The Billboard Hot 100:
Exploring Six Decades of Number One Singles
In this report, I will be analysing data relating to each weekly number one single from the Billboard Hot 100, from the chart's inception in August 1958 to the present day. The data was sourced from Spotify and Wikipedia, and contains both musical analysis (such as tempo, loudness and key), and chart performance data (such as chart date and total weeks at number one). I will explore the data to investigate, among other things:
- How number one singles have changed over time;
- Whether number one singles tend to have any particular musical features in common; and
- To what extent a number one single's performance (in terms of total weeks at number one) can be explained by any of its musical attributes.
I will report my findings in a 'stream of consciousness' style, digging deeper into trends as they appear, before presenting a selection of the most significant results in a final 'Key findings' section.
Total weeks at number one
## Min. 1st Qu. Median Mean 3rd Qu. Max. ## 1.000 1.000 2.000 2.876 4.000 16.000
Let's begin by exploring the distribution of total weeks spent at number one for each track. We can see that the majority of number one singles maintain their spot for three weeks or less (the median is two weeks), while the record for most weeks at number one is 16 (achieved by Mariah Carey and Boyz II Men with "One Sweet Day" in 1995). Even more impressively, this was managed in a single 16-week run, which was eventually stopped by Céline Dion with "Because You Loved Me" (itself managing an impressive six week stay at the top).
## title artist_1 year total_weeks ## 810 "One Sweet Day" Mariah Carey 1995 16 ## 780 "I Will Always Love You" Whitney Houston 1992 14 ## 797 "I'll Make Love to You" Boyz II Men 1994 14 ## 818 "Macarena" Los Del Rio 1996 14 ## 922 "We Belong Together" Mariah Carey 2005 14 ## 981 "I Gotta Feeling" The Black Eyed Peas 2009 14 ## 1048 "Uptown Funk" Mark Ronson 2015 14 ## 778 "End of the Road" Boyz II Men 1992 13 ## 838 "The Boy Is Mine" Brandy 1998 13 ## 858 "Smooth" Santana 1999 12
This was not an isolated success for Mariah Carey and Boyz II Men, as both artists separately hold joint second-place with "We Belong Together" and "End of the Road", respectively, with 14 weeks each. Interestingly, the top 10 most successful number ones (judged by weeks at number one), all occurred in 1992 or later, with seven in the 1990s alone.
Total number one singles per year
Note, throughout this section I have excluded 2017 data from charts that depict annual totals to avoid potentially misleading trends. I have included 2017 data in all charts that depict rates and ratios. For the avoidance of doubt, a "unique" number one is a number one single that has not held the position previously (in any year). 'Entry/entries' refers to the consecutive period(s) of time during which the single was number one.
The above charts explore this trend further, demonstrating a downward trend in the number of unique number ones per year following a general high in the 1970s (with an all time peak in 19751). This surprised me, because the general wisdom is that the barriers to making and releasing music have been getting lower over time, while streaming services like Spotify and Apple Music give fans unprecedented access to millions of records. And yet, we seem to be consolidating our listening around a smaller number of hit songs.
2008 was particularly emblematic of this trend, with 22 Hot 100 number ones in total, only 14 of them unique. It was also the year with the highest number of third-entry number ones, with "Bleeding Love" (Leona Lewis), "Live Your Life" (T.I. featuring Rihanna) and "Whatever You Like" (also T.I.) clinching the top spot three times each, for a total of four, six and seven weeks, respectively.
1 Note that the data set counts double A-side records as separate releases, therefore 1975 has 36 unique number ones because of "I'm Sorry" and "Calypso" by John Denver. If we consider this to be one release, both 1974 and 1975 are tied at 35 unique number ones.
As you might expect, we can also see a similar decline in the number of unique artists that achieve a number one hit in any given year. We can explore this trend from a different angle by plotting the ratio of unique artists to unique number one tracks for each year.
I have highlighted the point at which the ratio equals 1.0, which would indicate a year in which each number one single was released by a different artist. Ratios of less than one suggest a select group of artists dominating the charts, while ratios of greater than one suggest that one or more number ones included featured artists.
This chart suggests that while the early years of the Hot 100 may have had many more unique number one tracks, they tended to be from a relatively smaller pool of artists in any given year. Take 1964-65, in which 16 of the 47 unique number one singles in that period were recorded by The Beatles or The Supremes (10 and six, respectively). Not to mention the repeat number ones achieved by Bobby Vinton, Herman's Hermits, Four Tops, The Byrds and The Rolling Stones in the same period!
Similar feats of chart domination within a single year were achieved by Elvis Presley in 1960, The Jackson 5 in 1970, and Paula Abdul and Milli Vanilli in 1989.
That the ratio only dipped below 1.0 twice after 1995 suggests that, even if number ones are maintaining their status at the top for more weeks at a time, we are exposed to more unique artists per number one track than ever before. This is down to the rise in the use of featured / guest artists, which is charted below.
The use of guest artists in pop music is evidently a relatively modern phenomenon, likely related to the rise in prominence of hip hop from the '90s onwards. Features are a staple of the genre and are used as a means of appealing to two different fan bases.
In a pop music context, collaborations are also used as a means of generating crossover hits that appeal to fans of two different genres, thereby increasing their marketing potential. Katy Perry is an artist that has deployed this to notable effect, with three of her nine (!) number one singles featuring hip hop artists.
Summary findings - chart performance
Having analysed the overall performance of Billboard Hot 100 number one singles, we've identified a number of intriguing insights:
- The vast majority of number ones only maintain their position for 1-3 weeks. However, this has changed dramatically in the last 25 years as fewer unique singles make it to number one, with a corresponding increase in the average number of weeks spent at the top.
- There has been an increase in the number of songs enjoying multiple reigns at number one, but no song has ever been number one more than three times.
- The average number of artists per track has increased significantly since 1995, highlighting the trend of 'featured artists'.
These insights all raise interesting questions: are we, as seems to be the case, consolidating our listening around a smaller number of tracks, despite having more choice than ever before? Are music-on-demand services leading to us coalesce around the same blockbuster hits, rather than broadening our horizons?
It's worth pointing out that the methodology behind the Hot 100 has changed over the years to ensure the chart stays relevant. In its early years, the Hot 100 was based entirely on in-store sales and radio airplay. However, a truly seismic shift occured in 1991, when Billboard began using electronic sales data compiled by SoundScan to produce its charts. The effect was immediate, and has been cited as playing a major role in the careers of rock, country and hip hop artists like Nirvana, Garth Brooks and N.W.A. As far as the Hot 100 is concerned, it also "ushered in an era of repetition", which is borne out in the data.
More recent changes to the methodology included adding digital sales in 2005, streaming (from certain services) in 2007, and Spotify streams and YouTube views in 2013 (the most recent revision). According to Billboard, "the current formula targets a ratio of sales (35-45%), airplay (30-40%) and streaming (20-30%)".
It is important to consider that by including streaming data as well as sales data, fans' listening habits are playing an increasingly important role in chart compilation. For example, we now include the casual fan or even non-fan, who checks out a new single online, possibly adds it to a playlist, but would never consider purchasing it.
Streaming services also represent the music industry's attempt to create a new business model after the disruption of P2P sharing in the early '00s. A recent article in the FT notes that "US streaming revenues rose to $3.9bn last year — 51 per cent of a total of $7.7bn. The latter is only half the industry’s sales in 1999, before the outbreak of digital piracy, but growth has returned." With labels competing in a less profitable industry, it is possible that they now commit a greater proportion of their marketing resources to a smaller number of singles with strong hit potential, with the result being fewer, but more successful (in chart terms) number ones.
Having interrogated the chart data, I still have some questions: what is it about some tracks that makes us gravitate towards them? Can it be explained scientifically? And how on earth did "Macarena" stay number one for 14 weeks in a single run?!
By examining the musical features of each track, I hope to gain further insight.
## Min. 1st Qu. Median Mean 3rd Qu. Max. ## 61.85 99.99 116.60 118.50 131.40 213.80
The chart above shows the distribution of track tempos (in beats per minute), which is approximately normal with some positive skew. 73.6% of tracks have tempos within one standard deviation of the mean - that is, in the range 91.8-145.1 bpm.
When charted over time, we can see the mean tempo change with the era. While always hovering around the long run mean of 118.5 bpm (denoted by the dashed red line), there were periods of significant deviation. Several of these appear to coincide with the rise of distinct musical styles within pop, including British rock (mid '60s), disco (mid-late '70s), modern R&B ('90s), and EDM (late '00s and early '10s).
## Min. 1st Qu. Median Mean 3rd Qu. Max. ## 1.582 3.206 3.811 3.838 4.369 9.782
Plotting a histogram of tracks by duration, we can see that this is also an approximately normal distribution, with tracks concentrated closely around the mean duration of 3.8 minutes.
The shortest track is "Stay" by Maurice Williams & The Zodiacs, which according to Wikipedia "...remains the shortest single ever to reach the top of the American record charts, at 1 minute 36 seconds in length."
At the other end of the spectrum is "Stars on 45", released by the group of the same name in 1981. This was a medley of a number of popular songs from previous years, and was released as a number of different edits of varying duration. The version in our data set is 9 minutes 47 seconds long.
By plotting track duration over time, we can see that number one singles have become longer on average over time. However, this trend has been reversing since 1999, with average durations recently declining below the long term mean of 3.8 minutes (depicted by the dashed red line). Perhaps an indication of shortening attention spans?
By charting the frequency of particular keys, we can see that number one singles are more frequently written in major keys than minor, perhaps suggesting that pop music fans may be more drawn to music that is 'happy' in tone.
We can also observe that the distribution of minor keys is more uniform than that of major, for which the most popular key (C Major) occurs 5.1x more frequently than the least popular (D♯/Eb Major). The most popular minor key (A Minor) was used in 2.7x more number one singles than the joint least popular (D♯/Eb Minor and G♯/Ab Minor).
It is interesting to note that the most popular minor key is the relative minor of the most popular major key. In musical terms, this means they have the same number of sharp/flat notes in their respective scales (in this case, none). However, the data do not suggest that the presence of sharp or flat notes in a given key necessarily makes it more or less suitable for popular music; the second most widely-used key is C♯/Db Major, which has either five flats or seven sharps, depending on the harmony of the piece.
## ## Chi-squared test for given probabilities ## ## data: table(subset(billboard, mode == "Major")$key) ## X-squared = 154.29, df = 11, p-value < 2.2e-16
## ## Chi-squared test for given probabilities ## ## data: table(subset(billboard, mode == "Minor")$key) ## X-squared = 22.447, df = 11, p-value = 0.02113
By performing chi-squared goodness-of-fit tests on the distributions of major and minor keys at the 95% confidence level, we can confirm that neither of the respective distributions were likely to have occurred by chance - in other words, certain keys appear to be more favoured by writers of number one pop singles than others. However, the data do not tell us whether this is a feature unique to number one singles (and hence a possible factor in their popularity), or if it can also be found in pop music more generally. It would be useful to compare the sample data with a much broader data set to test these findings further.
Perhaps unsurprisingly, the vast majority of top-selling singles are written in 4/4 time, which lends itself well to the sort of catchy rhythms and driving beats I would associate chart music with. In fact, I was surprised that any 5/4 songs would have made it to number one and decided to investigate further.
## title artist_1 year ## 748 "One More Try" Timmy T 1991 ## 915 "Lean Back" Terror Squad 2004 ## 920 "Candy Shop" 50 Cent 2005 ## 993 "Not Afraid" Eminem 2010 ## 998 "Like a G6" Far East Movement 2010 ## 1054 "The Hills" The Weeknd 2015
Sure enough, manually reviewing the above list of songs confirms that they are all in 4/4 time, suggesting that Spotify's beat analysis software has some room for improvement!
The data on 3/4 songs appears to be more accurate, though I was still curious, given that this is a relatively uncommon time signature in pop music. I suspected that perhaps this was more common in older hits. The histogram below, which shows the frequency of 3/4 time number one singles over the life of the Hot 100, confirms this suspicion.
## Min. 1st Qu. Median Mean 3rd Qu. Max. ## -22.590 -11.090 -8.392 -8.827 -6.257 -1.185
While dynamics are certainly a feature of musical performance, loudness is more a factor of studio production and mastering. Given recorded audio has a practical ceiling at 0 dB, a negatively skewed distribution like that depicted in the above chart is unsurprising.
By plotting loudness over time, we can see that number one singles have followed a well-documented trend in getting louder over the years2. It is interesting to note however that the change was not gradual, but rather fluctuated around -10 dB before increasing rapidly from 1995 to 2003. The trend has since stabilised, perhaps because at approximately -5 dB, there isn't much room for an increase without producing unpleasant distortion.
While certainly more attention-grabbing, louder tracks tend to have lower dynamic ranges - an arguably less 'musical' outcome.
2 I should note that there will be additional variance present in the data due to some audio analysis unavoidably being based on recent remasters of the original recordings.
The chart above shows a fascinating trend: the emergence of explicit lyrics within number one singles. It appears that this is a fairly recent phenomenon, with only five explicit singles topping the charts in the 44 years to 2001 (the first only appearing in 1984). Since 2001 however, with the exception of 2012, there hasn't been a year without at least one number one single carrying a parental advisory label.
2017 is off to a particularly profane start, with two of the three new number one singles thus far using explicit lyrics ("Starboy" by The Weeknd feat. Daft Punk and "Bad and Boujee" by Migos feat. Lil Uzi Vert). The only other number one this year, a re-entry of "Black Beatles" by Rae Sremmurd feat. Gucci Mane, also uses explicit lyrics, bringing the proportion to a full 75% of the total Hot 100 number ones in 2017 so far (as of April). A sign of the times?
As well as more traditional musical features, Spotify also tries to quantify the 'feel' of a track through a number of measures, including:
- Danceability - "a combination of musical elements including tempo, rhythm stability, beat strength, and overall regularity.";
- Energy - "a perceptual measure of intensity and activity...perceptual features contributing to this attribute include dynamic range, perceived loudness, timbre, onset rate, and general entropy."; and
- Valence - "the musical positiveness conveyed by a track. Tracks with high valence sound more positive (e.g. happy, cheerful, euphoric), while tracks with low valence sound more negative (e.g. sad, depressed, angry)."
The above chart shows the distribution of danceability, energy, and valence scores for every track in our data set. We can see that number one singles tend to score in the upper range for each of these measures, particularly for valence. Given this is a measure of musical 'positivity', this finding seems to back up my earlier assertion about fans being drawn to 'happy' music.
## danceability energy valence ## Min. :0.1450 Min. :0.0264 Min. :0.0383 ## 1st Qu.:0.5250 1st Qu.:0.4640 1st Qu.:0.4340 ## Median :0.6410 Median :0.6090 Median :0.6680 ## Mean :0.6228 Mean :0.6044 Mean :0.6259 ## 3rd Qu.:0.7300 3rd Qu.:0.7560 3rd Qu.:0.8310 ## Max. :0.9780 Max. :0.9890 Max. :0.9790
The mean valence for Hot 100 number one singles is 0.6259, which is notably higher than the average valence that Spotify found in 2013 when testing the top 5,000 songs by year of release. That analysis showed that the average valence was consistently around 0.5 across the decades.
## ## One Sample t-test ## ## data: subset(billboard, select = "valence") ## t = 16.685, df = 1068, p-value < 2.2e-16 ## alternative hypothesis: true mean is greater than 0.5 ## 95 percent confidence interval: ## 0.6134651 Inf ## sample estimates: ## mean of x ## 0.6258864
By performing a one-sample t-test at the 95% confidence level, we can see that, assuming 0.5 is the true average valence score for pop music in general, number one singles are significantly more likely to have higher valence scores on average. This does not necessarily prove that music fans are more likely to buy or stream 'happy' sounding music; getting a number one record relies on a number of factors, not least heavy promotion by the record's label to build awareness. It could be the case that label executives are more comfortable committing large marketing budgets to songs that make them feel happy!
## title artist_1 year mode valence total_weeks ## 1041 "Happy" Pharrell Williams 2014 Major 0.962 10
To illustrate this with an example, take the song "Happy" by Pharrell Williams from 2014. This song is about as happy as it gets, from the harmony, to the feel, to the lyrics themselves! Unsurprisingly, it scores 0.962 for valence. Whether or not this was a contributing factor to its monster 10 weeks at number one isn't clear, but these findings suggest it can't have hurt its chances!
Summary findings - musical analysis
- Tempo has been relatively consistent over time, fluctuating around 118 bpm with notable periods of higher or lower tempos.
- Loudness has not been consistent however, with tracks increasing in volume particularly between 1995 and 2003. Loudness now sits near the practical ceiling of 0 dB.
- 70.5% of number one singles are between 2.8 and 4.8 minutes long. The average duration of number one singles has declined in recent years.
- It is extremely rare for a number one single to be in anything other than 4/4 time.
- Number one singles are 2.5x more likely to be written in major keys (than minor). C Major is the most popular key overall.
- The presence of explicit lyrics in number one singles has gone from being non-existent to de rigueur.
- Number one singles tend to score highly for danceability, energy, and valence.
- Comparisons with a wider study on valence in popular music suggest that number one singles are significantly likely to have higher than average valence scores.
Musical features and chart performance - is there a connection?
Having explored overall trends in both the chart performance and musical features of Hot 100 number one singles, I am interested in investigating whether there are any potential relationships between the two. Does a particular key or tempo predict a longer stay at the top of the chart? Or perhaps it's all down to danceability?
Given the major changes in chart performance observed in the post-1991 era, this investigation will focus only on data from 1992 onwards.
Chart performance by key
The above chart plots the total weeks at number one for every track in our data set (from 1992 to 2017), against each track's key. Note I added some horizontal jitter to reduce the overplotting somewhat.
We observed earlier that D♯/Eb was the least popular root note for number one songs, in both major and minor tonalities. This certainly appears to be consistent in the modern era of the Hot 100, with only five number one songs written in these keys since 1992. Notably, these are the only keys never to have achieved a total stay at number one of longer than five weeks.
We can also see that of the 30 songs to achieve a total stay of 10 weeks or longer (i.e. the top 10% best performing number ones), only nine were written in a minor key. And no song in a minor key has maintained a number one position for longer than 14 weeks.
## Min. 1st Qu. Median Mean 3rd Qu. Max. ## 1.000 2.000 3.000 4.349 6.000 16.000
By plotting box plots of each key, we can see that some keys do seem to perform better than others, with C♯/Db Minor, D Major and D♯/Eb Major all having markedly higher median values than the population median (three weeks).
I decided to explore this further by plotting the mean total weeks at number one for each key.
We can see that major keys tend to perform much more in line with the overall mean of 4.349 total weeks at number one (denoted by the dashed red line) than minor keys do. Also the number of major keys that outperform the overall mean is equal to those that underperform (six), while the split is 4:8 (respectively) for minor keys.
I decided to test the two best and two worst-performing keys to see whether their average performance deviated from the overall population (of post-1991 number ones) in a statistically significant way. I accomplished this by using one-tailed z-tests at the 95% significance level; the results are printed below:
## Key Mean weeks n z p ## 1 D Major 5.58 19 1.593 0.056 ## 2 C♯/Db Minor 5.64 14 1.439 0.075 ## 3 D♯/Eb Minor 1.50 2 -1.197 0.116 ## 4 C Minor 2.11 9 -1.995 0.023
We can see that for the two best-performing keys, D Major and C♯/Db Minor, the p-value is greater than 0.05, so we must accept the null hypothesis: that the true mean total weeks spent at number one for these keys is the same as that of the overall population.
The same is true for D♯/Eb Minor, the worst performing key. However, for C Minor, the second worst-performing key in the modern era of Hot 100 number ones, the p-value is less than 0.05, and hence we must accept the alternative hypothesis: that the true mean total weeks spent at number one for number ones written in C Minor is lower than the population mean.
In summary, we have shown that the key that a number one song is written in is generally not a good predictor of how many weeks that song may remain at number one. However, songs written in C Minor may tend to underperform against the overall mean.
It is important to place that result in context: total weeks at number one is an imperfect proxy for success, because it is not an independent outcome. There can only be a single number one at any given time. Therefore if two 'perfect' number one songs were released in the same time period, neither would likely live up to its full potential as far as this metric is concerned.
Also, the keys used in this investigation have been determined by Spotify, using software-based analysis. While I tested the data against a sample of manually determined keys and found it to be reasonably accurate, there is room for significant error. Hip hop songs in particular appear to be difficult for the software to analyse correctly, perhaps because of the lack of a prominent vocal melody.
While it certainly would have been a great story if it turned out all the most successful number ones were written in, say, D Major, that doesn't seem to be the case. Which is probably for the best, at least as far as the future diversity of pop music is concerned!
Chart performance by tempo
The chart above shows the relationship between total weeks at number one and tempo, which doesn't exhibit any strong correlation. In fact, calculating Pearson's r gives a value of 0.010 - pretty much zero!
Correlation with weeks at number one
Based on these initial findings, I was curious: does performance (total weeks at number one) correlate strongly with any of our variables?
## Var1 Var2 Correlation ## 14 weeks_1 total_weeks 0.9053539 ## 16 energy loudness 0.6869597 ## 18 valence energy 0.5125767 ## 20 energy acousticness -0.5108228 ## 22 valence danceability 0.4693328 ## 24 acousticness loudness -0.3337757 ## 26 valence loudness 0.3294917 ## 28 valence acousticness -0.3211362 ## 30 danceability acousticness -0.2836001 ## 32 valence duration_ms -0.2687879
The table above shows every pair of (numeric) variables in the data set with a correlation of 0.25 or greater (positive or negative). We can see therefore that total weeks at number one is not strongly correlated with any of our musical variables, suggesting that there may not be any magic formula for writing a top performing number one hit. Which is as we'd expect; not only is music difficult to break down into such a simple set of building blocks, but successful number ones likely owe much of their longevity to the artist's marketing team for generating the necessary buzz.
I would be interested in expanding this research further by looking at a much larger set of songs together with their highest achieved chart position, and investigating whether any correlation exists between chart position and the variables charted above. A similar investigation was carried out in 2011 by two master's degree students at Rutgers University, whose conclusions reflect many of my own findings:
"...there are a few general 'tips' you can follow to better your chances of having a top ten hit song on the Billboard Hot 100. First, keep your tempo at a medium pace, around 120 beats per minute. Write in a major key. Make your song danceable and around 4.5 minutes in length, and 'turn it up to 11'."
One thing that does leap out from the correlation analysis above is the number of semi-strong relationships between Spotify's own musical variables. I decided to expand this analysis to the original data set (i.e. 1958-2017) to see if it held true:
## Var1 Var2 Correlation ## 10 energy loudness 0.6946068 ## 12 energy acousticness -0.5644538 ## 14 valence energy 0.4733018 ## 16 valence danceability 0.4627233 ## 18 acousticness loudness -0.4025845 ## 20 danceability acousticness -0.3901702 ## 22 energy danceability 0.3406847 ## 24 acousticness duration_ms -0.3021656 ## 26 danceability loudness 0.2645731
For the most part, it seems to be consistent with the post-1991 data. We can see that high scores for 'acousticness' (a measure of whether a track is acoustic or not) tend to accompany lower energy scores. Perhaps not massively surprising. It also seems that higher scores for energy and danceability tend to go hand in hand with higher scores for valence, which I've charted below:
While there is certainly a high level of variance, the correlation between the three variables is pretty clear. In fact, danceability and energy explain up to 43.8% of the variance in valence.
Does this have any practical application? The short answer is "no" - Spotify calculates all of these measures itself without disclosing the precise methodology, so producing a track to be as energetic and 'danceable' as possible may not translate into a higher valence score. And even if it did, while our earlier analysis showed that number one singles tend to have higher valence scores than music in general, the valence itself likely plays a very small part in the overall chances of any given song making it to the top of the Billboard Hot 100.
Thousands of bedroom producers around the world are writing songs every day that, given the right exposure, could have a realistic shot. Take the story of Menace, the UK-based producer of the beat behind "Panda", Desiigner's 2016 breakout hit. Menace was working in a mobile phone shop when Desiigner came across his beat on YouTube. That chance discovery, amplified by Kanye West later discovering Desiigner's song and sampling it on "Father Stretch My Hands Pt. 2" gave Menace a number one single that knocked chart stalwarts Drake and Rihanna off the top spot. "Panda" managed a two week stint at number one, placing it in the top 66% of Hot 100 number ones.
Incidentally, that song has a valence score of only 0.256.
These charts together tell two sides of the same story: since 1992, we have been listening to a smaller number of unique number one songs than we had previously. However, we are also exposed to a far greater number of artists per number one song than ever before, largely because of the rise in the "featured artist" trend that sees two or more artists collaborate together on one track.
This chart depicts the (relatively) recent increase in the use of explicit lyrics in Hot 100 number ones, which I found fascinating for a number of reasons:
- The trend is open to all sorts of interpretations, from those who might want to opine on the change in morals over time to those who seek confirmation that music "isn't what it used to be".
- As a fan of hip hop, a genre not known for its delicate language, this to me effectively chronicles the genre's rise in popular music.
- It effectively invalidates the work of the Parents Music Resource Center, a group that sought to exercise a certain level of censorship over music, bringing about the introduction of the 'Parental Advisory' label on CDs in 1990. The subsequent rise in number one singles with explicit content shows that these warnings were either ineffectual, or perhaps even brought greater attention to genres like hip hop, aiding their rise.
Musical features and chart performance
This chart effectively answers the question that sparked this whole investigation: I wanted to know whether number one singles were all written in a small handful of keys and/or whether a particular key could predict greater commercial success.
Essentially the answer to both of these questions is "no": pop music remains diverse and there is no "magic key" that can predict success (although the key of C Minor may predict underperformance).
The investigation also showed that certain keys (like C Major) are more favoured by number one songwriters than others, and major keys are more favoured than minor keys overall, which I found interesting, if not too surprising.
However, the popularity of C#/Db Major surprised me a lot. It can be an awkward key to play on certain instruments, so its status as the second most frequently used key in number one singles makes me question the accuracy of Spotify's key analysis software. This may unfortunately render some of my findings less conclusive than I had hoped.
This investigation was very much a labour of love, with a long data gathering and wrangling process paying off in a big way when I was finally able to dig in to the data and start exploring. The process involved:
- Manually compiling a Spotify playlist of every Hot 100 number one;
- Using Spotify's API to access musical data for every track;
- Scraping chart performance data from Wikipedia; and
- Marrying the two data sets together using fuzzy string matching.
While gathering the data was time-consuming and painstaking at times, I learnt a lot about pop music history and am satisfied with the finished product. The accuracy of certain variables is sadly less perfect than I'd hoped - the 5/4 time signatures I documented in the body of the report give an idea of the limitations of musical analysis software. But in aggregate the data paint an interesting picture, with several relationships to explore.
As well as those presented in the key findings above, I was really interested to see the way average tempo has moved over time, seemingly reflecting wider musical trends. It was also interesting to see how many of my findings fit into wider narratives, whether about shortening song durations, the rise in profane lyrics or the big changes within the Hot 100 chart compilation process itself.
As referenced throughout, I would be extremely interested in expanding much of this same analysis across a wider data set, incorporating non-number ones and trying to assess key differences between different singles of varying commercial success. The difference in average valence I stumbled across, between number ones and music more generally, looks potentially interesting, and I'd like to explore it further.