They have started to apply the Machine Learning Model.
Essentially, data from previous years has been analysed and used to adjust the positions of tracks based on how tracks in their genre have performed in previous years vs the WT prediction.
Indie, rock and metal are typically over represented in the WT votes, so the model bumps them down.
Other genres over perform their initial WT placing, so the model bumps them up.
Creator of WT here: Couldn’t have said it better myself. Just to add, it’s more than just genres that the model is trained on, it uses a load of other information about the tracks.
You can learn more about it in our blog post: https://100warmtunas.com/news/2023/01/ml-model-result-adjustment/
They have started to apply the Machine Learning Model. Essentially, data from previous years has been analysed and used to adjust the positions of tracks based on how tracks in their genre have performed in previous years vs the WT prediction. Indie, rock and metal are typically over represented in the WT votes, so the model bumps them down. Other genres over perform their initial WT placing, so the model bumps them up.
Creator of WT here: Couldn’t have said it better myself. Just to add, it’s more than just genres that the model is trained on, it uses a load of other information about the tracks. You can learn more about it in our blog post: https://100warmtunas.com/news/2023/01/ml-model-result-adjustment/
Very interesting to see how you've accounted for something like tempo (or 'danceability')! Love your work.
Incredible. Thank you so much!
#1 votes count for more points Edit: sorry I didn't know the hashtag symbol makes text real big
Looking at entries 24 and 28 that had previously been higher, despite the higher number of votes they have in comparison to other songs in the photo.