Predicting the 2023 Hottest 100

  • Soil:
    By: Alfie Chadwick Date: January 26, 2024 Flower
  • Like many Australians, I spent my last Saturday in January getting hyped for the Triple J Hottest 100 countdown. And for the past few years, there has been a project run by 100 Warm Tunas that has been remarkably accurate at predicting the results of the countdown.

    Warm Tunas makes predictions by scraping social media posts for people’s votes and then collating them as a sample of all votes. While this method is highly effective, I feel that it misses the point a bit when it comes to understanding why a song is popular.

    Therefore, this year, I have set out to determine the top songs in the 2023 countdown without relying on anything related to the voting itself.

    My Hypotheses

    Heading into this, I have a few ideas as to factors that will make a song perform well in the countdown:

    Plays on Triple J

    I feel this factor is pretty self-explanatory. If a song is being played a lot on Triple J, it’s most likely popular with the listener base and will get more votes in the Hottest 100.

    Chart Success

    This one is a bit weirder, as I don’t think that just getting to number one in the ARIA charts will make you a top pick for Triple J listeners. Otherwise, the countdown would be topped by the year’s biggest pop hits. If a song is too popular in the mainstream, it seems to fall out of favor with Triple J listeners. However, there are some notable exceptions to this, such as “Bad Guy” by Billie Eilish and “Thrift Shop” by Macklemore, which both took out the top spot in their respective years.

    Time of Release and Peak

    This idea is commonly thrown around when talking about the Oscars, so I feel that it’s probably going to be applicable to the Hottest 100 as well. Being at peak popularity when people are voting is probably going to be useful. Similarly, a song that hung around for a long time will probably be voted for more than a song that only hung around for a week.

    Play Data

    I gathered the data for all plays on Triple J for the last 8 years from their API, which left me with a dataset that looks like this:

    Number of Plays

    To me, the most obvious indicator of a song’s popularity is the number of plays it receives. So, we can start by examining that.

    These plots give us a good insight into the trends in how Triple J selects songs. We have a lot of songs with almost no plays, which are mostly songs that are being presented to the audience to gauge their reaction. If they become popular, the songs will be played frequently, indicated by the absence of songs with 40-60 plays. However, very few songs receive excessive playtime, with only a handful surpassing 200 plays.

    We can also observe the impact of being released early in the year, as these songs have more opportunities to be played throughout the year, resulting in a downward slope for each year.

    How Total Plays Impact Success

    Looking at the rankings, we can see that the total number of plays doesn’t have a massive impact on performance. A song can have five plays or a hundred, and it seems to have a similar outcome in the rankings.

    There is a slight downward trend for songs getting over 120 plays, as these are the absolute most played songs for the year. However, this status still doesn’t guarantee a top spot.

    Accounting for Time

    A thought I had while looking at the absolute play data is that it disproportionately rewards songs that were released earlier in the year.

    To address this, I have compiled some statistics that consider the peak of the songs, which should eliminate any advantage for being released at the beginning of the year.

    Again, we can see that there is some useful information, with the peak plays per week showing that songs which have a big peak generally perform well in the final rankings. However, as with the absolute count of plays, there doesn’t seem to be a hard and fast rule.

    Chart Success

    The ARIA charts collate music sales and streaming data within Australia and produce a weekly list of the top 50 most popular songs. A GitHub user has been kind enough to compile all of these lists, so we can simply load them and compare the chart results to a song’s position in the Hottest 100.

    The first thing to note is that these plots are much sparser than the rest. This is because many songs played on Triple J don’t make it into the top 50 at all, even though they make it into the Hottest 100.

    For the songs that did make it into the ARIA charts and hung around, they consistently performed well in the countdown. Examples include “Bad Guy” by Billie Eilish and “Dance Monkey” by Tones and I, which claimed the 1st and 4th spots in their respective years.

    However, the predictive power of this statistic is again quite limited. Many songs that performed well in the Hottest 100 had poor chart success. For instance, “Redbone” by Childish Gambino took the 5th spot in 2015 despite only spending a single week in the charts at rank 42.

    From this chart, we can see that songs that make the charts are outperforming songs that don’t. But more importantly, it shows us that making the charts is not a deal-breaker on whether or not a song will perform well in the Hottest 100.

    Timing

    Another thing I wanted to look at was when and how the songs peaked in the play data. Maybe being the popular song would help the song perform around the time that voting is open, which may help with its performance in the final rankings.

    Looking at the above plots, we can see that the week of release or peak really doesn’t matter when looking at the final results.

    I went on to see if the shape of the peaks looks different for well-performing songs versus poorly performing songs, and again, nothing seems particularly interesting or different between the two.

    Where we are going wrong

    So it seems that all of my hypotheses are incorrect, and I believe the reason for this is that there is too much variation among the top 100. This is because these songs are already considered the best of the year from a pool of nearly 4000.

    Looking at this plot, we can see right away that a song that made the Hottest 100 got more plays than those that didn’t, but also that plenty of songs that didn’t make the 100 got a comparable number of plays.

    Screw it XGBoost

    I think the direction to go here is to see if we can use ML to find any trends that aren’t showing up in the plots.

    To do this, we are going to use XGBoost to train a model to predict the rank of the song using all the stats I wrote out above. The only thing I changed was taking the first play data and setting it to be the month rather than the day to reduce overfitting. For any song that didn’t make it into the 100, I set the rank to be 101, as it could be the 101st most popular song that year.

    A nice thing about XGBoost is that it can provide insight into the most important factors it uses to predict the results. From the above plots, we can see that the peak of the song on triple J and its total plays contribute significantly to the predictive power.

    Interestingly, the chart scores seem to have little effect. However, this can be justified by considering the fact that many songs that make the top 100 never make the charts.

    Now that we have the model, we can evaluate its performance in predicting the Hottest 100 by applying it to the play data from 2022.

    2022 Predicted Countdown

    Song Artist Actual
    b.o.t.a. eliza rose 2
    say nothing flume 1
    glimpse of us joji 10
    bad habit steve lacy 4
    first class jack harlow 12
    about damn time lizzo 7
    sitting up spacey jane 6
    get inspired genesis owusu 17
    in the wake of your leave gang of youths 9
    shirt sza 20
    hardlight spacey jane 3
    stars in my eyes ball park music 8
    stranger days skegss 19
    god is a freak peach prc 16
    it’s been a long day spacey jane 5
    thousand miles the kid laroi 33
    backseat of my mind thelma plum 21
    2 be loved lizzo 36
    facts of life lime cordiale 15
    doja central cee 39

    2022 Real Countdown

    Song Artist Predicted
    say nothing flume 2
    b.o.t.a. eliza rose 1
    hardlight spacey jane 11
    bad habit steve lacy 4
    it’s been a long day spacey jane 15
    sitting up spacey jane 7
    about damn time lizzo 6
    stars in my eyes ball park music 12
    in the wake of your leave gang of youths 9
    glimpse of us joji 3
    gay 4 me g flip 29
    first class jack harlow 5
    new gold gorillaz 24
    delilah fred again 27
    facts of life lime cordiale 19
    god is a freak peach prc 14
    get inspired genesis owusu 8
    stranger days skegss 13
    shirt sza 10
    backseat of my mind thelma plum 17

    From this, I reckon the model is doing pretty well, so lets have a look at my final predictions for the hottest 100 of 2023.

    My Final Predictions

    The list below seems pretty reasonable, with Doja Cat taking the top spot and my pick for number one, Rush, sitting in 10th. There seems to be a big lean towards pop and a lack of your classic Triple J-style indie rockers, but that might just be the turnout for this year.

    Song Artist
    paint the town red doja cat
    vampire olivia rodrigo
    what was i made for? billie eilish
    sprinter dave
    nanana peggy gou
    love type poolclvb
    green honda benee
    super ego babe rainbow
    kill bill sza
    rush troye sivan
    greedy tate mcrae
    strangers kenya grace
    sweat you out my system maya
    chemical post malone
    adored royel otis
    bad idea right? olivia rodrigo
    lovin on me jack harlow
    houdini dua lipa
    got me started troye sivan
    barbie world nicki minaj
    lost without you san cisco
    super-vision dice
    agora hills doja cat
    blak britney miss kaninna
    glue song beabadoobee
    ruthless hooligan hefs
    attention doja cat
    james dean tash sultana
    never felt so alone labrinth
    my love mine all mine mitski
    water tyla
    still have room hockey dad
    do it again benee
    trippin up the jungle giants
    i wish you roses kali uchis
    dash of speed rum jungle
    too much the kid laroi
    eyes ahead dice
    spin me like your records pacific avenue
    adore u fred again
    dogtooth tyler
    pets and drugs the rubens
    imposter syndrome lime cordiale
    into your room holly humberstone
    love again the kid laroi
    up lee
    pegasus arlo parks
    exploding angie mcmahon
    stockholm dice
    calling metro boomin
    must be nice ruel
    better love eliza rose
    your funeral maya
    don’t let me down gus dapperton
    the worst person alive g flip
    conceited lola young
    messed up holy holy
    lost in the rush telenova
    candle flame jungle
    one of your girls troye sivan
    minivan the rions
    perfect for you peach prc
    prescription remi wolf
    change laurel
    angel pinkpantheress
    sinner the last dinner party
    midwest vacations
    pretty girl ice spice
    good enough g flip
    take what you want the rions
    i wanna dance royel otis
    rhyme dust mk
    boys light up chillinit
    make up your mind cordae
    viper room thornhill
    weightless arlo parks
    letting go angie mcmahon
    can’t play myself skepta
    black mascara. raye
    feel alive cat
    tied up! genesis owusu
    fine day anthem skrillex
    we don’t talk about it thelma plum
    mrs. hollywood gojo
    am i dreaming metro boomin
    comma’s onefour
    lola maya
    it’s cool to be in love greta stanley
    since i have a lover 6lack
    prada cass
    lil boo thang paul russell
    nothing matters the last dinner party
    going kokomo royel otis
    all my life lil durk
    brain freeze northeast party house
    healing molly millington
    sticky kito
    rakata the jungle giants
    lost the breakup maisie peters
    f u goodbye peach prc