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Explanation of Bradley-Terry here: https://stats.stackexchange.com/a/131270/60526

It's such a great and simple algorithm. I feel like it deserves to be more widely known.

I used it at Dyson to evaluate really subjective things like how straight a tress of hair is - pretty much impossible to say if you just look at a photo, but you can ask a bunch of people to compare two photos and say which looks straighter, then you can get an objective ranking.

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Yeah absolutely. In your link, it iterates on _ = ^{_}, until it finds the fixed point.

In our training pipeline, we had to convert the fixed point iteration to be on _ directly for numerical stability. I have a post on that here!: https://hackmd.io/x3_EkXGKRdeq-rNHo_RpZA

Bradley-Terry also very cleanly turns into a loss function that you can do gradient descent on, which will cause your model to efficiently learn Elo scores! Our calculations are at: https://hackmd.io/eOwlF7O_Q1K4hj7WZcYFiw

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