  We propose and investigate feasibility of a novel task that consists in
finding e-sports talent using multimodal Twitch chat and video stream data. In
that, we focus on predicting the ranks of Counter-Strike: Global Offensive
(CS:GO) gamers who broadcast their games on Twitch. During January 2019-April
2019, we have built two Twitch stream collections: One for 425 publicly ranked
CS:GO gamers and one for 9,928 unranked CS:GO gamers. We extract neural
features from video, audio and text chat data and estimate modality-specific
probabilities for a gamer to be top-ranked during the data collection
time-frame. A hierarchical Bayesian model is then used to pool the evidence
across modalities and generate estimates of intrinsic skill for each gamer. Our
modeling is validated through correlating the intrinsic skill predictions with
May 2019 ranks of the publicly profiled gamers.
