I’m all but finished with my thesis now. I’ve titled it “Elo Regression: Extending the Elo Rating System.” The idea behind the work is to create a modified version of the Elo rating system that handles ratings over time in a principled way. Since Elo ratings are updated over time, you can look at the way the ratings change to get an ad hoc model of skill over time. But this ad hoc method has some pathologies. For example, we might observe someone’s rating climbing and then sharply plummeting in such a way that the obvious explanation is that they became overrated temporarily, and then their rating was corrected, which can definitely happen in Elo. Then, if we want to look back and see how strong different players were at different times, we should probably discount that temporary spike somehow. The methods from my thesis will do this automatically. You can read it on my GitHub here. There’s also a slide deck in that same repository, if you just want to see the main ideas.