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 & the inference algorithm at the Knowledge Discovery and Data Mining (KDD) conference in Anchorage, Alaska. It explains all the technical details [1]
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 & the inference algorithm at the Knowledge Discovery and Data Mining (KDD) conference in Anchorage, Alaska. It explains all the technical details [1]
[1]: https://arxiv.org/abs/1903.07746