Counter-Strike on Twitch; Valorant on YouTube and Asia

Jan–May 2026 S‑tier events: Counter‑Strike had 56.4% of Hours Watched on Twitch, led by English, Russian and Brazilian‑Portuguese viewers. Valorant split 49.9% Twitch/43.7% YouTube and reached more Asian languages.

Data cover S‑tier tournaments classified on Liquipedia held between January 1 and May 31, 2026. The sample measures Hours Watched for Counter‑Strike and Valorant across platforms and languages during that period.

On platforms, Counter‑Strike concentrated most viewership on Twitch. Twitch accounted for 56.4% of Counter‑Strike S‑tier Hours Watched, YouTube generated 37.0%, Kick supplied about 6.0% and TikTok 0.6%. Counter‑Strike recorded below 0.1% on other platforms.

Valorant’s platform mix was more even between two major services. Twitch made up 49.9% of Valorant S‑tier Hours Watched and YouTube 43.7%. Kick provided 1.6% of Valorant hours, TikTok 2.5% and other platforms 2.4%. Regional Asian streaming services such as SOOP and CHZZK contributed to the “other” category for Valorant.

Language breakdowns differed between the games. Counter‑Strike viewership clustered around three languages: English represented 33.91% of Hours Watched, Russian 30.64% and Brazilian‑Portuguese 14.18%. No other single language exceeded 4.0% in the Counter‑Strike sample. Ukrainian accounted for 3.71%, followed by smaller shares for Polish, French, Mongolian, Turkish and Spanish. Mongolian growth paralleled the international rise of The MongolZ, while Turkish presence linked to local teams such as Aurora Gaming.

Valorant showed wider language distribution. English made up 38.15% of Hours Watched, Brazilian‑Portuguese 13.62% and Japanese 13.34%. Spanish reached 7.73%. Other measurable language segments included Thai, Korean, Vietnamese, Indonesian and Hindi.

The dataset is limited to Liquipedia‑classified S‑tier events in the first five months of 2026 and reports Hours Watched across those tournaments. The platform and language figures above describe how viewership for each title was distributed across services and language audiences in that sample.

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