value between these variables is -0.0952.
The set of correlation values for these properties against 𝑉 ̄ is shown in Table 5. Considering
correlation strength classified as, Very Weak Correlation: |𝑟| < 0.2, Weak Correlation: 0.2 ≤
|𝑟| < 0.4, Moderate Correlation: 0.4 ≤ |𝑟| < 0.6, Strong Correlation: 0.6 ≤ |𝑟| < 0.8, Very
Strong Correlation: |𝑟| ≥ 0.8 [12], Table 5 highlights how our 𝑉 ̄ variety measure has only very
weak correlation to all these properties. This suggests the measure is robust across the varying
properties for our artists, and that comparisons between artists are not unduly impacted by
these variations.
825
Table 5
Correlation Data Analysis
Property Name Correlation Min Value Max Value Standard Deviation
Number of Tours -0.1786 5 38 6.2613
Total Number of Shows -0.0725 209 3541 518.9224
Length of Tours 0.0908 23.87 393.4 35.7931
Average Show Length 0.0952 12.87 57.52 5.7834
H-Index 0.0630 28 126 15.6733
Artist Start Date (Groups Only) 0.0933 1962-01-01 2011-01-01 –
Active Time in years (Groups Only) -0.0952 5.999 62.52 11.6033
bob−dylan
slayer
korn
elton−john−and−billy−joel
3000
elton−john
30
yes elton−john
elton−john−and−billy−joel
kiss jethro−tull
Total Number of Shows
the−cure megadeth
iron−maiden iron−maiden
Number of Tours
queensrÿche kiss
bruce−springsteen bruce−springsteen
def−leppard in−flames frank−zappa
metallica 2000 def−leppard yes
rush
20 marilyn−manson dream−theater slayer queensrÿche deep−purple
ghost
zz−top weezer incubus aerosmith metallica dream−theater
slipknot
rush acdc
depeche−mode weird−al−yankovic aerosmith
the−who
van−halen grateful−dead depeche−mode judas−priest
mötley−crüe u2
judas−priest megadeth jethro−tull bon−jovi
frank−turner korn
eagles acdc bon−jovi van−halen u2
pixies mötley−crüe frank−turner
primus wilco r.e.m. umphrey's−mcgee marilyn−manson muse
eagles red−hot−chili−peppers
madonna pearl−jam coldplay
red−hot−chili−peppers 1000 tina−turner zz−top deftones r.e.m.
10 pink−floyd paramore beck billy−joel foo−fighters
billy−joel
britney−spears roger−waters slipknot eric−clapton wilco
coldplay frank−zappa
sigur−rós cher
britney−spears madonna
roger−waters cheap−trick yo−la−tengo the−killers taylor−swift
beyoncé nofx taylor−swift ghost pink−floyd
cher the−killers beyoncé opeth pearl−jam cheap−trick umphrey's−mcgee
paramore beck
yo−la−tengo
sigur−rós velvet−revolver
you−me−at−six nofx
0 0
−100 −50 0 50 100 −100 −50 0 50 100
Variety Variety
(a) Number of Tours Vs 𝑉 ̄ (b) Total Number of Shows Vs 𝑉 ̄
Figure 13: Number of Tours and Total Number of Shows Correlation Analysis
826
400 cher
bob−dylan tina−turner
150
the−killers
Length of Tours (Average Shows PerTour)
Length of Tours (Average Shows PerTour)
placebo
arctic−monkeys rise−against coldplay
300 kasabian
acdc the−national
imagine−dragons red−hot−chili−peppers
roger−waters deep−purple
queens−of−the−stone−age bon−jovi
the−black−keys
100 nightwish
biffy−clyro faith−no−more
iron−maiden
oasiscéline−dion taylor−swift
weird−al−yankovic
def−leppard editors bruce−springsteen billy−joel
britney−spears eagles van−halen green−day r.e.m.
beyoncé foo−fighters
200 rush eric−clapton
depeche−mode frank−turner
radiohead
pink−floyd
cher arcade−fire
tina−turner madonna u.d.o. wilco
arctic−monkeys
the−killers jethro−tull
placebo rise−against cheap−trick
the−national coldplay 50 slayer yellowcard
roger−waters acdc primus pearl−jam yo−la−tengo
deep−purple kasabian bon−jovi slipknot
korn opeth
iron−maiden the−offspringbeck umphrey's−mcgee
100 britney−spears red−hot−chili−peppers sigur−rós the−cure incubus
limp−bizkit frank−zappa
beyoncé linkin−park neil−young
ghost nofx
rush pink−floyd frank−turner paramore jane's−addiction the−flaming−lips
wilco
madonna cheap−trick yo−la−tengo rob−zombie
grateful−dead
slipknot beck pearl−jam
umphrey's−mcgee
sigur−rós nofx
ghost rob−zombie grateful−dead
0 0
−100 −50 0 50 100 −100 −50 0 50 100
Variety Variety
(a) Length of Tours Vs 𝑉 ̄ (b) Length of Tours Vs 𝑉 ̄ (sans Bob Dylan)
Figure 14: Length of Tours Correlation Analysis
60
nofx
bob−dylan
bruce−springsteen
pearl−jam 100
anthrax rush dream−theater
the−cure
paul−mccartney weird−al−yankovic jethro−tull
r.e.m.
40
Overall Careear H−Index
u2
depeche−mode queensrÿche david−bowie
Average Show Length
iron−maiden van−halen muse elton−john bon−jovi pearl−jam
the−cure neil−young madonna yes
primus grateful−dead metallica placebo aerosmith taylor−swift
linkin−park paul−mccartney the−offspring def−leppard slayer
cheap−trick primus coldplay red−hot−chili−peppers
paramore opeth scorpions roger−waters
kiss
the−flaming−lips beck korn
eagles guns−n'−roses pixies pixies radiohead wilco foo−fighters
eagles
britney−spears nofx
rush beyoncé exodus heart wilco beyoncé zz−top frank−turner
zz−top
roger−waters limp−bizkit r.e.m. yo−la−tengo tina−turner green−day billy−joel umphrey's−mcgee
pink−floyd beck
ghost taylor−swift 50 slipknot yo−la−tengo
20 cher the−killers
pink−floyd ghost
rob−zombie limp−bizkit
britney−spears umphrey's−mcgee
sigur−rós sigur−rós lynyrd−skynyrd foals
lamb−of−god two−door−cinema−club
iron−maiden epica five−finger−death−punch
tool
foals velvet−revolver jane's−addiction
0 0
−100 −50 0 50 100 −100 −50 0 50 100
Variety Variety
(a) Average Show Length Vs 𝑉 ̄ (b) H-Index Vs 𝑉 ̄
Figure 15: Average Show Length and H-Index Correlation Analysis
827
noel−gallagher's−high−flying−birds the−rolling−stones
twenty−one−pilots lynyrd−skynyrd scorpions
ghost imagine−dragons 60 the−who
florence−+−the−machine two−door−cinema−club
yes jethro−tull
five−finger−death−punch you−me−at−six foals judas−priest
deep−purple
paramore zz−top
bullet−for−my−valentine aerosmith
kaiser−chiefs eagles queen journey
velvet−revolver acdc heart
arcade−fire pink−floyd iron−maiden kiss cheap−trick
the−killers black−sabbath
2000 lamb−of−god mastodon trivium rise−against rush van−halen the−cure u2
the−national
linkin−park interpol def−leppard exodus whitesnake
umphrey's−mcgee
Length of Active Time (Years)
kasabian coldplay depeche−mode mötley−crüe queensrÿche
slipknot disturbed biffy−clyro queens−of−the−stone−age
metallica anthrax bon−jovi
nickelback foo−fighters
guns−n'−roses pixies nofx
sigur−rós weezer limp−bizkit wilco 40 slayer primus u.d.o. yo−la−tengo
Active Start Date
korn him radiohead nine−inch−nails jane's−addiction iced−earth green−day
in−flamesoasis pearl−jam
tool incubus deftones opeth tool deftones pearl−jam
opeth iced−earth incubus
nine−inch−nails marilyn−manson green−day in−flames r.e.m.
radiohead
jane's−addiction alice−in−chains u.d.o. sigur−rós wilco
korn
pixies primus yo−la−tengo limp−bizkit foo−fighters
guns−n'−roses nofx slipknot disturbed biffy−clyro
dream−theater nightwish interpol coldplay umphrey's−mcgee
mötley−crüe the−cult bon−jovi red−hot−chili−peppers linkin−park
anthrax rise−against the−national
slayer metallica queensrÿche avenged−sevenfold
1980 depeche−mode arcade−fire the−killers
r.e.m. paramore
def−leppard exodus whitesnake 20 you−me−at−six kaiser−chiefs
the−cure motörhead five−finger−death−punch foals
tom−petty−and−the−heartbreakers oasis
iron−maiden acdc u2 imagine−dragons
kiss cheap−trick ghost florence−+−the−machine
van−halen twenty−one−pilots two−door−cinema−club
journey heart noel−gallagher's−high−flying−birds
queen aerosmith
eagles judas−priest
rush
black−sabbath
zz−top
yes deep−purple jethro−tull velvet−revolver
pink−floyd scorpions grateful−dead
lynyrd−skynyrd the−who
0
the−rolling−stones
1960
−100 −50 0 50 100 −100 −50 0 50 100
Variety Variety
(a) Artist Start Date (Groups Only) Vs 𝑉 ̄ (b) Artist Active Years (Groups Only) Vs 𝑉 ̄
Figure 16: Start Date and Active Years Correlation Analysis (Groups Only)
828