E-Sports Talent Scouting Based on Multimodal Twitch Stream Data
This addresses talent scouting for e-sports organizations, but it is incremental as it applies existing multimodal methods to a new domain.
The paper tackled the problem of predicting e-sports talent by using multimodal Twitch stream data to estimate the ranks of CS:GO gamers, achieving validation through correlation with future ranks.
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.