100 Warm Tunas has a new home!

100 Warm Tunas has found a new home this year! This year’s results, along with the past 2 years can now be found at 100warmtunas.com. The existing domain (100-warm-tunas.nickwhyte.com) will simply perform a 301 permanent redirect to the new domain, so all existing inbound links should be unaffected.

100 Warm Tunas 2017 Prediction Analysis

Over the space of 6 weeks, 100 Warm Tunas collected a large sum of data and chugged away at it to make some predictions about what the Hottest 100 of 2017 would look like. Along the way we encountered a bug in the collection process, however data was backfilled and showed that I had collected a sample size around the same as in 2016.


  • 100 Warm Tunas collected 7,216 entries (7.3% less than 2016 🔻)
  • 100 Warm Tunas tallied 67,085 votes across these entries (2.6% more than 2017 🔺). This is due to improvements in 100 Warm Tunas’ counting and recognition process.
  • Triple J counted 2,386,133 votes.
  • Therefore, 100 Warm Tunas, collected a sample of 2.8%. Not bad! (The same as in 2016).
  • Warm Tunas predicted 8 out of the top 10 songs (Same as 2016) (Ignoring order)
  • Warm Tunas predicted 16 out of the top 20 songs (3 less than in 2016, where 19 out of 20 were predicted) (Ignoring order).
  • Warm Tunas predicted 83 out of the 100 songs played in the countdown. (1 less than in 2016) (Ignoring order)

Overall, even though the sample size was reasonably consistent between 2016 and 2017, it is clear that the results collected in 2016 were more accurate.

Technical Analysis

The results this year definitely show a more accurate 1st place prediction (predicting HUMBLE. to win), as opposed to last year where the top two positions were placed out of order, however looking at the data, it looks as though all other aspects of the prediction stayed almost the same.

To start this analysis, lets take a look at the top 10 of the official countdown and match it up with their predicted places in Warm Tunas:

Artist Title ABC Rank Tunas Rank Difference
Kendrick Lamar HUMBLE. 1 1 0
Gang Of Youths Let Me Down Easy 2 3 1
Angus & Julia Stone Chateau 3 6 3
Methyl Ethel Ubu 4 4 0
Gang Of Youths The Deepest Sighs, The Frankest Shadows 5 2 3
Lorde Green Light 6 8 2
PNAU Go Bang 7 5 2
Thundamentals Sally {Ft. Mataya} 8 10 2
Vance Joy Lay It On Me 9 15 6
Gang Of Youths What Can I Do If The Fire Goes Out? 10 13 3
Peking Duk & AlunaGeorge Fake Magic 12 16 4
Khalid Young Dumb & Broke 13 24 11
Lorde Homemade Dynamite 14 30 16
Vera Blue Regular Touch 15 11 4
Jungle Giants, The Feel The Way I Do 16 32 16
Baker Boy Marryuna {Ft. Yirrmal} 17 12 5
Ball Park Music Exactly How You Are 18 14 4
Killers, The The Man 19 19 0
Peking Duk Let You Down {Ft. Icona Pop} 20 38 18

Lets pull apart this table and grab some statistics about how we did with our prediction:

Predicted Out Of Top N Percentage
8 10 80.0%
16 20 80.0%
22 30 73.3%
33 40 82.5%
42 50 84.0%
50 60 83.3%
62 70 88.6%
68 80 85.0%
78 90 86.7%
83 100 83.0%

So from the above data, it’s apparent that once again:

  • The average error for the top ten ranks was 2.2 positions (an increase from 2016’s 1.9 positions)
  • Warm Tunas predicted 8 out of the top 10 songs
  • Warm Tunas predicted 16 out of the top 20 songs
  • Warm Tunas predicted 83 out of the 100 songs played in the countdown.

That’s not a bad result at all!

The average rank prediction error, grouped into divisions of 10 is provided below. It shows that it’s difficult to predict where songs will place once you leave the top 50:

ABC Position Warm Tunas Avg Error
1-10 1.9000
11-20 8.2000
21-30 14.3000
31-40 12.5000
41-50 15.2000
51-60 24.7000
61-70 18.2000
71-80 29.9000
81-90 34.1000
91-100 29.5000

To compare Warm Tuna’s predictions vs actual rankings, a scatter plot has been provided below. We can see as we get closer to rank 1, the 100 Warm Tunas prediction gets better and converges upon the actual rankings played out on the day.

Fortunately this year around, 100 Warm Tunas was able to successfully predict the winner of the countdown. The reason this prediction was able to be made was because the sample collected clearly indicated HUMBLE. as an outlier. – an entire 5% higher than the next track, predicted to place 2nd.

Anyway, that’s a wrap. See you later this year for 100 Warm Tunas 2018 edition!

100 Warm Tunas 2017 Update 🔥💯

100 Warm Tunas has been happily chugging away for the last month or so. I’ve obtained a fair amount of media coverage too.

A couple of days back, I posted the site to the triplej subreddit. Someone replied to the post telling me my vote count was significantly less than what they had been counting by hand, which made me somewhat suspicious – was there a bug in my Instagram scraping library that I built?

Well, after a bit of debugging early this morning, I found that there was indeed a bug. Not a bug with my scraping library, but rather a bug with how I was using the library:

-    for page in ig.fetch_pages('triplej', per_page=10):
+    for page in ig.fetch_pages(hashtag, per_page=10):
         for post in page.posts:
             if post.is_video:
                 logger.info("Skipping {} because it's a video".format(post.shortcode))

For those who are programmers, you’ll probably spot the issue here. For those who aren’t, the issue is that I have been using a hardcoded string to collect Instagram votes, when I thought I was collecting a handful of hashtags.

This has now been rectified and I have kicked off a full re-scrape to back-fill the data.

100 Warm Tunas 2017

Last year I predicted the top 3 in Triple J’s hottest 100 (ignoring order). This year I’m back at it once again with an updated webpage and a Spotify playlist.

Results are collected, optimised, and processed multiple times per day. Instagram images tagged with #hottest100 and a few others are included for counting.

Happy voting!

Feel free to check out the results from 2016, 2016 results analysis, and the process taken in 2015.

100 Warm Tunas 2016 Prediction Analysis

It’s been a long time since the Hottest 100 of 2016 was aired. Unfortunately, I never really got around to publishing some analysis I performed on the prediction results. Fortunately, I managed to find some time recently!

Looking from afar, the results don’t look fantastic (when you compare them to my results from 2015 at least). The prediction unfortunately predicted the top two places out of order, however did manage to predict the third place correctly.

Lets take a look at the Top 10 of Triple J’s list and match it up with 100 Warm Tunas:

Triple J Rank vs Tuna Rank

Looking at this we see most predictions we can find some learnings:

  • The average error for the top ten rank was 1.9 rank positions.
  • If 100 Warm Tunas ignored rank and simply guessed the top ten, it would have predicted 8 of the top 10 songs.
  • If 100 Warm Tunas ignored rank and simply guessed the top 3 songs to win, it would have predicted all 3 songs. Woo!

Lets dive into a chart that shows error for all ranks:

Rank error per position

From this chart, we can deduce that the further away from position 1 we become, the higher the error. This information alone isn’t very useful. We can get a better understanding of error by finding the average for each ranking group:

Average Rank error per group

As we get closer to rank 1, the results become more and more accurate, however they are not perfect. This is more obvious if we use a scatter plot to compare Triple J ranks against Warm Tunas predictions:

Triple J vs Tunas Scatter Plot

It’s clear now that as we get closer to rank 1, the 100 Warm Tunas prediction gets better and converges upon the actual rankings played out on the day. However, unfortunately this year the difference between rank 1 and rank 2 was way too close to call - just 0.67% of voting volume was separating the two. A difference that was not enough to provide an accurate prediction of the winner.

Overall, whilst 100 Warm Tunas 2016 did get the two top positions out of order, it’s understandable as to why this happened. Hopefully this year there is a greater difference between ranks, giving further ability to predict the winner in position #1.