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 diferent 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 diferent 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 difering artist attributes, such as the number of tours and show lengths.
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 medi2a5][. 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 expectations, and the degree of variety in an artist’s performances is an intriguing topic with broader applications, such as historical live performance recommender system1]s. [
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 featuring deep cuts, and everything in between. Additional correlation analysis of our concept of variety explores how our measure remains robust across artists with difering characteristics, such as the number of tours or average show lengths. For more information about the project and its data, see the project’sGitHub repositor,y3 and to interactively explore the data and the proposed approach, see the project’Isnteractive Web App4.
The rest of the paper is structured as follows: Section 2 covers background literature, Section
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 scal2e3][, 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].
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 [
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” [
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 diferent 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 performances 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 diferent artists is carried out, to explore the prominence of unique (and quite unique) song occurrence rates. Comparisons highlight how The Grateful 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 2[0]. 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 diferent songs, resulting in a value of 1 if the the set-list is identical to the previous set, and -1 if it is completely diferent. From this, an overall average is derived for each artist, from which comparisons and clustering of diferent artists can be performed2[8]. 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 variety, contrasting their approach with other artist’s that play identical sets nigh2tl1y].[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 diversity2[9].
In our approach, raw artist tour data is first collected and processed, then utilized in various stages of analysis, as illustrated in Figu1r.eCommunities such as MusicBrain5z 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.
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 Figur2e. 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 AP8It,he MBID for each artist name is acquired, along with additional Music Brainz artist information including their Gen
8https://musicbrainz.org/doc/MusicBrainz_API (utilising the musicbrainz API wrapper R package https://github.com/dmi3kno/musicbrainz der (one of Male, Female, or Group), and theSitrart 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 selection 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 AP1I0. This data is stored as depicted in the Artist table in Figu2r.e
The determined single genres for each artist result in a large number of diferent 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 Tab1l.eThis data is stored as depicted in the Genre table in Figur2e.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 Figu2r.eFor 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 recorde12d.The song information is stored as depicted in the Song table 9For 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”. 10https://api.setlist.fm/docs/1.0/index.html (utilising the SetListR wrapper R package https://github.com/fusionet24/SetListR 11The assignment of a single genre to each artist, followed by the mapping of these genres to a broader set of generalized 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 crossover3[2]. Given such complexities, the automation of genre classification represents a compelling area for future exploration. 12Additionally, tours that are empty (made up of only empty shows) are removed, as are any artists for which all in Figure2.
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’sGitHub repository1.3 All of the data acquired is stored as raw data, as depicted in Figu1,rteo then be utilised within the analysis stage.
Before beginning analysis, pre-processing of the raw dataset is performed. Within our analysis, 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 minimum 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. 13https://github.com/EdAbel/setlist-variety shows from the tour1.4 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 parameter values utilised are shown in Tabl2e. Following the pre-processing stage, we begin the analysis at individual tour level.
For a tour, each (unique) song has a Play Count ( ), 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) values can be normalised with respect to the number of shows in the tour. For a tour, a Relative Play Count ( ) for each song played on the tour can be computed via: = ( PC ) ∗ 100 (1) Where is the value of the -th song and is the number of shows in the tour. A 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 values, where the y-axis denote s value, and the x-axis denotes song number where songs are sorted with respect to values high to low. For example, Bruce Springsteen (and the E-Street Band’s) 2023 tour, is shown in Figu3rae,and Coldplay’s Music of the Spheres 2023 Tour is shown in Figu3rbe. 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 oSfhelf, 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’sShelf 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 ( ) value, denoting what top percentile of tour songs are to be considered part of the shelf, a Shelf Val uecan be calculated as the ratio of a 14Such 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. yoC 06 a l from high to low with respect to the i r
values: tour’s songs that are in its shelf. Given the tour’s set oXf songs, of length , sorted as, 1 to , The Shelf Songs are selected as the top
percentile ofX: and is calculated via:
X = { 1, 2, … , }
where 1 ≥ 2 ≥ ⋯ ≥ tour’s shows, and the corresponding value represents the ratio of the tour’s songs that are played at 90+% of the tour’s shows. So, an value of 0.25 would represent that 25% of the tour’s value of 10% would select all the songs that have been played at 90% or more of a songs are played at 90% or more of its shows.
Tail - The notion of a tour’sTail 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 Figure3. Given a Tail Size ( ) value, denoting what bottom percentile of tour songs are to be considered a part of the tail, a Tail Val uecan be calculated as the ratio of a tour’s songs that are in its tail. TheTail Songs are selected as the bottom percentile ofX:
Tail Songs = Bottom Percentile= { ∈ X| ≤ (X)} and is calculated via: 0 8 0 2 yoC 06 a l =
|Shelf Songs| = |Tail Songs| (2) (3) (4) (5) (6) = = be identified as the set of songs that have = 100 ratio of a tour’s songs that are in this subset.
A value of 10% would select the songs that are played at most at 10% of a tour’s shows, and the correspondin g value represents the ratio of the tour’s songs that are played at 10% or less of the tour’s shows. So, a value of 0.4 would represent that 40% of the tour’s songs are played at 10% or less of its shows. songs that are played at 100% of the tour’s shows. This subset of song1s0(0%′ 100%’ers - In the set of songs making up the tour’s shelf, there exists a subset of 0 or more ) can . A 100%’ers Value is calculated as the that are played only once during the whole tour. This subset of son g s ( Uniques - In the set of songs making up a tour’s tail, there exists a subset of 0 or more songs ) can be identified as the set of songs that have a = 1 . A Uniques Value is calculated as the ratio of a tour’s songs that are in this subset. (7) (8) Ratio Value
calculated.
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 calculated. Similarity, the set of tail songs minus those that are covers can be determined and a Tail Minus Covers
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 Figure4a, and Coldplay’s Music of the Spheres 2023 Tour in Figure4b. 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 , and
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 AppendiAx.
Such analysis can be utilised to explore and compare diferent tours from the same artist and to compare tours of diferent artists. For example, regarding other Bruce Springsteen tours, the plot for the Wrecking ball tour is shown in Figu4rceand the plot for the Bruce Springsteen on Broadway tour shown in Figur4ed. 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 Figur4ee, 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 Song No. 40
Song No.
P
P R
R 0
0 0 10 20 30 40 50 60 70 0 20 40 60 80 100 120 140 C 06 e itv 40 C 06 e itv 40 t n u o y a l C 06 e itv 40 a l e is shown in Figure4f, 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.
10 Song No.
15 Song No.
Cover Cover
Cover Cover Cover Cover 0 0 1 0 8 0 2 0 0 1 0 8 0 2 t n u o y a l t n u o y a l a l e C 06 e itv 40 0 0 1 0 8 0 2 t n u o y a l (e) Taylor Swift - Speak Now World Tour (f) Pink Floyd - The Wall Tour
For a single tour, its Shelf Val ueand Tail Valu e 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 Figure5a. 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 Valueand Uniques Value can be calculated. These pair of values represent values equal to or less than the to ura’nsd 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 Fig5ubr.e
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 Figur5ec. 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 and Tail Minus Covers Valu e . 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 Fi5gdu.re
Such analysis can be utilised to explore and compare the careers of diferent artists. For example, Figure6a shows the career of Iron Maiden, and Figur6eb 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 Figur6ec. Here, we observe how after her ifrst 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 diferent shelf and tail values for the tour’s of Pink Floyd are shown in Figur6ed. From this plot we observe stark diferences 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.
For an artist, Shel f and Tail 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 v ālcuaen be calculated via: 0 twoyo−ud−omore−−cainte−pemsjli−tixaoch−enadc−rl−jvuoeebhyanr−tkehaaltniosudne.−d−rb−.jooicll.hhyn−iejpofqsreiumlceféueolsnaignslrseaj−e−toetdhff−iluroiomtlnh−−pedtu−−elbsaltindzoofknafifeixttrh−aa−ancenghkr−oeeopz−asaepmmwpa−oiprittllrrhca−eicojakembriecc−rkceldar−p.ehto.omnt−.cfohoibl−i−iflilpgye−hpfjtorepaerenslrks−turner yuom−lpah−rteeyn'sg−omcgee −25 lacpuinxhaiieym−bselcalolcokikliwn−cgsasar−bdobfa−ttrlheivoiundmbreifafyrma−d−citloyhhreeoeapatiedhdcreeafatuor2tnetrhisksiarets−ytah−abesgie−aacnninoasnttiadorsnc−abatlrdoue−cb−meof−ianrser−spjorivignmirgeasteghteniene−−endk−aildcyleroarlsgdopnlasy taylor−swift
interpol the−cult incjuobuursney whittehsen−aokffespbrionbg−dylan judas−priest exodus oasis lfeh zzra−mtomp sinte−ifnlametosolkirsosb−zmoemtabnlileeicwan−icftkoheuelnb−daro−cwlgkleilnoirgyrdye−−ssatol−nyeasnkovic nS−50 avenged−sevevnefolvledt−revolver acdc disturbed fall−out−boy aeM slalinysektiiirgnnua−pr−pa−tarurarórkmsneorcrehekroopreenathgledlasedvf−yal−neg−pahpgaaalrednpaquul−emenccartney iron−maiden depeche−mode
slipknot beyoncé −75 madonna rush 0 25
50 Mean Tail creating a scatter plot of these results, as shown in Figu8r.e 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 variety1.5
From Figures7 and 8’s data, additional analysis can compute overall averages for each generalized genre. Calculated genre averages, for Tail and Shelf values are shown in Fi9gau.re 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 Figu9br.e 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 Figur7eand 8’s data can, through division of the 2-dimensional plot 15Similarity 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. twyoo−ud−omoer−−caint−iecspmheijx−idamh−−eaecurrgalpiv.ucrdrieab−xt.hytoicee.lasfeupcllt−téoodlnnine−rea.jer−ode.hdmdnipo−.rnhimboaityul−eloysrc−−ohljsaiojlmei−e−ttlihptehrenopg−bfpooteeuotcrhl−flskrefai−pgnnehnkaacto−eihtrmfwzirlxoe−saianlajpcagapopmil−nater−icdkrfaragnokn−stuumrnpehrrey's−mcgee biffy−clyro green−day whitesnake oasis where is the Tail value of tourand is the Shelf value of tour. 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 ̄ value of an artist’s tours can be calculated via: 25
50 Mean Tail Minus Covers = − =̄ 1 ∑ =1
GeneralizedGenre a Alternative a ElectronicAndDance a Folk a Metal a Pop a Progressive a Punk a Rock 75 100 (11) (12)
Alternative
Punk Metal Rock
Progressive ElectronicAndDance
Pop
Folk lf e h nS−50 a e M −75 −100
Alternative Rock Punk
Folk ElectronicAndDManectael Progressive
Pop
Mea5n0Tail 0 25 75 100 0 25
Mea5n0Tail (a) All Artists - Tail and Shelf analysis with Genr(be) All Artists - Tail without covers and Shelf with
Averages out covers analysis with Genre Averages where ̄ is the average of the individual tou r values, represents the Shelf value for To u,r and is the number of Tours for the artist. The set of all artists and t hē ivralues, ordered with respect to ̄ , and coloured with respect to generalized genre, is shown in Figu10r,eproviding an overall visualisation of a corpus of artists with respect to variety.
To examine the robustness of our notion of variety for comparing diferent artists, despite their varying characteristics, such as the number of tours, the length of performances, and activity during diferent time periods, we conducted a correlation analysis between o ū rVariety measure and such properties. Tabl3eshows the correlation results for seven artist properties, along with definitions of each property. The Correlation values are the correlation levels found between each of the seven properties and our ̄ measure. Here, correlation is calculated with respect to Pearson Correlation CoefÏcient. For fuller descriptions and discussions of each of these properties see AppendixC, which also contains visualisation scatter plots of each property against our ̄ measure. Table3 highlights how our measure has only very weak correlation to these properties1,6 suggesting our measure is robust for analysis between artists, and will not be unduly bias by, for example, diferent artists having more tours or longer shows. 16Where the semantics of correlation strength can be classified as - Very Weak Correlatio|n|:< 0.2 , Weak Correlation:0.2 ≤ || < 0.4 , Moderate Correlation0:.4 ≤ || < 0.6 , Strong Correlation0:.6 ≤ || < 0.8 , Very Strong Correlation|:| ≥ 0.8 [12] umphrey's−mcgee yo−la−tengo frank−turner red−hot−chili−peppers bil y−joel taylor−swift
r.e.m. pearl−jam cheap−trick frank−zappa coldplay the−kil ers 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−tul
u.d.o. tom−pet y−and−the−heartbreakers rise−against primus deftones limp−bizkit arcade−fire thirty−seconds−to−mars bob−dylan iced−earth elton−john−and−bil y−joel elton−john kasabian kaiser−chiefs radiohead
foals the−flaming−lips
bif y−clyro nick−cave−&−the−bad−seeds
heart the−of spring
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
yel owcard florence−+−the−machine dream−theater
incubus papa−roach oasis pixies jane's−addiction the−who editors black−sabbath new−found−glory bul et−for−my−valentine franz−ferdinand deep−purple anthrax volbeat neil−young enter−shikari testament placebo weird−al−yankovic lacuna−coil blink−182 peter−gabriel weezer the−used hel oween 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−rol ing−stones megadeth exodus motörhead marilyn−manson noel−gal agher's−high−flying−birds
interpol ozzy−osbourne
kiss kil switch−engage fal −out−boy
metal ica 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 GeneralizedGenre ElectronicAndDance Folk Metal Pop Punk Rock Progressive 0 0 1 − 0 5 − 0 0 5
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 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 approach explores the impact of cover songs on these notions of variety, and explores variety comparisons between diferent musical genres. The analysis of variety highlighted the diversity 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 difering 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. Generally, 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 enrich our dataset and model, ofering potential for incremental improvements. Additionally, future work will investigate incorporating our analysis into live music recommender systems, which suggest items based on user preferences4[]. Given that factors such as variety and diversity have become increasingly important in this field 1[8], our analysis may provide valuable insights. [1] E. Abel and A. Goddard. “A Live Concert Performance Recommender System Utilizing User Ideal and Antithesis Ideal Setlist Preferences”. I1n4:th International Conference on Smart Computing and Artificial Intelligence . IEEE Computer Society Press, 2023, pp. 330– 335. [2] E. Abel and A. GoddardT.he Art Behind Bruce Springsteen’s Setlist Composition as Part of His Stagecraft . 2024. [3] E. Abel, L. Mikhailov, and J. Keane. “Inconsistency Reduction in decision making via Multi-objective Optimisation”. InE:uropean Journal of Operational Research (2017). doi: 10.1016/j.ejor.2017.11.044. url: http://linkinghub.elsevier.com/retrieve/pii/S0377221717 31055X.
A. BullardA.rctic Monkeys blasted for ‘changing lyrics and rhythm of best known songs’ leaving fans unable to singalong - MyLondon. 2023. url: https://www.mylondon.news/w hats-on/music-nightlife-news/arctic-monkeys-blasted-changing-lyrics-271041. 14 [8] E. C. Callahan and C. CarneyT.he politics and power of Bob Dylan’s live performances : play a song for me. 2024, p. 229. url: https://www.routledge.com/The-Politics-and-Pow er-of-Bob-Dylans-Live-Performances-Play-a-Song-for-Me/Callahan-Carney/p/book/9 781032315416. [9] J. R. Castillo and M. J. Flores. “Web-Based Music Genre Classification for Timeline Song Visualization and Analysis”. InI:EEE Access 9 (2021), pp. 18801–18816. doi: 10.1109/acc ess.2021.3053864. [10] P. Chianca. “Springsteen’s stage success”. In:Bruce Springsteen and Popular Music. Routledge, 2018, pp. 178–188. doi:10.4324/9781315672144-15/springsteen-stage-success-pet er-chianca. [11] C. Dalla Riva.Swifties vs. Deadheads: A Meditation on Live Music . 2023. url: https://chri sdallariva.substack.com/p/swifties-vs-deadheads-a-meditation?utm%5C%5Fsource=pu blication-searc h. [12] J. D. Evans. Straightforward Statistics for the Behavioral Sciences. Brooks/Cole Publishing
Company, 1996. [13] E. Gleadow.Bob Dylan divides fans by ’doing whatever he wants’ and snubbing hit songs at shows - Mirror Online. 2024. url: https://www.mirror.co.uk/3am/celebrity-news/bobdylan-divides-fans-doing-3309576.6 [14] J. E. Hirsch. “An index to quantify an individual’s scientific research output”. InP:roceedings of the National Academy of Sciences of the United States of America 102.46 (2005), p. 16569. doi: 10.1073/pnas.0507655102. url: https://www.ncbi.nlm.nih.gov/pmc/article s/PMC1283832/. [15] R. Johnston. How to Stream Bruce Springsteen 2024 Tour Online. 2024. url: https://www .billboard.com/culture/product-recommendations/watch-bruce-springsteen-tour-onlin e-streaming-1235669883/.
D. Kreps. Bruce Springsteen’s Poignant Cover of Prince’s ’Purple Rain’. 2016. url: https : //www.rollingstone.com/music/music-news/see-bruce-springsteens-poignant-cover-of -princes-purple-rain-171985./ [18]
M. Kunaver and T. Požrl. “Diversity in recommender systems – A survey”.KInn:owledgeBased Systems 123 (2017), pp. 154–162. doi: 10.1016/j.knosys.2017.02.009. [19] A. Lerch, C. Arthur, A. Pati, and S. Gururani. “An Interdisciplinary Review of Music Performance Analysis”. InT:ransactions of the International Society for Music Information Retrieval 3.1 (2020), pp. 221–245. doi: 10.5334/tismir.53. url: https://transactions.ismir .net/articles/10.5334/tismir.5.3 [20] C. Love. On Repeat. Are artists trotting out the same old set lists gig after gig? Tech. rep.
[21] B. Mathis-Lilley. “Secrets of the Radiohead Set List”. InN:ew York Magazine (2006). url: https://nymag.com/arts/all/process/17306./ [22] F. C. Moss, R. Lieck, and M. Rohrmeier. “Computational modeling of interval distributions in tonal space reveals paradigmatic stylistic changes in Western music history”. In: Humanities and Social Sciences Communications 11.1 (2024), p. 684. url: https://doi.org /10.1057/s41599-024-03168-1.
M. Mulder and E. Hitters. “Visiting pop concerts and festivals: measuring the value of an integrated live music motivation scale”. InC:ultural Trends 30.4 (2021), pp. 355–375. url: https://doi.org/10.1080/09548963.2021.191673 8. [23] [25] [26] [27] [30] [24] T. Murray.Taylor Swift unexpectedly covers Calvin Harris, Rihanna hit during Liverpool show. 2024. url: https://www.independent.co.uk/arts-entertainment/music/news/taylo r-swift-this-is-what-you-came-for-eras-tour-b2563075.htm.l MusicNews. Steve Van Zandt Defends Static Bruce Springsteen Setlists. 2023. url: https://v ermilioncountyfirst.com/2023/03/29/steve-van-zandt-defends-static-bruce-springsteen -setlists/.
M. Pandey. Crowd-pleasers: The art of choosing the perfect setlist. 2024. url: https://www .bbc.com/news/articles/c4nn9expp04 o.
D. Pattie. Rock Music in performance. Palgrave Macmillan, 2007, pp. 1–188. doi1:0.1057 /9780230593305/cover. [28] R. Radburn and C. Love.Digging into concert setlist data: Which artists play the same songs over and over? Tech. rep. Tableau, 2018. url:https://www.tableau.com/blog/data-music -which-artists-use-same-old-setlists-gig-after-g.ig [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-spot.ify M. Rodriguez, V. Gintautas, and A. Pepe. “A Grateful Dead Analysis: The Relationship Between Concert and Listening Behavior”. InF:irst Monday 14 (2008). doi: 10.5210/fm.v 14i1.2273.
To aid selection of pertinent and parameters, and to aid understanding our of dataset, sensitively analysis experimentation was performed exploring the average percentage of tour songs that are contained within diferent combined shelf and tail size parameter values. The impact of experimentation with diferent sized shelf and tail values is shown in Figu1r1e. The x-axis denotes diferent 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.
Figure11 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 of, 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 diferences within the results when the data is subsetted for features such as genre.
From calculations of a pair of̄ and ̄ 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 Fig1u2ar,ehere, 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 Figur1e2a. The membership constraints of each of the 8 clusters can be defined in terms of mean shelf ( )̄ and mean tail ( ̄) value ranges, and assigned semantic ordinal names such as: 80 t n ce70 r e P lfe60 h S nd50 A il a nT40 a e M30 40 50 60 Combined Shelf and Tail Values 1. Very High Variety: Where the diference between the mean tail and mean shelf value is greater than or equal to 75. 2. High Variety: Where the diference between the mean tail and mean shelf value is greater than or equal to 50 and less than 75. 3. Moderate Variety: Where the diference between the mean tail and mean shelf value is greater than or equal to 25 and less than 50. 4. Low Variety: Where the diference between the mean tail and mean shelf value is greater than or equal to 0 and less than 25. 5. Low Uniformity: Where the diference between the mean tail and mean shelf value is greater than or equal to -25 and less than 0. 6. Moderate Uniformity: Where the diference between the mean tail and mean shelf value is greater than or equal to -50 and less than -25. 7. High Uniformity: Where the diference 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 diference 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 Figur1e2b. Here, we observe that when covers are excluded, some artists shift in the plot to the extent that they belong to a diferent 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 Tabl4e. 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. The explored properties and results are shown in Ta5b,laend visualisation scatter plots of each property against ou r ̄ measure are shown in Figure1s3 – 16. Next, each property is outlined and discussed.
Number of Tours: Our dataset includes artists with varying numbers of tours. Figu1r3ea compares each artist’s tour count with thei r̄, 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 thei r̄is shown in Figure13b. Here, the plot highlights there is no strong relationship between these variables; the Pearson Correlation
Length of Tours: Within our dataset, the number of shows within our artists’ tours difers. Comparisons between each artist’s average tour show count and th eir̄is shown in Figure14a, 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 Figur1e4b.
Average Show Length: For our set of artists, their average show length, in terms of the
number of songs in the set-lists, varies. Comparisons of each artist’s average show Length and their
̄ is shown in Figure15a, 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 1[
The set of correlation values for these properties again̄sits shown in Table5. Considering correlation strength classified as, Very Weak Correlation||: < 0.2 , Weak Correlation:0.2 ≤ || < 0.4 , Moderate Correlation0:.4 ≤ || < 0.6 , Strong Correlation0:.6 ≤ || < 0.8 , Very Strong Correlation|:| ≥ 0.8 [12], Table5 highlights how ou r ̄ 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. kiss
yes rush acdc
weird−al−yankovic aerosmith depeche−mode judas−priest korn
megadeth jethro−tul bon−jovi mötley−crüe eagles marilyn−manson muse cher −50 slayer korn kiss
yes the−cure megadeth elton−john−and−bil y−joel elton−john rush judas−priest eagles acdc pixies
frank−zappa bon−jovi frank−turner primus wilco r.e.m.
umphrey's−mcgee nofx
opeth sigur−rós velvet−revolver you−me−at−six u2 beck nofx yo−la−tengo 0 Variety
̄ (a) Number of Tours Vs 0 Variety
̄ (b) Total Number of Shows Vs cher
tina−turner rush roger−waters deep−purple the−black−keys korn opeth
yel owcaprrdimus ghost linkin−park
neil−young paramore rob−zombie
the−offspringbeck the−cure incubus limp−bizkit frank−zappa jane's−addiction the−flaming−lips
nofx grateful−dead cheap−trick pearl−jam yo−la−tengo
umphrey's−mcgee acdc
placebo arctic−monkeys weird−al−yankovic
wilco jethro−tul r.e.m. frank−turner ra200 T th100
0 60 40 h t g n e L w o h S e g a r v20 A 0 −100 −50 100 −100 −50 50 100 (a)
D ylan) a r t
0 100 x e d n I − H r a e e r a C e v O 0 Variety 0
̄ nofx beck green−day rush paul−mccartney the−cure
dream−theater weird−al−yankovic jethro−tul depeche−mode queensrÿche david−bowie iron−maiden van−halen muse elton−john bon−jovi pearl−jam britney−spears beyoncé madonna def−leppard slayer roger−waters
korn eagles tina−turner yes kiss pink−floyd ghost lamb−of−god slipknot cher sigur−rós placebo aerosmith primus taylor−swift coldplay red−hot−chili−peppers pixies radiohead wilco foo−fighters frank−turner bil y−joel
umphrey's−mcgee yo−la−tengo rob−zombie five−finger−death−punch cher tina−turner v A A ( ( s o o g g n n e L L roger−waters acdc britney−spears rush pink−floyd
slipknot ghost beck frank−turner nofx u u P P Vs nofx anthrax eagles guns−n'−roses pixies zz−top
exodus the−cure neil−young linkin−park paul−mccartney rush
beyoncé roger−waters ghost heart limp−bizkit wilco r.e.m.
foals 0 Variety (a)
̄ −50 50 100 −100 −50 50 100 and S e v A1980 ghost paramore velvet−revolver foals marilyn−manson slipknot korn opeth
weezer him
radiohead tool incubus deftones green−day rush mötley−crüe dream−theater anthrax slayer metal ica queensrÿche
exodus whitesnake u2 acdc
kiss queen
aerosmith yes deep−purple jethro−tul nofx eagles −50
0 Variety kasabian coldplay foo−fighters pixies primus tom−petty−and−the−heartbreakers zz−top eagles acdc yes deep−purple queen
journey kiss
heart rush def−leppard exodus whitesnake jethro−tul aerosmith u2 ghost anthrax pixies tool incubus slayer slipknot opeth korn disturbed paramore five−finger−death−punch biffy−clyro deftones pearl−jam arcade−fire the−kil ers oasis foals two−door−cinema−club velvet−revolver primus u.d.o.
nofx O Vs
Vs ̄ 16: Start Date and Active Years