Pairwise Comparisons with Flexible Time-Dynamics — mentioned by lum

Tue, 06 Aug 2019 16:37:13 GMT

Hi HN, together with colleagues from EPFL I developed a new statistical model that can be used to (a) understand & visualize the skill of sports teams and players, and (b) to predict outcomes of matches. The model is trained on historical data. In short: it's an extension of the Elo rating system & TrueSkill. The way we improve upon these is by letting the (latent) skill of players & teams change over time in a more flexible way. I developed it as a Python library, with a (hopefully) easy-to-use API. I'd be happy to get your feedback! Today I'm presenting the paper we wrote about the model &…

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Pairwise Comparisons with Flexible Time-Dynamics · HackerPapers discussions

Pairwise Comparisons with Flexible Time-Dynamics — mentioned by lum