Posted in: Games, Twitch, Video Games | Tagged: Hearthstone, overwatch, Twitch

Twitch Adds In New Integration Tools For Hearthstone & Overwatch

Published Thu, 17 Aug 2017 13:45:21 -0500

Last updated Thu, 17 Aug 2017 18:38:49 -0500

Today, Twitch announced a new addition to their platform specifically geared toward players and fans of Overwatch and Hearthstone. Right now, streamers can log in and find a new menu that appears over the Overwatch directory and Hearthstone directory, which will allow "viewers to filter channels by hero played on Overwatch streams, or by game mode, player rank, number of wins, and hero class on Hearthstone streams."

That's going to be a very handy tool for broadcasters who are looking to attract certain audiences to their stream or wish to show off how well they're doing in a particular game. The game directory filters were made possible by the team behind ClipMine, which is helping Twitch analyze their own content going out and will help better refine searches. We got a couple quotes below from key figures involved with the new integration tools, as we patiently await to see how people will utilize these.

"When we recently introduced metadata filtering options for one of our top esports games, we not only received positive reception from its fans, but demand from the greater community to have access to similar discovery tools for other titles," said JT Gleason, Director, Integration Success, at Twitch. "Since Overwatch and Hearthstone are also among our most popular competitive games whose players take to Twitch to improve their skills by watching others play, we focused on how we can improve discovery for them. Our new metadata filters now make it easy to find more granular aspects of gameplay that previously required a lot more searching."

"Video game streams have a very rich structure that has been difficult to exploit for the purpose of enabling content discovery," said Zia Syed, director of engineering, Twitch and founder of ClipMine. "By employing computer vision and machine learning developed at ClipMine, we have been able to recreate that structure in a reliable, scalable and cost effective way. This enables us to match creators and viewers in a very precise manner opening up new ways of content discovery on Twitch."