# E-Sports Talent Scouting Based on Multimodal Twitch Stream Data

## TL;DR

This paper explores a novel approach to identify e-sports talent by analyzing multimodal Twitch stream data, including video, audio, and chat, to predict gamer ranks using neural features and Bayesian modeling.

## Contribution

It introduces a new multimodal data-driven method for e-sports talent scouting, combining neural feature extraction and hierarchical Bayesian modeling to estimate gamer skill.

## Key findings

- High correlation between predicted intrinsic skill and actual gamer ranks
- Effective integration of video, audio, and chat data for skill estimation
- Demonstrated feasibility of automated e-sports talent prediction

## Abstract

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.